case study on writing

How to Write a Case Study: Bookmarkable Guide & Template

Published: November 30, 2023

Earning the trust of prospective customers can be a struggle. Before you can even begin to expect to earn their business, you need to demonstrate your ability to deliver on what your product or service promises.

Sure, you could say that you're great at X or that you're way ahead of the competition when it comes to Y. But at the end of the day, what you really need to win new business is cold, hard proof.

One of the best ways to prove your worth is through a compelling case study. In fact, HubSpot’s 2020 State of Marketing report found that case studies are so compelling that they are the fifth most commonly used type of content used by marketers.

Below, I'll walk you through what a case study is, how to prepare for writing one, what you need to include in it, and how it can be an effective tactic. To jump to different areas of this post, click on the links below to automatically scroll.

Case Study Definition

Case study templates, how to write a case study.

• How to Format a Case Study

Business Case Study Examples

A case study is a specific challenge a business has faced, and the solution they've chosen to solve it. Case studies can vary greatly in length and focus on several details related to the initial challenge and applied solution, and can be presented in various forms like a video, white paper, blog post, etc.

In professional settings, it's common for a case study to tell the story of a successful business partnership between a vendor and a client. Perhaps the success you're highlighting is in the number of leads your client generated, customers closed, or revenue gained. Any one of these key performance indicators (KPIs) are examples of your company's services in action.

When done correctly, these examples of your work can chronicle the positive impact your business has on existing or previous customers and help you attract new clients.

Free Case Study Templates

Showcase your company's success using these three free case study templates.

• Data-Driven Case Study Template
• Product-Specific Case Study Template
• General Case Study Template

You're all set!

Click this link to access this resource at any time.

Why write a case study? 

I know, you’re thinking “ Okay, but why do I need to write one of these? ” The truth is that while case studies are a huge undertaking, they are powerful marketing tools that allow you to demonstrate the value of your product to potential customers using real-world examples. Here are a few reasons why you should write case studies. 

1. Explain Complex Topics or Concepts

Case studies give you the space to break down complex concepts, ideas, and strategies and show how they can be applied in a practical way. You can use real-world examples, like an existing client, and use their story to create a compelling narrative that shows how your product solved their issue and how those strategies can be repeated to help other customers get similar successful results.  

2. Show Expertise

Case studies are a great way to demonstrate your knowledge and expertise on a given topic or industry. This is where you get the opportunity to show off your problem-solving skills and how you’ve generated successful outcomes for clients you’ve worked with. 

3. Build Trust and Credibility

In addition to showing off the attributes above, case studies are an excellent way to build credibility. They’re often filled with data and thoroughly researched, which shows readers you’ve done your homework. They can have confidence in the solutions you’ve presented because they’ve read through as you’ve explained the problem and outlined step-by-step what it took to solve it. All of these elements working together enable you to build trust with potential customers.

4. Create Social Proof

Using existing clients that have seen success working with your brand builds social proof . People are more likely to choose your brand if they know that others have found success working with you. Case studies do just that — putting your success on display for potential customers to see. 

All of these attributes work together to help you gain more clients. Plus you can even use quotes from customers featured in these studies and repurpose them in other marketing content. Now that you know more about the benefits of producing a case study, let’s check out how long these documents should be. 

How long should a case study be?

The length of a case study will vary depending on the complexity of the project or topic discussed. However, as a general guideline, case studies typically range from 500 to 1,500 words. 

Whatever length you choose, it should provide a clear understanding of the challenge, the solution you implemented, and the results achieved. This may be easier said than done, but it's important to strike a balance between providing enough detail to make the case study informative and concise enough to keep the reader's interest.

The primary goal here is to effectively communicate the key points and takeaways of the case study. It’s worth noting that this shouldn’t be a wall of text. Use headings, subheadings, bullet points, charts, and other graphics to break up the content and make it more scannable for readers. We’ve also seen brands incorporate video elements into case studies listed on their site for a more engaging experience. 

Ultimately, the length of your case study should be determined by the amount of information necessary to convey the story and its impact without becoming too long. Next, let’s look at some templates to take the guesswork out of creating one. 

To help you arm your prospects with information they can trust, we've put together a step-by-step guide on how to create effective case studies for your business with free case study templates for creating your own.

Tell us a little about yourself below to gain access today:

And to give you more options, we’ll highlight some useful templates that serve different needs. But remember, there are endless possibilities when it comes to demonstrating the work your business has done.

1. General Case Study Template

Do you have a specific product or service that you’re trying to sell, but not enough reviews or success stories? This Product Specific case study template will help.

This template relies less on metrics, and more on highlighting the customer’s experience and satisfaction. As you follow the template instructions, you’ll be prompted to speak more about the benefits of the specific product, rather than your team’s process for working with the customer.

4. Bold Social Media Business Case Study Template

You can find templates that represent different niches, industries, or strategies that your business has found success in — like a bold social media business case study template.

In this template, you can tell the story of how your social media marketing strategy has helped you or your client through collaboration or sale of your service. Customize it to reflect the different marketing channels used in your business and show off how well your business has been able to boost traffic, engagement, follows, and more.

5. Lead Generation Business Case Study Template

It’s important to note that not every case study has to be the product of a sale or customer story, sometimes they can be informative lessons that your own business has experienced. A great example of this is the Lead Generation Business case study template.

If you’re looking to share operational successes regarding how your team has improved processes or content, you should include the stories of different team members involved, how the solution was found, and how it has made a difference in the work your business does.

Now that we’ve discussed different templates and ideas for how to use them, let’s break down how to create your own case study with one.

• Get started with case study templates.
• Determine the case study's objective.
• Establish a case study medium.
• Find the right case study candidate.
• Contact your candidate for permission to write about them.
• Ensure you have all the resources you need to proceed once you get a response.
• Download a case study email template.
• Define the process you want to follow with the client.
• Ensure you're asking the right questions.
• Layout your case study format.
• Publish and promote your case study.

1. Get started with case study templates.

Telling your customer's story is a delicate process — you need to highlight their success while naturally incorporating your business into their story.

If you're just getting started with case studies, we recommend you download HubSpot's Case Study Templates we mentioned before to kickstart the process.

2. Determine the case study's objective.

All business case studies are designed to demonstrate the value of your services, but they can focus on several different client objectives.

Your first step when writing a case study is to determine the objective or goal of the subject you're featuring. In other words, what will the client have succeeded in doing by the end of the piece?

The client objective you focus on will depend on what you want to prove to your future customers as a result of publishing this case study.

Your case study can focus on one of the following client objectives:

• Complying with government regulation
• Lowering business costs
• Becoming profitable
• Generating more leads
• Closing on more customers
• Generating more revenue
• Expanding into a new market
• Becoming more sustainable or energy-efficient

3. Establish a case study medium.

Next, you'll determine the medium in which you'll create the case study. In other words, how will you tell this story?

Case studies don't have to be simple, written one-pagers. Using different media in your case study can allow you to promote your final piece on different channels. For example, while a written case study might just live on your website and get featured in a Facebook post, you can post an infographic case study on Pinterest and a video case study on your YouTube channel.

Here are some different case study mediums to consider:

Written Case Study

Consider writing this case study in the form of an ebook and converting it to a downloadable PDF. Then, gate the PDF behind a landing page and form for readers to fill out before downloading the piece, allowing this case study to generate leads for your business.

Video Case Study

Plan on meeting with the client and shooting an interview. Seeing the subject, in person, talk about the service you provided them can go a long way in the eyes of your potential customers.

Infographic Case Study

Use the long, vertical format of an infographic to tell your success story from top to bottom. As you progress down the infographic, emphasize major KPIs using bigger text and charts that show the successes your client has had since working with you.

Podcast Case Study

Podcasts are a platform for you to have a candid conversation with your client. This type of case study can sound more real and human to your audience — they'll know the partnership between you and your client was a genuine success.

4. Find the right case study candidate.

Writing about your previous projects requires more than picking a client and telling a story. You need permission, quotes, and a plan. To start, here are a few things to look for in potential candidates.

Product Knowledge

It helps to select a customer who's well-versed in the logistics of your product or service. That way, he or she can better speak to the value of what you offer in a way that makes sense for future customers.

Remarkable Results

Clients that have seen the best results are going to make the strongest case studies. If their own businesses have seen an exemplary ROI from your product or service, they're more likely to convey the enthusiasm that you want prospects to feel, too.

One part of this step is to choose clients who have experienced unexpected success from your product or service. When you've provided non-traditional customers — in industries that you don't usually work with, for example — with positive results, it can help to remove doubts from prospects.

Recognizable Names

While small companies can have powerful stories, bigger or more notable brands tend to lend credibility to your own. In fact, 89% of consumers say they'll buy from a brand they already recognize over a competitor, especially if they already follow them on social media.

Customers that came to you after working with a competitor help highlight your competitive advantage and might even sway decisions in your favor.

5. Contact your candidate for permission to write about them.

To get the case study candidate involved, you have to set the stage for clear and open communication. That means outlining expectations and a timeline right away — not having those is one of the biggest culprits in delayed case study creation.

Most importantly at this point, however, is getting your subject's approval. When first reaching out to your case study candidate, provide them with the case study's objective and format — both of which you will have come up with in the first two steps above.

To get this initial permission from your subject, put yourself in their shoes — what would they want out of this case study? Although you're writing this for your own company's benefit, your subject is far more interested in the benefit it has for them.

Benefits to Offer Your Case Study Candidate

Here are four potential benefits you can promise your case study candidate to gain their approval.

Brand Exposure

Explain to your subject to whom this case study will be exposed, and how this exposure can help increase their brand awareness both in and beyond their own industry. In the B2B sector, brand awareness can be hard to collect outside one's own market, making case studies particularly useful to a client looking to expand their name's reach.

Employee Exposure

Allow your subject to provide quotes with credits back to specific employees. When this is an option for them, their brand isn't the only thing expanding its reach — their employees can get their name out there, too. This presents your subject with networking and career development opportunities they might not have otherwise.

Product Discount

This is a more tangible incentive you can offer your case study candidate, especially if they're a current customer of yours. If they agree to be your subject, offer them a product discount — or a free trial of another product — as a thank-you for their help creating your case study.

Backlinks and Website Traffic

Here's a benefit that is sure to resonate with your subject's marketing team: If you publish your case study on your website, and your study links back to your subject's website — known as a "backlink" — this small gesture can give them website traffic from visitors who click through to your subject's website.

Additionally, a backlink from you increases your subject's page authority in the eyes of Google. This helps them rank more highly in search engine results and collect traffic from readers who are already looking for information about their industry.

6. Ensure you have all the resources you need to proceed once you get a response.

So you know what you’re going to offer your candidate, it’s time that you prepare the resources needed for if and when they agree to participate, like a case study release form and success story letter.

Let's break those two down.

Case Study Release Form

This document can vary, depending on factors like the size of your business, the nature of your work, and what you intend to do with the case studies once they are completed. That said, you should typically aim to include the following in the Case Study Release Form:

• A clear explanation of why you are creating this case study and how it will be used.
• A statement defining the information and potentially trademarked information you expect to include about the company — things like names, logos, job titles, and pictures.
• An explanation of what you expect from the participant, beyond the completion of the case study. For example, is this customer willing to act as a reference or share feedback, and do you have permission to pass contact information along for these purposes?
• A note about compensation.

Success Story Letter

As noted in the sample email, this document serves as an outline for the entire case study process. Other than a brief explanation of how the customer will benefit from case study participation, you'll want to be sure to define the following steps in the Success Story Letter.

7. Download a case study email template.

While you gathered your resources, your candidate has gotten time to read over the proposal. When your candidate approves of your case study, it's time to send them a release form.

A case study release form tells you what you'll need from your chosen subject, like permission to use any brand names and share the project information publicly. Kick-off this process with an email that runs through exactly what they can expect from you, as well as what you need from them. To give you an idea of what that might look like, check out this sample email:

8. Define the process you want to follow with the client.

Before you can begin the case study, you have to have a clear outline of the case study process with your client. An example of an effective outline would include the following information.

The Acceptance

First, you'll need to receive internal approval from the company's marketing team. Once approved, the Release Form should be signed and returned to you. It's also a good time to determine a timeline that meets the needs and capabilities of both teams.

The Questionnaire

To ensure that you have a productive interview — which is one of the best ways to collect information for the case study — you'll want to ask the participant to complete a questionnaire before this conversation. That will provide your team with the necessary foundation to organize the interview, and get the most out of it.

The Interview

Once the questionnaire is completed, someone on your team should reach out to the participant to schedule a 30- to 60-minute interview, which should include a series of custom questions related to the customer's experience with your product or service.

The Draft Review

After the case study is composed, you'll want to send a draft to the customer, allowing an opportunity to give you feedback and edits.

The Final Approval

Once any necessary edits are completed, send a revised copy of the case study to the customer for final approval.

Once the case study goes live — on your website or elsewhere — it's best to contact the customer with a link to the page where the case study lives. Don't be afraid to ask your participants to share these links with their own networks, as it not only demonstrates your ability to deliver positive results and impressive growth, as well.

9. Ensure you're asking the right questions.

Before you execute the questionnaire and actual interview, make sure you're setting yourself up for success. A strong case study results from being prepared to ask the right questions. What do those look like? Here are a few examples to get you started:

• What are your goals?
• What challenges were you experiencing before purchasing our product or service?
• What made our product or service stand out against our competitors?
• What did your decision-making process look like?
• How have you benefited from using our product or service? (Where applicable, always ask for data.)

Keep in mind that the questionnaire is designed to help you gain insights into what sort of strong, success-focused questions to ask during the actual interview. And once you get to that stage, we recommend that you follow the "Golden Rule of Interviewing." Sounds fancy, right? It's actually quite simple — ask open-ended questions.

If you're looking to craft a compelling story, "yes" or "no" answers won't provide the details you need. Focus on questions that invite elaboration, such as, "Can you describe ...?" or, "Tell me about ..."

In terms of the interview structure, we recommend categorizing the questions and flowing them into six specific sections that will mirror a successful case study format. Combined, they'll allow you to gather enough information to put together a rich, comprehensive study.

Open with the customer's business.

The goal of this section is to generate a better understanding of the company's current challenges and goals, and how they fit into the landscape of their industry. Sample questions might include:

• How long have you been in business?
• How many employees do you have?
• What are some of the objectives of your department at this time?

Cite a problem or pain point.

To tell a compelling story, you need context. That helps match the customer's need with your solution. Sample questions might include:

• What challenges and objectives led you to look for a solution?
• What might have happened if you did not identify a solution?
• Did you explore other solutions before this that did not work out? If so, what happened?

Discuss the decision process.

Exploring how the customer decided to work with you helps to guide potential customers through their own decision-making processes. Sample questions might include:

• How did you hear about our product or service?
• Who was involved in the selection process?
• What was most important to you when evaluating your options?

Explain how a solution was implemented.

The focus here should be placed on the customer's experience during the onboarding process. Sample questions might include:

• How long did it take to get up and running?
• Did that meet your expectations?
• Who was involved in the process?

Explain how the solution works.

The goal of this section is to better understand how the customer is using your product or service. Sample questions might include:

• Is there a particular aspect of the product or service that you rely on most?
• Who is using the product or service?

End with the results.

In this section, you want to uncover impressive measurable outcomes — the more numbers, the better. Sample questions might include:

• How is the product or service helping you save time and increase productivity?
• In what ways does that enhance your competitive advantage?
• How much have you increased metrics X, Y, and Z?

10. Lay out your case study format.

When it comes time to take all of the information you've collected and actually turn it into something, it's easy to feel overwhelmed. Where should you start? What should you include? What's the best way to structure it?

To help you get a handle on this step, it's important to first understand that there is no one-size-fits-all when it comes to the ways you can present a case study. They can be very visual, which you'll see in some of the examples we've included below, and can sometimes be communicated mostly through video or photos, with a bit of accompanying text.

Here are the sections we suggest, which we'll cover in more detail down below:

• Title: Keep it short. Develop a succinct but interesting project name you can give the work you did with your subject.
• Subtitle: Use this copy to briefly elaborate on the accomplishment. What was done? The case study itself will explain how you got there.
• Executive Summary : A 2-4 sentence summary of the entire story. You'll want to follow it with 2-3 bullet points that display metrics showcasing success.
• About the Subject: An introduction to the person or company you served, which can be pulled from a LinkedIn Business profile or client website.
• Challenges and Objectives: A 2-3 paragraph description of the customer's challenges, before using your product or service. This section should also include the goals or objectives the customer set out to achieve.
• How Product/Service Helped: A 2-3 paragraph section that describes how your product or service provided a solution to their problem.
• Results: A 2-3 paragraph testimonial that proves how your product or service specifically benefited the person or company and helped achieve its goals. Include numbers to quantify your contributions.
• Supporting Visuals or Quotes: Pick one or two powerful quotes that you would feature at the bottom of the sections above, as well as a visual that supports the story you are telling.
• Future Plans: Everyone likes an epilogue. Comment on what's ahead for your case study subject, whether or not those plans involve you.
• Call to Action (CTA): Not every case study needs a CTA, but putting a passive one at the end of your case study can encourage your readers to take an action on your website after learning about the work you've done.

When laying out your case study, focus on conveying the information you've gathered in the most clear and concise way possible. Make it easy to scan and comprehend, and be sure to provide an attractive call-to-action at the bottom — that should provide readers an opportunity to learn more about your product or service.

11. Publish and promote your case study.

Once you've completed your case study, it's time to publish and promote it. Some case study formats have pretty obvious promotional outlets — a video case study can go on YouTube, just as an infographic case study can go on Pinterest.

But there are still other ways to publish and promote your case study. Here are a couple of ideas:

Lead Gen in a Blog Post

As stated earlier in this article, written case studies make terrific lead-generators if you convert them into a downloadable format, like a PDF. To generate leads from your case study, consider writing a blog post that tells an abbreviated story of your client's success and asking readers to fill out a form with their name and email address if they'd like to read the rest in your PDF.

Then, promote this blog post on social media, through a Facebook post or a tweet.

Published as a Page on Your Website

As a growing business, you might need to display your case study out in the open to gain the trust of your target audience.

Rather than gating it behind a landing page, publish your case study to its own page on your website, and direct people here from your homepage with a "Case Studies" or "Testimonials" button along your homepage's top navigation bar.

Format for a Case Study

The traditional case study format includes the following parts: a title and subtitle, a client profile, a summary of the customer’s challenges and objectives, an account of how your solution helped, and a description of the results. You might also want to include supporting visuals and quotes, future plans, and calls-to-action.

Image Source

The title is one of the most important parts of your case study. It should draw readers in while succinctly describing the potential benefits of working with your company. To that end, your title should:

• State the name of your custome r. Right away, the reader must learn which company used your products and services. This is especially important if your customer has a recognizable brand. If you work with individuals and not companies, you may omit the name and go with professional titles: “A Marketer…”, “A CFO…”, and so forth.
• State which product your customer used . Even if you only offer one product or service, or if your company name is the same as your product name, you should still include the name of your solution. That way, readers who are not familiar with your business can become aware of what you sell.
• Allude to the results achieved . You don’t necessarily need to provide hard numbers, but the title needs to represent the benefits, quickly. That way, if a reader doesn’t stay to read, they can walk away with the most essential information: Your product works.

The example above, “Crunch Fitness Increases Leads and Signups With HubSpot,” achieves all three — without being wordy. Keeping your title short and sweet is also essential.

2. Subtitle

Your subtitle is another essential part of your case study — don’t skip it, even if you think you’ve done the work with the title. In this section, include a brief summary of the challenges your customer was facing before they began to use your products and services. Then, drive the point home by reiterating the benefits your customer experienced by working with you.

The above example reads:

“Crunch Fitness was franchising rapidly when COVID-19 forced fitness clubs around the world to close their doors. But the company stayed agile by using HubSpot to increase leads and free trial signups.”

We like that the case study team expressed the urgency of the problem — opening more locations in the midst of a pandemic — and placed the focus on the customer’s ability to stay agile.

3. Executive Summary

The executive summary should provide a snapshot of your customer, their challenges, and the benefits they enjoyed from working with you. Think it’s too much? Think again — the purpose of the case study is to emphasize, again and again, how well your product works.

The good news is that depending on your design, the executive summary can be mixed with the subtitle or with the “About the Company” section. Many times, this section doesn’t need an explicit “Executive Summary” subheading. You do need, however, to provide a convenient snapshot for readers to scan.

In the above example, ADP included information about its customer in a scannable bullet-point format, then provided two sections: “Business Challenge” and “How ADP Helped.” We love how simple and easy the format is to follow for those who are unfamiliar with ADP or its typical customer.

4. About the Company

Readers need to know and understand who your customer is. This is important for several reasons: It helps your reader potentially relate to your customer, it defines your ideal client profile (which is essential to deter poor-fit prospects who might have reached out without knowing they were a poor fit), and it gives your customer an indirect boon by subtly promoting their products and services.

Feel free to keep this section as simple as possible. You can simply copy and paste information from the company’s LinkedIn, use a quote directly from your customer, or take a more creative storytelling approach.

In the above example, HubSpot included one paragraph of description for Crunch Fitness and a few bullet points. Below, ADP tells the story of its customer using an engaging, personable technique that effectively draws readers in.

5. Challenges and Objectives

The challenges and objectives section of your case study is the place to lay out, in detail, the difficulties your customer faced prior to working with you — and what they hoped to achieve when they enlisted your help.

In this section, you can be as brief or as descriptive as you’d like, but remember: Stress the urgency of the situation. Don’t understate how much your customer needed your solution (but don’t exaggerate and lie, either). Provide contextual information as necessary. For instance, the pandemic and societal factors may have contributed to the urgency of the need.

Take the above example from design consultancy IDEO:

“Educational opportunities for adults have become difficult to access in the United States, just when they’re needed most. To counter this trend, IDEO helped the city of South Bend and the Drucker Institute launch Bendable, a community-powered platform that connects people with opportunities to learn with and from each other.”

We love how IDEO mentions the difficulties the United States faces at large, the efforts its customer is taking to address these issues, and the steps IDEO took to help.

6. How Product/Service Helped

This is where you get your product or service to shine. Cover the specific benefits that your customer enjoyed and the features they gleaned the most use out of. You can also go into detail about how you worked with and for your customer. Maybe you met several times before choosing the right solution, or you consulted with external agencies to create the best package for them.

Whatever the case may be, try to illustrate how easy and pain-free it is to work with the representatives at your company. After all, potential customers aren’t looking to just purchase a product. They’re looking for a dependable provider that will strive to exceed their expectations.

In the above example, IDEO describes how it partnered with research institutes and spoke with learners to create Bendable, a free educational platform. We love how it shows its proactivity and thoroughness. It makes potential customers feel that IDEO might do something similar for them.

The results are essential, and the best part is that you don’t need to write the entirety of the case study before sharing them. Like HubSpot, IDEO, and ADP, you can include the results right below the subtitle or executive summary. Use data and numbers to substantiate the success of your efforts, but if you don’t have numbers, you can provide quotes from your customers.

We can’t overstate the importance of the results. In fact, if you wanted to create a short case study, you could include your title, challenge, solution (how your product helped), and result.

8. Supporting Visuals or Quotes

Let your customer speak for themselves by including quotes from the representatives who directly interfaced with your company.

Visuals can also help, even if they’re stock images. On one side, they can help you convey your customer’s industry, and on the other, they can indirectly convey your successes. For instance, a picture of a happy professional — even if they’re not your customer — will communicate that your product can lead to a happy client.

In this example from IDEO, we see a man standing in a boat. IDEO’s customer is neither the man pictured nor the manufacturer of the boat, but rather Conservation International, an environmental organization. This imagery provides a visually pleasing pattern interrupt to the page, while still conveying what the case study is about.

9. Future Plans

This is optional, but including future plans can help you close on a more positive, personable note than if you were to simply include a quote or the results. In this space, you can show that your product will remain in your customer’s tech stack for years to come, or that your services will continue to be instrumental to your customer’s success.

Alternatively, if you work only on time-bound projects, you can allude to the positive impact your customer will continue to see, even after years of the end of the contract.

10. Call to Action (CTA)

Not every case study needs a CTA, but we’d still encourage it. Putting one at the end of your case study will encourage your readers to take an action on your website after learning about the work you've done.

It will also make it easier for them to reach out, if they’re ready to start immediately. You don’t want to lose business just because they have to scroll all the way back up to reach out to your team.

To help you visualize this case study outline, check out the case study template below, which can also be downloaded here .

You drove the results, made the connection, set the expectations, used the questionnaire to conduct a successful interview, and boiled down your findings into a compelling story. And after all of that, you're left with a little piece of sales enabling gold — a case study.

To show you what a well-executed final product looks like, have a look at some of these marketing case study examples.

1. "Shopify Uses HubSpot CRM to Transform High Volume Sales Organization," by HubSpot

What's interesting about this case study is the way it leads with the customer. This reflects a major HubSpot value, which is to always solve for the customer first. The copy leads with a brief description of why Shopify uses HubSpot and is accompanied by a short video and some basic statistics on the company.

Notice that this case study uses mixed media. Yes, there is a short video, but it's elaborated upon in the additional text on the page. So, while case studies can use one or the other, don't be afraid to combine written copy with visuals to emphasize the project's success.

2. "New England Journal of Medicine," by Corey McPherson Nash

When branding and design studio Corey McPherson Nash showcases its work, it makes sense for it to be visual — after all, that's what they do. So in building the case study for the studio's work on the New England Journal of Medicine's integrated advertising campaign — a project that included the goal of promoting the client's digital presence — Corey McPherson Nash showed its audience what it did, rather than purely telling it.

Notice that the case study does include some light written copy — which includes the major points we've suggested — but lets the visuals do the talking, allowing users to really absorb the studio's services.

3. "Designing the Future of Urban Farming," by IDEO

Here's a design company that knows how to lead with simplicity in its case studies. As soon as the visitor arrives at the page, he or she is greeted with a big, bold photo, and two very simple columns of text — "The Challenge" and "The Outcome."

