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Overview of the Problem-Solving Mental Process

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

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

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

General Problem-solving Process

Introduction

The following is a general problem-solving process that characterizes the steps that can be followed by any discipline when approaching and rationally solving a problem. When used in conjunction with reasoning and decision-making skills, the process works well for one or more participants. Its main purpose is to guide participants through a procedure for solving many types of problems that have a varying level of complexity.

More importantly, the process is both descriptive and prescriptive. This means it can be used to look at past, present, and potential future problems and their solutions in a clear systematic way that is consistent and able to be generalized. At each step along the way to a solution, various types of research must be conducted to successfully accomplish the steps of the process and thus arrive at an effective solution that is viable. A description of research follows the problem solving process. In both the problem solving and research processes, good decision-making, critical-thinking and self-assessment is vital to a high quality result. At each step in the process, the problem-solver may need to go back to earlier steps and reexamine decisions made. It is this revisiting of earlier choices that make the process iterative and allows for improvement of the final outcomes.

Steps in the General Problem-solving Process

• Become aware of the problem
• Define the problem
• Choose the particular problem to be solved
• Identify potential solutions
• Evaluate the valid potential solutions to select the best one
• Develop an action plan to implement the best solution

Become Aware of the Problem

The first step of any problem-solving process is becoming aware. This awareness can be generated from inside or outside the individual. Many times the awareness is part of a stated task or assignment given to the individual by someone else. In other cases, a person can observe a specific problem or a clear gap in knowledge that they feel must be addressed. In the end, as long as a problem is perceived by oneself or others, awareness of this problem is achieved. However, the level of awareness and the research associated with this level is vital to the initiation of the problem solving process.

Define the Problem

After the problem is recognized, research is conducted. Initially, research must be done to help define the problem as well as identify the assumptions being made and determine the parameters of the situation.

In the end, the main purpose of this step is to evaluate the constraints on the problem and the problem solver to better understand the goals that are trying to be reached. Once these goals are identified, the objectives that must be attained in order to reach the goals can be specified and utilized to help narrow the scope of the problem. Once the goals and objectives are clearly understood, the problem to be solved can be selected. An easy way to think of goals and objectives is that goals are what you hope to achieve while objectives are how you will go about accomplishing the goal.

Just as research might have been the impetus for engaging in the problem solving process—it made the problem-solver aware—research is vital to the specification of parameters and assumptions. The heart of this step is the series of decisions made to narrow the scope of the problem made by the problem-solver. Parameters are those factual boundaries and constraints set by the problem statement or discovered through research. Assumptions by contrast are those constraints that the problem-solver sets without having incontrovertible factual backing for those decisions. A clear understanding of the assumptions being made when engaging in the process is important. If an unsatisfactory outcome is reached, it may be necessary to adjust these assumptions. Even if the final solution is arrived at, knowing one’s assumptions assists the problem-solver in explaining and defending their conclusions.

Choose Which Problem to Be Solved

Once a goal and set of objectives has been specified and the parameters and assumptions have been identified, it is necessary to choose a particular problem to solve. Any large problem can be broken into smaller problems that are in turn broken into even smaller problems to be addressed. Each problem is an achievable goal that consists of objectives. Each of these objectives is a sub-problem that must be solved first in order to solve the larger overarching problem.

There are many different reasons to choose a particular problem to solve. It is important to do risk assessment on the problems involved and examine why the problem is being solved. There are many reasons why a particular problem is chosen as the one to solve. For example, the problem might be the most important, most immediate, most far reaching, or most politically important at the moment. Whatever the choice, the individual or group must have clear reasons why they choose the problem to be solved.

Once the aspects of the problem are known, the problem must be phrased as a question that each solution can answer affirmatively. An example of a problem statement might be "How might I increase the use of problem solving techniques by college graduates of four year universities in America today?" This specific type of question has four separate parts: question statement, active verb, object, and parameters and assumptions.

The first part is the question statement which transforms the problem into a question to be answered. It takes the form "How might I" or "In what ways might I." If the process is being undertaken by a group, it should be phrased as we instead of I. At times, an individual or a group may examine an issue concerning a third party. For example, students may work on problems facing their institution or that must be handled by the government. In this case, the question might become, "how might our school," or "In what way might the United States government." In all of these cases, the object is to create a question that must be answered as well as specify the group who is designated to answer it. Each solution must then apply to that group and be able to be accomplished by them as well.

            Next is the active verb or the action used to solve the problem. Some of the most useful of these active verbs are the ones that describe change without specifying an absolute end or any one action. For example: Accelerate, alleviate, broaden, increase, minimize, reduce, and stabilize. It is important to realize that the stronger the verb, the more difficult it might be to accomplish workable solutions. For example, it is easier to reduce crime than to eliminate it. Keep this in mind when choosing verbs because verb choice is vital to good solution finding. If necessary, two or more verbs can be used and should be separated by the following conjunctions: And, Or, or And/Or. To assist in the verb choice process, some active verbs are listed below:

Active Verbs

Figure 2 is a list action verbs that can be used when formulating a problem statement.

            The third part of the problem statement is the object of the sentence that relates to the problem being solved. The object states what is being acted upon by the verb to help solve the problem. Each solution must directly or indirectly affect this object. In our earlier statement, "How might I increase the use of problem solving techniques by college graduates of four year universities in America today?" the object is "use of problem solving."

            Finally, the parameters and assumptions that are bounding the solution are listed. These help to focus the solutions that are generated. Though parameters are not necessary, they are often useful to help limit and focus the scope of the process. Be careful not to leave too broad a problem. Broad problems lead to a wide number of solutions that can be difficult to choose between and implement with weak or ineffectual results. At the same time, an overly narrow problem statement can lead to a small number of solutions that provide little useable results. In our example, "college graduates of four year universities in America today?" are the parameters. This is identified with the conjunction ‘by’ and is used to mark who should have the use of problem solving increased.

            Once the problem statement is phrased properly, solutions can be generated. However, it is important to note that this statement might have to be modified as more research becomes available or as the remainder of the process is worked through. As the process is iterated, small modifications to the problem statement can be made and refinements in the scope and specificity accomplished through changes in the verb, object and parameters.

Identify Potential Solutions

Once the problem statement has been chosen, it is necessary to generate potential solutions. This is the most creative portion of the process. Even so, conducting research into existing solutions to the problem or similar problems is helpful to generate workable solutions. The main criteria for judging solutions in this step is simply whether or not they answer the problem statement with a ‘yes.’ At this point, it may also be possible to eliminate some solutions because they do not agree with commonly held moral and ethical guidelines. Even though not stated specifically, these guidelines are understood and assumed to be upheld when reviewing solutions. For example, a solution to global pollution might be to kill every human. This is obviously not a good solution even though it would give a ‘yes’ answer to the question of "How might we minimize global air pollution caused by humans?"

When working in groups, it is important to work together to generate solutions. Also, it should be realized that the solution process takes time depending upon the problem complexity. At this point, do not judge solutions for more than their ability to answer the stated problem questions with a "yes" because they will be evaluated more closely in the next step. Many times it is possible to use discarded solutions to develop new ideas for solutions. However, it is important to be able to distinguish between similar solutions. Saying the same thing in ten different ways may not be ten different solutions. Try to group similar solutions together. If all the solutions fall into one group, then perhaps the best solution is to implement that group with different variations for different cases of the problems. Just as there are many unique problems, the solutions to these problems are all unique and need to be adapted to the particular situations being discussed. This will be addressed in the last section of the problem solving process.

Evaluate the Valid Potential Solutions to Select a Best Solution

Once a list of potential solutions has been generated, the evaluation process can begin. First, a list of criteria for judging all solutions equally must be chosen. It is vital to eliminate personal bias towards particular solutions as well as to utilize a consistent set of criteria to evaluate all solutions fairly. For example: most cost effective, most socially acceptable, most easily implemented, most directly solves the problem, most far reaching effects, most lasting effects, least government intervention required, least limiting to development, or quickest to implement. It is important to have research and logical reasons for the criteria chosen as well as factual support for the rankings given to a particular solution for each criteria.