Immediately, IDEO has communicated two of the case study's major pillars. And while that's great — the company created a solution for vertical farming startup INFARM's challenge — it doesn't stop there. As the user scrolls down, those pillars are elaborated upon with comprehensive (but not overwhelming) copy that outlines what that process looked like, replete with quotes and additional visuals.

4. "Secure Wi-Fi Wins Big for Tournament," by WatchGuard

Then, there are the cases when visuals can tell almost the entire story — when executed correctly. Network security provider WatchGuard can do that through this video, which tells the story of how its services enhanced the attendee and vendor experience at the Windmill Ultimate Frisbee tournament.

5. Rock and Roll Hall of Fame Boosts Social Media Engagement and Brand Awareness with HubSpot

In the case study above , HubSpot uses photos, videos, screenshots, and helpful stats to tell the story of how the Rock and Roll Hall of Fame used the bot, CRM, and social media tools to gain brand awareness.

6. Small Desk Plant Business Ups Sales by 30% With Trello

This case study from Trello is straightforward and easy to understand. It begins by explaining the background of the company that decided to use it, what its goals were, and how it planned to use Trello to help them.

It then goes on to discuss how the software was implemented and what tasks and teams benefited from it. Towards the end, it explains the sales results that came from implementing the software and includes quotes from decision-makers at the company that implemented it.

7. Facebook's Mercedes Benz Success Story

Facebook's Success Stories page hosts a number of well-designed and easy-to-understand case studies that visually and editorially get to the bottom line quickly.

Each study begins with key stats that draw the reader in. Then it's organized by highlighting a problem or goal in the introduction, the process the company took to reach its goals, and the results. Then, in the end, Facebook notes the tools used in the case study.

Showcasing Your Work

You work hard at what you do. Now, it's time to show it to the world — and, perhaps more important, to potential customers. Before you show off the projects that make you the proudest, we hope you follow these important steps that will help you effectively communicate that work and leave all parties feeling good about it.

Editor's Note: This blog post was originally published in February 2017 but was updated for comprehensiveness and freshness in July 2021.

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How to write a case study — examples, templates, and tools

It’s a marketer’s job to communicate the effectiveness of a product or service to potential and current customers to convince them to buy and keep business moving. One of the best methods for doing this is to share success stories that are relatable to prospects and customers based on their pain points, experiences, and overall needs.

That’s where case studies come in. Case studies are an essential part of a content marketing plan. These in-depth stories of customer experiences are some of the most effective at demonstrating the value of a product or service. Yet many marketers don’t use them, whether because of their regimented formats or the process of customer involvement and approval.

A case study is a powerful tool for showcasing your hard work and the success your customer achieved. But writing a great case study can be difficult if you’ve never done it before or if it’s been a while. This guide will show you how to write an effective case study and provide real-world examples and templates that will keep readers engaged and support your business.

In this article, you’ll learn:

What is a case study?

How to write a case study, case study templates, case study examples, case study tools.

A case study is the detailed story of a customer’s experience with a product or service that demonstrates their success and often includes measurable outcomes. Case studies are used in a range of fields and for various reasons, from business to academic research. They’re especially impactful in marketing as brands work to convince and convert consumers with relatable, real-world stories of actual customer experiences.

The best case studies tell the story of a customer’s success, including the steps they took, the results they achieved, and the support they received from a brand along the way. To write a great case study, you need to:

• Celebrate the customer and make them — not a product or service — the star of the story.
• Craft the story with specific audiences or target segments in mind so that the story of one customer will be viewed as relatable and actionable for another customer.
• Write copy that is easy to read and engaging so that readers will gain the insights and messages intended.
• Follow a standardized format that includes all of the essentials a potential customer would find interesting and useful.
• Support all of the claims for success made in the story with data in the forms of hard numbers and customer statements.

Case studies are a type of review but more in depth, aiming to show — rather than just tell — the positive experiences that customers have with a brand. Notably, 89% of consumers read reviews before deciding to buy, and 79% view case study content as part of their purchasing process. When it comes to B2B sales, 52% of buyers rank case studies as an important part of their evaluation process.

Telling a brand story through the experience of a tried-and-true customer matters. The story is relatable to potential new customers as they imagine themselves in the shoes of the company or individual featured in the case study. Showcasing previous customers can help new ones see themselves engaging with your brand in the ways that are most meaningful to them.

Besides sharing the perspective of another customer, case studies stand out from other content marketing forms because they are based on evidence. Whether pulling from client testimonials or data-driven results, case studies tend to have more impact on new business because the story contains information that is both objective (data) and subjective (customer experience) — and the brand doesn’t sound too self-promotional.

Case studies are unique in that there’s a fairly standardized format for telling a customer’s story. But that doesn’t mean there isn’t room for creativity. It’s all about making sure that teams are clear on the goals for the case study — along with strategies for supporting content and channels — and understanding how the story fits within the framework of the company’s overall marketing goals.

Here are the basic steps to writing a good case study.

1. Identify your goal

Start by defining exactly who your case study will be designed to help. Case studies are about specific instances where a company works with a customer to achieve a goal. Identify which customers are likely to have these goals, as well as other needs the story should cover to appeal to them.

The answer is often found in one of the buyer personas that have been constructed as part of your larger marketing strategy. This can include anything from new leads generated by the marketing team to long-term customers that are being pressed for cross-sell opportunities. In all of these cases, demonstrating value through a relatable customer success story can be part of the solution to conversion.

2. Choose your client or subject

Who you highlight matters. Case studies tie brands together that might otherwise not cross paths. A writer will want to ensure that the highlighted customer aligns with their own company’s brand identity and offerings. Look for a customer with positive name recognition who has had great success with a product or service and is willing to be an advocate.

The client should also match up with the identified target audience. Whichever company or individual is selected should be a reflection of other potential customers who can see themselves in similar circumstances, having the same problems and possible solutions.

Some of the most compelling case studies feature customers who:

• Switch from one product or service to another while naming competitors that missed the mark.
• Experience measurable results that are relatable to others in a specific industry.
• Represent well-known brands and recognizable names that are likely to compel action.
• Advocate for a product or service as a champion and are well-versed in its advantages.

Whoever or whatever customer is selected, marketers must ensure they have the permission of the company involved before getting started. Some brands have strict review and approval procedures for any official marketing or promotional materials that include their name. Acquiring those approvals in advance will prevent any miscommunication or wasted effort if there is an issue with their legal or compliance teams.

3. Conduct research and compile data

Substantiating the claims made in a case study — either by the marketing team or customers themselves — adds validity to the story. To do this, include data and feedback from the client that defines what success looks like. This can be anything from demonstrating return on investment (ROI) to a specific metric the customer was striving to improve. Case studies should prove how an outcome was achieved and show tangible results that indicate to the customer that your solution is the right one.

This step could also include customer interviews. Make sure that the people being interviewed are key stakeholders in the purchase decision or deployment and use of the product or service that is being highlighted. Content writers should work off a set list of questions prepared in advance. It can be helpful to share these with the interviewees beforehand so they have time to consider and craft their responses. One of the best interview tactics to keep in mind is to ask questions where yes and no are not natural answers. This way, your subject will provide more open-ended responses that produce more meaningful content.

4. Choose the right format

There are a number of different ways to format a case study. Depending on what you hope to achieve, one style will be better than another. However, there are some common elements to include, such as:

• An engaging headline
• A subject and customer introduction
• The unique challenge or challenges the customer faced
• The solution the customer used to solve the problem
• The results achieved
• Data and statistics to back up claims of success
• A strong call to action (CTA) to engage with the vendor

It’s also important to note that while case studies are traditionally written as stories, they don’t have to be in a written format. Some companies choose to get more creative with their case studies and produce multimedia content, depending on their audience and objectives. Case study formats can include traditional print stories, interactive web or social content, data-heavy infographics, professionally shot videos, podcasts, and more.

5. Write your case study

We’ll go into more detail later about how exactly to write a case study, including templates and examples. Generally speaking, though, there are a few things to keep in mind when writing your case study.

• Be clear and concise. Readers want to get to the point of the story quickly and easily, and they’ll be looking to see themselves reflected in the story right from the start.
• Provide a big picture. Always make sure to explain who the client is, their goals, and how they achieved success in a short introduction to engage the reader.
• Construct a clear narrative. Stick to the story from the perspective of the customer and what they needed to solve instead of just listing product features or benefits.
• Leverage graphics. Incorporating infographics, charts, and sidebars can be a more engaging and eye-catching way to share key statistics and data in readable ways.
• Offer the right amount of detail. Most case studies are one or two pages with clear sections that a reader can skim to find the information most important to them.
• Include data to support claims. Show real results — both facts and figures and customer quotes — to demonstrate credibility and prove the solution works.

6. Promote your story

Marketers have a number of options for distribution of a freshly minted case study. Many brands choose to publish case studies on their website and post them on social media. This can help support SEO and organic content strategies while also boosting company credibility and trust as visitors see that other businesses have used the product or service.

Marketers are always looking for quality content they can use for lead generation. Consider offering a case study as gated content behind a form on a landing page or as an offer in an email message. One great way to do this is to summarize the content and tease the full story available for download after the user takes an action.

Sales teams can also leverage case studies, so be sure they are aware that the assets exist once they’re published. Especially when it comes to larger B2B sales, companies often ask for examples of similar customer challenges that have been solved.

Now that you’ve learned a bit about case studies and what they should include, you may be wondering how to start creating great customer story content. Here are a couple of templates you can use to structure your case study.

Template 1 — Challenge-solution-result format

• Start with an engaging title. This should be fewer than 70 characters long for SEO best practices. One of the best ways to approach the title is to include the customer’s name and a hint at the challenge they overcame in the end.
• Create an introduction. Lead with an explanation as to who the customer is, the need they had, and the opportunity they found with a specific product or solution. Writers can also suggest the success the customer experienced with the solution they chose.
• Present the challenge. This should be several paragraphs long and explain the problem the customer faced and the issues they were trying to solve. Details should tie into the company’s products and services naturally. This section needs to be the most relatable to the reader so they can picture themselves in a similar situation.
• Share the solution. Explain which product or service offered was the ideal fit for the customer and why. Feel free to delve into their experience setting up, purchasing, and onboarding the solution.
• Explain the results. Demonstrate the impact of the solution they chose by backing up their positive experience with data. Fill in with customer quotes and tangible, measurable results that show the effect of their choice.
• Ask for action. Include a CTA at the end of the case study that invites readers to reach out for more information, try a demo, or learn more — to nurture them further in the marketing pipeline. What you ask of the reader should tie directly into the goals that were established for the case study in the first place.

Template 2 — Data-driven format

• Start with an engaging title. Be sure to include a statistic or data point in the first 70 characters. Again, it’s best to include the customer’s name as part of the title.
• Create an overview. Share the customer’s background and a short version of the challenge they faced. Present the reason a particular product or service was chosen, and feel free to include quotes from the customer about their selection process.
• Present data point 1. Isolate the first metric that the customer used to define success and explain how the product or solution helped to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 2. Isolate the second metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 3. Isolate the final metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Summarize the results. Reiterate the fact that the customer was able to achieve success thanks to a specific product or service. Include quotes and statements that reflect customer satisfaction and suggest they plan to continue using the solution.
• Ask for action. Include a CTA at the end of the case study that asks readers to reach out for more information, try a demo, or learn more — to further nurture them in the marketing pipeline. Again, remember that this is where marketers can look to convert their content into action with the customer.

While templates are helpful, seeing a case study in action can also be a great way to learn. Here are some examples of how Adobe customers have experienced success.

Juniper Networks

One example is the Adobe and Juniper Networks case study , which puts the reader in the customer’s shoes. The beginning of the story quickly orients the reader so that they know exactly who the article is about and what they were trying to achieve. Solutions are outlined in a way that shows Adobe Experience Manager is the best choice and a natural fit for the customer. Along the way, quotes from the client are incorporated to help add validity to the statements. The results in the case study are conveyed with clear evidence of scale and volume using tangible data.

The story of Lenovo’s journey with Adobe is one that spans years of planning, implementation, and rollout. The Lenovo case study does a great job of consolidating all of this into a relatable journey that other enterprise organizations can see themselves taking, despite the project size. This case study also features descriptive headers and compelling visual elements that engage the reader and strengthen the content.

Tata Consulting

When it comes to using data to show customer results, this case study does an excellent job of conveying details and numbers in an easy-to-digest manner. Bullet points at the start break up the content while also helping the reader understand exactly what the case study will be about. Tata Consulting used Adobe to deliver elevated, engaging content experiences for a large telecommunications client of its own — an objective that’s relatable for a lot of companies.

Case studies are a vital tool for any marketing team as they enable you to demonstrate the value of your company’s products and services to others. They help marketers do their job and add credibility to a brand trying to promote its solutions by using the experiences and stories of real customers.

When you’re ready to get started with a case study:

• Think about a few goals you’d like to accomplish with your content.
• Make a list of successful clients that would be strong candidates for a case study.
• Reach out to the client to get their approval and conduct an interview.
• Gather the data to present an engaging and effective customer story.

Adobe can help

There are several Adobe products that can help you craft compelling case studies. Adobe Experience Platform helps you collect data and deliver great customer experiences across every channel. Once you’ve created your case studies, Experience Platform will help you deliver the right information to the right customer at the right time for maximum impact.

To learn more, watch the Adobe Experience Platform story .

Keep in mind that the best case studies are backed by data. That’s where Adobe Real-Time Customer Data Platform and Adobe Analytics come into play. With Real-Time CDP, you can gather the data you need to build a great case study and target specific customers to deliver the content to the right audience at the perfect moment.

Watch the Real-Time CDP overview video to learn more.

Finally, Adobe Analytics turns real-time data into real-time insights. It helps your business collect and synthesize data from multiple platforms to make more informed decisions and create the best case study possible.

Request a demo to learn more about Adobe Analytics.

https://business.adobe.com/blog/perspectives/b2b-ecommerce-10-case-studies-inspire-you

https://business.adobe.com/blog/basics/business-case

https://business.adobe.com/blog/basics/what-is-real-time-analytics

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How to Write a Case Study | Examples & Methods

What is a case study?

A case study is a research approach that provides an in-depth examination of a particular phenomenon, event, organization, or individual. It involves analyzing and interpreting data to provide a comprehensive understanding of the subject under investigation. 

Case studies can be used in various disciplines, including business, social sciences, medicine ( clinical case report ), engineering, and education. The aim of a case study is to provide an in-depth exploration of a specific subject, often with the goal of generating new insights into the phenomena being studied.

When to write a case study

Case studies are often written to present the findings of an empirical investigation or to illustrate a particular point or theory. They are useful when researchers want to gain an in-depth understanding of a specific phenomenon or when they are interested in exploring new areas of inquiry. 

Case studies are also useful when the subject of the research is rare or when the research question is complex and requires an in-depth examination. A case study can be a good fit for a thesis or dissertation as well.

Case study examples

Below are some examples of case studies with their research questions:

These examples demonstrate the diversity of research questions and case studies that can be explored. From studying small businesses in Ghana to the ethical issues in supply chains, case studies can be used to explore a wide range of phenomena.

Outlying cases vs. representative cases

An outlying case stud y refers to a case that is unusual or deviates significantly from the norm. An example of an outlying case study could be a small, family-run bed and breakfast that was able to survive and even thrive during the COVID-19 pandemic, while other larger hotels struggled to stay afloat.

On the other hand, a representative case study refers to a case that is typical of the phenomenon being studied. An example of a representative case study could be a hotel chain that operates in multiple locations that faced significant challenges during the COVID-19 pandemic, such as reduced demand for hotel rooms, increased safety and health protocols, and supply chain disruptions. The hotel chain case could be representative of the broader hospitality industry during the pandemic, and thus provides an insight into the typical challenges that businesses in the industry faced.

Steps for Writing a Case Study

As with any academic paper, writing a case study requires careful preparation and research before a single word of the document is ever written. Follow these basic steps to ensure that you don’t miss any crucial details when composing your case study.

Step 1: Select a case to analyze

After you have developed your statement of the problem and research question , the first step in writing a case study is to select a case that is representative of the phenomenon being investigated or that provides an outlier. For example, if a researcher wants to explore the impact of COVID-19 on the hospitality industry, they could select a representative case, such as a hotel chain that operates in multiple locations, or an outlying case, such as a small bed and breakfast that was able to pivot their business model to survive during the pandemic. Selecting the appropriate case is critical in ensuring the research question is adequately explored.

Step 2: Create a theoretical framework

Theoretical frameworks are used to guide the analysis and interpretation of data in a case study. The framework should provide a clear explanation of the key concepts, variables, and relationships that are relevant to the research question. The theoretical framework can be drawn from existing literature, or the researcher can develop their own framework based on the data collected. The theoretical framework should be developed early in the research process to guide the data collection and analysis.

To give your case analysis a strong theoretical grounding, be sure to include a literature review of references and sources relating to your topic and develop a clear theoretical framework. Your case study does not simply stand on its own but interacts with other studies related to your topic. Your case study can do one of the following: 

• Demonstrate a theory by showing how it explains the case being investigated
• Broaden a theory by identifying additional concepts and ideas that can be incorporated to strengthen it
• Confront a theory via an outlier case that does not conform to established conclusions or assumptions

Step 3: Collect data for your case study

Data collection can involve a variety of research methods , including interviews, surveys, observations, and document analyses, and it can include both primary and secondary sources . It is essential to ensure that the data collected is relevant to the research question and that it is collected in a systematic and ethical manner. Data collection methods should be chosen based on the research question and the availability of data. It is essential to plan data collection carefully to ensure that the data collected is of high quality

Step 4: Describe the case and analyze the details

The final step is to describe the case in detail and analyze the data collected. This involves identifying patterns and themes that emerge from the data and drawing conclusions that are relevant to the research question. It is essential to ensure that the analysis is supported by the data and that any limitations or alternative explanations are acknowledged.

The manner in which you report your findings depends on the type of research you are doing. Some case studies are structured like a standard academic paper, with separate sections or chapters for the methods section , results section , and discussion section , while others are structured more like a standalone literature review.

Regardless of the topic you choose to pursue, writing a case study requires a systematic and rigorous approach to data collection and analysis. By following the steps outlined above and using examples from existing literature, researchers can create a comprehensive and insightful case study that contributes to the understanding of a particular phenomenon.

Preparing Your Case Study for Publication

After completing the draft of your case study, be sure to revise and edit your work for any mistakes, including grammatical errors , punctuation errors , spelling mistakes, and awkward sentence structure . Ensure that your case study is well-structured and that your arguments are well-supported with language that follows the conventions of academic writing .  To ensure your work is polished for style and free of errors, get English editing services from Wordvice, including our paper editing services and manuscript editing services . Let our academic subject experts enhance the style and flow of your academic work so you can submit your case study with confidence.

All You Wanted to Know About How to Write a Case Study

What do you study in your college? If you are a psychology, sociology, or anthropology student, we bet you might be familiar with what a case study is. This research method is used to study a certain person, group, or situation. In this guide from our dissertation writing service , you will learn how to write a case study professionally, from researching to citing sources properly. Also, we will explore different types of case studies and show you examples — so that you won’t have any other questions left.

What Is a Case Study?

A case study is a subcategory of research design which investigates problems and offers solutions. Case studies can range from academic research studies to corporate promotional tools trying to sell an idea—their scope is quite vast.

What Is the Difference Between a Research Paper and a Case Study?

While research papers turn the reader’s attention to a certain problem, case studies go even further. Case study guidelines require students to pay attention to details, examining issues closely and in-depth using different research methods. For example, case studies may be used to examine court cases if you study Law, or a patient's health history if you study Medicine. Case studies are also used in Marketing, which are thorough, empirically supported analysis of a good or service's performance. Well-designed case studies can be valuable for prospective customers as they can identify and solve the potential customers pain point.

Case studies involve a lot of storytelling – they usually examine particular cases for a person or a group of people. This method of research is very helpful, as it is very practical and can give a lot of hands-on information. Most commonly, the length of the case study is about 500-900 words, which is much less than the length of an average research paper.

The structure of a case study is very similar to storytelling. It has a protagonist or main character, which in your case is actually a problem you are trying to solve. You can use the system of 3 Acts to make it a compelling story. It should have an introduction, rising action, a climax where transformation occurs, falling action, and a solution.

Here is a rough formula for you to use in your case study:

Problem (Act I): > Solution (Act II) > Result (Act III) > Conclusion.

Types of Case Studies

The purpose of a case study is to provide detailed reports on an event, an institution, a place, future customers, or pretty much anything. There are a few common types of case study, but the type depends on the topic. The following are the most common domains where case studies are needed:

• Historical case studies are great to learn from. Historical events have a multitude of source info offering different perspectives. There are always modern parallels where these perspectives can be applied, compared, and thoroughly analyzed.
• Problem-oriented case studies are usually used for solving problems. These are often assigned as theoretical situations where you need to immerse yourself in the situation to examine it. Imagine you’re working for a startup and you’ve just noticed a significant flaw in your product’s design. Before taking it to the senior manager, you want to do a comprehensive study on the issue and provide solutions. On a greater scale, problem-oriented case studies are a vital part of relevant socio-economic discussions.
• Cumulative case studies collect information and offer comparisons. In business, case studies are often used to tell people about the value of a product.
• Critical case studies explore the causes and effects of a certain case.
• Illustrative case studies describe certain events, investigating outcomes and lessons learned.

Need a compelling case study? EssayPro has got you covered. Our experts are ready to provide you with detailed, insightful case studies that capture the essence of real-world scenarios. Elevate your academic work with our professional assistance.

Case Study Format

The case study format is typically made up of eight parts:

• Executive Summary. Explain what you will examine in the case study. Write an overview of the field you’re researching. Make a thesis statement and sum up the results of your observation in a maximum of 2 sentences.
• Background. Provide background information and the most relevant facts. Isolate the issues.
• Case Evaluation. Isolate the sections of the study you want to focus on. In it, explain why something is working or is not working.
• Proposed Solutions. Offer realistic ways to solve what isn’t working or how to improve its current condition. Explain why these solutions work by offering testable evidence.
• Conclusion. Summarize the main points from the case evaluations and proposed solutions. 6. Recommendations. Talk about the strategy that you should choose. Explain why this choice is the most appropriate.
• Implementation. Explain how to put the specific strategies into action.
• References. Provide all the citations.

How to Write a Case Study

Let's discover how to write a case study.

Setting Up the Research

When writing a case study, remember that research should always come first. Reading many different sources and analyzing other points of view will help you come up with more creative solutions. You can also conduct an actual interview to thoroughly investigate the customer story that you'll need for your case study. Including all of the necessary research, writing a case study may take some time. The research process involves doing the following:

• Define your objective. Explain the reason why you’re presenting your subject. Figure out where you will feature your case study; whether it is written, on video, shown as an infographic, streamed as a podcast, etc.
• Determine who will be the right candidate for your case study. Get permission, quotes, and other features that will make your case study effective. Get in touch with your candidate to see if they approve of being part of your work. Study that candidate’s situation and note down what caused it.
• Identify which various consequences could result from the situation. Follow these guidelines on how to start a case study: surf the net to find some general information you might find useful.
• Make a list of credible sources and examine them. Seek out important facts and highlight problems. Always write down your ideas and make sure to brainstorm.
• Focus on several key issues – why they exist, and how they impact your research subject. Think of several unique solutions. Draw from class discussions, readings, and personal experience. When writing a case study, focus on the best solution and explore it in depth. After having all your research in place, writing a case study will be easy. You may first want to check the rubric and criteria of your assignment for the correct case study structure.

Read Also: ' WHAT IS A CREDIBLE SOURCES ?'

Although your instructor might be looking at slightly different criteria, every case study rubric essentially has the same standards. Your professor will want you to exhibit 8 different outcomes:

• Correctly identify the concepts, theories, and practices in the discipline.
• Identify the relevant theories and principles associated with the particular study.
• Evaluate legal and ethical principles and apply them to your decision-making.
• Recognize the global importance and contribution of your case.
• Construct a coherent summary and explanation of the study.
• Demonstrate analytical and critical-thinking skills.
• Explain the interrelationships between the environment and nature.
• Integrate theory and practice of the discipline within the analysis.

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Case Study Outline

Let's look at the structure of an outline based on the issue of the alcoholic addiction of 30 people.

Introduction

• Statement of the issue: Alcoholism is a disease rather than a weakness of character.
• Presentation of the problem: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there.
• Explanation of the terms: In the past, alcoholism was commonly referred to as alcohol dependence or alcohol addiction. Alcoholism is now the more severe stage of this addiction in the disorder spectrum.
• Hypotheses: Drinking in excess can lead to the use of other drugs.
• Importance of your story: How the information you present can help people with their addictions.
• Background of the story: Include an explanation of why you chose this topic.
• Presentation of analysis and data: Describe the criteria for choosing 30 candidates, the structure of the interview, and the outcomes.
• Strong argument 1: ex. X% of candidates dealing with anxiety and depression...
• Strong argument 2: ex. X amount of people started drinking by their mid-teens.
• Strong argument 3: ex. X% of respondents’ parents had issues with alcohol.
• Concluding statement: I have researched if alcoholism is a disease and found out that…
• Recommendations: Ways and actions for preventing alcohol use.

Writing a Case Study Draft

After you’ve done your case study research and written the outline, it’s time to focus on the draft. In a draft, you have to develop and write your case study by using: the data which you collected throughout the research, interviews, and the analysis processes that were undertaken. Follow these rules for the draft:

• Your draft should contain at least 4 sections: an introduction; a body where you should include background information, an explanation of why you decided to do this case study, and a presentation of your main findings; a conclusion where you present data; and references.
• In the introduction, you should set the pace very clearly. You can even raise a question or quote someone you interviewed in the research phase. It must provide adequate background information on the topic. The background may include analyses of previous studies on your topic. Include the aim of your case here as well. Think of it as a thesis statement. The aim must describe the purpose of your work—presenting the issues that you want to tackle. Include background information, such as photos or videos you used when doing the research.
• Describe your unique research process, whether it was through interviews, observations, academic journals, etc. The next point includes providing the results of your research. Tell the audience what you found out. Why is this important, and what could be learned from it? Discuss the real implications of the problem and its significance in the world.
• Include quotes and data (such as findings, percentages, and awards). This will add a personal touch and better credibility to the case you present. Explain what results you find during your interviews in regards to the problem and how it developed. Also, write about solutions which have already been proposed by other people who have already written about this case.
• At the end of your case study, you should offer possible solutions, but don’t worry about solving them yourself.

Use Data to Illustrate Key Points in Your Case Study

Even though your case study is a story, it should be based on evidence. Use as much data as possible to illustrate your point. Without the right data, your case study may appear weak and the readers may not be able to relate to your issue as much as they should. Let's see the examples from essay writing service :

‍ With data: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there. Without data: A lot of people suffer from alcoholism in the United States.

Try to include as many credible sources as possible. You may have terms or sources that could be hard for other cultures to understand. If this is the case, you should include them in the appendix or Notes for the Instructor or Professor.

Finalizing the Draft: Checklist

After you finish drafting your case study, polish it up by answering these ‘ask yourself’ questions and think about how to end your case study:

• Check that you follow the correct case study format, also in regards to text formatting.
• Check that your work is consistent with its referencing and citation style.
• Micro-editing — check for grammar and spelling issues.
• Macro-editing — does ‘the big picture’ come across to the reader? Is there enough raw data, such as real-life examples or personal experiences? Have you made your data collection process completely transparent? Does your analysis provide a clear conclusion, allowing for further research and practice?

Problems to avoid:

• Overgeneralization – Do not go into further research that deviates from the main problem.
• Failure to Document Limitations – Just as you have to clearly state the limitations of a general research study, you must describe the specific limitations inherent in the subject of analysis.
• Failure to Extrapolate All Possible Implications – Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings.

How to Create a Title Page and Cite a Case Study

Let's see how to create an awesome title page.

Your title page depends on the prescribed citation format. The title page should include:

• A title that attracts some attention and describes your study
• The title should have the words “case study” in it
• The title should range between 5-9 words in length
• Your name and contact information
• Your finished paper should be only 500 to 1,500 words in length.With this type of assignment, write effectively and avoid fluff

Here is a template for the APA and MLA format title page:

There are some cases when you need to cite someone else's study in your own one – therefore, you need to master how to cite a case study. A case study is like a research paper when it comes to citations. You can cite it like you cite a book, depending on what style you need.

Citation Example in MLA ‍ Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies. Boston: Harvard Business Publishing, 2008. Print.

Citation Example in APA ‍ Hill, L., Khanna, T., & Stecker, E. A. (2008). HCL Technologies. Boston: Harvard Business Publishing.

Citation Example in Chicago Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies.

Case Study Examples

To give you an idea of a professional case study example, we gathered and linked some below.

Eastman Kodak Case Study

Case Study Example: Audi Trains Mexican Autoworkers in Germany

To conclude, a case study is one of the best methods of getting an overview of what happened to a person, a group, or a situation in practice. It allows you to have an in-depth glance at the real-life problems that businesses, healthcare industry, criminal justice, etc. may face. This insight helps us look at such situations in a different light. This is because we see scenarios that we otherwise would not, without necessarily being there. If you need custom essays , try our research paper writing services .

Get Help Form Qualified Writers

Crafting a case study is not easy. You might want to write one of high quality, but you don’t have the time or expertise. If you’re having trouble with your case study, help with essay request - we'll help. EssayPro writers have read and written countless case studies and are experts in endless disciplines. Request essay writing, editing, or proofreading assistance from our custom case study writing service , and all of your worries will be gone.

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What Is A Case Study?

How to cite a case study in apa, how to write a case study, related articles.

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The ultimate guide to writing a good case study

Your prospect has done their research. They’ve made a list of requirements. They’ve compared several possible solutions (including yours). They’ve been to your website and had conversations with a salesperson. And they’ve narrowed their search down to your product and your competitor. On paper, both products look similar. But your prospect is still on the fence.

So what’s it going to take for them to go with yours? 

Probably something that convinces them that your product gets results. 

Enter the case study—tiebreaker extraordinaire, and your best friend. 

In this post, we’ll look at:

• What a case study is and why you need one
• 3 elements of a good case study
• How to prep for a case study
• 5 steps to writing your case study
• Tips for making a good case study great
• 5 real-life case study examples

🔍  Are you looking for some case study examples? Check out this free eBook housing five case study examples.

📙 Get the eBook

What is a case study and why should you create one?

A case study is basically a document (it can be a video too) that outlines how a customer used your product to overcome a problem. It’s real-world proof that your product works and gets results.

If your product or service has helped customers get great results, a case study will help you showcase those results to your future customers. They’re an excellent way to attract more business, and can mean the difference between a lost opportunity and a really good end-of-quarter. 

What makes a good case study? 

First, it’s helpful to highlight what makes case studies bad: most are painfully boring. What they have in research and detail, they lack in a cohesive, consumable story. They list numbers and contain data, but the reader isn’t sure what it all means or why it’s relevant to their problem. They end up existing as technical documents that do little to persuade or excite anyone—and that’s unfortunate because they have the potential to be a powerful sales tool that can help you close big deals in the decision-making phase. 

So how do you write a good one, then? Here are three characteristics every good case study should have:

It’s digestible

There’s no hard rule on how long a case study should be. But it’s always a good idea to ask “ How short can we make it? ” A good case study avoids the unnecessary minutiae, knows what it’s trying to say, and communicates it quickly and without ambiguity. With a few exceptions, effective case studies are concise and, clear. 

It’s thorough

On the other side of the length equation, being thorough is also important. While the case study is all about making impressive claims about how a product helped someone achieve a certain result, it also needs to explain how it happened. Good case studies include key details that show how the customer got from A to B using the product—something you don’t get with customer reviews . Don’t make your reader work too hard to visualize the story. If you can use images and videos, use them!

It’s a story

Yes, case studies are sales tools. But the ones really worth reading tell a compelling story with a beginning, middle, and end. They beg to be read all the way through. Often, they present a problem that creates tension and demands a solution. And remember, in this story, the customer is the hero—not you. 

6 steps to find a good case study

Before you start actually writing, there’s a bit of prep work you’ll need to do to make sure your case study is amazing. (This is where good customer service teamwork will really come in handy since your customer support team will have the best intel.)

1. Choose your customer

You may have many customers who’ve seen great results using your product. But you can’t just pick a name out of a hat and showcase their results; they may not be right for your audience or their results may not be typical. For example, don’t feature an enterprise company when most of your customers are small businesses. Or claiming that your clients have a 90% customer retention rate when most of them see 70% on average (still impressive, though).  When considering which customer to use, start by creating a list of customers that meet these criteria:

They’ve seen good results with your product or service

The numbers are what really matter. So choose customers that have seen strong results using your product. But be careful about showcasing exceptionally good results if they’re not likely to be repeated by most.

RingCentral: W2O

They have a respected and recognizable brand

Strong brands give your product instant social proof. They prove that you’re established and trustworthy. That alone can make you a front-runner in the decision-making process. After all, if Big Brand X trusts you, so can a prospect.

They’re a typical customer

Good results don’t carry as much weight when they’re achieved by companies in other industries or verticals. Identify current customers that are similar to your target audience. If you sell enterprise software, choose enterprise customers. If you’re a consultant in the healthcare industry, choose a customer that works in healthcare. 

With your list in hand, you can start reaching out. Picking up the phone can be a lot more effective than sending an email. It’s more personal, lets you build rapport, and is harder to ignore than an email. 

Try to get in touch with customers who use or are very familiar with your product or service—someone who can speak to results. Tell them you’re interested in writing a case study and you’d love to hear more about the results they’ve achieved. Be clear about what the process involves on their part—whether it’s a list of questions in an email, a phone call, or if it involves a camera and crew.  

If you’ve provided value, your customer is more likely to see you as a partner rather than a vendor and, hopefully, will be happy to participate. Remember, you’re also shining a spotlight on their own success. So it’s a win-win.  

That said, you may hear “no” a few times, too. Don’t get discouraged. Some customers will decline for different reasons, regardless of the results they’ve achieved with your product. 

Don’t just use a personal phone to call your customers and interview them. Use a communications app that has a phone calling feature instead. Not only would it show your business as the caller ID (instead of a shady phone number they’re not familiar with), some apps let you record conversations too to make it easy to go back and analyze your conversations (just remember to ask first).

2. Begin your research

Start collecting information about your customer. This is easier if you work as a team. From sales to marketing to customer service, everyone who’s been in touch with customer will have insight about their experience. They can help you understand what your customers do and sell, and what challenges they’re facing. Identify the stakeholders you need to speak with—anyone in the company who uses your product—from the CEO to the marketing intern. Collect stats, even ones you don’t think are relevant—they may be later. 

3. Ask the right questions

Smart questions get insightful answers. Here are some examples of great questions to start with: 

“What were some of the bigger challenges you faced before using our product?”

“How does our product help you reach your individual goals?”

“Which key metrics have improved most since using our product/service?”

“Which parts of your business have been impacted most, and how?”

“How long did it take to roll out our product?”

But don’t stop there. Use these questions to segue into deeper, more targeted questions that underscore the real-world benefits of your product. Let the conversation flow naturally—this is the magic of interviews. You can’t always plan for what interesting topics come up next.

4. Identify your target audience

Beyond your customer’s industry, consider who the target audience of the case study is. Who will see it? Who does it need to influence? While it’s often high-level executives who make large purchase decisions, employees at all levels can act as a champion for your product or brand. Your case study may have to persuade an IT worker that your product or service is going to make their job easier, while it needs to convince the CFO that they’ll see a real return on investment. 

5. Identify the top three things you want to highlight

During the initial research phase, you’ve likely uncovered a lot of interesting information about your customer and their experiences with your product. While it might be tempting to use it all, your case study should quickly and clearly communicate the value of your product. Go through this information and identify the three most important business results you want to communicate in the case study. 

Stats and key performance indicators (KPIs) to consider using in your case study:

• Ramp up time: How long did it take to get started with your product? Did it improve any other facet of their workflow? 
• Sales results: How did the product impact your customer’s bottom line?
• Total return on investment (ROI): How long did it take to earn more than they spent on your product? 
• Productivity increases: Which teams saw improvements in process and workflow? And now much? 

Here’s how RollWorks shows off the amazing ROI that their customers, Payscale, got with them .

6. Choose your format

A case study doesn’t have to exist only as a PDF attachment in a late-stage deal email (although there’s nothing wrong with that). Consider the format. Think about who’s going to read it (or watch it). Do you want to turn this into fancy interactive content ? Does your prospect have the time and interest to dig into the details? Or do they just want the facts? Choose the format that you think best engages the audience that you’re selling to.

Report format

This long-form document has been the gold standard for B2B case studies for many years. This format is effective when the subject matter is complex and demands detail. Remember, a CTO who’s evaluating large-scale business communications platforms for a multi-year deal is going to want more information than a marketing manager who’s evaluating a new social media ad platform:

Here’s how Zendesk presented their case study with IDC as a report .

Keeping things short and sweet is often the best way to get your message heard. By focusing on the key points, you can highlight the biggest wins at just a glance. Most report format case studies can be easily condensed into a one-page document. This is ideal for prospects (and salespeople) who are short on time and prefer something they can quickly scan—like this Adzerk case study with Reddit :

Few things can tell a story the way that video can, and case studies are no exception. They give you an unmatched level of creative freedom and storytelling using music, lighting, pacing, and voice that can evoke emotions and persuade someone using more than just numbers and facts. And at just a couple of minutes long, they can do a lot of heavy lifting in not a lot of time. 

Dropbox: Expedia

Infographic

People love infographics. They’re an excellent way to convey important data in a simple, eye-pleasing way. If your case study requires you to use a lot of data to prove a point—or if visualizing data can make the results more clear—building an infographic case study can be a great investment. 

5 key steps for writing your case study

Congrats. You’ve done the research. You’ve made the calls. You’ve pored over all the details. Now, all you have to do is write. Here are five simple steps that’ll help you create a powerful case study that champions your customer and clearly showcases the real-world value of your products or services. 

1. Introduce the customer

Set the stage for your case study with an introduction. Briefly explain who your customer is with a bit of background information that can include their industry, product, company size, and location. You don’t have to dig into the nuts and bolts of their business, but you do want the reader to understand who they are and what they do. The more color you can provide here, the more impactful it’ll be when you show the awesome results this customer saw because they chose you.

2. State the problem

Every product or service is a solution to a problem. Explain the problem (or problems) that you helped your customer overcome. Describe the larger impact of the issue. Maybe it was customers leaving. Perhaps it was bad leads—or good leads that were never followed up on. Use this as an opportunity to clearly show what was at stake, and make sure you leave the jargon out of it. Frame the problem in simple terms that any reader can understand. 

3. Introduce your product

This is where you begin solving the problem. Briefly introduce your product and what it does. Start on a general level, then apply it to the challenge the customer was experiencing. Talk about which teams or individuals used your product and how they used it. Be sure to make the connection between the customer’s problem and your solution crystal clear. 

4. Show results

The big reveal. What kind of results was your customer able to achieve using your product or service? Speak to how they solved the problem descriptively, but also with cold, hard numbers. Not everything can be measured in numbers (sometimes, peace of mind is a powerful benefit all on its own), but whenever you can, back up your story with the stats. At the very least, this will make it easy for a CFO—or a prospect who wants to buy—to justify buying your product.

For example: 

The customer saw a 33% increase in web traffic, a large influx of social media activity, and a 10% boost in revenue over the duration of the campaign . 

5. Prove it

Don’t forget to show your math. How you get the results is just as important as the results themselves.  What specific steps were taken to get those results? Not only will this help validate your claims, it makes it easier to envision how the reader may be able to achieve them, too. 

8 tips to write a great case study

1. avoid jargon .

As a subject matter expert in your line of work, it can be tempting to go into as much jargony detail as possible. This is normal as it’s often the language we use at work every day. But remember that your customer probably doesn’t speak that language. When in doubt, use an app like Hemingway to make sure you’re writing at a level that most people can understand.  

2. Spend time on your title

It’s tempting to use the case study’s most interesting or impressive KPI as your title. But that also gives away the ending before the story begins, and skips details that are important for context in the process. Try writing a title that piques interest without being a spoiler. 

3. Edit. Then edit again. 

Once you’ve got your first draft completed (and the jargon removed), edit the case study. A few best practices here:

• Look for and eliminate unnecessary adjectives. 
• Speak in an active voice. 
• Look for details that get in the way of the story. 

And then do it all over again until you can’t edit it down anymore without losing the essence of the story. 

4. A picture is worth a thousand words

This is especially true when you’re talking about a block of text that’s trying to communicate a chunk of data. Well-designed charts, graphs, images, or infographics can do the heavy lifting of several pages of text in just seconds. They can also help break up large pieces of text, making the case study easier to read—and nicer to look at. After all, the end goal is to have these read all the way through.

Here’s an example of a graphic from a longer CPA Canada infographic (that includes a short case study embedded inside it): 

5. Pull quotes

Hard data and results are good. But a customer quote is a great piece of social proof and adds a human element to your case study. And that makes your results more believable. Customer quotes can also be used outside of your testimonial too—try adding it on your website, landing pages, or email marketing campaigns or welcome emails to get more people to check out your products and buy online. Here’s an example of what that looks like:

6. Make it scannable

Some people will take the time to read your case study front to back and absorb every detail. Some won’t give it more than a single glance. And sometimes, that person is the decision-maker. Make the most important results easy to spot, read, and retain at a glance. Write headings that are descriptive—if someone just scanned them, would they be able to get the gist of the story? Consider putting a summary at the very beginning of the study, or call out impressive results in a larger font size. 

7. Record your interviews

Ditch the pen and paper. If you’re conducting one-on-one interviews over the phone, you can save yourself a lot of time and energy by recording the conversation (with your customer’s consent, of course). There are tools that can make this easier too—you might find one or two in your marketing stack . For example, you could use RingCentral’s Zapier integration to transcribe your conversation into a text file. 

8. Don’t forget the call to action (CTA)

Your prospect is excited because your case study has done an excellent job of showing how your product or service can help drive results for customers. Now, how do they get in touch with you to learn more? Whether it’s a button that links to your website, an email address, or a phone number, make sure there’s an easy way of getting in touch with you in the case study. 

5 examples of great case studies from real-life companies

Mailchimp: make a connection in real life with postcards.

What we like about it: The title doesn’t give everything away all at once, and the case study tells a story with a beginning, middle, and end. The sections are clearly titled and organized, and the results are easy to find. As a bonus: the video adds a believable human element.

LinkedIn: How Adobe achieves alignment and ABM success with LinkedIn

What we like about it: It’s detailed without being a novella. It understands and speaks to the enterprise customer. The key points are in bullet format and easy to read. The important wins are highlighted. And the video makes the content easy to engage with. 

Hootsuite: How Meliá became one of the most influential hotel chains on social media

What we like about it: The title makes you want to read the whole customer story. They’ve embdedded a well-produced video high on the page, so you can choose to watch it before you read on. The design and layout of the page makes the content and images easy to consume, and the results can’t be missed. Also, they weren’t shy about adding CTAs. 

Slack: So yeah, we tried Slack

What we like about it: This case study follows the tried and true format of customer, problem, solution, and results. It uses humor and relatable characters throughout to support the story and keep your attention. And it’s only two minutes long so it gets the point across quickly. 

Assetworks: South Carolina School Board Insurance Trust

What we like about it: This case study tackles the otherwise complex and technical topic, and simplifies it as an infographic using images to make the results clear. It’s concise and easy to follow because you can see the math without actually doing any math. 

The final word on building a great case study…

Sure, an ad or boosted social media post (more on social media best practices here) can make someone aware of your brand or that your product exists, and a landing page can tell them how your product can solve their problem. 

But there’s nothing quite as powerful as someone else singing your praises. 

And that’s exactly what a case study does. Spend the time to do it right and it has the potential to deliver huge ROI no matter how big or small your company is. And not just once—but over and over again.

Originally published Feb 05, 2020, updated Oct 19, 2022

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Writing A Case Study

A Complete Case Study Writing Guide With Examples

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Simple Case Study Format for Students to Follow

Understand the Types of Case Study Here

Brilliant Case Study Examples and Templates For Your Help

Many writers find themselves grappling with the challenge of crafting persuasive and engaging case studies. 

The process can be overwhelming, leaving them unsure where to begin or how to structure their study effectively. And, without a clear plan, it's tough to show the value and impact in a convincing way.

But don’t worry!

In this blog, we'll guide you through a systematic process, offering step-by-step instructions on crafting a compelling case study. 

Along the way, we'll share valuable tips and illustrative examples to enhance your understanding. So, let’s get started.

• 1. What is a Case Study? 
• 2. Types of Case Studies
• 3. How To Write a Case Study - 9 Steps
• 4. Case Study Methods
• 5. Case Study Format
• 6. Case Study Examples
• 7. Benefits and Limitations of Case Studies

What is a Case Study? 

A case study is a detailed analysis and examination of a particular subject, situation, or phenomenon. It involves comprehensive research to gain a deep understanding of the context and variables involved. 

Typically used in academic, business, and marketing settings, case studies aim to explore real-life scenarios, providing insights into challenges, solutions, and outcomes. They serve as valuable tools for learning, decision-making, and showcasing success stories.

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Types of Case Studies

Case studies come in various forms, each tailored to address specific objectives and areas of interest. Here are some of the main types of case studies :

• Illustrative Case Studies: These focus on describing a particular situation or event, providing a detailed account to enhance understanding.
• Exploratory Case Studies: Aimed at investigating an issue and generating initial insights, these studies are particularly useful when exploring new or complex topics.
• Explanatory Case Studies: These delve into the cause-and-effect relationships within a given scenario, aiming to explain why certain outcomes occurred.
• Intrinsic Case Studies: Concentrating on a specific case that holds intrinsic value, these studies explore the unique qualities of the subject itself.
• Instrumental Case Studies: These are conducted to understand a broader issue and use the specific case as a means to gain insights into the larger context.
• Collective Case Studies: Involving the study of multiple cases, this type allows for comparisons and contrasts, offering a more comprehensive view of a phenomenon or problem.

How To Write a Case Study - 9 Steps

Crafting an effective case study involves a structured approach to ensure clarity, engagement, and relevance. 

Here's a step-by-step guide on how to write a compelling case study:

Step 1: Define Your Objective

Before diving into the writing process, clearly define the purpose of your case study. Identify the key questions you want to answer and the specific goals you aim to achieve. 

Whether it's to showcase a successful project, analyze a problem, or demonstrate the effectiveness of a solution, a well-defined objective sets the foundation for a focused and impactful case study.

Step 2: Conduct Thorough Research

Gather all relevant information and data related to your chosen case. This may include interviews, surveys, documentation, and statistical data. 

Ensure that your research is comprehensive, covering all aspects of the case to provide a well-rounded and accurate portrayal. 

The more thorough your research, the stronger your case study's foundation will be.

Step 3: Introduction: Set the Stage

Begin your case study with a compelling introduction that grabs the reader's attention. Clearly state the subject and the primary issue or challenge faced. 

Engage your audience by setting the stage for the narrative, creating intrigue, and highlighting the significance of the case.

Step 4: Present the Background Information

Provide context by presenting the background information of the case. Explore relevant history, industry trends, and any other factors that contribute to a deeper understanding of the situation. 

This section sets the stage for readers, allowing them to comprehend the broader context before delving into the specifics of the case.

Step 5: Outline the Challenges Faced

Identify and articulate the challenges or problems encountered in the case. Clearly define the obstacles that needed to be overcome, emphasizing their significance. 

This section sets the stakes for your audience and prepares them for the subsequent exploration of solutions.

Step 6: Detail the Solutions Implemented

Describe the strategies, actions, or solutions applied to address the challenges outlined. Be specific about the decision-making process, the rationale behind the chosen solutions, and any alternatives considered. 

This part of the case study demonstrates problem-solving skills and showcases the effectiveness of the implemented measures.

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Step 7: Showcase Measurable Results

Present tangible outcomes and results achieved as a direct consequence of the implemented solutions. Use data, metrics, and success stories to quantify the impact. 

Whether it's increased revenue, improved efficiency, or positive customer feedback, measurable results add credibility and validation to your case study.

Step 8: Include Engaging Visuals

Enhance the readability and visual appeal of your case study by incorporating relevant visuals such as charts, graphs, images, and infographics. 

Visual elements not only break up the text but also provide a clearer representation of data and key points, making your case study more engaging and accessible.

Step 9: Provide a Compelling Conclusion

Wrap up your case study with a strong and conclusive summary. Revisit the initial objectives, recap key findings, and emphasize the overall success or significance of the case. 

This section should leave a lasting impression on your readers, reinforcing the value of the presented information.

Case Study Methods

The methods employed in case study writing are diverse and flexible, catering to the unique characteristics of each case. Here are common methods used in case study writing:

Conducting one-on-one or group interviews with individuals involved in the case to gather firsthand information, perspectives, and insights.

• Observation

Directly observing the subject or situation to collect data on behaviors, interactions, and contextual details.

• Document Analysis

Examining existing documents, records, reports, and other written materials relevant to the case to gather information and insights.

• Surveys and Questionnaires

Distributing structured surveys or questionnaires to relevant stakeholders to collect quantitative data on specific aspects of the case.

• Participant Observation

Combining direct observation with active participation in the activities or events related to the case to gain an insider's perspective.

• Triangulation

Using multiple methods (e.g., interviews, observation, and document analysis) to cross-verify and validate the findings, enhancing the study's reliability.

• Ethnography

Immersing the researcher in the subject's environment over an extended period, focusing on understanding the cultural context and social dynamics.

Case Study Format

Effectively presenting your case study is as crucial as the content itself. Follow these formatting guidelines to ensure clarity and engagement:

• Opt for fonts that are easy to read, such as Arial, Calibri, or Times New Roman.
• Maintain a consistent font size, typically 12 points for the body text.
• Aim for double-line spacing to maintain clarity and prevent overwhelming the reader with too much text.
• Utilize bullet points to present information in a concise and easily scannable format.
• Use numbered lists when presenting a sequence of steps or a chronological order of events.
• Bold or italicize key phrases or important terms to draw attention to critical points.
• Use underline sparingly, as it can sometimes be distracting in digital formats.
• Choose the left alignment style.
• Use hierarchy to distinguish between different levels of headings, making it easy for readers to navigate.

If you're still having trouble organizing your case study, check out this blog on case study format for helpful insights.

Case Study Examples

If you want to understand how to write a case study, examples are a fantastic way to learn. That's why we've gathered a collection of intriguing case study examples for you to review before you begin writing.

Case Study Research Example

Case Study Template

Case Study Introduction Example

Amazon Case Study Example

Business Case Study Example

APA Format Case Study Example

Psychology Case Study Example

Medical Case Study Example

UX Case Study Example

Looking for more examples? Check out our blog on case study examples for your inspiration!

Benefits and Limitations of Case Studies

Case studies are a versatile and in-depth research method, providing a nuanced understanding of complex phenomena. 

However, like any research approach, case studies come with their set of benefits and limitations. Some of them are given below:

Tips for Writing an Effective Case Study

Here are some important tips for writing a good case study:

• Clearly articulate specific, measurable research questions aligned with your objectives.
• Identify whether your case study is exploratory, explanatory, intrinsic, or instrumental.
• Choose a case that aligns with your research questions, whether it involves an individual case or a group of people through multiple case studies.
• Explore the option of conducting multiple case studies to enhance the breadth and depth of your findings.
• Present a structured format with clear sections, ensuring readability and alignment with the type of research.
• Clearly define the significance of the problem or challenge addressed in your case study, tying it back to your research questions.
• Collect and include quantitative and qualitative data to support your analysis and address the identified research questions.
• Provide sufficient detail without overwhelming your audience, ensuring a comprehensive yet concise presentation.
• Emphasize how your findings can be practically applied to real-world situations, linking back to your research objectives.
• Acknowledge and transparently address any limitations in your study, ensuring a comprehensive and unbiased approach.