Once the criteria are chosen, they should be given a weighting. In most cases, all the criteria have the same weight. However, it is possible to give other weightings to criteria so that a particular factor is seen as more important. Many times, the cost, time to complete, or political nature of a project is more important than other factors and so that criteria may have a higher ranking than others used to judge.

            Once the criteria are chosen and weighted, all qualified solutions must then be ranked. Two types of procedures for ranking exist. If the number of solutions is large, usually greater than ten, an independent ranking must be conducted to narrow the number of choices. Each solution is listed along one side of a grid and then given a score for each criteria from 1-5 where 5 is the highest (other ranges can be used). The rankings for the various criteria are then totaled and a score for each solution is reported. These scores are compared to create a subset of solutions that have the highest score.

            If the number of solutions is initially small or the independent ranking has been conducted, the remaining solutions are placed into a grid with the criteria for a comparative analysis. Though all the solutions may be seen as good, the comparative analysis gives the best solution. The total number of solutions listed gives the range of numbers for each criteria. For example, if there are six (6) solutions, then the rankings will go from 1-6 with 6 being the highest. Each solution is ranked for each criteria in comparison to the other solutions for that criteria. However, within a criteria no two solutions can have the same number. If two are equal, the adjacent numbers should be added and then divided by 2. The result is then placed in the space for each solution. See the charts below for an example. If the question being asked was "How might we control development in order to preserve the integrity and character of the town of Bedminster?"

Sample Table of Potential Solutions

Figure A3. 3 is a list of the potential solutions to be evaluated.

Sample Table of Evaluation Criteria

Figure 4 is a list of the criteria to be used to evaluate the potential solutions.

Sample Table of a Comparative Analysis

Figure 5 is a comparative analysis of the solutions from the table in figure 6 based upon the listed criteria shown in figure 7 for the problem stated earlier. The values used for scoring range from 6 as most satisfies criteria to 1 that least satisfies criteria.

            Once all the solutions are ranked for all criteria and the weighting is applied appropriately, the scores for each solution are totaled. The highest score is then the best solution. If two solutions are close in score then there may be two solutions that are equally as good but differ in their strong points.

            It is important to remember that the criteria that are used to judge the solutions are reflective of the choices being made. Each criteria is a ruler or a gauge by which to measure an outcome. Different rulers will yield different results so be sure to choose the proper rulers as well as use them properly. In order to choose the correct ruler and interpret it in the correct way, it is necessary to understand many different disciplines and the tools they use. In the end, however, each individual must have good decision-making skills to choose and use criteria.

Develop an Action Plan to Implement the Solution

After selecting the best solution, it is necessary to give some thought to the way in which it might be implemented. Giving insight into funding, potential problems with implementing the solution, and the time frame of the solution is necessary for any workable solution to a problem. Not all solutions can be implemented. Unforeseen problems may arise as solutions are tested and put to work. Many times, unexpected resistance to solutions can be encountered. Other times, unacceptable results can require that another solution be used.

In some circumstances the problem may have been originally selected incorrectly, have been misunderstood, or have changed as a result of research or altered circumstances. In the end, mistakes happen and the action plan helps the problem solver be prepared for such eventualities. In any event, the action plan can be used to make others aware of potential problems that might be faced while putting the selected solution into effect. Even when solving a current problem, this process will automatically assist the problem solver in thinking of potential problems and thus assist in avoiding unwanted outcomes. Whatever the outcome, it is vital to understand that the choices made during this entire process rely upon research.

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A Powerful Methodology for Creative Problem Solving

By the Mind Tools Content Team

Projects don't always run smoothly. Even with all the analysis and data you need at your fingertips, sometimes you just can't see a way forward. At times like these, you need to develop creative solutions to the problems you face.

Chances are you already know about brainstorming , which can help with this sort of situation. But brainstorming depends on intuition and the existing knowledge of team members, and its results are often unpredictable and unrepeatable.

TRIZ, however, is a problem-solving philosophy based on logic, data and research, rather than on intuition.

It draws on the past knowledge and ingenuity of thousands of engineers to speed up creative problem solving for project teams. Its approach brings repeatability, predictability and reliability to the problem-solving process and delivers a set of dependable tools.

This article walks you through the essentials of TRIZ.

What is TRIZ?

TRIZ is the Russian acronym for the "Theory of Inventive Problem Solving," an international system of creativity developed in the U.S.S.R. between 1946 and 1985, by engineer and scientist Genrich S. Altshuller and his colleagues.

According to TRIZ, universal principles of creativity form the basis of innovation. TRIZ identifies and codifies these principles, and uses them to make the creative process more predictable.

In other words, whatever problem you're facing, somebody, somewhere, has already solved it (or one very like it). Creative problem solving involves finding that solution and adapting it to your problem.

TRIZ is most useful in roles such as product development, design engineering, and process management. For example, Six Sigma quality improvement processes often make use of TRIZ.

The Key TRIZ Tools

Let's look at two of the central concepts behind TRIZ: generalizing problems and solutions, and eliminating contradictions.

1. Generalizing Problems and Solutions

The primary findings of TRIZ research are as follows:

• Problems and solutions are repeated across industries and sciences. By representing a problem as a "contradiction" (we explore this later in this article), you can predict creative solutions to that problem.
• Patterns of technical evolution tend to repeat themselves across industries and sciences.
• Creative innovations often use scientific effects outside the field where they were developed.

Using TRIZ consists of learning these repeating patterns of problem and solution, understanding the contradictions present in a situation, and developing new methods of using scientific effects.

You then apply the general TRIZ patterns to the specific situation that confronts you, and discover a generalized version of the problem.

Figure 1, below, illustrates this process.

Figure 1 – The TRIZ Problem-Solving Method

Here, you take the specific problem that you face and generalize it to one of the TRIZ general problems. From the TRIZ general problems, you identify the general TRIZ solution you need, and then consider how you can apply it to your specific problem.

The TRIZ databases are actually a collection of "open source" resources compiled by users and aficionados of the system (such as the 40 Principles and 76 Standard Solutions, which we look at, below).

2. Eliminating Contradictions

Another fundamental TRIZ concept is that there are fundamental contradictions at the root of most problems. In many cases, a reliable way to solve a problem is to eliminate these contradictions.

TRIZ recognizes two categories of contradictions:

• The product gets stronger (good), but the weight increases (bad).
• Service is customized to each customer (good), but the service delivery system gets complicated (bad).
• Training is comprehensive (good), but it keeps employees away from their assignments (bad).

The key technical contradictions are summarized in the TRIZ Contradiction Matrix . As with all TRIZ resources, it takes time and study to become familiar with the Contradiction Matrix.

• Software should be complex (to have many features), but simple (to be easy to learn).
• Coffee should be hot (to be enjoyed), but cool (to avoid burning the drinker).
• An umbrella should be large (to keep the rain off), but small (to be maneuverable in a crowd).

You can solve physical contradictions with the TRIZ Separation Principles . These separate your requirements according to basic categories of Space, Time and Scale.

How to Use TRIZ Principles – an Example

Begin to explore TRIZ by applying it to a simple, practical problem.

For example, consider the specific problem of a furniture store in a small building. The store wants to attract customers, so it needs to have its goods on display. But it also needs to have enough storage space to keep a range of products ready for sale.

Using TRIZ, you can establish that the store has a physical contradiction. The furniture needs to be large (to be useful and attractive), but also small (to be stored in as little space as possible). Using TRIZ, the store owners generalize this contradiction into a general problem and apply one of the 40 Principles of Problem Solving – a key TRIZ technique – to it.

They find a viable general solution in Principle 1 – Segmentation. This advocates dividing an object or system into different parts, or making it easy to take apart. This could lead the owners to devise flat-pack versions of their furniture, so that display models can take up the room that they need while inventory occupies much less space per unit. This is the specific solution.

You, too, can use the 40 Principles of Problem Solving, or the 40 Inventive Principles, and the Contradiction Matrix to help you with your problem-solving.

Five Top TRIZ Concepts and Techniques

TRIZ comes with a range of ideas and techniques beyond the basic principles outlined above. Some are conceptual and analytical, such as:

• The Law of Ideality. This states that any system tends to become more reliable throughout its life, through regular improvement.
• Functional Modeling, Analysis and Trimming. TRIZ uses these methods to define problems.
• Locating the Zones of Conflict. (This is known to Six Sigma problem-solvers as " Root Cause Analysis .")

Some are more prescriptive. For example:

• The Laws of Technical Evolution and Technology Forecasting . These categorize technical evolution by demand, function and system.
• The 76 Standard Solutions . These are specific solutions devised to a range of common problems in design and innovation.

You can use one such tool or many to solve a problem, depending on its nature.

TRIZ is a system of creative problem solving, commonly used in engineering and process management. It follows four basic steps:

• Define your specific problem.
• Find the TRIZ generalized problem that matches it.
• Find the generalized solution that solves the generalized problem.
• Adapt the generalized solution to solve your specific problem.

Most problems stem from technical or physical contradictions. Apply one of hundreds of TRIZ principles and laws to eliminate these contradictions, and you can solve the problem.

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• The Three Stages of the Problem-Solving Cycle

Essentially every problem-solving heuristic in mathematics goes back to George Polya’s How to Solve It ; my approach is no exception. However, this cyclic description might help to keep the process cognitively present.

A few months ago, I produced a video describing this the three stages of the problem-solving cycle: Understand, Strategize, and Implement. That is, we must first understand the problem, then we think of strategies that might help solve the problem, and finally we implement those strategies and see where they lead us. During two decades of observing myself and others in the teaching and learning process, I’ve noticed that the most neglected phase is often the first one—understanding the problem.

The Three Stages Explained

• What am I looking for?
• What is the unknown?
• Do I understand every word and concept in the problem?
• Am I familiar with the units in which measurements are given?
• Is there information that seems missing?
• Is there information that seems superfluous?
• Is the source of information bona fide? (Think about those instances when a friend gives you a puzzle to solve and you suspect there’s something wrong with the way the puzzle is posed.)
• Logical reasoning
• Pattern recognition
• Working backwards
• Adopting a different point of view
• Considering extreme cases
• Solving a simpler analogous problem
• Organizing data
• Making a visual representation
• Accounting for all possibilities
• Intelligent guessing and testing

I have produced videos explaining each one of these strategies individually using problems we have solved at the Chapel Hill Math Circle.

• Implementing : We now implement our strategy or set of strategies. As we progress, we check our reasoning and computations (if any). Many novice problem-solvers make the mistake of “doing something” before understanding (or at least thinking they understand) the problem. For instance, if you ask them “What are you looking for?”, they might not be able to answer. Certainly, it is possible to have an incorrect understanding of the problem, but that is different from not even realizing that we have to understand the problem before we attempt to solve it!

As we implement our strategies, we might not be able to solve the problem, but we might refine our understanding of the problem. As we refine our understanding of the problem, we can refine our strategy. As we refine our strategy and implement a new approach, we get closer to solving the problem, and so on. Of course, even after several iterations of this cycle spanning across hours, days, or even years, one may still not be able to solve a particular problem. That’s part of the enchanting beauty of mathematics.

I invite you to observe your own thinking—and that of your students—as you move along the problem-solving cycle!

[1] Problem-Solving Strategies in Mathematics , Posamentier and Krulik, 2015.

About the author: You may contact Hector Rosario at [email protected].

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Culture Development

Workplace problem-solving examples: real scenarios, practical solutions.

• March 11, 2024

In today’s fast-paced and ever-changing work environment, problems are inevitable. From conflicts among employees to high levels of stress, workplace problems can significantly impact productivity and overall well-being. However, by developing the art of problem-solving and implementing practical solutions, organizations can effectively tackle these challenges and foster a positive work culture. In this article, we will delve into various workplace problem scenarios and explore strategies for resolution. By understanding common workplace problems and acquiring essential problem-solving skills, individuals and organizations can navigate these challenges with confidence and success.

Understanding Workplace Problems

Before we can effectively solve workplace problems , it is essential to gain a clear understanding of the issues at hand. Identifying common workplace problems is the first step toward finding practical solutions. By recognizing these challenges, organizations can develop targeted strategies and initiatives to address them.

Identifying Common Workplace Problems

One of the most common workplace problems is conflict. Whether it stems from differences in opinions, miscommunication, or personality clashes, conflict can disrupt collaboration and hinder productivity. It is important to note that conflict is a natural part of any workplace, as individuals with different backgrounds and perspectives come together to work towards a common goal. However, when conflict is not managed effectively, it can escalate and create a toxic work environment.

In addition to conflict, workplace stress and burnout pose significant challenges. High workloads, tight deadlines, and a lack of work-life balance can all contribute to employee stress and dissatisfaction. When employees are overwhelmed and exhausted, their performance and overall well-being are compromised. This not only affects the individuals directly, but it also has a ripple effect on the entire organization.

Another common workplace problem is poor communication. Ineffective communication can lead to misunderstandings, delays, and errors. It can also create a sense of confusion and frustration among employees. Clear and open communication is vital for successful collaboration and the smooth functioning of any organization.

The Impact of Workplace Problems on Productivity

Workplace problems can have a detrimental effect on productivity levels. When conflicts are left unresolved, they can create a tense work environment, leading to decreased employee motivation and engagement. The negative energy generated by unresolved conflicts can spread throughout the organization, affecting team dynamics and overall performance.

Similarly, high levels of stress and burnout can result in decreased productivity, as individuals may struggle to focus and perform optimally. When employees are constantly under pressure and overwhelmed, their ability to think creatively and problem-solve diminishes. This can lead to a decline in the quality of work produced and an increase in errors and inefficiencies.

Poor communication also hampers productivity. When information is not effectively shared or understood, it can lead to misunderstandings, delays, and rework. This not only wastes time and resources but also creates frustration and demotivation among employees.

Furthermore, workplace problems can negatively impact employee morale and job satisfaction. When individuals are constantly dealing with conflicts, stress, and poor communication, their overall job satisfaction and engagement suffer. This can result in higher turnover rates, as employees seek a healthier and more supportive work environment.

In conclusion, workplace problems such as conflict, stress, burnout, and poor communication can significantly hinder productivity and employee well-being. Organizations must address these issues promptly and proactively to create a positive and productive work atmosphere. By fostering open communication, providing support for stress management, and promoting conflict resolution strategies, organizations can create a work environment that encourages collaboration, innovation, and employee satisfaction.

The Art of Problem Solving in the Workplace

Now that we have a clear understanding of workplace problems, let’s explore the essential skills necessary for effective problem-solving in the workplace. By developing these skills and adopting a proactive approach, individuals can tackle problems head-on and find practical solutions.

Problem-solving in the workplace is a complex and multifaceted skill that requires a combination of analytical thinking, creativity, and effective communication. It goes beyond simply identifying problems and extends to finding innovative solutions that address the root causes.

Essential Problem-Solving Skills for the Workplace

To effectively solve workplace problems, individuals should possess a range of skills. These include strong analytical and critical thinking abilities, excellent communication and interpersonal skills, the ability to collaborate and work well in a team, and the capacity to adapt to change. By honing these skills, individuals can approach workplace problems with confidence and creativity.

Analytical and critical thinking skills are essential for problem-solving in the workplace. They involve the ability to gather and analyze relevant information, identify patterns and trends, and make logical connections. These skills enable individuals to break down complex problems into manageable components and develop effective strategies to solve them.

Effective communication and interpersonal skills are also crucial for problem-solving in the workplace. These skills enable individuals to clearly articulate their thoughts and ideas, actively listen to others, and collaborate effectively with colleagues. By fostering open and honest communication channels, individuals can better understand the root causes of problems and work towards finding practical solutions.