To sum it up, creating a good case study involves careful thinking to share valuable insights and keep your audience interested. 

Stick to basics like having clear questions and understanding your research type. Choose the right case and keep things organized and balanced.

Remember, your case study should tackle a problem, use relevant data, and show how it can be applied in real life. Be honest about any limitations, and finish with a clear call-to-action to encourage further exploration.

However, if you are having issues understanding how to write a case study, it is best to hire the professionals.  Hiring a paper writing service online will ensure that you will get best grades on your essay without any stress of a deadline. 

So be sure to check out case study writing service online and stay up to the mark with your grades. 

Frequently Asked Questions

What is the purpose of a case study.

The objective of a case study is to do intensive research on a specific matter, such as individuals or communities. It's often used for academic purposes where you want the reader to know all factors involved in your subject while also understanding the processes at play.

What are the sources of a case study?

Some common sources of a case study include:

• Archival records
• Direct observations and encounters
• Participant observation
• Facts and statistics
• Physical artifacts

What is the sample size of a case study?

A normally acceptable size of a case study is 30-50. However, the final number depends on the scope of your study and the on-ground demographic realities.

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Dr. Barbara is a highly experienced writer and author who holds a Ph.D. degree in public health from an Ivy League school. She has worked in the medical field for many years, conducting extensive research on various health topics. Her writing has been featured in several top-tier publications.

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Keep reading

How to Write a Case Study: Step-by-Step Guide with Examples

• October 7, 2022

Content Manager at SocialBee

Why is learning how to write a case study so important?

Well, because it provides your customers with social proof and supporting evidence of how effective your products and services are. Moreover, it eliminates the doubt that usually makes clients give up on their next purchase.

That is why today we are going to talk about the step-by-step process of writing a case study . We prepared five business case study examples guaranteed to inspire you throughout the process.

Let’s get started!

What Is a Case Study?

A case study is a piece of content that focuses on a case from your business history. It describes the problems your client faced and the solutions you used to help them succeed.

The goal of a writing case study is to promote your business , so your aim should be to put together a compelling story with evidence that backs up all your claims.

Case studies use real-life examples to show your clients the quality and effectiveness of your products and services. It’s a marketing tool that provides credibility and it helps your potential clients gain confidence in your brand.

Case studies can be structured in different formats:

• A written document
• An infographic
• A blog post
• A landing page

Case Study Benefits

A great case study makes your potential customers want to benefit from the products and services that helped your client overcome their challenges. 

Here are the benefits of writing a case study:

• It is an affordable marketing practice
• It decreases the perceived risk of your potential clients
• It provides transparency
• It builds trust and credibility among prospective customers
• It makes your potential clients relate to the problem
• It provides your potential clients with a solution for their problems

How to Write a Case Study

Now that you know what a case study is, let’s get into the real reason why you are here — learning how to write an in-depth study.

Here is the step-by-step process of writing a case study:

• Identify the topic of your case study
• Start collaborating with a client
• Prepare questions for the interview
• Conduct the case study interview
• Structure your case study 
• Make it visual

Step 1: Identify the Topic of Your Case Study

A case study starts with a strategy. Choosing what you want to write about should be closely related to your business needs. More specifically, what service or product do you want to promote through your case study?

Because case studies focus on client challenges, business solutions, and results, you have to carefully pick the case that your potential clients will relate to the most. 

To communicate the benefits of your business, you should focus on a customer story that appeals to a specific segment of your audience . Consequently, you will target clients that relate to your customer example while providing a solution for their needs and pain points — your products and services.

Start by focusing all your research methods on identifying your customers’ main pain points. Then find examples of how your products or services have helped them overcome their challenges and achieve their goals .

Furthermore, to make sure you choose the best case study topic for your buyer persona , you should have a meeting with your sales/customer service team. Because they are in close contact with your customers, they will be able to tell you:

• The main challenges your clients face 
• The services/products that bring them the best results 

These are the main two pieces of information you want your case study to focus on.

Step 2: Start Collaborating with a Client

With a clear topic in mind, you have to find the best fit for your case study. 

However, that is not all. First, you must obtain the client’s permission. After all, your business story is theirs too.

So, craft an email to provide your client with an overview of the case study. This will help them make a decision. 

Your message should include:

• The case study format (video, written, etc.) and where it will be published (blog, landing page , etc.)
• The topic of the document
• The timeline of the process
• The information that will be included
• The benefits they get as a result of this collaboration (brand exposure, backlinks)

Additionally, you can offer to schedule a call or a meeting to answer all their questions and curiosities and provide a means for clear and open communication.

Once you receive a positive response from your client, you can continue with the next step of the process: the actual interview.

PRO TIP: A great way to ensure a smooth and safe collaboration between you and your client is to sign a legal release form before writing the case study. This will allow you to use their information and protect you from issues that may occur in the future. Moreover, if the client is not comfortable with revealing their identity, you can always offer them anonymity.

Step 3: Prepare Questions for the Interview

Now that you have the subject for your case study, it’s time to write and organize your interview in several sets of questions.

Don’t forget that the whole structure of your case study is based on the information you get from your customer interview.

So pay attention to the way you phrase the questions. After all, your goal is to gather all the data you need to avoid creating a back-and-forth process that will consume your client’s time and energy.

To help you create the best questionnaire, we created a set of case study questions and organized them into different categories. 

Here are the five main sections your case study interview should contain:

• The client’s background information
• The problem
• The start of the collaboration
• The solution
• The results

A. The Client’s Background Information

This part of the case study interview must give a comprehensive look into your customer’s business and allow your readers to get to know them better.

Here are some question ideas:

B. The Problem

Now it’s time to get into the reason your client came to you for assistance, the initial challenge that triggered your collaboration.

In this part of the interview process, you want to find out what made them ask for help and what was their situation before working with you.

You can ask your client the following case study questions:

C. The Start of the Collaboration

This part of the case study interview will focus on the process that made your collaboration possible. More specifically, how did your client research possible collaboration opportunities, and why they chose your business? 

This information will not only be informative for your future customers but will also give you a behind-the-scenes look into their decision-making process.

D. The Solution

It’s time to get into one of the most significant parts of the case study interview — the solution. Here you should discuss how your services have helped their business recover from the problems mentioned before.

Make sure you ask the right questions so you can really paint the picture of a satisfied customer.

Have a look at these question examples:

E. The Results

The best proof you can give to your customers is through your results. And this is the perfect opportunity to let your actions speak for themselves.

Unlike the other marketing strategies you use to promote your business, the content is provided by your customer, not by your team. As a result, you end up with a project that is on another level of reliability.

Here is how you can ask your client about their results:

Step 4: Conduct the Case Study Interview

Now that you have a great set of case study questions, it’s time to put them to good use.

Decide on the type of interview you want to conduct: face-to-face, video call , or phone call. Then, consult with your client and set up a date and a time when you are both available. 

It should be noted that during the interview it’s best to use a recording device for accuracy. Maybe you don’t have time to write down all the information, and you forget important details. Or maybe you want to be focused more on the conversational aspect of the interview, and you don’t want to write anything down while it’s happening.

Step 5: Structure Your Case Study 

The hard part is over. Now it’s time to organize all the information you gathered in an appealing format. Let’s have a look at what your case study should contain.

Here are the components of a case study:

• Engaging title
• Executive summary
• Client description 
• Introduction to the problem
• The problem-solving process
• Progress and results

A. Engaging Title

Putting that much work into a project, it would be a shame not to do your best to attract more readers. So, take into consideration that you only have a few seconds to catch your audience’s attention. 

You can also use a headline analyzer to evaluate the performance of your title.

The best case study titles contain:

• Relevant keywords
• Customer pain points
• Clear result

Case study example :

B. Executive Summary

Your executive summary should include a thesis statement that sums up the main points of your case study. Therefore, it must be clear and concise. Moreover, to make your audience curious, you can add a statistic or a relevant piece of data that they might be interested in.

Here is what you should include in your executive summary:

• The business you are writing about (only if the clients wants to make themselves known)
• Relevant statistics

C. Client Description 

Here is where you start to include the information you gained from your interview. Provide your readers with a clear picture of your client and create a context for your case study.

Take your client’s answers from the “Client Background” section of the interview and present them in a more appealing format.

D. Introduction to the Problem

In this section, use your client’s interview answers to write about the problem they were experiencing before working with you.

Remember to be specific because you want your audience to fully understand the situation and relate to it. At the end of the day, the goal of the case study is to show your potential customers why they should buy your services/products.

E. The Problem-Solving Process

Next, explain how your service/product helped your client overcome their problems. Moreover, let your readers know how and why your service/product worked in their case.

In this part of the case study, you should summarize: 

• The strategy used to solve the problem of your customer 
• The process of implementing the solution 

F. Progress and Results

Tell your readers about what you and your client have achieved during your collaboration. Here you can include:

• Graphics about your progress
• Business objectives they have achieved
• Relevant metrics 

Step 6: Make It Visual

To elevate the information you have written for your audience, you must make sure it’s appealing and easy to read. And a great way to achieve that is to use visuals that add value to your case study.

Here are some design elements that will make emphasize your text:

• Graphic symbols that guide the eye (arrows, bullet points, checkmarks, etc.)
• Charts, graphics, tables 
• Relevant screenshots from business reports
• The colors and fonts of your brand
• Your client’s logo

Platforms like Canva can really come in handy while designing your case study. It’s easy to use and it has multiple free slide templates and graphics that save you time and money.

PRO TIP: Share Your Case Study Across All Marketing Channels

A case study is a perfect example of evergreen content that can be reshared endlessly on your social media channels .

Aside from helping you maintain a consistent posting schedule with ease, case study-related posts will increase your credibility and push leads toward the bottom of your marketing funnel . Other examples of social proof evergreen content are reviews, testimonials, and positive social media mentions.

To keep track of all your evergreen posts and have them scheduled on a continuous loop, use a social media tool like SocialBee.

Create evergreen content categories where all your posts get reposted regularly on your social media channels. 

Start your 14-day trial today and start using SocialBee for free!

Aside from promoting your case study on social media, you can also feature it in your newsletter that you can create using email newsletter software , include it as a pop-up on your website, and even create a separate landing page dedicated to your customer study.

Share Your Case Study on Social Media with SocialBee!

Get to writing your own case study.

What do you think? Is writing a case study easier than you thought? We sure hope so.

Learning how to write a case study is a simple process once you understand the logical steps that go into it. So make sure you go over the guide a couple of times before you start documenting your customer success stories.

And remember that the goal of your case study is to attract more leads . Therefore you need to include tangible results and valuable details that will compel your audience to invest in your products and services.

Article written by

Content writer at SocialBee

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Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Case study definition

Case study, a term which some of you may know from the "Case Study of Vanitas" anime and manga, is a thorough examination of a particular subject, such as a person, group, location, occasion, establishment, phenomena, etc. They are most frequently utilized in research of business, medicine, education and social behaviour. There are a different types of case studies that researchers might use:

• Collective case studies

• Descriptive case studies

• Explanatory case studies

• Exploratory case studies

• Instrumental case studies

• Intrinsic case studies

Case studies are usually much more sophisticated and professional than regular essays and courseworks, as they require a lot of verified data, are research-oriented and not necessarily designed to be read by the general public.

How to write a case study?

It very much depends on the topic of your case study, as a medical case study and a coffee business case study have completely different sources, outlines, target demographics, etc. But just for this example, let's outline a coffee roaster case study. Firstly, it's likely going to be a problem-solving case study, like most in the business and economics field are. Here are some tips for these types of case studies:

• Your case scenario should be precisely defined in terms of your unique assessment criteria.

• Determine the primary issues by analyzing the scenario. Think about how they connect to the main ideas and theories in your piece.

• Find and investigate any theories or methods that might be relevant to your case.

• Keep your audience in mind. Exactly who are your stakeholder(s)? If writing a case study on coffee roasters, it's probably gonna be suppliers, landlords, investors, customers, etc.

• Indicate the best solution(s) and how they should be implemented. Make sure your suggestions are grounded in pertinent theories and useful resources, as well as being realistic, practical, and attainable.

• Carefully proofread your case study. Keep in mind these four principles when editing: clarity, honesty, reality and relevance.

Are there any online services that could write a case study for me?

Luckily, there are!

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The ordering process is fully online, and it goes as follows:

• Select the topic and the deadline of your case study.

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12 great case study examples (plus case study writing tips)

GatherContent Contributor, Writer

5 minute read.

Interviewed by:

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Lead with Content

How to put content at the centre of digital transformation.

Padma Gillen

Digital Content Consultant

This long-form content style is also becoming more common as more marketers discover its value. According to Hubspot’s 2021 State of Marketing report , more than 30% of marketers use case studies as a primary marketing media—up from 13% in 2020.

If you’re new to the world of case studies, we’ll be diving into what case studies are, why they’re important, and how to create your own. We’ll also highlight some compelling case study examples that you can learn from.

What is a case study?

A good case study highlights customer stories showing the following:

• The problems the business faced before using a product or service
• How the product or service proposed to solve the problems
• The before and after of using a product or service
• The measurable positive impact of the product or service on metrics such as click-through rate, website traffic, or sales

While case studies are most often product or service-focused, sometimes businesses use them to share their brand or founder story.

These types of case studies typically focus on organizational progress, such as how they grew their revenue or website traffic. One example is this Outfunnel case study on how the team saved over 80% of its time with user onboarding.

Why are case studies important?

They may not suit every business. But case studies are beneficial, for example, for helping SaaS brands reach future customers.

If they make sense for your industry, case studies should be an important part of your content marketing strategy for many reasons.

Three reasons you should incorporate them as soon as possible are:

• To provide value to your audience: At its core, the best marketing doesn’t just drive sales; it serves its audience. Case studies are a brilliant way to teach your audience tips they can incorporate into their businesses. It can also serve as research for industry experts to quote.
• To show off your expertise: A great case study is a perfect blend of data and storytelling. It showcases your expertise to your target audience, most likely dealing with similar issues. By telling a good story in your case studies, you’re essentially saying, “Look how we made everything better for X client—we can do that for you, too.”
• As social proof: Because case studies are available to the public, they’re undeniable social proof—better than hard-to-believe testimonials with client initials. This makes them extra valuable as MOFU and BOFU content ; they can drive sales at the click of a button.

Good to Know: Not sure how to use case studies? They work well as lead magnets, landing pages, repurposed blog posts, and, if you have the capacity, even video content!

12 real-life case study examples to bookmark

Reading about the mechanics of case studies is more straightforward than writing case studies from scratch.

That’s why we’ve gathered 12 real-life marketing case study examples you can review before you embark on creating yours.

1. GatherContent | University of Edinburgh

What works: In this great case study, GatherContent includes quotes from the client (the University of Edinburgh) about how their software has improved their content workflow. This adds a human element and will help readers with the same issues identify with the client.

View more GatherContent case studies .

2. Omniscient Digital | AppSumo

What works: Omniscient Digital includes client feedback in video format and shares the results they achieved in a digestible bullet point format.

3. Bit.ly | Vissla

What works: Besides hosting this case study on their website, Bit.ly provides a PDF link that can both be viewed online or downloaded. Plus, the PDF is visually appealing and easy to read.

4. Asana | Autodesk

What works: Asana leads with their impact and includes basic information about their client to the right of the page so the reader immediately gets bite-sized background information.

5. Shopify | Bombas

What works: Shopify includes a video in their case study, as well as multiple eye-catching images of Bombas products. This ensures that the case study serves both companies, possibly generating customer interest in Bombas socks.

6. Outfunnel | Alight Analytics

What works: Outfunnel has repurposed its case study into a blog post, which increases its visibility. The study is also full of client quotes, which adds valuable social proof.

7. Sapling | Zapier

What works: Sapling also shares quick preliminary information about Zapier on the left panel and includes several screenshots to show the impact of their product on the company’s processes.

8. BigCommerce | Skullcandy

What works: The quick metrics in bold hit readers quickly and highlight BigCommerce expertise to potential customers even before they read the entire case study.

9. Google Ads | L’Oreal

What works: Video format. Few things beat hearing the client praise the service and explain the process and results of the campaign in their own words.

10. ActiveCampaign | Your Therapy Source

What works: ActiveCampaign efficiently showcases the problems and solutions before delving into how they helped the client achieve desired results.

11. Intuit | Xenex Healthcare

What works: The main benefit is highlighted on the first page of the PDF and the rest of the study delves into the process and the nitty-gritty of the product’s impact.

12. Grayscale | Upwork

What works: This page features minimal text. It focuses on quotes from decision-makers at Upwork and ends with a call-to-action that will likely drive conversions.

How to write your own case study

How can you write engaging, effective case studies like the examples above? Here are six steps.

1. Identify a worthy case

Think of projects—either for yourself or for clients—that got outstanding results. Then, whittle it down to the cases that your target audience is most likely to relate to , perhaps because they experience the same problem or have the same goal as in the case.

2. Reflect on your chosen case

Once you’ve decided on the case you’ll start with, do some deeper reflection on the details. What was the project goal? What challenges did you encounter along the way? How did you overcome them to reach your goal?

3. Think about differentiation

Take the last step even further and think of anything you did differently than others might. Did you an experimental tactic or strategy or create a custom solution? If so, use those details to subtly show potential customers why they should be interested in what you have to offer.

4. Gather quotes

Next, get hard-hitting quotes from project stakeholders or clients. Having their thoughts on goals, project obstacles, the solutions provided, and the outcomes will make your description of the case more credible.

5. Draft your case study

Time to turn the details you’ve compiled into a case study draft. How? We’ll talk about the best format for case studies shortly.

6. Add visuals

Next, create visuals that will reinforce the main points of your case study. These could include:

• Charts or screenshots to show the change in metrics before and after the project
• An infographic to give a brief visual overview of the case
• Pictures of deliverables (e.g. a web design agency might show a picture of the new site it designed for a client)
• Product images such as screenshots from within your software that was used on the project

After any designated reviewers and approvers give their stamp of approval on the case study, it’s ready to be published and promoted!

What’s the best case study format?

We’ve seen A+ examples of case studies and gotten some more context on how to create them for your brand or organization. Now, it's time to get to work. As you do, remember to include the following vital sections in your case study format:

• Client name and profile
• The problem
• Your solution (and screenshots!)
• Before and after ( real results with data)
• Appealing visuals, photos, illustrations, infographics, charts, and graphs
• A memorable CTA

Ready to get started? Thankfully, you don’t have to go it alone.

GatherContent—a powerful tool for case study creation

GatherContent makes it possible to keep track of all your case study research —even while working with your marketing team. You don’t have to guess what stage the piece is at or consult another tool to know when your part is due or who to pass the torch to.

GatherContent is a content hub that helps you keep all your content creation in one place , whether you’re writing blog posts, email newsletters, social media posts, or case studies. With content modeling features like Components , you can effortlessly maintain brand identity throughout all your case studies.

Read more customer success stories here to learn more!

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Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

• Open access
• Published: 22 April 2024

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• Paola Mejia-Domenzain   ORCID: orcid.org/0000-0003-1242-3134 1 ,
• Jibril Frej   ORCID: orcid.org/0009-0009-0631-0636 1 ,
• Seyed Parsa Neshaei   ORCID: orcid.org/0000-0002-4794-395X 1 ,
• Luca Mouchel 1 ,
• Tanya Nazaretsky   ORCID: orcid.org/0000-0003-1343-0627 1 ,
• Thiemo Wambsganss 1 ,
• Antoine Bosselut   ORCID: orcid.org/0000-0001-8968-9649 1 &
• Tanja Käser   ORCID: orcid.org/0000-0003-0672-0415 1  

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback, finding it redundant and unhelpful. To address this issue, we present RELEX , an adaptive learning system designed to enhance procedural writing through personalized example-based learning. The core of our system is a multi-step example retrieval pipeline that selects a higher quality and contextually relevant example for each learner based on their unique input. We instantiate our system in the domain of cooking recipes. Specifically, we leverage a fine-tuned Large Language Model to predict the quality score of the learner’s cooking recipe. Using this score, we retrieve recipes with higher quality from a vast database of over 180,000 recipes. Next, we apply BM25 to select the semantically most similar recipe in real-time. Finally, we use domain knowledge and regular expressions to enrich the selected example recipe with personalized instructional explanations. We evaluate RELEX in a 2 x 2 controlled study (personalized vs. non-personalized examples, reflective prompts vs. none) with 200 participants. Our results show that providing tailored examples contributes to better writing performance and user experience.

Avoid common mistakes on your manuscript.

Introduction

Writing, decomposing, and revising texts are critical skills in many daily domains and professional environments. Procedural writing is a form of expository writing that promotes the replicability of procedures and the transfer of knowledge (Ambarwati & Listyani, 2021 ). Procedural texts are ubiquitous in many professions, examples include instruction manuals, algorithmic code (Ambarwati & Listyani, 2021 ), lab protocols, and cooking recipes (Alviana, 2019 ). Unfortunately, many learners struggle to write complete and high-quality procedural texts (Mejia-Domenzain et al., 2022 ; Ambarwati & Listyani, 2021 ).

Procedural writing is a so-called heuristic domain (Renkl et al., 2009 ), requiring a combination of knowledge of the learning domain (e.g., how to structure a procedural text) and the application domain (e.g., chemistry in the case of lab protocols). This domain dependence prevents the development of a single algorithmic solution for writing good procedural texts. In this context, learners can benefit from learning from examples. Learning from examples enables learners to "borrow" knowledge from others (Sweller, 1994 ) and abstract general rules that can be used to solve similar problems in the future. Prior research has mainly focused on example-based learning applied to highly structured tasks like mathematics and physics (Sweller, 1994 ; Hilbert et al., 2008 ; van Gog et al., 2008 ). Nevertheless, example-based learning has been studied in heuristic domains with no single correct solution (Renkl et al., 2009 ). In these contexts, the examples are often enriched to include instructional explanations that can reduce the cognitive load by emphasizing relevant characteristics (Schworm & Renkl, 2007 ; van Gog et al., 2008 ). However, the provided examples and instructional explanations are commonly static (Renkl, 2002 ): all learners are provided with the exact same content (e.g., a worked-example by an expert with instructional explanation), independent of their actual skill level. Hence, the provided examples and instructions might be too complex or not relevant to the user, hindering learning and motivation (van Gog et al., 2008 ; Alamri et al., 2020 ).

Providing tailored examples and feedback timely, therefore, has the potential to increase learner performance and experience. While there exists a large body of research on optimal task selection in structured domains (e.g., Bassen et al. ( 2020 )), only a few works have focused on retrieving examples tailored to the user’s context in heuristic domains. Existing research has, for example, employed feature-based similarity metrics (Hosseini & Brusilovsky, 2017 ; Pelánek, 2020 ) or unsupervised semantic sentence similarity methods (Zlabinger et al., 2020 ) to retrieve similar educational items. However, the majority of these works focused on retrieving similar (in terms of the input text provided by the user) expert-created examples, disregarding the actual skill level of the user.

Furthermore, there is also a vast research on providing personalized explanations and instructions for various writing tasks. Existing tools visualize the revision history of the user’s text (Afrin et al., 2021 ) or use an underlying domain-specific structure to enrich the user’s text with feedback and explanations (Wang et al., 2020 ). However, they do not provide suggestions or examples on how to correct the shortcomings in the user’s text.

In this paper, we present RELEX (REcipe Learning through EXamples), an effective and scalable learning system for procedural writing using personalized example-based learning. We have instantiated RELEX in the domain of cooking recipes because of its familiarity and practical relevance to culinary students and chef apprentices, as identified by prior work (Mejia-Domenzain et al., 2022 ). RELEX features a multi-step pipeline retrieving an example that is 1) relevant for the learner (i.e., similar in terms of topic), 2) of better quality than the learner’s text (i.e., tailored to the learner’s skill level), and 3) annotated with explanations and suggestions that the learner’s text is lacking. Our pipeline takes as input the learner’s recipe and predicts its quality using a fine-tuned Large Language Model (LLM). Then, it retrieves a set of texts with a higher quality (than the predicted quality) from a database containing over \(180'000\) rated recipes. Finally, the most semantically similar recipe is extracted from the retrieved candidate set using BM25 .

To evaluate RELEX , we conduct a \(2\times 2\) controlled study with 200 participants, in which we manipulate a) the adaptiveness of the provided example and annotations (adaptive vs. non-adaptive example and feedback), and b) the prompts for reflection (reflective prompts vs. none). We also run the same task with a control group receiving static procedural writing support only. With our analyses, we aim to address the following three research questions: What are the effects of providing a personalized example along with adaptive feedback and reflective guidance on learners’ experience (RQ1), writing performance (RQ2) and revising behavior (RQ3)?

Our results indicate that participants who received tailored examples revised their cooking recipes more, wrote them with higher quality, and had a more positive perception of the tool than the users without adaptive feedback.

Related Work and Conceptual Background

In this paper, we present the design and evaluation of a learning system for personalized example-based learning at scale, which is instantiated in the domain of procedural writing. Our study has therefore been influenced by related work in the areas of (1) learning procedural writing skills, (2) example-based learning in heuristic domains, and (3) adaptive learning.

Learning Procedural Writing Skills

Procedural writing, a form of expository writing, facilitates the transfer of knowledge and the replicability of procedures (Ambarwati & Listyani, 2021 ). This type of writing finds its applications in various fields, ranging from life sciences lab protocols to technical documentation and culinary recipes (Wieringa & Farkas, 1991 ; Mejia-Domenzain et al., 2022 ; Alviana, 2019 ).

While procedural writing is highly dependent on the subject matter, previous research (Wieringa & Farkas, 1991 ; Sato & Matsushima, 2006 ; Traga Philippakos, 2019 ; Adoniou, 2013 ) has identified three main qualities of high-quality procedural texts: structure, clarity, and specificity. Structure refers to the organization of the text like having appropriate sections. Clarity involves providing necessary details, and specificity refers to the use of appropriate, domain-specific vocabulary.