Collaboration and teamwork are essential for problem-solving in the workplace. By working together, individuals can leverage their diverse skills, knowledge, and perspectives to generate innovative solutions. Collaboration fosters a supportive and inclusive environment where everyone’s ideas are valued, leading to more effective problem-solving outcomes.

The ability to adapt to change is another important skill for problem-solving in the workplace. In today’s fast-paced and dynamic work environment, problems often arise due to changes in technology, processes, or market conditions. Individuals who can embrace change and adapt quickly are better equipped to find solutions that address the evolving needs of the organization.

The Role of Communication in Problem Solving

Communication is a key component of effective problem-solving in the workplace. By fostering open and honest communication channels, individuals can better understand the root causes of problems and work towards finding practical solutions. Active listening, clear and concise articulation of thoughts and ideas, and the ability to empathize are all valuable communication skills that facilitate problem-solving.

Active listening involves fully engaging with the speaker, paying attention to both verbal and non-verbal cues, and seeking clarification when necessary. By actively listening, individuals can gain a deeper understanding of the problem at hand and the perspectives of others involved. This understanding is crucial for developing comprehensive and effective solutions.

Clear and concise articulation of thoughts and ideas is essential for effective problem-solving communication. By expressing oneself clearly, individuals can ensure that their ideas are understood by others. This clarity helps to avoid misunderstandings and promotes effective collaboration.

Empathy is a valuable communication skill that plays a significant role in problem-solving. By putting oneself in the shoes of others and understanding their emotions and perspectives, individuals can build trust and rapport. This empathetic connection fosters a supportive and collaborative environment where everyone feels valued and motivated to contribute to finding solutions.

In conclusion, problem-solving in the workplace requires a combination of essential skills such as analytical thinking, effective communication, collaboration, and adaptability. By honing these skills and fostering open communication channels, individuals can approach workplace problems with confidence and creativity, leading to practical and innovative solutions.

Real Scenarios of Workplace Problems

Now, let’s explore some real scenarios of workplace problems and delve into strategies for resolution. By examining these practical examples, individuals can develop a deeper understanding of how to approach and solve workplace problems.

Conflict Resolution in the Workplace

Imagine a scenario where two team members have conflicting ideas on how to approach a project. The disagreement becomes heated, leading to a tense work environment. To resolve this conflict, it is crucial to encourage open dialogue between the team members. Facilitating a calm and respectful conversation can help uncover underlying concerns and find common ground. Collaboration and compromise are key in reaching a resolution that satisfies all parties involved.

In this particular scenario, let’s dive deeper into the dynamics between the team members. One team member, let’s call her Sarah, strongly believes that a more conservative and traditional approach is necessary for the project’s success. On the other hand, her colleague, John, advocates for a more innovative and out-of-the-box strategy. The clash between their perspectives arises from their different backgrounds and experiences.

As the conflict escalates, it is essential for a neutral party, such as a team leader or a mediator, to step in and facilitate the conversation. This person should create a safe space for both Sarah and John to express their ideas and concerns without fear of judgment or retribution. By actively listening to each other, they can gain a better understanding of the underlying motivations behind their respective approaches.

During the conversation, it may become apparent that Sarah’s conservative approach stems from a fear of taking risks and a desire for stability. On the other hand, John’s innovative mindset is driven by a passion for pushing boundaries and finding creative solutions. Recognizing these underlying motivations can help foster empathy and create a foundation for collaboration.

As the dialogue progresses, Sarah and John can begin to identify areas of overlap and potential compromise. They may realize that while Sarah’s conservative approach provides stability, John’s innovative ideas can inject fresh perspectives into the project. By combining their strengths and finding a middle ground, they can develop a hybrid strategy that incorporates both stability and innovation.

Ultimately, conflict resolution in the workplace requires effective communication, active listening, empathy, and a willingness to find common ground. By addressing conflicts head-on and fostering a collaborative environment, teams can overcome challenges and achieve their goals.

Dealing with Workplace Stress and Burnout

Workplace stress and burnout can be debilitating for individuals and organizations alike. In this scenario, an employee is consistently overwhelmed by their workload and experiencing signs of burnout. To address this issue, organizations should promote a healthy work-life balance and provide resources to manage stress effectively. Encouraging employees to take breaks, providing access to mental health support, and fostering a supportive work culture are all practical solutions to alleviate workplace stress.

In this particular scenario, let’s imagine that the employee facing stress and burnout is named Alex. Alex has been working long hours, often sacrificing personal time and rest to meet tight deadlines and demanding expectations. As a result, Alex is experiencing physical and mental exhaustion, reduced productivity, and a sense of detachment from work.

Recognizing the signs of burnout, Alex’s organization takes proactive measures to address the issue. They understand that employee well-being is crucial for maintaining a healthy and productive workforce. To promote a healthy work-life balance, the organization encourages employees to take regular breaks and prioritize self-care. They emphasize the importance of disconnecting from work during non-working hours and encourage employees to engage in activities that promote relaxation and rejuvenation.

Additionally, the organization provides access to mental health support services, such as counseling or therapy sessions. They recognize that stress and burnout can have a significant impact on an individual’s mental well-being and offer resources to help employees manage their stress effectively. By destigmatizing mental health and providing confidential support, the organization creates an environment where employees feel comfortable seeking help when needed.

Furthermore, the organization fosters a supportive work culture by promoting open communication and empathy. They encourage managers and colleagues to check in with each other regularly, offering support and understanding. Team members are encouraged to collaborate and share the workload, ensuring that no one person is overwhelmed with excessive responsibilities.

By implementing these strategies, Alex’s organization aims to alleviate workplace stress and prevent burnout. They understand that a healthy and balanced workforce is more likely to be engaged, productive, and satisfied. Through a combination of promoting work-life balance, providing mental health support, and fostering a supportive work culture, organizations can effectively address workplace stress and create an environment conducive to employee well-being.

Practical Solutions to Workplace Problems

Now that we have explored real scenarios, let’s discuss practical solutions that organizations can implement to address workplace problems. By adopting proactive strategies and establishing effective policies, organizations can create a positive work environment conducive to problem-solving and productivity.

Implementing Effective Policies for Problem Resolution

Organizations should have clear and well-defined policies in place to address workplace problems. These policies should outline procedures for conflict resolution, channels for reporting problems, and accountability measures. By ensuring that employees are aware of these policies and have easy access to them, organizations can facilitate problem-solving and prevent issues from escalating.

Promoting a Positive Workplace Culture

A positive workplace culture is vital for problem-solving. By fostering an environment of respect, collaboration, and open communication, organizations can create a space where individuals feel empowered to address and solve problems. Encouraging teamwork, recognizing and appreciating employees’ contributions, and promoting a healthy work-life balance are all ways to cultivate a positive workplace culture.

The Role of Leadership in Problem Solving

Leadership plays a crucial role in facilitating effective problem-solving within organizations. Different leadership styles can impact how problems are approached and resolved.

Leadership Styles and Their Impact on Problem-Solving

Leaders who adopt an autocratic leadership style may make decisions independently, potentially leaving their team members feeling excluded and undervalued. On the other hand, leaders who adopt a democratic leadership style involve their team members in the problem-solving process, fostering a sense of ownership and empowerment. By encouraging employee participation, organizations can leverage the diverse perspectives and expertise of their workforce to find innovative solutions to workplace problems.

Encouraging Employee Participation in Problem Solving

To harness the collective problem-solving abilities of an organization, it is crucial to encourage employee participation. Leaders can create opportunities for employees to contribute their ideas and perspectives through brainstorming sessions, team meetings, and collaborative projects. By valuing employee input and involving them in decision-making processes, organizations can foster a culture of inclusivity and drive innovative problem-solving efforts.

In today’s dynamic work environment, workplace problems are unavoidable. However, by understanding common workplace problems, developing essential problem-solving skills, and implementing practical solutions, individuals and organizations can navigate these challenges effectively. By fostering a positive work culture, implementing effective policies, and encouraging employee participation, organizations can create an environment conducive to problem-solving and productivity. With proactive problem-solving strategies in place, organizations can thrive and overcome obstacles, ensuring long-term success and growth.

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Problem Solving - 3 Basic Steps

Don't complicate it.

Problems can be confusing. Your problem-solving process shouldn’t make them more confusing. With a variety of different tools available, it’s common for people in the same company to use different approaches and different terminology. This makes problem solving problematic. It shouldn’t be.

Some companies use 5Whys , some use fishbone diagrams , and some categorize incidents into generic buckets like " human error " and " procedure not followed ." Some problem-solving methods have six steps, some have eight steps and some have 14 steps. It’s easy to understand how employees get confused.

6-sigma is another widely recognized problem-solving tool. It has five steps with its own acronym, DMAIC: define, measure, analyze, improve and control. The first two steps are for defining and measuring the problem . The third step is the analysis . And the fourth and fifth steps are improve and control, and address solutions .

3 Basic Steps of Problem Solving

As the name suggests, problem solving starts with a problem and ends with solutions. The step in the middle is the analysis. The level of detail within a problem changes based on the magnitude of an issue, but the basic steps of problem solving remain the same regardless of the type of problem:

Step 1. Problem

Step 2. analysis, step 3. solutions.

But these steps are not necessarily what everyone does. Some groups jump directly to solutions after a hasty problem definition. The analysis step is regularly neglected. Individuals and organizations don’t dig into the details that are essential to understand the issue. In the Cause Mapping® method, the point of root cause analysis is to reveal what happened within an incident—to do that digging.

Step 1. Problem

A complete problem definition consists of several different questions:

• What is the problem?
• When did it happen?
• Where did it happen?
• What was the total impact to each of the organization’s overall goals?

These four questions capture what individuals see as a problem, along with the specifics about the setting of the issue (the time and place), and, importantly, the overall consequences to the organization. The traditional approach of writing a problem description as a few sentences doesn’t necessarily capture the information needed for a complete definition. Some organizations see their problem as a single effect, but that doesn’t reflect the nature of an actual issue since different negative outcomes can occur within the same incident. Specific pieces of information are captured within each of the four questions to provide a thorough definition of the problem.

The analysis step provides a clear explanation of an issue by breaking it down into parts. A simple way to organize the details of an incident is to make a timeline . Each piece of the incident in placed in chronological order. A timeline is an effective way to understand what happened and when for an issue.

Ultimately, the objective of problem solving is to turn the negative outcomes defined in step 1 into positive results. To do so, the causes that produced the unwanted outcomes must be identified. These causes provide both the explanation of the issue as well as control points for different solution options. This cause-and-effect approach is the basis of explaining and preventing a problem solving. It’s why cause-and-effect thinking is fundamental for troubleshooting, critical thinking and effective root cause analysis.

Many organizations are under-analyzing their problems because they stop at generic categories like procedure not followed, training less than adequate or management systems . This is a mistake. Learning how to dig a littler further, by asking more Why questions, can reveal significant insight about those chronic problems that people have come to accept as normal operations.

A Cause Map™ diagram provides a way for frontline personnel, technical leads and managers to communicate the details of an issue objectively, accurately and thoroughly. A cause-and-effect analysis can begin as a single, linear path that can be expanded into as much detail as needed to fully understand the issue.

Solutions are specific actions that control specific causes to produce specific outcomes. Both short-term and long-term solutions can be identified from a clear and accurate analysis. It is also important for people to understand that every cause doesn’t need to be solved. Most people believe that 15 causes require 15 solutions. That is not true. Changing just one cause along a causal path breaks that chain of events. Providing solutions on more than one causal path provides additional layers of protection to further reduce the risk of a similar issue occurring in the future.

The Basics of Problem Solving Don't Change

These three steps of problem solving can be applied consistently across an organization from frontline troubleshooters to the executives. First principles should be the foundation of a company’s problem-solving culture. Overlooking these basics erodes critical thinking. Even though the fundamentals of cause-and-effect don’t change, organizations and individuals continue to find special adjectives, algorithms and jargon appealing. Teaching too many tools and using contrived terms such as “true root causal factors” is a symptom of ignoring lean principles. Don’t do that which is unnecessary.

Your problems may be complex, but your problem-solving process should be clear and simple. A scientific approach that objectively explains what happened and why (cause and effect) is sound. It’s the basis for understanding and solving a problem – any problem. It works on the farm, in the power plant, at the manufacturing company and at an airline. It works for the cancer researcher and for the auto mechanic. It also works the same way for safety incidents, production losses and equipment failures. Cause and effect doesn’t change. Just test it.

If you’re interested in seeing one of your problems dissected as a Cause Map diagram, send us an email or call the ThinkReliability office. We’ll arrange a call to step through your issue. You can also learn more about improving the way your organization investigates and prevents problems through one of our upcoming online webinars, short courses or workshops .

Want to learn more? Watch our 28-minute video on problem-solving basics.

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Seven Steps of Generic Problem Solving And Tips and Hints for Creating Actions at Meetings

by Martin Gummery | Dec 19, 2019 | Tools For Change | 0 comments

I thought I would share some work shared in the past.

Many years ago, when I first embarked on World Class Tools and Techniques and becoming a Management Consultant, I thought the business world would be perfect by 2020. Phew, I got that wrong!

Problems continue to plague companies and worse than that, companies still have the same problems repeating again and again. So here are some tips I learnt number of years ago.

1. Define The Problem

This is often where people struggle. They react to what they think the problem is. Instead, seek to understand more about why you think there’s a problem.

Ask yourself and others, the following questions:

• What can you see that causes you to think there’s a problem?
• Where is it happening?
• How is it happening?
• When is it happening?
• With whom is it happening? (HINT: Don’t jump to “Who is causing the problem?” When we’re stressed, blaming is often one of our first reactions. To be an effective manager, you need to address issues more than people.)
• Why is it happening?
• Write down a description of the problem in terms of “The following should be happening, but isn’t …” or “The following is happening and should be: …” As much as possible, be specific in your description, including what is happening, where, how, with whom and why. (It may be helpful at this point to use a variety of research methods.

Defining complex problems:

If the problem still seems overwhelming, break it down by repeating steps 1-7 until you have descriptions of several related problems.

Verifying your understanding of the problems:

It helps a great deal to verify your problem analysis for conferring with a peer or someone else.

Prioritize the problems:

If you discover that you are looking at several related problems, then prioritize which ones you should address first.

Note the difference between “important” and “urgent” problems. Often, what we consider to be important problems to consider are really just urgent problems. Important problems deserve more attention. For example, if you’re continually answering “urgent” phone calls, then you’ve probably got a more “important” problem and that’s to design a system that screens and prioritizes your phone calls.

Understand your role in the problem:

Your role in the problem can greatly influence how you perceive the role of others. For example, if you’re very stressed out, it’ll probably look like others are too, and you may resort too quickly to blaming and reprimanding others. Or, you are feeling very guilty about your role in the problem and you may ignore the accountabilities of others.

2. Look at potential causes for the problem

• It’s amazing how much you don’t know about what you don’t know. Therefore, in this phase, it’s critical to get input from other people who notice the problem and who are affected by it.
• It’s often useful to collect input from other individuals one at a time (at least at first). Otherwise, people tend to be inhibited about offering their impressions of the real causes of problems.
• Write down your opinions and what you’ve heard from others.
• Regarding what you think might be performance problems associated with an employee; it’s often useful to seek advice from a peer or your supervisor in order to verify your impression of the problem.
• Write down a description of the cause of the problem and in terms of what is happening, where, when, how, with whom and why.

3. Identify alternatives for approaches to resolve the problem

At this point, it’s useful to keep others involved (unless you’re facing a personal and/or employee performance problem). Brainstorm for solutions to the problem. Very simply put, brainstorming is collecting as many ideas as possible, and then screening them to find the best idea. It’s critical when collecting the ideas to not pass any judgment on the ideas — just write them down as you hear them. (A wonderful set of skills used to identify the underlying cause of issues is Systems Thinking.)