Previous research has found that learners often encounter difficulties when attempting to compose comprehensive and high-quality procedural texts (Mejia-Domenzain et al., 2022 ; Ambarwati & Listyani, 2021 ). Common mistakes, in the case of computer documentation and nuclear power plants procedures, are the incorrect order of steps, missing elements, lack of details, or ambiguous words that lead to confusion (Wieringa & Farkas, 1991 ). Similarly, the recipes documented by chef apprentices are often missing ingredients and exhibit a lack of detail and use of specific vocabulary (Mejia-Domenzain et al., 2022 ).

Given these challenges in writing procedural texts, the question arises: How can we effectively teach and instruct this skill? Effective feedback mechanisms for procedural writing have received limited attention. One notable investigated mechanism involved feedback through simulation: students were prompted to compose a procedural text detailing how to draw a geometrical figure and subsequently received feedback in the form of the figure drawn based on their instructions (Sato & Matsushima, 2006 ).

While there are general learning objectives (structure, clarity, and specificity), the dependence on the domain prevents the development of a single algorithmic solution for writing a good procedural text. In this context, learners can benefit from learning from examples. Previous research has investigated the efficacy of model-based instruction, where students observe a teacher demonstrating and verbally describing the procedure in action. Notably, studies have applied this approach in various scenarios, such as making a peanut butter and jelly sandwich (Traga Philippakos, 2019 ) and preparing a chicken sandwich (Alviana, 2019 ). Encouragingly, both works reported positive effects on the quality of procedural writing resulting from the implementation of the demonstration technique. Surprisingly, despite the proven benefits of using written worked examples in other genres, such as argumentation skills (Schworm & Renkl, 2007 ), their potential application in procedural writing remains largely unexplored.

Example-Based Learning in Heuristic Domains

Example-based learning is an effective method to acquire knowledge by observing and/or imitating what other people do, say, or write (Sweller, 1994 ). It allows learners to build a cognitive schema of how problems should be solved. In addition, learners can abstract general rules from the examples and ultimately transfer and adapt them to other problems (van Gog & Rummel, 2010 ). The vast majority of research on example-based learning has studied their effectiveness in well-structured tasks, such as algebra (Sweller, 1994 ) and physics (van Gog et al., 2008 ). More recently, worked-examples and solved-examples have been applied to non-algorithmic learning domains such as argumentative writing (Schworm & Renkl, 2007 ) and mathematical proof finding (Hilbert et al., 2008 ). In heuristic domains (Renkl et al., 2009 ), where no algorithmic solution can be provided (e.g. cooking recipes), learners acquire heuristics that help them find a solution. Examples in heuristic domains require learners to process two different content levels: (1) the learning domain (i.e., how to structure the solution) and (2) the exemplifying domain (i.e., the topic). In the case of cooking recipes, learners need to understand how to structure a procedural text (learning domain: procedural writing) and be familiar with the cooking domain (the exemplifying domain). Given the two content levels, these examples are referred to as double-content . In structural domains, worked-examples are usually annotated with the steps to solve the problem. In contrast, the double-content examples tend to be enriched with self-explanation prompts and/or additional instructional explanations.

Reflective Prompts . According to the self-explanation effect , learners benefit more from the examples if they can actively explain the examples to themselves (Wong et al., 2002 ). Furthermore, the quality of the self-explanations determines what is learned from the examples (Chi et al., 1989 ). However, frequently, learners’ self-explanations are superficial or passive. Thus, the application of prompts is a possible intervention to increase the quality and depth of the explanations. These prompts should stimulate the active processing of learning materials and direct attention to the central aspects (Schworm & Renkl, 2007 ). The use of self-regulated learning (SRL) prompts has been shown to foster conceptual knowledge (Roelle et al., 2012 ). Furthermore, SRL prompts (i.e., which aspects of the learning materials do you find interesting, useful, and convincing, and which not? ) have been used to help the learner focus on the central elements of examples (Nückles et al., 2009 ) or to guide learners to diagnose their deficiencies and be critical (Fan et al., 2017 ).

Instructional Explanations . Instructional explanations are another possibility to enrich examples. It has been demonstrated that in a first learning phase, instructional explanations improve the learning outcomes compared to when there are no explanations provided  (van Gog et al., 2008 ). However, these explanations can be detrimental later in the learning, since the provided information soon becomes redundant and the explanations increase the cognitive load and hinder learning. Instructional explanations have the following disadvantages in comparison to self-explanation (Renkl, 2002 ): (1) they are not adapted to the learner’s prior knowledge, so they can be redundant or too complex and hard to understand; (2) they are often not timely and therefore hard to integrate as part of the ongoing learner’s activities.

In a \(2\times 2\) study on the effect of self-explanation prompts and instructional explanations, the group that received only self-explanation prompts had the most favorable learning outcomes, whereas the group that received instructional explanations had the highest perception of learning (Schworm & Renkl, 2007 ). Nevertheless, the authors did not examine the use of adaptive instructional explanations. A first step in this direction has been taken by providing so-called faded examples in geometry learning (Schwonke et al., 2009 ). Students were shown complete worked-out examples at first; over time steps from the example were gradually removed. However, the missing steps and the selected examples were pre-determined and not chosen adaptively depending on the students.

To summarize, the provided examples, the reflective prompts, and the instructional explanations are commonly static: all learners are provided with the same content (e.g., a worked-example by an expert with instructional explanation). The examples and explanations are (1) not adapted to the learner’s prior knowledge, so they can be redundant and hence hinder learning (van Gog et al., 2008 ) and (2) not timely and relevant, hence decreasing engagement (Alamri et al., 2020 ). Providing personalized examples and instruction in a timely manner therefore has the potential to improve learning.

Adaptive Learning

Providing personalized examples and adaptive annotations and explanations translates into providing 1) personalized content (the example) and 2) personalized instruction.

Personalized Content . In content level adaptation, the learning objects (e.g., examples, tasks) are selected and adapted based on the content (e.g., current task, answer, knowledge state) of the user (Premlatha & Geetha, 2015 ). One approach to providing personalized content is to retrieve a tailored example from an existing collection. The collection consists of all the examples available, the query is the user’s context and the system ranks examples in the collection based on their similarity with the user’s context. Depending on the task to be learned, the user’s context can be the current task, the answer, the learner’s knowledge or any combination of these. Example retrieval involves three steps: (1) computing a similarity between the learner’s context and examples from the collection, (2) ranking the examples based on their similarity and (3) presenting the most similar or top- k examples to the learner. For instance, Hosseini and Brusilovsky ( 2017 ) used semantic-level similarity-based linking to recommend personalized examples to programming learners.  Pelánek ( 2020 ) explored feature-based (such as the occurrence of domain-specific keywords) and performance-based measures to compare the similarity of educational items in various domains. Furthermore,  Zlabinger et al. ( 2020 ) provided crowdworkers with personalized examples: they used unsupervised semantic sentence similarity methods to retrieve tailored expert-labeled examples.

Obtaining high-quality expert examples for learning purposes can be challenging and costly. In such cases, peer examples serve as an alternative, which, despite their potential loss in quality, can prove more effective in a learning scenario (Doroudi et al., 2016 ). However, evaluating the quality of peer examples poses its own challenge, as the perception of good quality varies among raters, tasks, and genres (Wilson et al., 2014 ). To address this issue, recent research has explored the application of LLMs, like BERT (Devlin et al., 2019 ) or GPT-models (Brown et al., 2020 ), for tasks such as automatically scoring essays (Mayfield & Black, 2020 ), rating recipe nutritional quality (Hu et al., 2022 ), and evaluating text generation (Sellam et al., 2020 ). These LLMs, being at the forefront of natural language processing (NLP) tasks (Devlin et al., 2019 ; Liu et al., 2019 ; Brown et al., 2020 ), offer a promising approach to predict the quality of examples in heuristic domains.

Personalized Instruction . In contrast to generic instruction, personalized instruction (or feedback or explanation) is dynamic, which means that different learners will receive different information (Bimba et al., 2017 ). While there is a range of research on providing personalized feedback and hints in structured domains such as mathematics (Paassen et al., 2018 ) or programming (Ahmed et al., 2020 ), less work has focused on giving automated fine-grained suggestions and explanations in heuristic domains such as expository writing.

Existing NLP-based writing support tools often provide holistic feedback on higher-level properties of the text such as grammar errors, fluency, or coherence (e.g., Grammarly  (Max et al., 2022 )). To provide more detailed guidance, other tools adopt alternative approaches. For instance, ArgRewrite (Afrin et al., 2021 ) visualizes revision histories by annotating a side-by-side comparison of two drafts, providing revision suggestions at the sentence and sub-sentence level. In contrast, ArguLens (Wang et al., 2020 ) utilizes a domain-specific structure by imposing an argumentation-enhanced representation, breaking the user text into argumentation components and standpoints. Despite these valuable contributions, none of the existing systems combine adaptive instruction with a comparison example that could leverage the potential of example-based learning.

RELEX - Learning With Personalized Examples

To study the effect of personalized example-based feedback on learners’ writing performance, revision behavior, and learning experience, we designed RELEX (REcipe Learning through EXamples). The primary purpose of RELEX is to facilitate procedural writing by providing students with tailored examples, accompanied by relevant annotations and reflective prompts. The tool aims to address three key aspects of procedural writing: (a) the organization and structure of texts, (b) the provision of requisite details for enhanced clarity, and (c) the appropriate utilization of specific vocabulary. In the following, we will describe the two main components of RELEX , the user interface and the personalized example retrieval pipeline.

Interaction flow: A learner requests feedback (F1) and receives a tailored example recipe (F2) with highlighted in-text elements (F3) and personalized explanations (F4). The learner is also prompted to reflect on the strengths and weaknesses of the recipes (F5)

User Interface

The user interface of RELEX is illustrated in Fig. 1 Footnote 1 . The main interface is shown on the upper part of the figure. The interface is split into two main panels: the Text Editor (left) where learners can write or edit recipes and request feedback by clicking "Analyze" ( \(F_1\) ) and the Personal Dashboard (right) displaying a selected example recipe with personalized annotations. This Personal Dashboard is again split vertically into two sections, listing suggestions to improve the recipe on the left ( \(F_4\) ) and showing the example recipe ( \(F_2\) ) with missing aspects in the learner’s recipe highlighted ( \(F_3\) ) on the right. Below the main interface (Fig. 1 bottom right), other types of recipe improvement tips together with a fragment of the example recipe that fulfills these suggestions are shown. More specifically, the bottom middle panel shows examples of tips on the specificity of ingredients and steps; and to the right, there are examples on the clarity of the steps. Finally, the Reflection Space ( \(F_5\) , bottom left) invites the user to carefully study the example recipe with the question What aspects of this example recipe do you find interesting, useful, convincing, and which not? ; and compare it to their own recipe with the question What deficiencies does your recipe have compared to the example recipe on the right? . The (synthetic) example in Fig.  1 illustrates these design functionalities. The learner has asked for feedback ( \(F_1\) ) on a recipe including chicken and is provided a similar recipe ("Louisiana Chicken") of higher quality (immediately visible by its clear structure) as an example ( \(F_2\) ). The highlights indicate the missing structural elements (for example, "List each ingredient separately", \(F_3\) ) and the left-hand pane of the personal dashboard suggests other tips on the structure like enumerating the steps ( \(F_4\) ). Other examples of \(F_3\) and \(F_4\) are shown below the main interface. In addition, the reflection panel ( \(F_5\) ) opens to the bottom left where the user answers the questions. The five design functionalities of RELEX (see Table 1 ) are based on design requirements derived from user interviews as well as from literature.

User Requirements . Given that the users should be the main focus of a design effort (Cooper et al., 2007 ), we conducted ten semi-structured user interviews (female-identifying: 6, male-identifying: 4) to better understand the specific user needs when using example-based learning in the context of procedural writing Footnote 2 . Participants described their past experiences with writing procedural texts, which included tutorials, lab protocols, technical manuals, and cooking recipes. One common difficulty they encountered when writing procedural texts was being too vague, missing steps, and having the readers struggle to reproduce the instructions they had written.

From these semi-structured interviews, we derived 22 user stories. The stories contained a multitude of detailed suggestions, such as the type of colors used for highlighting elements of the text or the request to see explanations for the highlighted elements. We clustered the different user stories based on the underlying topic and obtained five groups, from which we formed the following user requirements:

Examples should be relevant and similar so that the users can relate to them.

Users should be able to see more than one example in order to generalize and abstract the relevant elements.

The important elements of the text should be highlighted with different colors (indicating what each color means) to stimulate active processing.

The mechanism should have interactive explanations (e.g., when the mouse scrolls on top or clicks) of the highlighted text in the form of suggestions or questions (that can be dismissed) to help learners understand the underlying structure of the example.

The mechanism should include self-explanation and self-reflection prompts to foster active understanding of the example.

Literature Requirements . After deriving the user-centric requirements, we complemented them with the large body of literature on example-based learning (described in detail in “ Example-Based Learning in Heuristic Domains ”). The impact of this approach is highly dependent on the design of the examples utilized. With this regard, previous research examined various design aspects such as self-explanation prompts (Schworm & Renkl, 2007 ), content guidance (Renkl et al., 2009 ), and highlighting (Ringenberg & VanLehn, 2006 ). In their review paper, van Gog and Rummel ( 2010 ) synthesized these aspects and provide design guidelines for example processing. Similarly, Renkl ( 2002 ) derived design principles for instructional explanations. Drawing from the insights of these two review papers, we establish the literature-based design requirements of RELEX :

Active processing of examples should be stimulated by emphasizing important aspects of the procedure. This will help learners understand the underlying structure and transfer that knowledge to a different task (van Gog & Rummel, 2010 ).

Learners should be instructed to self-explain the example in order to foster active processing and understanding (van Gog & Rummel, 2010 ).

Examples and explanations should be presented on learners’ demand to ensure that they are appropriately timed and used in ongoing knowledge-building activities (Renkl, 2002 ).

Explanations should be short and minimalist to reduce the effort to process them (Renkl, 2002 ).

Explanations should not tell learners things that they already know or do not need to know (Renkl, 2002 ).

Table 1 illustrates the relationship between user and literature requirements and the corresponding functionalities of the tool. The design of these functionalities focused on meeting both the needs identified in the relevant literature and those expressed by users, with the goal of creating a tool that is both educationally effective and user-friendly. Specifically, we began by considering user requirements and then incorporated requirements derived from the literature where applicable. For instance, \(F_1\) caters to the users’ need for accessing multiple examples ( \(U_2\) ) and also aligns with the literature’s emphasis on the availability of on-demand examples ( \(L_3\) ). Similarly, \(F_3\) fulfills the requirement of highlighting important aspects of the learning material ( \(L_1\) ) by using different colors, a feature specifically requested by users ( \(U_3\) ), while also ensuring that redundancies are minimized ( \(L_5\) ). Furthermore, \(F_4\) supports the users’ desire for explanations ( \(U_4\) ) and the use of varied colors ( \(U_3\) ), while also adhering to the recommendation for brevity and minimalism in explanations ( \(L_4\) ). Additionally, \(F_5\) addresses the users’ preference for self-explanation prompts ( \(U_5\) ) in line with literature insights ( \(L_2\) ). Finally, \(F_2\) , which responds to the users’ desire for relevant and similar examples, represents an innovative aspect of our work.

Personalized Example Retrieval Pipeline

To retrieve tailored examples for learners, we propose a multi-step recipe selection pipeline. Our pipeline retrieves a personalized example recipe that satisfies the following constraints: 1) describing a similar dish, thus relevant to the learner, 2) of higher quality than the learner’s recipe, 3) annotated with explanations and suggestions based on identified weaknesses of the learner’s recipe, and 4) retrieved in real-time.

Hence, both the retrieved example and the highlighted suggestions are tailored to the learner’s content (e.g., the type of recipe) and skill level (e.g., the quality of the recipe). The pipeline is illustrated in Fig.  2 . It features an offline and an online component. The offline component (top, in green) consists of three main steps:

Preprocessing: a large cooking recipe database ( RecipeNLG ) is preprocessed to obtain the ratings for each recipe. We denote the resulting dataset of rated recipes as RELEXset .

Fine-Tuning: a general domain language model is fine-tuned on all recipes from RecipeNLG . This model is further fine-tuned on the regression task to predict the stars from RELEXset . We call this fine-tuned model RELEXset-predictor .

Recipe Annotation: the recipies from RELEXset are annotated using writing suggestions obtained from experts. We denote the resulting dataset of annotated, rated recipes as RELEX-sugg-set .

The online component (orange, Fig.  2 bottom), processes the learner’s recipe x in four steps:

Quality Prediction: the stars (quality) of x , denoted as S ( x ), is predicted using RELEXset-predictor .

Recipe Retrieval: all recipes of higher quality than x ( \(SB_x\) ) are retrieved from RELEX-sugg-set .

Relevance Filtering: only relevant recipes according to the missing suggestions are kept. We denote this filtered set as \(rel(SB_x)\) .

Recipe Similarity: recipe similarity is computed to output the most similar example recipe from \(rel(SB_x)\) .

In the following subsections, we describe each step of the offline and online components in detail.

Offline Training and Annotation

As seen in Fig. 2 , the offline phase consists of three steps: Preprocessing the database, training the cooking domain LLM for rating prediction, and annotating the recipes with improvement suggestions. Specifically, we quantify the quality of the recipes using crowd-sourced ratings, which allows us to sort the recipes based on the community’s perception. Then, we train a model to predict the rating (in the form of stars) a new recipe x would obtain, enabling us to extract recipes of higher quality than the recipe x from the database.

Preprocessing . We use RELEXset , a database composed of rated cooking recipes Footnote 3 . The recipes were extracted from RecipeNLG  (Bień et al., 2020 ), a publicly available dataset of clean and formatted versions of cooking recipes. Ratings are real numbers from 0 to 5. They were obtained from food.com , an online recipes site (Majumder et al., 2019 ). We remove from RELEXset all recipes with no ratings. As a result, RELEXset contains more than 180, 000 clean and formatted recipes with more than 700, 000 user ratings. One common problem with user ratings is that different users adopt different criteria and rating scales. Some users might, for example, be more tolerant than others and give higher ratings in general (Jin & Si, 2004 ). To mitigate this bias, following common practices in collaborative filtering models (Jin & Si, 2004 ), we standardize the ratings per user. We denote by \(R_y(x)\) the rating of user y for recipe x and by \(\hat{R}_y\) the average rating of user y across all recipes. Standardization consists in centering \(R_y(x)\) around \(\hat{R}_y\) with a unit standard deviation as follows: \(\widehat{R}_y\left( x\right) = \left( R_y\left( x\right) - \overline{R}_y\right) \bigg /\sqrt{\sum _{z \in \mathcal {X}} \frac{1}{|\mathcal {X}|} \left( R_y(z) - \overline{R}_y \right) ^2}\) with \(\mathcal {X}\) the set of all recipes in RELEXset . As the standardized rating cannot be computed when the standard deviation is 0, users who have only rated one recipe are automatically excluded from the analysis. To obtain a unique rating S ( x ) associated with each recipe, we average the standardized ratings across all users: \(S(x) = \sum _{y \in \mathcal {Y} }\widehat{R}_y\left( x\right) \big /|\mathcal {Y}|\) with \(\mathcal {Y}\) the set of all users in RELEXset . In the remaining part of the paper, we use “stars” to refer to the averaged user-standardized ratings.

Fine-Tuning on Recipes . Given that the recipes consist of text, we follow the recent advances in NLP (Devlin et al., 2019 ; Liu et al., 2019 ; Sanh et al., 2019 ; Brown et al., 2020 ) and use a pre-trained LLM to predict the quality (starts) of a recipe. The choice of pre-trained LLM is based on performance and efficiency. On the one hand, BERT, a widely-recognized LLM, employs self-attention mechanisms to generate context-aware word representations (Devlin et al., 2019 ). While BERT offers robust performance, RoBERTa, an enhanced version, surpasses it in various NLP benchmarks due to extensive training and hyperparameter optimization (Liu et al., 2019 ). On the other hand, RoBERTa’s computational demands are substantial, making it less ideal for real-time applications. To balance performance and efficiency, we opt for DistilRoBERTa, a streamlined version of RoBERTa (Sanh et al., 2019 ). Developed through knowledge distillation, DistilRoBERTa maintains much of RoBERTa’s efficacy but with reduced complexity, making it more suitable for our requirement of real-time prediction without relying on GPUs. This is in line with studies suggesting that increased prediction time can negatively impact user experience (Nah, 2003 ). Therefore, we initialize our predictor with the distilroberta-base checkpoint from HuggingFace’s transformers (Wolf et al., 2019 ).

It is worth noting that, distilroberta-base was trained on general texts from the internet and not specifically in the cooking domain. Following common practices (Gururangan et al., 2020 ; Sun et al., 2019 ), before fine-tuning the model for rating recipes, we first adapt distilroberta-base to the cooking domain by fine-tuning it on a Masked Language Modeling (MLM) task on the entire set of recipes from RecipeNLG . We will refer to the resulting model as RELEXset-MLM .

Fine-Tuning on a Regression Task . Given that we want to predict the averaged user-standardized rating (stars) of a recipe, we formulate the prediction stage as a regression task: for any given recipe denoted as x , the predictive model should output a real-valued star rating symbolized as S ( x ). Thus, we fine-tune RELEXset-MLM to predict the number of stars of recipes in RELEXset . We will refer to the obtained model as RELEXset-Predictor Footnote 4 . The model has six transformer layers, each with a hidden size of 768, and employs 12 attention heads. The intermediate layers in the transformers have a size of 3072. Moreover, the model uses GELU as its activation function and dropout rates for both attention probabilities and hidden layers are set to 0.1 Footnote 5 . Following, we use a fully connected neural network with one hidden layer that takes the [CLS] token final embedding as input and outputs the number of stars S ( x ). We optimize both RELEXset-MLM and RELEXset-Predictor using adam  (Kingma & Ba, 2015 ) with early stopping. Both RecipeNLG and RELEXset are split into train/validation/test sets with a ratio of 80/10/10. This ratio was chosen to provide sufficient data for training while also allowing adequate samples for validation and testing. Given the complexity of the model, the 80/10/10 split ensures that more data is available for training. Furthermore, given the large size of the dataset, \(10\%\) of the data points used for validation and testing are sufficient to validate and test effectively. We used the Kolmogorov-Smirnov test Footnote 6 , a nonparametric test of the equality of continuous probability distributions, to verify that there were no significant differences (train vs validation: \(p=.36\) , train vs test: .91, validation vs test: \(p=.75\) ) between the label distributions in the train, validation and test sets Footnote 7 . Learning rate, batch size, and weight decay were selected on the validation set using grid search from {1e-6, 1e-5, 2e-5, 3e-5, 5e-5, 1e-4}, {32, 64, 128, 256, 512} and {0.01, 0.02, 0.03, 0.05, 0.08, 0.1} respectively. We chose the best model (hereafter referred as RELEXset-Predictor ) based on the validation loss and tested its performance on the hold-out test set. RELEXset-Predictor achieved a mean absolute error (MAE) of 0.39 on the test set, which is slightly better than the baseline MAE of 0.42 (simply predicting the mean). Despite the difference not being significant, RELEXset-Predictor has the ability to generalize to new, unseen data, making it a more reliable tool for making predictions in real-world scenarios than the static baseline predictor. As outlined in “ Online Prediction and Selection ”, the subsequent stages of the pipeline are designed to address the prediction uncertainties by selecting recipes that fall within a quality range set above the MAE threshold to ensure that the recipe is perceived as better by the users.

Recipe Annotation . After choosing a targeted example recipe, we enrich the recipe with suggestions on how to improve the text. These suggestions are based on the three main aspects of high-quality procedural text (Wieringa & Farkas, 1991 ; Sato & Matsushima, 2006 ; Traga Philippakos, 2019 ): structure (i.e., clear organization of the text), clarity (i.e., appropriate degree of detail), and specificity (i.e., proper use of technical terms). The suggestions can be divided into general suggestions concerning the learning domain (i.e., how to write procedural text) and into suggestions specific to the exemplifying domain (cooking recipes). The domain-general suggestions are derived from the main qualities of good procedural text identified in previous work (Wieringa & Farkas, 1991 ; Sato & Matsushima, 2006 ; Traga Philippakos, 2019 ). The domain-specific suggestions are derived from "The Recipe Writer’s Handbook, Revised and Expanded" (Ostmann & Baker, 2001 ). In this handbook, two recipe book editors give punctual recommendations on how to write a good recipe in terms of the learning objectives (structure, specificity, and clarity). We use the keywords “specify” and “indicate” to retrieve 45 suggestions from the handbook. Table 2 lists all the domain-general suggestions as well as examples of domain-specific suggestions. There are four suggestions related to the structure and three suggestions related to the clarity of the text. For these two categories, there is a direct mapping between domain-general and domain-specific annotations. There are in total 38 recipe-specific suggestions related to the specificity of the steps and material Footnote 8 .

We transform the suggestions into explicit rules to be able to annotate each recipe for each of the 45 suggestions. Specifically, we classify each of the 45 suggestions as "followed", "missing", or "not relevant" for each recipe. For example, if the recipe does not require a pan, the suggestion to “ indicate the size and type of the pan ” is not relevant; on the other hand, if the recipe requires a “pan”, but the size (small, medium, large) or type (frying, skillet, non-stick, ceramic, etc) are not specified, the suggestion is “missing”. To facilitate this classification, we employ a rule-based system using regular expressions. This method allows for an automated annotation of the recipes. Our classification algorithm operates in two stages. Initially, it scans the recipe for keywords related to each suggestion (main keyword). Following the example, it would look for “pan” or synonyms. Subsequently, when a keyword is identified, the algorithm examines a 20-character range surrounding it to detect any mention of the specific characteristics detailed in the suggestion (supporting keywords). In our example, it would look for the size or type of the pan. This process is repeated for all suggestions, and the results are compiled into a dictionary. This dictionary reflects the status of each suggestion (followed, missing, or not relevant) for every recipe, including the specific locations where these criteria are met.

The previously described classification algorithm aims at ascertaining the presence of the specified keywords (supporting keywords) in proximity to another predetermined keyword (main keyword). We define “proximity” as a 20-character range to account for intervening descriptors (such as adjectives or qualifiers) that are typically positioned close to their corresponding nouns that might not be related to the suggestion. For example, for the suggestion about specifying the form of nuts (e.g., whole, halved, chopped, etc) in proximity to a nut’s name (e.g., walnut, almonds), a phrase like “slivered (form) blanched almonds (nut)” exemplifies a case where looking at the preceding or succeeding word fails to recognize the relationship due to the intervening descriptors. Given that the average word length in English is 4.8 Footnote 9 , we chose a 20-character range that is approximately 4 words apart. Empirical trials confirmed that this range effectively captures the necessary details in the majority of cases, striking a good balance between capturing essential information and excluding unrelated text that might pertain to other ingredients or elements rather than describing the main keyword.

To assess the rule-based annotation performance, we conducted an evaluation using a random sample of recipe segments. Two annotators, who are also authors of this work, were involved in this process. One of the annotators had participated in the generation of the rule-based regular expressions, while the other annotator was unfamiliar with the generation process. The choice of annotators was a pragmatic decision that allowed us to evaluate the rule-based model without the need for recruitment of external annotators. Per each suggestion, we randomly selected five recipe segments where the two-step annotation algorithm indicated that the suggestion was present and five segments where it was missing. The segments were shuffled and manually labeled to indicate whether the rule was being fulfilled or not. The Cohen’s Kappa score between the annotators was 0.85 (near perfect agreement (Landis & Koch, 1977 )) and the average accuracy was 0.95. We acknowledge that the choice of annotators could have induced a level of subjective interpretation. However, the random selection and shuffling of segments for annotation likely mitigated any subconscious biases. Moreover, the high inter-rater reliability indicates that the suggestions provided were clear and consistent, regardless of the annotators’ prior involvement in the process.

Online Prediction and Selection

The online part of the pipeline consists of retrieving a tailored comparison recipe for the user in real-time.

Quality Prediction . When a participant y asks for feedback on a recipe x , the first step consists in predicting the stars of the input recipe \(S_y(x)\) using RELEXset-Predictor .

Recipe Retrieval . In the next step, a candidate subset \(SB_x\) of recipes with higher quality (i.e., a higher stars value) is retrieved from RELEX-sugg-set . \(SB_x\) contains all the recipes with a rating withing the range \([S_y(x) + 0.4, S_y(x) + 0.8]\) . For example if the rating of the input recipe \(S_y(x)=1\) , \(SB_x\) will contain all the recipes with a standardized rating within the range [1.4, 1.8]. This range was chosen based on RELEXset-Predictor ’s MAE (0.39) as we did not want \(SB_x\) to contain recipes that fit within the error range of the predictor. Moreover, we wanted to show the user a peer recipe that is of better quality, but still similar enough for the user to relate to and not be discouraged by peer excellence (Rogers & Feller, 2016 ). We tested the selected range in a pilot study with 10 participants. We asked the participants to evaluate the level of the recipes seen in comparison to theirs, and the options were "much worse", "worse", "same level", "better" and "much better". None of the participants stated that the recipes were "much better", \(60\%\) perceived the recipe as better, and \(40\%\) as their same level.

Relevance Filtering . The next stage of the pipeline consists of filtering the candidate subset \(SB_x\) according to relevance. We consider that a recipe contains relevant feedback if it can exemplify how to successfully improve the input recipe x . To assess the relevance of the candidate recipe, we first identify the suggestions that are missing from the input recipe x . We then filter out from \(SB_x\) the recipes that do not contain relevant feedback. We postulate that a recipe contains relevant feedback if it follows at least one suggestion that is missing from x . We denote as \(rel\left( SB_x\right) \) the set of recipes from \(SB_x\) containing relevant feedback. To exemplify the filtering stage, let us consider the following minimal example: z is a recipe where the only suggestion classified as missing is " indicate the intensity of the heat ". Therefore, we will remove from \(SB_z\) all recipes that do not specify the intensity of the heat when they should have. Thus, the resulting set \(rel\left( SB_z\right) \) will contain only recipes that follow the suggestions: " indicate the intensity of the heat ".

Recipe Similarity . The final step of the online pipeline aims to retrieve from \(rel\left( SB_x\right) \) the recipe that is most similar to x . We compute the recipe-recipe similarity using BM25  (Robertson & Walker, 1994 ), a Bag-of-Word Information Retrieval model. Our main motivation for using BM25 instead of a LLM fine-tuned for text similarity such as cpt-text  (Neelakantan et al., 2022 ) is efficiency. Indeed, constraint \(C_5\) enforces our pipeline to work in real-time and because in some cases \(rel\left( SB_x\right) \) can contain more than 100, 000 recipes, we decided to use an efficient Bag-of-Word model. We evaluated the similarity computation time for 100 random recipes in the worst-case scenario (with 100, 000 comparisons) and we found that the computation time was on average 0.8 seconds ( \(\sigma = 0.1\) seconds) on a laptop with an Apple M1 processor. After computing the pair-wise similarities between x and all recipes in \(rel\left( SB_x\right) \) , we return the recipe with the highest similarity.

Experimental Design

To evaluate RELEX , we conducted a controlled user study, where we asked participants to complete three procedural writing tasks in the domain of cooking recipes using our system. In the following, we will describe the study design, participants, procedure, and the employed measures in detail.

We employed a fully randomized between-subjects design, encompassing two main factors: feedback type (adaptive vs. non-adaptive) and reflection guidance (with vs. without prompts). This resulted in four distinct treatment groups, each experiencing a specific combination of feedback and reflection instructions. To provide a basis for comparison, we also included a control group ( CG ), which received general static rules on how to write a cooking recipe, representing the traditional approach to support recipe writing without the provision of a peer example. The subjects were randomly assigned to one of the five conditions. The experiment task and questions were exactly the same for all groups; we only manipulated the adaptivity and the reflective prompts between participants. The adaptive feedback encompasses the tailored example recipe along with personalized in-text highlighting and explanations; and the reflective prompts refers to the Reflective Space where the learner is promoted to compare the recipes.

Each group used a different version of RELEX . Figure 3 shows the interface for the four treatment groups experiencing varying levels of adaptive feedback and reflection guidance. The grid has two axes: Reflective Prompts and Adaptive Feedback. Each axis has two options: With and without. Thus, each quadrant represents a distinct group differentiated by the presence or absence of adaptive feedback and reflection prompts. The interface for \(G_{R}^{A}\) including reflective prompts and adaptive feedback is displayed in the upper left quadrant. \(G_{R}^{A}\) used RELEX with all relevant functionalities including adaptive feedback (i.e., tailored example annotated with personalized in-text highlighting and explanations) and reflection prompts. The interface for \(G_{NR}^{A}\) is shown in the upper right quadrant. Accordingly, \(G_{NR}^{A}\) used RELEX without the reflection prompts. Next, as seen in the lower left quadrant, \(G_{R}^{NA}\) used RELEX without adaptive feedback, but with reflection prompts. Lastly, \(G_{NR}^{NA}\) (lower right quadrant) without reflective prompts and without adaptive feedback. Subjects in this group were displayed a pre-selected recipe from the database. Specifically, we pre-selected five complete (in terms of structure and level of detail, see “ Personalized Example Retrieval Pipeline ”), but not perfect recipes (in terms of stars, see also “ Personalized Example Retrieval Pipeline ”) from the database. We chose to not display perfect recipes in order to keep the impression of a peer recipe. Furthermore, we made sure that the five pre-selected recipes covered a range of cooking methods (e.g., dessert, hot dish, etc.). Finally, the CG did not see an example recipe; instead, an instruction manual on how to write recipes was displayed in the right pane of the tool.

Illustration of the study setup using a randomized 2 (feedback type: adaptive vs. non-adaptive) x 2 (reflection guidance: with vs. without prompts) between-subjects design resulting in four treatment groups

Participants

We recruited 200 paid participants from Prolific to conduct a controlled experiment. We chose Prolific as a platform since past research on behavioral research platforms reported the highest response quality and sample diversity for Prolific (Peer et al., 2017 ). To avoid an overlarge diversity in our sample, we recruited participants in the age range from \(18-30\) with at least an undergraduate degree as the highest completed education level. We excluded participants who did not complete the post-test or had technical problems, remaining with 187 participants for our analyses. Table  3 summarizes the demographic information per group. We did not find significant differences between the groups in terms of age ( \({\chi }^2(4) = 1.07, p = .90\) ) or gender ( \({\chi }^2(8) = 7.49, p = .48\) ) as indicated by a non-parametric Kruskal-Wallis test Footnote 10 . The median time spent on the study was 70 minutes. Participants were paid \(9\pounds \) per hour.

The experiment consisted of three main parts that were the same for all groups: (1) a pre-survey (including a pre-test), (2) three procedural writing tasks (in the domain of cooking recipes), and (3) a post-survey (including a post-test). Different from the three main tasks centered on composing cooking recipes, the pre-test and post-test were situated in a distinct domain: furniture assembly. The different domain was chosen in order to study whether the users could transfer the acquired procedural writing skills to another task.

Pre-Survey . The experiment began with a pre-survey, where we a) controlled the effectiveness of the randomization using two different constructs (see Table 4 ) and b) conducted a pre-test for procedural writing skills in the domain of furniture assembly. We started by asking each participant three questions about their previous cooking experience and documenting their recipes. Next, we captured participants’ attitudes towards technology (Agarwal & Karahanna, 2000 ). Both constructs were measured on a 7-point Likert scale (1: totally disagree to 7: totally agree, with 4 as a neutral statement). Finally, we assessed participants’ procedural writing skills in a transfer domain. Specifically, we asked participants to write the instructions to assemble an IKEA piece of furniture (a TINGBY table) based on a step-to-step diagram (illustration only, no text available). Participants were requested to spend five minutes solving the task.

Procedural writing assignment . In the procedural writing part of the experiments, we asked the participants to perform three cooking recipe writing tasks. The task was: "It’s a Sunday afternoon and your best friend calls you with a very hectic voice: they need to prepare a dish for their family who is going to visit in the evening. Your friend asks you to provide them with three different cooking recipes to choose from. Be aware that your friend has very little cooking experience and therefore you have to write the recipe as structured and understandable as possible." All groups were asked to watch an introduction video on the usage of the tool before the first recipe-writing task.

Post-Survey. The experiment ended with the post-survey. First, we conducted the post-test, where participants were asked to write instructions on how to assemble a different piece of IKEA furniture (an EKET cube) based on a step-by-step diagram (illustration only). We made sure that the difficulty of assembly was similar for both tests. As in the pre-test, participants were asked to spend five minutes on the task. Next, we measured the users’ perception using different constructs from literature (see Table 4 ). Again, all behavioral constructs were measured on a 7-point Likert scale (1: totally disagree to 7: totally agree, with 4 a neutral statement). Finally, participants answered five qualitative questions about the usage of the tool, the impact of RELEX on participants’ recipe writing, and user experience.

Measures and Analysis

To investigate the impact of our system, we studied learners’ writing performance on the task and the transfer task. Moreover, the impact on learners’ perception was assessed using a post-survey with qualitative questions. Finally, we assessed the impact on learners’ revision behavior using a keystroke analysis.

Task Performance . To assess participants’ progress in recipe writing skills, we used each participant’s first recipe (i.e., their first submission) as an initial evaluation and their last revised recipe (i.e., their last submission) as a final evaluation. Specifically, we computed two scores for each recipe: the predicted stars ( \(S_y(x)\) ) and the quality score ( \(Q_y(x)\) ). The first score, the predicted stars ( \(S_y(x)\) ), was obtained using the model fine-tuned to predict the recipes’ stars ( RELEXset-Predictor , see “ Offline Training and Annotation ”). We gave as input the recipe written by the participant and the model returned the predicted stars. The second score is a quality/completeness score based on the quality criteria (structure, clarity, specificity) implemented by the set of suggestions derived in “ Offline Training and Annotation ”. We computed the quality score \(Q_y(x)\) for a recipe x from a participant y based on \(A_{x}\) , the set of suggestions relevant to recipe x . For each suggestion \(r_i \in A_{x}\) , we computed a score \(s_{y,r_i} \in \{0,1\}\) , where 1 indicates that the suggestion was followed and 0 indicates that the suggestion was missing. We then computed the quality score as \(Q_y(x)=\sum s_{y,r_i}/|A_x|\) . The quality score, therefore, measures the ratio of followed rules for a recipe.

Transfer Performance . To evaluate the pre-and post-test tasks, we assessed the learning objectives of procedural texts. We thus adopted the subset of suggestions regarding structure, clarity, and specificity described in Table 2 . We made adjustments to \(r_8\) and \(r_9\) to better suit the context of furniture assembly. Specifically, for the specificity of materials ( \(r_8\) ), we examined the level of detail provided in describing the materials, such as explicitly naming them as wood or metal. For the specificity of steps ( \(r_9\) ), we assessed how accurately the components were referred to, including terms like screws, pegs, grooves, and knobs. Similar to measuring task performance in terms of suggestions, for each relevant suggestion \(r_i\) with, \(i \in \{1,...,9\}\) , we computed a quality score \(s_{y,r_i} \in \{0,1\}\) , where 1 indicates that the requested suggestion is followed and 0 indicates that the suggestion is missing. The overall transfer score of the task was then calculated as \(T_y(task)=\sum s_{y,r_i}/9\) .

Perception . We analyzed participants’ open responses with topic modeling. We used BERTTopic  (Grootendorst, 2022 ), a technique that incorporates the contextual information of the text by clustering embeddings generated by pre-trained transformer-based language models. We used Sentence-BERT  (Reimers & Gurevych, 2019 ) to embed the sentences in the fixed-size representation required by BERTTopic . More specifically, we used all-mpnet-base-v2 checkpoint from HuggingFace’s Transformers. We split the participants’ answers into sentences v and clustered them to obtain the topics z . The topics extracted by BERTTopic are described in terms of the most important words and their relevance. We interpreted them and assigned names to each cluster. In a next step, we computed for each sentence v the probability \(p_{v,z}\) of belonging to each cluster z . We considered that a sentence v belongs to a cluster z if \(p_{v,z} > 0.3\) to allow for sentences to be categorized into at most three topics. We then grouped the sentences by participant y to obtain the set of topics \(Z_y\) for their entire text answer. As an example, assume that the answer of a participant y consisted of three sentences \(v_1\) , \(v_2\) and \(v_3\) with assigned topics: \(v_1\) - topics A , B , \(v_2\) - topic B , and \(v_3\) - topics A , C , D . In this case, the set of topics associated with the text answer of participant y is \(Z_y = {A,B,C,D}\) .

Revision Behavior . To study users’ revision behavior, we analyzed the changes made to their recipes after receiving feedback. Based on this feedback, participants were instructed to refine their recipe. This process of analysis and improvement was not limited to a single iteration; participants could engage in multiple cycles of revision. Thus, we define a "revision" to be the set of edits (deletion, insertion, and changes) executed after receiving feedback on the recipe submission. For example, if a user requests feedback, reviews an example recipe, and subsequently makes several changes to their recipe, we consider the sequence of modifications as a single revision. If the user then proceeds to engage with the "Analyze" function once more, making additional edits to the recipe, this subsequent round of alterations is classified as a second revision. Following previous work on revision behavior and analyzing keystrokes (Mouchel et al., 2023 ; Zhu et al., 2019 ), we computed the following two features: revision time (time spent revising) and total number of revisions (number of times recipe was edited and re-submitted).

In this study, we sought to examine the effects of adaptive feedback and reflective prompts on learners’ perception (RQ1), procedural writing skills (RQ2), and revision behavior (RQ3). To achieve this, we conducted a comprehensive analysis, both quantitative and qualitative, on the data gathered from the post-survey, procedural writing assessments, and the pre- and post-test. In the following analyses we present the p -values resulting from the analysis, the effect sizes are available at: https://github.com/epfl-ml4ed/relex/tree/main/docs/effect-sizes.pdf . In a first preparatory step, we verified the randomization by checking for differences between the five groups at the beginning of the study. A Kruskal-Wallis test Footnote 11 confirmed that there were no differences in participants’ procedural writing skills as measured by their quality scores \(T_y(pre)\) (see “ Measures and Analysis ”) achieved on the pretest task ( \({\chi }^2(4) = 4.85\) , \(p = .30\) ). For the pre-survey, we obtained the construct score by averaging the items in each construct (all factor loadings were greater than 0.7) and found no significant differences either in participants’ previous experience with documenting cooking recipes ( \({\chi }^2(4) = 4.83\) , \(p = .30\) ) and attitudes towards technology ( \({\chi }^2(4) = 4.2\) , \(p =.37\) ). Lastly, we analyzed how long participants took to complete the study. On average, participants took 73 minutes. Again, we found no significant differences ( \({\chi }^2(4) = 8.15\) , \(p = .09\) ) between the average duration time per group (78 minutes for \(G_{R}^{A}\) ; 73 minutes for \(G_{NR}^{A}\) ; 64 minutes for \(G_{R}^{NA}\) ; 79 minutes for \(G_{NR}^{NA}\) ; and 70 minutes for CG .)

RQ1: Impact on Learners’ Experience

To answer our first research question, we analyzed participants’ user experience and perception. Based on the findings of Schworm and Renkl ( 2006 ), we hypothesized that the perceived learning gain, usefulness, behavioral intention, and attitude towards use would be higher in the groups with adaptive feedback: \(G_{R}^{A}\) , \(G_{NR}^{A}\) (H1-1) . In addition, in line with Venkatesh and Bala ( 2008 ), we hypothesized that the perceived ease of use would be the highest in the CG and the lowest in \(G_{R}^{A}\) given that the CG used the version with the simplest interface and functionality (H1-2) .

Quantitative Analysis . In a first analysis, we compared the post-survey constructs between groups using the Kruskal-Wallis test 11 . The results confirmed significant differences between groups concerning perceived usefulness ( \({\chi }^2(4) = 14.30\) , \(p < .01\) ) and behavioral intention ( \({\chi }^2(4) = 14.20\) , \(p < .01\) ). To further investigate the specific differences within these constructs, we performed a pairwise comparison using the Wilcoxon Rank Sum test, correcting for multiple comparisons via a Benjamini-Hochberg (BH) procedure.

Figure 4 depicts the distribution per group and construct, with statistically significant differences marked with * ( \(p < .05\) ) and ** ( \(p < .01\) ). We observe that participants from the group receiving both adaptive feedback and reflective prompts ( \(G_{R}^{A}\) ) perceived the tool as more useful than the participants from the groups without adaptive feedback ( \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) ) and the control group ( CG ). Likewise, participants in \(G_{NR}^{A}\) (adaptive feedback, no reflective prompts) also reported higher perceived usefulness than participants in \(G_{R}^{NA}\) . It is worth mentioning that the only variant between these two groups was the presence of adaptive feedback. Moreover, regarding the behavioral intention, both \(G_{R}^{A}\) and \(G_{NR}^{A}\) (the groups with adaptive feedback) exhibit significantly higher scores than all other groups.

Post-survey answers comparison between control and treatment groups. Statistically significant differences between groups are indicated with * ( \(p < .05\) ) and ** ( \(p < .01\) )

In a subsequent analysis, we investigated the differences between the groups that received adaptive feedback ( \(G_{R}^{A}\) and \(G_{NR}^{A}\) ) and the ones that did not ( \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) ). We found that the groups with adaptive feedback had significantly higher scores in four out of five constructs: perceived usefulness ( \({\chi }^2(1) = 11.46\) , \(p < .001\) ); attitudes toward use ( \({\chi }^2(1) = 5.2\) , \(p < .01\) ); behavioral intention ( \({\chi }^2(1) = 12.08\) , \(p < .001\) ); and perceived learning gain ( \({\chi }^2(1) = 6.07\) , \(p < 0.01\) ). Interestingly, there were no significant differences in the perceived ease of use.

Perception Analysis . In our subsequent analysis, we delved into participants’ open-text responses to gain deeper insights into the observed effects from the post-survey. Specifically, we first examined the responses to the question "What did you like?". The responses reflected a positive reception of the system’s features, including comparative viewing of recipes, in-text highlighting, ease of use, helpful suggestions, and educational insights. The most frequently mentioned aspect, noted by 16% of participants, was the opportunity to see other recipes. This feature was particularly appreciated for its comparative aspect, as highlighted by a participant from \(G_{NR}^{NA}\) : "[I liked] that I could compare my recipe with another, which makes you want to improve yours to a higher standard." The next notable aspect was in-text highlighting, valued by 11% of participants. A participant from \(G_{R}^{A}\) described this feature as "useful to quickly identify areas, and it helps you learn and observe things you can improve quite intuitively." Ease of use was also a significant point of appreciation. Participants described the system as "really intuitive, user-friendly" and "clear, easy to use and methodical ." Additionally, participants praised the quality of the suggestions offered. Comments like "I liked that it gave useful suggestions that are actually valuable to a beginner" and "It gives me tips and advice on how I can improve the wording and formatting of my recipe, so I can easily make these changes to improve the clarity and how clear my recipe is" were common. Finally, the educational insights provided by the system were highlighted. One participant mentioned, "it allowed me to gain a better perspective on how to write instructions in a clearer and more concise manner. It helped me to focus on problem areas that I subconsciously missed because it has become ingrained into my writing style. Overall, I would say that it made me more aware of my writing foibles and allowed me to thus tackle those problems and improve." Another added, "Despite reading a lot of recipes in the past, I do think that it very quickly guided me to writing more concise and easier to understand instructions. I like how quickly I learned using it, as well as how it leads you to figure out how to write good instructions rather than simply telling you a strict set of rules you must use."

Next, we examined participants’ feedback on potential improvements to the tool. Not surprisingly, \(12\%\) of participants in the control group ( CG ) proposed personalized content. One participant suggested, "I would change the recipe suggestions to be directly relevant for each written recipe. For example, after the first recipe, I added numbers to each step in the following two recipes, but still received the same feedback, so it became less useful." Similarly, \(16\%\) and \(4\%\) of the participants in \(G_{NR}^{NA}\) and \(G_{R}^{NA}\) , respectively, which were shown pre-selected recipes without using our pipeline, mentioned "adaptivity" as a potential area of improvement, suggesting to: "Limit the returned recipes to related dishes only." Interestingly, some participants in \(G_{R}^{A}\) , where participants received semantically similar examples, also expressed a desire for even more similar examples. One participant noted: "I would offer example recipes that have the same ingredients as the user’s recipe." . Another participant added "I may improve my recipe by adding ingredients that I did not previously add before to make it taste better." Furthermore, practical suggestions for future tool iterations included the ability to scan handwritten recipes, eliminating the need to retype them, and the integration of real-time tips and advice during recipe composition.

In summary, participants who received personalized examples ( \(G_{R}^{A}\) and \(G_{NR}^{A}\) ) reported significantly higher perceived usefulness, attitudes toward use, behavioral intention, and perceived learning gain compared to the other conditions, confirming (H1-1) . Interestingly, participants in the control group ( CG ), unaware of the other conditions, suggested the incorporation of adaptive feedback and content personalization, while participants in groups \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) recommended showing more tailored and similar recipes. However, contrary to our expectations, there were no significant differences in the perceived ease of use between the conditions. As a result, we reject (H1-2) and conclude that the example-selection pipeline does not impose any perceivable burden or complexity on users.

RQ2: Effect on Learners’ Writing Performance

To answer the second research question, we analyzed learners’ writing performance (quantitatively) and participants’ open-text answers (qualitatively). We analyzed the users’ change in performance on the recipe task as well as on the furniture assembly task (transfer task). For the in-task performance, we hypothesized that learners who received adaptive feedback would outperform those who did not, because the highlighted elements and explanations reduce the cognitive load needed to capture the main elements (Sweller, 1994 ), enabling participants to learn faster and perform better on the task (H2-1) . In contrast, for the performance on the transfer task, we hypothesized that the participants who received reflective prompts would perform better, because of the generation effect that states that self-generated information is better retained and learned (Renkl, 2002 ) (H2-2) .

Effect on learners’ task performance . To test H2-1 , we used a repeated-measures ANOVA for the predicted stars \(S_y(x)\) and quality score \(Q_y(x)\) (see “ Measures and Analysis ”) with the conditions ( \(G_{R}^{A}\) , \(G_{NR}^{A}\) , \(G_{R}^{NA}\) , \(G_{NR}^{NA}\) and CG ) as the between-subjects and the test time (pre-score, post-score) as a within-subject factor. Subsequently, we proceeded with pairwise comparisons using the Wilcoxon Rank Sum test with BH corrections to investigate the differences between the various conditions.

In the quality score ( \(Q_y(x)\) ) analysis, we found a significant effect of test time ( \(F(1,186)=84.4, p<.0001\) ). Test time refers to the different measurements of the quality score through time, i.e., how the scores change from the first to the last recipe. Thus, a significant effect of test time means that the quality scores changed significantly over the course of the experiment. As seen in Fig.  5 (top left), the scores in general increased from the first to the last recipes. In addition, there was also a significant interaction effect ( \(F(4,186)=2.6, p<.05\) ), which indicates that the effect of time on quality scores differed depending on the experimental condition. This is also visible in Fig.  5 (top left) where some groups exhibit a steeper slope than others. This is further reinforced by a non-significant condition factor in the between-subjects analysis ( \(F(4,186)=1.65, p=.10\) ), which suggests that there were no inherent differences between the participants in the different groups. Planned pairwise comparisons confirmed the observed differences in Fig.  5 (top left). The users in \(G_{R}^{A}\) improved significantly more than the users in \(G_{R}^{NA}\) ( \(p<.05\) ). Likewise, users in \(G_{NR}^{A}\) performed significantly better than users in \(G_{R}^{NA}\) ( \(p<.05\) ) and CG ( \(p<.05\) ).

In a subsequent analysis, we investigated the differences between the groups with and without adaptive feedback (Fig. 5 top middle) as well as with and without reflective prompts (Fig. 5 top right). Planned comparisons revealed that the users with adaptive feedback improved significantly more than the users without ( \(p<.01\) ) from the first to the last recipe.