4. Select an approach to resolve the problem

When selecting the best approach, consider:

• Which approach is the most likely to solve the problem for the long term?
• Which approach is the most realistic to accomplish for now? Do you have the resources? Are they affordable? Do you have enough time to implement the approach?
• What is the extent of risk associated with each alternative?

(The nature of this step, in particular, in the problem solving process is why problem solving and decision making are highly integrated.)

5. Plan the implementation of the best alternative (this is your action plan)

• Carefully consider “What will the situation look like when the problem is solved?”
• What steps should be taken to implement the best alternative to solving the problem? What systems or processes should be changed in your organization, for example, a new policy or procedure? Don’t resort to solutions where someone is “just going to try harder”.
• How will you know if the steps are being followed or not? (these are your indicators of the success of your plan)
• What resources will you need in terms of people, money and facilities?
• How much time will you need to implement the solution? Write a schedule that includes the start and stop times, and when you expect to see certain indicators of success.
• Who will primarily be responsible for ensuring implementation of the plan?
• Write down the answers to the above questions and consider this as your action plan.
• Communicate the plan to those who will be involved in implementing it and, at least, to your immediate supervisor.

(An important aspect of this step in the problem-solving process is continual observation and feedback.)

6. Monitor implementation of the plan

Monitor the indicators of success:

• Are you seeing what you would expect from the indicators?
• Will the plan be done according to schedule?
• If the plan is not being followed as expected, then consider: Was the plan realistic? Are there sufficient resources to accomplish the plan on schedule? Should more priority be placed on various aspects of the plan? Should the plan be changed?

7. Verify if the problem has been resolved or not

One of the best ways to verify if a problem has been solved or not is to resume normal operations in the organization. Still, you should consider:

• What changes should be made to avoid this type of problem in the future? Consider changes to policies and procedures, training, etc.
• Lastly, consider “What did you learn from this problem solving?” Consider new knowledge, understanding and/or skills.
• Consider writing a brief memo that highlights the success of the problem solving effort, and what you learned as a result. Share it with your supervisor, peers and subordinates.

Martin Gummery, Managing Director, NewLeaf International Ltd

Tel: 01905 425209

Email: [email protected]

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Complementary approaches to problem solving in healthcare and public health: implementation science and human-centered design

Elizabeth chen.

1 Department of Health Behavior, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2 Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA

Megan C Roberts

3 Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Complementary approaches to problem solving in healthcare and public health: implementation science and human-centered design”: Combining implementation science and human-centered design approaches is novel and these complementary approaches can be applied together to optimize the integration of evidence-based practices within clinical and public health settings.

Implications

Practice: Human-centered design (HCD) methods can be used to consistently operationalize implementation strategies.

Policy: HCD and implementation science (IS), when used together, can provide an avenue for developing stakeholder engaged policy interventions and implementation strategies.

Research: Integrating HCD and IS is a novel approach and future research should be aimed at understanding which HCD strategies are most effective for operationalizing implementation strategies and how IS can be used to inform and evaluate HCD research.

INTRODUCTION

The timely, effective adoption and implementation of evidence-based practices, interventions, tools, programs, and policies (hereafter referenced as evidence-based practices) is important to improve health care delivery and patient outcomes. Implementation science (IS), user-centered design (UCD), and human-centered design (HCD) are three research approaches that focus on translating research evidence into the real world. In their recent article titled “A glossary of user-centered design strategies for implementation experts,” Dopp et al. [ 1 ] established a precedent for combining IS and UCD approaches and offered a new glossary of UCD strategies IS experts could use. In this commentary, we build upon this work by combining IS and HCD approaches and offering a how-to guide for IS experts to operationalize implementation strategies using HCD methods. Combining IS and HCD approaches is novel to health care research and practice, and we believe that these complementary approaches can be applied together to optimize the integration of evidence-based practices within clinical and public health settings.

IMPLEMENTATION SCIENCE

IS explores methods to effectively translate evidence-based care, interventions, and policies into practice to improve health [ 2 ]. By accounting for context and multilevel determinants, researchers and practitioners may better address implementation challenges for evidence-based practices and maximize their potential benefits on population health. The field leverages dozens of frameworks, theories, and conceptual models [ 3 ] to inform IS and uses a variety of measures and study designs [ 4 ] to understand implementation processes and develop and test implementation strategies [ 5 ]. More specifically, IS theories and frameworks can help (a) identify factors that may influence implementation processes or outcomes, (b) provide guidance for conceptualizing an implementation challenge and inform study hypotheses, including how to overcome barriers to implementation, and (c) select and tailor implementation strategies to address delivery gaps.

Implementation strategies promote the integration of evidence-based practices into public health and health care settings. Powell et al. [ 6 ] identified 73 implementation strategies in their Expert Recommendations for Implementing Change study, of which many involve stakeholder engagement, such as conducting educational meetings, clinical reminders, and conducting local needs assessments to improve implementation outcomes, such as acceptability, adoption, appropriateness, costs, feasibility, fidelity, penetration, and sustainability [ 7 ]. These strategies can be selected to address specific multilevel barriers to implementation and improve implementation outcomes, which, in turn, strengthens the health impact of evidence-based practices [ 7 ]. For example, if a needs assessment uncovers low provider awareness of an evidence-based practice to improve asthma inhaler adherence, then educational meetings with providers may be an effective implementation strategy for increasing adoption of this practice. Methods for selecting and refining implementation strategies for a given context are continuing to be developed. Some recommended approaches for selecting strategies include conjoint analysis, simulation modeling, intervention mapping, and concept mapping, among others [ 8–10 ].

In addition to implementation frameworks, outcomes, and strategies, a broad variety of study designs can be used to study implementation, including effectiveness-implementation hybrid designs (which includes effectiveness and implementation research aims and data collection); mixed methods (integrating qualitative and quantitative methods); factorial designs (e.g., sequential multiple assignment randomized implementation trial); two-level nested randomized designs; cluster randomized control trials; crossover designs; and simulation models among others [ 5 , 11 , 12 ]. Taken together, the field has utilized a set of research methods to rigorously study and evaluate the implementation of evidence-based practices into public health and clinical settings.

HUMAN-CENTERED DESIGN

HCD is a repeatable, creative approach to problem-solving that brings together what is desirable to humans with what is technologically feasible and economically viable [ 13 ]. Dopp et al. [ 1 ] offer a glossary of USD strategies for IS experts, which focuses on the individual for which a solution is designed (e.g., patient or practitioner), whereas HCD focuses on the individual, those who are around them, and the systems in which the individual is a part. Dopp et al. [ 1 ] offered this when comparing HCD to UCD:

The closely related approach of human-centered design more explicitly seeks to integrate an innovation into human activities and systems by considering individuals beyond primary users (including those who interact indirectly with the innovation, such as clinic leaders who oversee implementation, as well as those who are unintentionally affected by it, such as family members of patients) in the design process.

Given the multiple levels of influence (e.g., patient, provider, clinic, organization, and system) that can impact successful implementation, IS experts could benefit from combining a multilevel, HCD approach to operationalizing implementation strategies.

Over the past 30 years, HCD has evolved from diverse disciplines, including computer science, visual design, and architecture, and has been primarily embraced in the private sector [ 14 , 15 ]. However, the public sector has started to embrace HCD [ 16 ]. Recently, public health researchers have started to apply HCD approaches and methodologies to community-based participatory research projects as a way to better understand the experiences of end users (i.e., intended beneficiaries) and to codevelop health interventions with them [ 17 , 18 ]. For this commentary, the authors rely on the HCD process as defined by IDEO, a leading global design company, which has successfully used HCD to create groundbreaking products like Palm pilots and Oral-B toothbrushes [ 19 ].

IDEO’s HCD process for problem-solving consists of three distinct phases: the inspiration phase, the ideation phase, and the implementation phase [ 13 , 19 ]. After identifying a particular problem for which a solution is desired, designers’ (i.e., those engaged in HCD) first aim is to build empathy toward and draw inspiration from individual users (e.g., patients, patients’ families, clinicians, and staff) through in-depth conversations and experiences in Phase 1 of HCD [ 18 ]. The purpose of this first phase is not to arrive at a solution; instead, the goals are to more completely understand the intended users, the barriers (i.e., “pain points” in HCD) they have experienced given the problem, and the solutions (i.e., “workarounds”) they have found [ 13 ]. Second, in the ideation phase, designers generate numerous ideas for how to solve the problem, informed by the users’ thoughts, feelings, and experiences. Third, in the implementation phase, designers quickly prototype (i.e., test) the different ideas with users to solicit immediate feedback. This is achieved through designing short experiments with low-fidelity prototypes. Low-fidelity prototypes are simple versions of a solution, often paper based, that are quickly produced to test broad concepts [ 13 ]. Prototyping allows for the recombination and refinement of these concepts into a solution that is desirable, feasible, and viable for a specific set of users. These short iteration cycles help to secure buy-in by repeatedly engaging collaborators, which also allows for a smoother, broader implementation of the product or service at the conclusion of the project [ 13 , 18 ]. To make HCD more accessible to the general public, IDEO’s nonprofit arm, IDEO.org, published The Field Guide to Human-Centered Design in 2015 [ 13 ]. This field guide includes HCD mindsets, methods, case studies, and resources.

COMBINING HCD AND IS APPROACHES

We typically consider IS when there is an evidence-based practice with proven efficacy that has not yet been effectively implemented in health care or community settings. Through IS, researchers can develop and test strategies to improve care delivery of evidence-based practices [ 5 ]. We might consider HCD when developing a new intervention. Both fields acknowledge the importance of multiple stakeholder perspectives, iterative study cycles to optimize outcomes of interest, and consideration of the end users to improve implementation in real-world settings. Based on these complementary strengths, we believe that IS and HCD can be combined to provide “client-centered” approaches for implementing health care and public health practices, and we offer two ways to conceptualize how to integrate the two approaches.

First, we could view HCD as a process that occurs toward the beginning of the translational research pathway (i.e., discovery), and IS on the distal end of the pathway. Indeed, the final phase of HCD includes an implementation phase, so there are opportunities to integrate these two fields in the effort to develop patient-, provider-, and system-centered implementation strategies across the research continuum. IS frameworks, measures, and study designs could play a key role in strengthening the rigor of HCD research projects in the implementation phase.

Second, we could view HCD as a practical method for operationalizing implementation strategies. As previously outlined, IS leverages strategies to optimize the delivery of interventions and stakeholder engagement is paramount. HCD offers IS a set of methods (i.e., activities) to engage with intended beneficiaries [ 13 , 18 , 19 ]. Therefore, HCD may provide a new approach for selecting, optimizing, and operationalizing implementation strategies.

HCD methods may be particularly useful for operationalizing implementation strategies [ 6 ] within four of the nine broader implementation strategy categories identified by Waltz et al. [ 20 ]: use evaluative strategies, adapt and tailor to context, develop stakeholder interrelationships, and engage consumers. Publications have provided guidance on how to select, tailor, and specify the 73 implementations strategies [ 6 , 9 ], but there is still little guidance for how to execute specific implementation strategies; that is—how do researchers actually apply these implementation strategies in the field? For example, if researchers want to employ “involving patients/consumers and family members” as an implementation strategy in their research, how do they operationalize this implementation strategy? Operationalizing implementation strategies through the use of low-cost, accessible HCD methods could help researchers and practitioners assess which implementation strategies are most acceptable and feasible, as well as how these strategies should be executed. Using HCD methods to operationalize implementation strategies will also provide implementation scientists a shared language with those who practice HCD and vice versa. Fig. 1 below summarizes the interrelationship between HCD and IS and illustrates how combining these approaches can impact population health. In order to further illustrate how HCD can enhance IS and how IS can enhance HCD, we present the case study below.

How human-centered design and implementation science can lead to public health impact.

CASE STUDY: THE REAL TALK APP

This case study reports the development of a new mobile app where the first author and her team used HCD as the approach for intervention development and implementation. We will report the activities completed by the team in the development and implementation of the Real Talk app and note (a) where HCD methods offered ways to operationalize IS strategies and (b) where IS could have enhanced this HCD project in identifying determinants of implementation and offering ways to evaluate both effectiveness and implementation outcomes.

About the Real Talk app

In 2017, the first author and the two other cofounders of the technology nonprofit MyHealthEd, Inc., applied HCD to build and launch the first version of their Real Talk app for teenage users aged 13–15 [ 24 ]. To date, the app has more than 15,000 users in all 50 states and in more than 125 countries. The purpose of the app is to build a community for teens around taboo health issues, such as sexual health and mental health, and let users know that they are not alone. In the app, users can browse, share, and react to stories on a variety of topics, as well as connect with high-quality online resources from organizations like amaze.org and TeensHealth.

How HCD can enhance IS

While the MyHealthEd, Inc., team did not apply an explicit IS framework through their design work, they did apply several implementation strategies, including: (a) involve patients/consumers and family; (b) conduct cyclical small tests of change; and (c) intervene with patients/consumers to enhance uptake/adherence ( Table 1 ). The team applied these implementation strategies by using the Inspiration, Ideation, and Implementation methods from IDEO.org’s field guide [ 13 ] as described below. The examples below illustrate how HCD methods could be used to operationalize IS strategies.

Implementation strategies and aligned design thinking methods from IDEO.org’s The Field Guide to Human-Centered Design

Involving patients/consumers and family

In order to operationalize “involving patients/consumers and family” as an implementation strategy, the MyHealthEd, Inc., team involved teenagers aged 13–15 (intended users) early in the HCD process. IDEO.org’s field guide [ 13 ] offers a number of specific HCD methods (i.e., activities) to involve end users that include activities like Card Sorts, Conversation Starters, a Guided Tour, or a Resource Flow. The team used the field guide’s Card Sort method to answer questions regarding where teenagers felt most comfortable talking about sex and/or relationships. In order to do this, the team created cards with the following options: school, home, bus, church, friend’s house, and other. Then, the team asked the teenagers to rank the cards in terms of comfort level. After meeting with teenagers across the country and completing the same activity, the team quickly realized that teenagers did not want to talk about sex and/or relationships in school, so they moved away from thinking that they might implement their intervention in schools. This, along with other insights gained through the formative research process, led to a direct-to-consumer approach via a native smartphone app rather than a school-based approach.

Conduct cyclical small tests of change

As part of the ideation phase, the team then conducted dozens of small cyclical tests of change to get feedback from teenagers and other stakeholders (e.g., parents, health teachers, school administrators, and faith leaders) on different features and design elements in the app. This HCD phase directly relates to the implementation strategy for conducting cyclical small tests of change but adds a more specific methodology. The Real Talk app’s user interface and user experience designers used the software InVision to create clickable prototypes of the different app screens. Then, the team used the IDEO.org’s field guide [ 13 ] Rapid Prototyping method to share the InVision prototypes with intended users, collect reactions and data, and make adjustments. For example, the team heard from intended users that they would prefer to interact with sexual health content via stories rather than facts or statistics. Teens also wanted the ability to share their own stories through the app, so the MyHealthEd, Inc., team rapidly tested different versions of the story submission experience. One major test compared a form-based study submission experience (e.g., users submit their entire story by typing it into a box) with a chatbot experience (e.g., users respond to prompts from a chatbot to share their stories piece by piece). After testing these two options, the team found that the majority of their intended users preferred the more interactive chatbot because it was as easy and familiar as text messaging a friend. This resulted in building the interactive story submission feature rather than the form-based feature.