Regarding the predicted stars ( \(S_y(x)\) ) analysis, we found a significant effect of test time, with participants’ predicted stars improving significantly across recipes ( \(F(1,186)=19.2, p<.0001\) ). There was no main effect of the condition, and planned comparisons revealed no differences between the conditions.

Performance on recipe task (in terms of quality score) and transfer task. The error bars show the standard deviation

Effect on learners’ transfer performance . In a next analysis, we also used a repeated-measures ANOVA to assess performance improvements on the transfer task. Figure 5 (bottom left) illustrates the score change between participants’ pre- and post-test for the five conditions. While the CG seems to do worse than the other four conditions, we only found a significant effect of test time ( \(F(1,186)=104, p<.0001\) ). This suggests that on average, all the participants improved on the transfer task (see Fig. 5 (bottom left)). For example, \(24\%\) of the participants, who did not enumerate the steps in the pre-test, enumerated the steps in the post-test. Moreover, we reviewed the tests and noted that only two participants included a title in the pre-test, while 26 participants added it in the post-test. We also investigated the differences between the groups with and without adaptive feedback (Fig. 5 bottom middle) as well as with and without reflective prompts (Fig. 5 bottom right) and found no significant effects.

Perception Analysis . To relate the observed effects on performance to participants’ perceived performance, we again examined the survey’s open-text answers. After each recipe, participants were asked to describe the changes they made in their recipes. \(20\%\) of the participants referred to enumerating: "I numbered the steps to make the order clearer. It was a good point and will allow who is cooking to quickly find the step they need" ( \(G_{NR}^{A}\) ). Most of the consecutive popular topics referred to the recipe suggestions and explanations, for example, "specifying the size and type of pan" ( \(10\%\) ), "using more appropriate terms than add like mix, stir, beat" ( \(9\%\) ). Interestingly, despite not having direct suggestions, some participants in \(G_{R}^{NA}\) , \(G_{NR}^{NA}\) , and CG made similar changes. For example, a participant in \(G_{R}^{NA}\) mentioned: "I added a size measurement to my description of a baking pan because I realised it is helpful to have these details available for new bakers who are unsure of what sizes these things ought to be" .

In addition, as observed in the post-test, no participant in the CG mentioned adding a title and only \(3\%\) of the participants in \(G_{NR}^{A}\) mentioned it. In comparison, \(12\%\) and \(13\%\) of the participants from \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) said they added a title; a participant from \(G_{R}^{NA}\) wrote: "I [originally] did not give my recipe a title. I saw that in the example recipe and realised stating the title would help the presentation" .

Additionally, to comprehend the impact of the reflective prompts, we examined how participants in groups \(G_{R}^{A}\) and \(G_{R}^{NA}\) responded regarding their utilization of these prompts. Among the participants, \(27\%\) mentioned that the prompts were useful in identifying areas of improvement, with one participant expressing, "I had to actually think about where I was going wrong and what was good about the example" . For \(12\%\) of the participants, the reflective prompts acted as a means of introspection, leading them to consider ways to enhance their own recipe writing. One participant explained, "It forced me to be introspective about my own recipe writing and thus think of ways to improve my instructions." However, a small percentage (7%) of the participants expressed a dislike for the prompts. For instance, one participant conveyed, "Not much, the reflective questions were just a part to write what I was already thinking." This observation could provide some insight into why we did not observe a significant effect of the reflective prompts on performance.

In summary, our findings support H2-1 as we observed significant differences in task performance between groups with and without adaptive feedback. However, contrary to our expectations, we did not find any significant differences in task performance between groups with and without reflective prompts, leading us to reject H2-2 . Furthermore, the results from the perception analyses indicate that participants from all groups demonstrated a good understanding of the basic elements of a procedural text.

RQ3: Effect on Learners’ Revision Behavior

In addressing our final research question, we studied how users revised their recipes after receiving feedback. We formulated two hypotheses to explore this aspect. Firstly, we hypothesized that the groups with reflection prompts would invest more time in the revision process. Participants in these groups were required to answer the reflective questions, and we anticipated that this reflective practice would lead them to approach revisions with a critical mindset, spending more time contemplating potential improvements ( H3-1 ). Additionally, we hypothesized that groups receiving adaptive feedback would continue revising over time, as the feedback provided would remain pertinent and applicable to their writing efforts ( H3-2 ).

Quantitative Analysis . Firstly, we investigated users’ average revision time (time spent revising the recipes). We compared the revision features between groups using the Kruskal-Wallis test 11 , confirming that there were significant differences between groups for time ( \({\chi }^2(4) = 12.2\) , \(p < .01\) ). Then, to investigate the differences between groups, we performed post-hoc Wilcox pairwise comparison Footnote 12 .

We found that users in group \(G_{NR}^{NA}\) spent significantly less time revising than users in \(G_{R}^{A}\) ( \(p < .05\) ) and \(G_{NR}^{A}\) ( \(p < .05\) ). However, we did not find any significant difference between the groups with and without reflecting prompts, thus rejecting H3-1 .

Next, we examined how the time spent varied between the three recipes users wrote. Figure 6 (left) illustrates the revision times of all five conditions for their first, second and third recipe. We observe that over time, users from all groups spend less time revising. It is worth noting that in the first recipe the users in \(G_{R}^{A}\) spent on average more than twice as much time (346 seconds) as the users in \(G_{R}^{NA}\) (166 seconds, \(p < .05\) ), suggesting that the adaptive features prolongated the time users revised the recipes.

Revision behavior: time spent revising, number of revisions, and the percentage of declared no changes

Furthermore, when analyzing the number of revisions, we also found significant differences in the overall number of revisions per group ( \({\chi }^2(4) = 23.6\) , \(p < .001\) ). In particular, group \(G_{NR}^{A}\) revised their recipes more than the rest of the groups ( \(G_{R}^{A}\) , \(p < .05\) ; \(G_{R}^{NA}\) , \(p < .001\) ; \(G_{NR}^{NA}\) , \(p < .001\) ; CG , \(p < .01\) ).

Moreover, we examined the number of revisions per recipe and found that there was also a general declining trend in the average number of revisions (see Fig. 6 (middle)). Analogously to the general results, in the first recipe, the users in \(G_{NR}^{A}\) revised their recipe significantly more than the groups with no adaptive feedback ( \(p < .01\) for \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) ). Likewise, in the second recipe, \(G_{NR}^{A}\) had significantly more revisions than \(G_{R}^{NA}\) ( \(p = .005\) ), \(G_{NR}^{NA}\) ( \(p = .006\) ) and CG ( \(p = .02\) ). Despite the fact that users in \(G_{NR}^{A}\) also reduced their revision count throughout all three recipes, they consistently maintained a higher average number of revisions compared to the other groups. This finding supports hypothesis H3-2 , indicating that certain users who received adaptive feedback still perceived it as interesting or valuable enough to ask for it again. Nonetheless, it is notable that for the first recipe, users in \(G_{NR}^{A}\) revised more than users in \(G_{R}^{A}\) , despite both having adaptive feedback. It might be possible that the reflective prompts increased the cognitive load for \(G_{R}^{A}\) , leading to less revisions.

Perception Analysis . As mentioned earlier, after each submission, participants were asked to describe the changes they made to improve their recipes. Figure 6 (right) shows the percentage of participants reporting not making any changes for their first, second, and third recipes. We observe that for all groups, a large majority of users reported changes, with the percentage of participants not improving their recipe, increased from the first to the last recipe. Not surprisingly, group CG had the steepest increase: \(29\%\) of participants in this group reported making no changes to their last recipe. One participant in this group mentioned that The Analyze button just outputs the same suggestions every time, so I knew already what it wanted, and I didn’t need to make any changes . This suggests that the feedback became redundant as it was static and there were no changes. In contrast, \(88\%\) ( \(G_{R}^{A}\) ) and \(86\%\) ( \(G_{NR}^{A}\) ) of the participants in the adaptive feedback groups continued to report changes they made to the recipe. A big portion of the changes reported by the users (83%), came from (or were very similar to) the suggestion given by the system. Interestingly, in the first recipe, most changes were related to the structure of the recipe, for example: "I added the ingredients list and made it step by instructions. I made these steps to make it easier to follow." Whereas in the second and third recipes, most comments referred to the specificity of the instructions and the steps, for example, one participant of \(G_{NR}^{A}\) mentioned: "I described exactly when to move onto a next step and what to look out for in a mixture in order to proceed" .

In summary, we reject H3-1 as we did not see the groups with reflection prompts spending more time revising. Moreover, our quantitative and qualitative analyses support H3-2 indicating that groups with adaptive feedback perceive the example recipe and annotations as relevant, while suggestions for the other groups started to feel redundant.

Discussion and Conclusion

In this paper, we presented RELEX , an adaptive learning system for enhancing procedural writing skills. RELEX features a real-time retrieval pipeline, enabling personalized example-based learning at scale. Our multi-step pipeline selects higher quality and semantically relevant examples for learners based on their input and provides suggestions on how to improve their writing. We evaluated RELEX with 200 users to analyze the effects of personalized examples and reflective prompts on users’ writing performance, perceived experience, and revision behavior.

Impact on learners’ experience (RQ1) . Our results show that providing adaptive feedback on procedural writing skills has a positive impact on the user experience (RQ1). As we hypothesized ( H1-1: Adaptive feedback will lead to heightened perceptions of learning gain, usefulness, behavioral intention, and more positive attitudes towards usage among learners ), learners who received personalized recipes and adaptive feedback ( \(G_{R}^{A}\) and \(G_{NR}^{A}\) ) judged the perceived learning gain, the perceived usefulness, the behavioral intention for continuous use, and the attitude towards use significantly better than those who did not receive adaptive feedback ( \(G_{R}^{NA}\) and \(G_{NR}^{NA}\) ). These results are coherent with previous work (Wambsganss et al., 2020 ), where the group with adaptive feedback had a significantly higher intention to use. Moreover, our analysis of open answers exemplifies the positive reactions participants had towards seeing another recipe, in-text highlighted elements, and adaptive suggestions. A positive perception plays an important role in the long-term success of learning tools and their potential to foster learning (Kirkpatrick, 1994 ).

Against our expectations and different from Fan et al. ( 2017 ), we did not find any significant differences between the groups regarding the perceived ease of use ( H1-2: The ease of use will be perceived as most favorable in the groups with simpler interfaces ). We originally hypothesized that the users would find the complete interface (including the personalized example, adaptive explanations, in-text highlighting, and reflective prompts) hard to understand. Venkatesh and Bala ( 2008 ) define perceived ease of use as the degree to which a person believes that using the tool with be free of effort. Thus, we expected the extra features like reflection and suggestions to represent an effort for the users. Nevertheless, when analyzing the qualitative comments, the third highest-ranked topic was the "intuitiveness" of the tool. This suggests that the design iterations with users contributed to an intuitive design, where the special features and elements do not hinder the ease of use.

Impact on learners’ writing performance (RQ2) . Moreover, we investigated the effects of the design elements (personalized example, adaptive feedback and prompts) on performance (RQ2). Our results confirm our hypothesis ( H2-1: Adaptive feedback will improve in-task writing performance ), showing that participants in the adaptive feedback groups improved their recipe quality and completeness significantly more than the participants in the non-adaptive groups. The perception analysis suggests that the in-text highlighted elements helped identify the areas of opportunity quickly. Previous work (van Gog et al., 2008 ) found that extra information and explanations were beneficial in terms of learning gains at first, but hindered performance later on as the information quickly became redundant. In our study, we overcome that challenge in \(G_{R}^{A}\) and \(G_{NR}^{A}\) by only showing explanations that are relevant based on the user’s recipe. This adaptivity could also explain the observed performance differences given that \(G_{R}^{NA}\) - CG received redundant explanations regardless of the user’s input. This is in line with the perception analysis, where the participants in the CG mentioned that the suggestions became less useful when they were redundant.

We also studied whether participants in the groups with reflective prompts were able to generalize better when asked to transfer the skills to another domain ( H2-2: Reflective prompts will improve the writing performance in a transfer task ). Our results reject our hypothesis. We hypothesize that the duration of the user study was too short (only three recipes) to unfold the self-explanation effect (Wong et al., 2002 ). Alternatively, as noted by one of the participants, it is possible that even without writing, the participants were already explaining the example to themselves.

Surprisingly, all groups improved on the transfer task (furniture assembly). We observed that, on average, participants improved their text \(15\%\) in terms of quality (structure and specificity). This suggests that participants were able to grasp the principles of the learning domain (procedural writing) and apply them to a different exemplifying domain (furniture assembly). Furthermore, our results from H2-1 and the perception analysis indicate that participants also learned elements specific to the cooking domain (e.g., specifying the heat intensity). In H2-1 we observed significant differences when measuring the improvements from both content levels. We therefore hypothesize that the five approaches (experimental conditions) are similarly effective in teaching general procedural writing skills (from the learning domain). Yet, the conditions incorporating adaptive feedback also enhance learners’ understanding in a specialized area within procedural writing: cooking recipe writing (exemplifying domain). On average, participants improved the structure and organization of their procedural text by \(15\%\) , including enumerating the steps, listing the materials, having separate sections for materials and steps, and adding appropriate sub-headings. According to the double-content description provided by Renkl et al. ( 2009 ), these elements belong to the content level of the learning domain of procedural writing (i.e., how to structure a procedural text in general), i.e. participants were able to grasp the fundamental structural elements of procedural writing by practicing only in the example domain.

Impact on learners’ revision behavior (RQ3) . In the last analysis, against our expectations, we did not find that the use of reflective questions led to extended periods of revision. On the contrary, we found that users who received adaptive feedback spent more time revising ( H3-1: Reflective prompts will increase the duration of revision times ) than the users without adaptive feedback. Moreover, we observed that in general the time spent revising, as well as the number of revisions, decreased from the first to the last recipe. It is indeed interesting that despite the users spending less time revising, the recipes are of higher quality (as seen in RQ2 ). As the perception analysis revealed, the users made fewer changes because they had already incorporated some of the feedback. Zhu et al. ( 2020 ) also observed a decline in the revision time with multiple tasks and hypothesized that the users became more familiar with the content and the feedback resulting in less time reading feedback and making changes.

As expected, users with adaptive feedback continued to revise more in their second and third recipes ( H3-2: Adaptive feedback will result in an increased number of revisions ). The percentage of users in groups with adaptive feedback that report making no changes in the last recipe is lower than in the other groups. van Gog and Rummel ( 2010 ) observed instructional explanations becoming redundant and irrelevant over time; it seems that providing personalized examples and annotations indeed helps reducing this effect. These results complement the results from RQ1 , it is possible that the users perceived the tool as more useful if they engaged more with the feedback and spent more time making changes.

Literature Contributions . Our study contributes to and expands prior research in two main literature streams.

First, we contribute to the literature stream of artificial intelligence (AI) for example-based learning in heuristic domains. Most prior research (van Gog et al., 2008 ; van Gog & Rummel, 2010 ; Renkl et al., 2009 ; Renkl, 2002 ) on example-based learning uses static examples: both the examples and explanations are created by experts and all the learners see the exact same content, independent of their input. In contrast to past literature, RELEX provides examples tailored to the needs of the learner in terms of topic (i.e. similar content) and skill level. Instead of providing a perfect expert example, we provide a peer example of better quality, but still attainable. Furthermore, we also personalize the instructional explanations based on the input text of the learner. Additionally, we enhance the adaptive feedback by incorporating reflective prompts, leveraging the documented benefits found in the existing literature (Schworm & Renkl, 2007 ; Wong et al., 2002 ; Chi et al., 1989 ; Roelle et al., 2012 ).

Second, we contribute to the literature around SRL in AI systems. By including prompts for self-evaluation within the design of RELEX , we shed light on the combination of reflective prompts and personalized content and their effect on learning experiences and learning outcomes. Despite the qualitative comments on the helpfulness of the prompts and the positive effects from previous work (Roelle et al., 2012 ; Schworm & Renkl, 2007 ; van Gog & Rummel, 2010 ), we did not see a significant effect on our quantitative outcome variables for perception or performance. This opens new lines of future research to investigate how to best integrate reflective prompts into adaptive systems.

RELEX contrasts with previous approaches to instruct procedural writing skills by focusing on personalization and adaptivity. In comparison to previous works (Traga Philippakos, 2019 ; Sato & Matsushima, 2006 ; Alviana, 2019 ) where the instructional materials are static, meaning that all students received the same examples, in RELEX the example is chosen to cater to individual learning needs. Moreover, in comparison to instructional group approaches (Traga Philippakos, 2019 ), in RELEX each student can learn at their own pace and different from (Sato & Matsushima, 2006 ), it does not require external readers to give feedback. Furthermore, in contrast to other approaches of example-based learning (Sweller, 1994 ; van Gog et al., 2008 ; Renkl et al., 2009 ; Renkl, 2002 ), in our work, not only do we provide a personalized example, but we also offset the common disadvantage of instructional explanations being redundant or too complex. By annotating the examples with instructional explanations adapted to the learner’s prior text, we ensure their relevance.

Limitations and Future Work . One of the big challenges of enriching examples in example-based learning is the relevance of the explanations (Renkl, 2002 ). Despite the participants’ positive perception of the suggestions, they were extracted from "The Recipe Writer’s Handbook, Revised and Expanded" (Ostmann & Baker, 2001 ) and inevitably include the authors’ bias. For example, there are more suggestions for ingredients used in Western cuisine. The implication of this is that at scale, learners who write recipes from Western cuisine could benefit more from relevant suggestions. Future lines of work should investigate these biases and how to mitigate them.

Another limitation emerging from the database is that the prediction model was trained on user ratings that can be subjective. In addition, the ratings were given for a recipe as a whole, combining writing quality and taste. We examined the comments associated with the ratings and found that high-rated recipes (five stars) often had comments appreciating the clarity of instructions, as exemplified by remarks like " I really appreciate the instructions about using the spoon when cutting the potatoes. This is a well-written recipe." ; and "This was easy enough to prepare on a worknight and assembly was so easy when following the well-written directions" . Conversely, recipes with low ratings were often criticized for their lack of clarity and order, as indicated by comments such as "This recipe is written in a way that is impossible to attempt to follow or understand. It is a disaster." ; and "Very frustrated with the directions. They are not orderly whatsoever." . This suggests that even if a recipe is tasty, unclear writing can hinder its reproducibility, leading to low ratings. However, we acknowledge that a recipe with excellent writing but an unfamiliar or unappealing taste might also receive low ratings. In future studies, it would be beneficial to separate the variables taste and writing quality to more accurately assess their individual impacts on user ratings.

This complexity extends to the predictive task, where RELEXset-Predictor attempts to account for both taste and writing quality, leading to only minor improvement over a static baseline. We have therefore made our code and models publicly available Footnote 13 , encouraging future research to enhance predictive accuracy, for example through the integration of new SOTA models. The design of the subsequent stages of the pipeline attempts to mitigate the limitations of RELEXset-Predictor . Overall, participants perceived the adaptive recipes as useful and edited the recipes accordingly. However more rigorous, quantitative assessments are needed to investigate the influence of the model performance and the chosen quality range on user perception. Furthermore, RELEX offers a promising approach for learners to improve their recipe writing skills by integrating both the learning domain (procedural writing) and the exemplifying domain (cooking). Despite its effectiveness, its scope is limited to these specific areas. One main takeaway for the research community is the demonstrated importance of adaptivity and personalization in example-based learning, particularly in enhancing user engagement and performance outcomes. In future work, the example-selection pipeline can be adapted to cater to other learning domains. For instance, journal writing (Roelle et al., 2012 ), high school instruction (Hilbert et al., 2008 ), or argumentative writing (Schworm & Renkl, 2007 ). Transferring RELEX to a different exemplifying or learning domain requires two main ingredients: 1) multiple examples with associated evaluations, ratings, or grades, and 2) domain-specific suggestions regarding example annotation. The selection pipeline (see “ Personalized Example Retrieval Pipeline ”) can be used to fine-tune an NLP model to predict the evaluations of the examples. Then, the model name and domain-specific suggestions can be added to the code base of RELEX to run the application. By extending the tool’s capabilities to various educational contexts, we anticipate a broader impact and potential benefits for learners across different domains Footnote 14 .

In the future, we envision expanding the scope and applicability of our findings by conducting replication studies in real-world settings, such as classrooms with chef apprentices. This approach would help address the ecological validity of the results and provide insights into the effectiveness of RELEX in practical educational contexts. Additionally, we plan to explore the long-term effects of RELEX by conducting a longitudinal study, assessing how repeated usage of the tool impacts learners’ procedural writing skills over an extended period.

The demo version of RELEX is available at https://go.epfl.ch/relex

The detailed interview questions can be found on https://github.com/epfl-ml4ed/relex/blob/main/docs/user-interviews.pdf

RELEXset can be downloaded from https://github.com/epfl-ml4ed/relex/readme.md

RELEXset-MLM is available at https://huggingface.co/paola-md/RELEXset-MLM and RELEXset-Predictor is available at https://huggingface.co/paola-md/RELEXset-Predictor/

The architecture configuration is available at https://huggingface.co/paola-md/RELEXset-Predictor/blob/main/config.json

After a significant Shapiro-Wilk test on the three sets ( \(p=0\) )

Visual validation available at: https://github.com/epfl-ml4ed/relex/blob/main/docs/split-verification.ipynb

Complete list of suggestions and classification rules available at https://github.com/epfl-ml4ed/relex/docs/recipe-suggestions-rules.pdf .

https://norvig.com/mayzner.html

We checked for normality using a Shapiro-Wilk test and verified equal variances using Levene’s test and found that for both age and gender, the assumptions of ANOVA were not satisfied.

We checked for normality using a Shapiro-Wilk test and verified equal variances using Levene’s test and found the assumptions of ANOVA were not satisfied.

correcting for multiple comparisons via BH procedure.

For those interested in replicating or building upon our work, we have made the implementation code and instructions for domain transfer available at https://github.com/epfl-ml4ed/relex/readme.md

For those interested in replicating or building upon our work, we have made the implementation code and instructions for domain transfer available at https://github.com/epfl-ml4ed/relex/readme.md .

Adoniou, M. (2013). Drawing to support writing development in english language learners. Language and Education , 27 (3), 261–277. Retrieved from https://doi.org/10.1080/09500782.2012.704047

Afrin, T., Kashefi, O., Olshefski, C., Litman, D., Hwa, R., & Godley, A. (2021). Effective interfaces for student-driven revision sessions for argumentative writing. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. Retrieved from https://doi.org/10.1145/3411764.3445683

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly , 24 (4), 665–694. Retrieved 2022-09-13 from http://www.jstor.org/stable/3250951

Ahmed, U.Z., Srivastava, N., Sindhgatta, R., & Karkare, A. (2020). Characterizing the pedagogical benefits of adaptive feedback for compilation errors by novice programmers. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training (pp. 139–150). ACM. Retrieved from https://doi.org/10.1145/3377814.3381703

Alamri, H., Lowell, V., Watson, W., & Watson, S.L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education , 52 (3), 322–352. Retrieved from https://doi.org/10.1080/15391523.2020.1728449

Alviana, V. (2019). The effect of recipe demonstration technique on students’ writing competence in procedural text. Journal of Languages and Language Teaching, 7 (2), 128–131.

Article   Google Scholar  

Ambarwati, S., & Listyani, L. (2021). Procedural essay writing: Students’ problems and strategies. LLT Journal: A Journal on Language and Language Teaching, 24 (2), 364–379.

Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D. & Mitchell, J.C. (2020). Reinforcement learning for the adaptive scheduling of educational activities. In: CHI ’20: CHI Conference on Human Factors in Computing Systems (pp. 1–12). ACM. Retrieved from https://doi.org/10.1145/3313831.3376518

Bień, M., Gilski, M., Maciejewska, M., Taisner, W., Wisniewski, D., & Lawrynowicz, A. (2020). RecipeNLG: A cooking recipes dataset for semi-structured text generation. In: Proceedings of the 13th International Conference on Natural Language Generation (pp. 22–28). ACL. Retrieved from https://aclanthology.org/2020.inlg-1.4

Bimba, A.T., Idris, N., Al-Hunaiyyan, A., Mahmud, R.B., & Shuib, N.L.B.M. (2017). Adaptive feedback in computer-based learning environments: a review. Adaptive Behavior , 25 (5), 217–234. Retrieved from https://doi.org/10.1177/1059712317727590

Brown, T.B., & et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems 33 . Retrieved from https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

Chi, M.T., Bassok, M., Lewis, M.W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science , 13 (2), 145–182. Retrieved from https://doi.org/10.1016/0364-0213(89)90002-5

Compeau, D.R., & Higgins, C.A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly , 19 (2), 189–211. Retrieved from http://www.jstor.org/stable/249688

Cooper, A., Reimann, R., & Cronin, D. (2007). About face 3: the essentials of interaction design (3rd edition). Wiley Pub.

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , 13 (3), 319–340. Retrieved 2022-09-13 from http://www.jstor.org/stable/249008

Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Association for Computational Linguistics. Retrieved from https://doi.org/10.18653/v1/n19-1423

Doroudi, S., Kamar, E., Brunskill, E., & Horvitz, E. (2016). Toward a learning science for complex crowdsourcing tasks. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2623–2634). ACM. Retrieved from https://doi.org/10.1145/2858036.2858268

Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2017). Scaling reflection prompts in large classrooms via mobile interfaces and natural language processing. Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 363–374). ACM. Retrieved from https://doi.org/10.1145/3025171.3025204

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure . Retrieved from arXiv:2203.05794

Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N.A. (2020). Don’t stop pretraining: Adapt language models to domains and tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8342–8360). ACL. Retrieved from https://doi.org/10.18653/v1/2020.acl-main.740

Hilbert, T.S., Renkl, A., Kessler, S., & Reiss, K. (2008). Learning to prove in geometry: Learning from heuristic examples and how it can be supported. Learning and Instruction , 18 (1), 54–65. Retrieved from https://doi.org/10.1016/j.learninstruc.2006.10.008

Hosseini, R., & Brusilovsky, P. (2017). A study of concept-based similarity approaches for recommending program examples. New Review of Hypermedia and Multimedia , 23 (3), 161–188. Retrieved from https://doi.org/10.1080/13614568.2017.1356878

Hu, G., Ahmed, M., & L’Abbé, M.R. (2022). Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared to traditional methods. The American Journal of Clinical Nutrition , 553–563. Retrieved from https://doi.org/10.1016/j.ajcnut.2022.11.022

Jin, R., & Si, L. (2004). A study of methods for normalizing user ratings in collaborative filtering. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 568–569). ACM. Retrieved from https://doi.org/10.1145/1008992.1009124

Kingma, D.P., & Ba, J. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations . Retrieved from arXiv:1412.6980

Kirkpatrick, D. L. (1994). Evaluating training programs: The four levels . San Francisco: Berrett-Koehler Publishers.

Google Scholar  

Landis, J.R., & Koch, G.G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics , 33 (2), 363–374. Retrieved from http://www.jstor.org/stable/2529786

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D. & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach . Retrieved from arXiv:1907.11692

Majumder, B.P., Li, S., Ni, J., & McAuley, J. (2019). Generating personalized recipes from historical user preferences. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 5976–5982). ACL. Retrieved from https://doi.org/10.18653/v1/D19-1613

Max, L., Alex, S., & Dmytro, L. (2022). Grammarly . Retrieved from https://app.grammarly.com/

Mayfield, E., & Black, A.W. (2020). Should you fine-tune bert for automated essay scoring? In: Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 151–162). Association for Computational Linguistics. Retrieved from https://doi.org/10.18653/v1/2020.bea-1.15

Mejia-Domenzain, P., Marras, M., Giang, C., Cattaneo, A., & Käser, T. (2022). Evolutionary clustering of apprentices’ self- regulated learning behavior in learning journals. IEEE Transactions on Learning Technologies , 1–14. Retrieved from https://doi.org/10.1109/TLT.2022.3195881

Mouchel, L., Wambsganss, T., Mejia-Domenzain, P., & Käser, T. (2023). Understanding revision behavior in adaptive writing support systems for education. International Conference on Educational Data Mining , 445–452. Retrieved from arXiv:2306.10304

Nah, F.F. (2003). A study on tolerable waiting time: How long are web users willing to wait? 9th Americas Conference on Information Systems (p. 285). AIS. Retrieved from http://aisel.aisnet.org/amcis2003/285

Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J.M., Tworek, J. & et al. (2022). Text and code embeddings by contrastive pre-training. Retrieved from arXiv:2201.10005

Nückles, M., Hübner, S., & Renkl, A. (2009). Enhancing self-regulated learning by writing learning protocols. Learning and Instruction , 19 (3), 259–271. Retrieved from https://doi.org/10.1016/j.learninstruc.2008.05.002

Ostmann, B.G.O., & Baker, J.L. (2001). The recipe writer’s handbook, revised and expanded . Harvest.

Paassen, B., Hammer, B., Price, T.W., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining , 10 (1), 1–35. Retrieved from arXiv:1708.06564

Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70 , 153–163.

Pelánek, R. (2020). Measuring similarity of educational items: An overview. IEEE Transactions on Learning Technologies , 13 (2), 354–366. Retrieved from https://doi.org/10.1109/TLT.2019.2896086

Premlatha, K.R., & Geetha, T.V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review , 44 (4), 443–465. Retrieved from https://doi.org/10.1007/s10462-015-9432-z

Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 3980–3990). ACL. Retrieved from https://doi.org/10.18653/v1/D19-1410

Renkl, A. (2002). Worked-out examples: instructional explanations support learning by self-explanations. Learning and Instruction , 12 (5), 529–556. Retrieved from https://doi.org/10.1016/S0959-4752(01)00030-5

Renkl, A., Hilbert, T., & Schworm, S. (2009). Example-based learning in heuristic domains: A cognitive load theory account. Educational Psychology Review , 21 (1), 67–78. Retrieved from https://doi.org/10.1007/s10648-008-9093-4

Ringenberg, M.A., & VanLehn, K. (2006). Scaffolding problem solving with annotated, worked-out examples to promote deep learning. Intelligent Tutoring Systems (pp. 625–634). Springer Berlin Heidelberg.

Robertson, S.E., & Walker, S. (1994). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. Proceedings of the 17th Annual International Conference on Research and Development in Information Retrieval. (pp. 232–241). ACM. Retrieved from https://doi.org/10.1007/978-1-4471-2099-5_24

Roelle, J., Krüger, S., Jansen, C., & Berthold, K. (2012). The use of solved example problems for fostering strategies of self-regulated learning in journal writing. Education Research International. (2012). 751625. Retrieved from https://doi.org/10.1155/2012/751625

Rogers, T., & Feller, A. (2016). Discouraged by peer excellence: Exposure to exemplary peer performance causes quitting. Psychological Science , 27 (3), 365–374. Retrieved from https://doi.org/10.1177/0956797615623770

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter . Retrieved from arXiv:1910.01108

Sato, K., & Matsushima, K. (2006). Effects of audience awareness on procedural text writing. Psychological Reports , 99 (1), 51–73. Retrieved from https://doi.org/10.2466/pr0.99.1.51-73

Schwonke, R., Renkl, A., Krieg, C., Wittwer, J., Aleven, V., & Salden, R. (2009). The worked-example effect: Not an artefact of lousy control conditions. Computers in Human Behavior , 25 (2), 258–266. Retrieved from https://doi.org/10.1016/j.chb.2008.12.011

Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education , 46 (4), 426–445. Retrieved from https://doi.org/10.1016/j.compedu.2004.08.011

Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology , 99 (2), 285–296. Retrieved from https://doi.org/10.1037/0022-0663.99.2.285

Sellam, T., Das, D., & Parikh, A.P. (2020). BLEURT: learning robust metrics for text generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7881–7892). ACL. Retrieved from https://doi.org/10.18653/v1/2020.acl-main.704

Slade, C., & Downer, T. (2020). Students’ conceptual understanding and attitudes towards technology and user experience before and after use of an eportfolio. Journal of Computing in Higher Education , 32 (3), 529–552. Retrieved from https://doi.org/10.1007/s12528-019-09245-8

Sun, C., Qiu, X., & Xu, Y. (2019). How to fine-tune BERT for text classification? Chinese Computational Linguistics - 18th China National Conference (Vol. 11856, pp. 194–206). Springer. Retrieved from https://doi.org/10.1007/978-3-030-32381-3_16

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction , 4 (4), 295–312. Retrieved from https://doi.org/10.1016/0959-4752(94)90003-5

Traga Philippakos, Z.A. (2019). Effects of strategy instruction with an emphasis on oral language and dramatization on the quality of first graders’ procedural writing. Reading & Writing Quarterly , 35 (5), 409–426. Retrieved from https://doi.org/10.1080/10573569.2018.1547233

van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review , 22 (2), 155–174. Retrieved from https://doi.org/10.1007/s10648-010-9134-7

van Gog, T., Paas, F., & van Merriënboer, J.J. (2008). Effects of studying sequences of process-oriented and product-oriented worked examples on troubleshooting transfer efficiency. Learning and Instruction , 18 (3), 211–222. Retrieved from https://doi.org/10.1016/j.learninstruc.2007.03.003

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences , 39 (2), 273–315. Retrieved from https://doi.org/10.1111/j.1540-5915.2008.00192.x

Wang, W., Arya, D.M., Novielli, N., Cheng, J., & Guo, J.L.C. (2020). Argulens: Anatomy of community opinions on usability issues using argumentation models. Conference on Human Factors in Computing Systems (pp. 1–14). ACM. Retrieved from https://doi.org/10.1145/3313831.3376218

Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., Leimeister, J.M. (2020). AL: an adaptive learning support system for argumentation skills. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–14)

Wieringa, D.R., & Farkas, D.K. (1991). Procedure writing across domains: nuclear power plant procedures and computer documentation. Proceedings of the 9th Annual International Conference on Systems Documentation (pp. 49–58).

Wilson, J., Olinghouse, N.G., & Andrada, G.N. (2014). Does automated feedback improve writing quality? Learning Disabilities: A Contemporary Journal , 12 (1), 93–118. Retrieved from https://eric.ed.gov/?id=EJ1039856

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., & Brew, J. (2019). Huggingface’s transformers: State-of-the-art natural language processing. Retrieved from arXiv:1910.03771

Wong, R.M., Lawson, M.J., & Keeves, J. (2002). The effects of self-explanation training on students’ problem solving in high-school mathematics. Learning and Instruction , 12 (2), 233–262. Retrieved from https://doi.org/10.1016/S0959-4752(01)00027-5

Zhu, M., Liu, O.L., & Lee, H.-S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education , 143 , 103668. Retrieved from https://doi.org/10.1016/j.compedu.2019.103668

Zhu, M., Zhang, M., & Deane, P. (2019). Analysis of Keystroke Sequences in Writing Logs. ETS Research Report Series , 2019 (1), 1–16. Retrieved from https://doi.org/10.1002/ets2.12247

Zlabinger, M., Sabou, M., Hofstätter, S., Sertkan, M., & Hanbury, A. (2020). DEXA: supporting non-expert annotators with dynamic examples from experts. Proceedings of the 43rd International conference on research and development in Information Retrieval (pp. 2109–2112). ACM. Retrieved from https://doi.org/10.1145/3397271.3401334

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Ai vs human writing: the enduring value of human quality.

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• Writing Tips

What Is B2B Content Writing?

• By  Lynn Godson
• Apr 20, 2024

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B2B content writing, or business-to-business content writing, involves creating written content for businesses that sell products and services to other businesses.

In this post, we’ll look at:

• What B2B content writing involves
• The different types of B2B written content
• The difference between B2B and B2C (business-to-customer) content
• The day-to-day responsibilities of a B2B content writer
• How you can become a great B2B content writer

Read on to discover more about B2B content writing!

B2B content writing is creating written material aimed at other businesses and business-focused publications. There are various types of B2B writing, including:

• Blog posts and articles
• Press releases
• Case studies
• White papers
• Social media content 
• Website content (e.g., product landing pages )

A B2B content writer’s goal is to attract and engage potential customers. To do this, they typically highlight a business problem and then explain how to solve it. Take a look at this example from HubSpot: When Is the Best Time to Post on Instagram in 2023? In this blog post, the writer highlights a problem (knowing when is best to post on Instagram) and then provides several solutions – including using their social media management software.

Creating great B2B content is one of the best ways for a business to develop its brand voice, improve its reputation, and build trust with customers (both existing and potential). When a business becomes known for accurate, engaging, useful B2B content – and thought leadership content – they gain brand authority . Increased brand authority means greater reach and more business customers. And it’s not just other businesses. Effective B2B content writing can draw the attention of other industry influencers, such as economists, professional bodies, journalists, and media outlets. In contrast, poor quality B2B content can very quickly hit reputations hard, and that can be difficult to recover from.

Let’s return to our HubSpot example. Although their ultimate goal is to sell their product, Hubpsot also provides the reader with tons of valuable, free information. They include new research data, useful infographics, and insights from industry experts. All of this helps to build trust and credibility, increasing the likelihood that the reader will purchase at some point in the future.

How Is B2B Content Different from B2C Content?

B2B content writing and B2C content writing are two distinct types of content marketing, with some notable differences.

As we’ve highlighted above, B2B content is aimed at other businesses. B2C content, in contrast, is aimed at individual consumers. This difference in target audience determines not only the actual content but also the style, tone, and vocabulary used. B2B content tends to be more formal, using a professional tone and industry-specific jargon and vocabulary. This webpage from our partner, Proofed, is a good example of B2B content writing:

Proofreading and Editing for Market Intelligence Firms

B2C content, however, is usually more informal. It can be chatty, friendly, and often humorous and tends to use more casual vocabulary. Here’s an example from our blog:

How to Write AI Prompts for Content Creation

Of course, many businesses produce both B2B and B2C content. For instance, a restaurant business might want to promote its menu to individual diners (B2C) and promote its corporate hospitality packages to business customers (B2B). A good content writer will always keep the desired target audience in mind and tailor their writing to suit them.

What Does a B2B Content Writer Do?

The exact day-to-day responsibilities of a B2B content writer will vary depending on the business. However, certain skills and competencies are expected of every B2B content writer. A B2B content writer will:

• Tailor content for different channels and audiences. A B2B content writer needs to be able to write clear, concise, engaging copy – whatever the context. This means understanding the pain points and motivations of the audience and knowing how to structure content to effectively present them with solutions. Like any good content writer, a B2B content writer needs to know how to use persuasive language , inspire action , and avoid common pitfalls like fluff in writing.
• Follow brand voice and style guidelines. A B2B content writer will usually need to follow brand voice guidelines and/or an in-house style guide . A brand’s voice is an important part of its marketing strategy. Not only does it help the brand stand out from the crowd, but it can also help build credibility. The more consistent and familiar the brand, the more reliable a source of information it becomes. 
• Conduct thorough research. All credible B2B content is based on in-depth research . A B2B content writer needs to be able to identify credible sources , gather relevant evidence, and incorporate it into their content.
• Establish thought leadership. As we mentioned earlier, thought leadership content can be a brilliant tool for businesses wanting to expand their reach and build authority in a particular industry. A good B2B content writer will formulate pieces that demonstrate expertise and innovation in a particular field – and in doing so attract the attention of other businesses, media outlets, and influential thinkers. 
• Apply search engine optimization (SEO) techniques. Producing SEO-friendly content is a vital part of B2B content writing for websites and business blogs. A B2B content writer needs to know how to write content that incorporates relevant keywords and satisfies Google’s E-E-A-T guidelines . They may also need to write meta descriptions .  The application of SEO techniques ensures that content reaches the top spots in online searches and reaches a wider audience.

B2B Writing Samples

We’ve already seen some examples of B2B blog posts. Let’s look at some examples of other types of B2B content writing.

Case studies are real-world examples of a business customer using the product or service another business provides. They can include technical details of the product in addition to how the product or service helped the customer.

Here’s an example of a case study from National Education Group , a British educational training and online safety for schools provider. The case study starts with an introduction to the customer and then goes on to quote one of the customer’s senior managers, who sets the context and explains the benefit they derived from the service.

Landing Page

This landing page , from the website of chartered accounts Wright Vigar, gives information about the sectors they serve and how they can help each one. It’s written with the specific needs of each sector in mind, so we can see that research and thought have gone into each potential business customer’s needs. The vocabulary is technical and specific to each industry sector, and the tone is professional but not intimidating.

The email below is from a professional membership organization to its business members. It’s about an awards evening hosted by a business specializing in finance, HR, and office support.

EMFA entries close at midnight on Friday 29th March!

Founded in 2019, the East Midlands Finance Awards (EMFA) is the only event that celebrates finance and accountancy professionals across the region.

We’re thrilled to have already received submissions from a range of fantastic local businesses. Read on to learn more about each award and to submit your nomination!

We promise a wonderful evening where local businesses can come together to celebrate their staff and let their hair down. Alongside the main ceremony, our informal event also includes food and drink, with live entertainment and guest speakers.

The language is designed to encourage members to nominate people to receive an award and to attend the evening itself. The use of exclamation points and words such as “thrilled,” “fantastic,” and “wonderful” create urgency, atmosphere, and expectation. The message gives reasons why member businesses should engage and attend, and it explains the benefits of responding positively to the invitation.

Press Release

B2B press releases focus on new, or newly refreshed, products and services. They are written to appeal to the businesses that might use them. This press release from American Express targets small and medium-sized businesses. It explains the pain points that their newly refreshed business credit card can help address and gives details of the added benefits users will now receive.

Business owners have challenging, around-the-clock jobs, and they need rewards that can be used for business trips to meet with clients, suppliers, or team members – or that can be used for personal trips to recharge and find new inspiration. The enhanced Hilton Honors American Express Business Card is designed to help Card Members balance work and life, rewarding business owners across both.

White Paper

This white paper from a market research firm provides a comprehensive guide to customer journey mapping for B2B marketers. It highlights a problem – poor customer retention – and provides an evidence-based solution. There are infographics and links to further reading throughout.

More and more companies now actively recognize that customers are their biggest asset; with no customers to buy our products and services, there is no business at all. People who have a positive customer experience are more likely to spend more with that supplier, pay a premium for that service, and recommend the supplier based on the experience it delivers. People who change to another supplier/brand are more likely to do so because of poor service, and they are equally likely to broadcast this.

The content isn’t overly promotional or salesy, but CTA buttons (‘contact us’, ‘learn more’) are strategically placed at regular intervals.

Becoming A Freelance Writer

B2B writers can be on the payroll of large corporations, but many are freelance content writers and have a diverse and interesting portfolio of clients. If the flexibility of a freelance writing career appeals to you, taking our course, Becoming A Freelance Writer, can be a terrific way to kickstart your new career. Find out more here !

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A Unified General Education Pathway

"...the transfer process is still unnecessarily complex, confusing and difficult for the majority of students to navigate." — Assembly Bill 928, The Student Transfer Achievement Reform (STAR) Act 2021

More than 50% of CSU students are transfer students, arriving primarily from the California Community Colleges system. In an effort to simplify their pathway to a four-year degree, the Student Transfer Achievement Reform Act (AB 928) creates a singular, lower-division General Education (GE) pattern for both California State University and University of California transfer admissions. This pattern, called Cal-GETC, was approved by all three higher education intersegmental partners via the Intersegmental Committee of Academic Senates in spring 2023. When Cal-GETC is implemented in fall 2025, it will become the only transfer GE pattern offered by California community colleges.

The STAR Act is meant to support student success and equity, helping to ease access, simplify advisement across segments, eliminate barriers and carve a clear path to a four-year degree across California's educational segments.

Recognizing a growing trend of first-time, first-year students arriving to the CSU with college credit, including 60% of CSU first-year applicants who have earned college credit, the Chancellor's Office has recommended a unified pathway. Historically, the CSU has had one unified GE pattern for all students—CSU GE Breadth. Changes to Title 5 California Code of Regulations ensure the CSU continues to provide one unified GE pattern whether students enroll as first-time, first-year students or transfer students.

GE Informational Webinar, April 15, 2024

An informational webinar was held on Monday, April 15, 2024 hosted by Interim Associate Vice Chancellor of Academic and Faculty Programs Laura Massa and Assistant Vice Chancellor and State University Dean Brent Foster. Questions posed in this webinar will be posted shortly.

On March 27, 2024, the CSU Board of Trustees approved proposed changes to Title 5 CSU General Education that modify CSU GE Breadth to mirror the Cal-GETC pattern and units.

The Chancellor’s Office will support campuses and faculty through the implementation processes, including through resources to support faculty release, written guidance and stipends for faculty effort during off-contract periods. Each campus will determine the application of units that are not included in Cal-GETC.

Changes to CSU General Education

The update to CSU GE removes five units from the GE pattern. It does this by:

• Including a one-unit laboratory for Biological or Physical Sciences
• Not including one of three Arts or Humanities courses (in Area C)
• Not including Area E, Lifelong Learning and Self-Development

The five units removed from GE will be returned to campuses to determine how to utilize.

About the Student Transfer Achievement Reform Act

Authored by Assemblymember Marc Berman and approved in 2021, Assembly Bill 928 consolidates two existing general education pathways for California Community College students into a single pathway to either the CSU or UC system. It also requires that community colleges place incoming students on an Associate Degree for Transfer (ADT) pathway, if one exists for their major, on or before August 1, 2024.

Key Terms and Definitions

What is Cal-GETC? Cal-GETC is a new GE pattern that will be implemented in fall 2025. As a result of its implementation, California Community Colleges will no longer offer the current CSU GE Breadth and Intersegmental General Education Transfer Curriculum (IGETC) patterns.

What is IGETC? The Intersegmental General Education Transfer Curriculum, or IGETC, is designed for the community college student who wants to be eligible to transfer to either the CSU or the UC systems. 

What is CSU GE Breadth? CSU GE Breadth is the current General Education pattern for all CSU students whether they are first-time first-year students or transfer students. Following the approval of the CSU Board of Trustees on March 27, 2024, starting in fall 2025 CSU GE will mirror Cal-GETC in areas and units.

What is an ADT? The Associate Degree for Transfer (ADT) allows California Community College students who meet the CSU's minimum eligibility requirements guaranteed priority admission to the CSU, though not necessarily to a particular campus or major. Students earn a two-year associate degree (no more than 60 units) that is fully transferrable towards a CSU bachelor's degree.

Additional Resources

GE Informational Seminar May 2023

AB 928 Bill Text

ADT Intersegmental Implementation Committee

The Intersegmental Committee of the Academic Senates (ICAS)

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

Read More About

Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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AI + Machine Learning , Azure AI , Azure AI Services , Azure AI Studio , Azure OpenAI Service , Best practices

AI study guide: The no-cost tools from Microsoft to jump start your generative AI journey

By Natalie Mickey Product Marketing Manager, Data and AI Skilling, Azure

Posted on April 15, 2024 4 min read

The world of AI is constantly changing. Every day it seems there are new ways we can work with generative AI and large language models. It can be hard to know where to start your own learning journey when it comes to AI. Microsoft has put together several resources to help you get started. Whether you are ready to build your own copilot or you’re at the very beginning of your learning journey, read on to find the best and free resources from Microsoft on generative AI training.

Build intelligent apps at enterprise scale with the Azure AI portfolio

Azure AI fundamentals

If you’re just starting out in the world of AI, I highly recommend Microsoft’s Azure AI Fundamentals course . It includes hands on exercises, covers Azure AI Services, and dives into the world of generative AI. You can either take the full course in one sitting or break it up and complete a few modules a day.

Learning path: Azure AI fundamentals

Course highlight: Fundamentals of generative AI module

Azure AI engineer

For those who are more advanced in AI knowledge, or are perhaps software engineers, this learning path is for you. This path will guide you through building AI infused applications that leverage Azure AI Services, Azure AI Search, and Open AI.

Course highlight: Get started with Azure OpenAI Service module

Let’s get building with Azure AI Studio

Imagine a collaborative workshop where you can build AI apps, test pre-trained models, and deploy your creations to the cloud, all without getting lost in mountains of code. In our newest learning path , you will learn how to build generative AI applications like custom copilots that use language models to provide value to your users.

Learning path: Create custom copilots with Azure AI Studio (preview)

Course highlight: Build a RAG-based copilot solution with your own data using Azure AI Studio (preview) module

Dive deep into generative AI with Azure OpenAI Service

If you have some familiarity with Azure and experience programming with C# or Python, you can dive right into the Microsoft comprehensive generative AI training.

Learning path: Develop generative AI solutions with Azure OpenAI Service

Course highlight: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service module

Cloud Skills Challenges

Microsoft Azure’s Cloud Skills Challenges are free and interactive events that provide access to our tailored skilling resources for specific solution areas. Each 30-day accelerated learning experience helps users get trained in Microsoft AI. The program offers learning modules, virtual training days, and even a virtual leaderboard to compete head-to-head with your peers in the industry. Learn more about Cloud Skills Challenges here , then check out these challenges to put your AI skills to the test.

Invest in App Innovation to Stay Ahead of the Curve

Challenges 1-3 will help you prepare for Microsoft AI Applied Skills, scenario-based credentials. Challenges 4 and 5 will help you prepare for Microsoft Azure AI Certifications, with the potential of a 50% exam discount on your certification of choice 1 .

Challenge #1: Generative AI with Azure OpenAI

In about 18 hours, you’ll learn how to train models to generate original content based on natural language input. You should already have familiarity with Azure and experience programming with C# or Python. Begin now!

Challenge #2: Azure AI Language

Build a natural language processing solution with Azure AI Language. In about 20 hours, you’ll learn how to use language models to interpret the semantic meaning of written or spoken language. You should already have familiarity with the Azure portal and experience programming with C# or Python. Begin now!

Challenge #3: Azure AI Document Intelligence

Show off your smarts with Azure AI Document Intelligence Solutions. In about 21 hours, you’ll learn how to use natural language processing (NLP) solutions to interpret the meaning of written or spoken language. You should already have familiarity with the Azure portal and C# or Python programming. Begin now!

Challenge #4: Azure AI Fundamentals

Build a robust understanding of machine learning and AI principles, covering computer vision, natural language processing, and conversational AI. Tailored for both technical and non-technical backgrounds, this learning adventure guides you through creating no-code predictive models, delving into conversational AI, and more—all in just about 10 hours.

Complete the challenge within 30 days and you’ll be eligible for 50% off the cost of a Microsoft Certification exam. Earning your Azure AI Fundamentals certification can supply the foundation you need to build your career and demonstrate your knowledge of common AI and machine learning workloads—and what Azure services can solve for them. Begin now!

Challenge #5: Azure AI Engineer

Go beyond theory to build the future. This challenge equips you with practical skills for managing and leveraging Microsoft Azure’s Cognitive Services. Learn everything from secure resource provisioning to real-time performance monitoring. You’ll be crafting cutting-edge AI solutions in no time, all while preparing for Exam AI-102 and your Azure AI Engineer Associate certification . Dive into interactive tutorials, hands-on labs, and real-world scenarios. Complete the challenge within 30 days and you’ll be eligible for 50% off the cost of a Microsoft Certification exam 2 . Begin now!

Finally, our free Microsoft AI Virtual Training Days are a great way to immerse yourself in free one or two-day training sessions. We have three great options for Azure AI training:

• Azure AI Fundamentals
• Generative AI Fundamentals
• Building Generative Apps with Azure OpenAI Service

Start your AI learning today

For any and all AI-related learning opportunities, check out the Microsoft Learn AI Hub including tailored AI training guidance . You can also follow our Azure AI and Machine Learning Tech Community Blogs for monthly study guides .

• Microsoft Cloud Skills Challenge | 30 Days to Learn It – Official Rules
• https://developer.microsoft.com/en-us/offers/30-days-to-learn-it/official-rules#terms-and-conditions

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