Intervene with patients/consumers to enhance uptake/adherence

Prior to implementation and dissemination, the MyHealthEd, Inc., team was also very intentional about engaging with teenagers to develop strategies together to increase uptake (i.e., app downloads) and adherence (app usage). Through using the Co-Creation Session method from IDEO.org’s field guide [ 13 ], the team convened a group of teenagers to design alongside them by empowering them to jointly create and brand the solution. Specifically, the team worked with teenagers to name the health app. Teenagers came up with the name “Real Talk” because it captured the raw or “cringey” nature of the stories submitted by other teenage users, but it did not overtly signal that the app covers sexual health education topics. Teenagers wanted a resource like this to be discreet and this insight informed the app logo (two generic white chat bubbles without signals to sexual health content). Lastly, the team held multiple Co-Creation Sessions for teenagers to design and pitch their own sexual health apps. Both the drawings and language that the teenagers used to pitch their app concepts to other teenagers shaped the language and images shown for the app as it is advertised in the iTunes App Store. The description reads:

Real Talk is a community for teens packed with real stories about cringey moments. Browse through stories, search for topics that matter most to you, and use emojis to share your reactions. You can also share your own story directly in the app - it’s as easy as texting with a friend. Join thousands of teens who already use and love Real Talk. With totally relatable stories, you won’t feel as alone as you go through the struggles of growing up [ 24 ].

Additional language offered by teenagers in Co-Creation sessions was used in other marketing and outreach materials. Applying this HCD Co-Creation Session method led to an app description that was more teen-friendly than what the MyHealthEd, Inc., team initially envisioned before collaborating with the teens. The use of this specific HCD method provided a protocol to inform the language used to attract new users of the app. In the first year of launching the app, Real Talk was downloaded more than 10,000 times by teenagers across the globe.

How IS can enhance HCD

While the MyHealthEd, Inc., team considered implementation from the start, they did not employ an IS framework or study design. As mentioned earlier, IS can enhance HCD by identifying multilevel determinants of implementation and offering more rigorous evaluation of an evidence-based practice.

IS frameworks that focus on multilevel determinants of implementation can provide structure to studying the implementation of an evidence-based practice. HCD largely considers determinants for implementation on the individual level in the case of Real Talk app from the perspective of teens and their families. However, when considering the implementation of an evidence-based practice, it is essential to consider the multilevel determinants that impact an individual’s use of that practice. Investigating multilevel determinants iteratively throughout the development and evaluation of the Real Talk app could help hone in on the appropriate implementation strategies, as well as provide a more holistic view of the effectiveness of the practice. For example, exploration of multilevel determinants for implementing Real Talk outside of patients and families could prevent disconnects between the patient and their providers, clinics, retail stores, and pharmacists, who also play a role in their sexual health. As individuals act within systems, it is important to study, act upon, and evaluate across multiple levels rather than within a vacuum on the individual level. A number of IS frameworks do well in systematically providing a multilevel perspective on implementation determinants and processes.

Next, IS frameworks, measures, and study designs could provide structure for evaluating the effectiveness and implementation of practices, particularly throughout the rapid prototyping and cyclical experiments. In addition to assessing determinants of implementation, as mentioned above, IS frameworks are available to provide structure to the evaluation of the implementation of evidence-based practices and newly developed innovative solutions to improve health [ 7 , 23 ]. Implementation outcomes have been outlined by the field and include measures such as acceptability, appropriateness, feasibility, and costs, among others [ 7 ]. Assessing these implementation outcomes, as well as effectiveness outcomes, is essential for understanding the total impact of Real Talk on adolescent sexual health outcomes. Hybrid effectiveness-implementation designs allow for more rigorous testing and documentation of rapid prototyping cycles by incorporating the exploration of not only effectiveness outcomes but also implementation outcomes. For example, teenagers could have been randomized to view one of three sets of marketing materials each with different content. Then, the team could assess the implementation outcomes (e.g., acceptability, appropriateness, and download rates) and the effectiveness outcomes (e.g., sexual health knowledge) of the teenagers and determine which of these three sets of marketing materials leads to the strongest outcomes. These data on implementation outcomes are key for optimizing, scaling-up, and implementing the intervention in different settings (i.e., scale out) if found to be effective and, if not effective, may point to reasons why the intervention failed to have the intended impact on health.

Overall, HCD offers specific methods that can readily operationalize implementation strategies to improve the translation of health innovations into practice. Using HCD to execute implementation strategies provides a set of tools for implementation researchers to develop and test implementation strategies associated with health interventions. Additionally, IS offers specific approaches to identifying and analyzing multilevel systems and barriers to implementation, as well as rigorous study designs that would enhance HCD research by providing guidance for how to document and evaluate the iterative, cyclical experiments [ 22 , 23 ]. By combining the processes and tools from HCD and IS, we believe that health care and public health researchers can develop a common language to improve implementation outcomes and health outcomes for patients and communities.

Funding: Megan Roberts is funded through the National Center for Advancing Translational Sciences, National Institutes of Health through Grant KL2TR002490.

Compliance with Ethical Standards

Conflicts of Interest: E.C. was a cofounder and employee of MyHealthEd, Inc., the technology 501 (c)3 nonprofit that created the Real Talk app mentioned in this manuscript, from August 2016 through June 2019. G.N. and M.C.R. declare that they have no conflicts of interest.

Authors’ Contributions: E.C. and M.C.R. conceptualized this study; E.C., M.C.R, and G.N. analyzed the data; and E.C., M.C.R. and G.N. co-authored this manuscript.

Ethical Approval: This article does not contain any studies with human participants performed by the authors. This article does not contain any studies with animals performed by any of the authors.

Informed Consent: This study does not involve human participants and informed consent was, therefore, not required.

A framework for supporting systems thinking and computational thinking through constructing models

• Open access
• Published: 24 July 2022
• Volume 50 , pages 933–960, ( 2022 )

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• Namsoo Shin   ORCID: orcid.org/0000-0002-5900-2073 1 ,
• Jonathan Bowers 1 ,
• Steve Roderick 2 ,
• Cynthia McIntyre 2 ,
• A. Lynn Stephens 2 ,
• Emil Eidin 1 ,
• Joseph Krajcik 1 &
• Daniel Damelin 2  

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We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

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Introduction

The primary goals of science education for all learners are to explain natural phenomena, solve problems, and make informed decisions about actions and policies that may impact their lives, their local environments, and our global community. Models—representations that abstract and simplify a system by focusing on key features—have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena across many fields (Harrison & Treagust, 2000 ; National Research Council, 2012 ; Schwarz et al., 2017 ). For example, En-ROADS, a computational model and simulator developed by Climate Interactive, an environmental think tank, has been used to educate members of the U.S. Congress, the U.S. State Department, the Chinese government, and the office of the UN Secretary-General on climate change on the impacts of proposed policies on global warming. The model was also a centerpiece of multiple presentations at the UN Climate Change Conference in Scotland in 2021 (Madubuegwn et al., 2021 ).

Modeling, which includes developing, testing, evaluating, revising, and using a model (National Research Council, 2012 ; Schwarz et al., 2017 ; Sengupta et al., 2013 ), necessitates many different thinking processes such as problem decomposition (Grover & Pea, 2018 ), causal reasoning (Levy & Wilensky, 2008 ), pattern recognition (Berland & Wilensky, 2015 ), algorithmic thinking (Lee & Malyn-Smith, 2020 ), and analysis and interpretation of data (Shute et al., 2017 ). These thinking processes often necessitate systems thinking and computational thinking, which are intrinsically linked to modeling (Richmond, 1994 ; Wing, 2017 ).

Richmond ( 1994 ) defines systems thinking (ST) as “the art and science of making reliable inferences about behavior by developing an increasingly deep understanding of underlying structure” (p. 139). To help manage the complexities of climate change, experts use a systems thinking approach to guide decision making and inform policy design (Holz et al., 2018 ; Sterman & Booth Sweeney, 2007). In the context of modeling, ST helps us to comprehend the causal connections between the components of a model––the elements of a problem or phenomenon that affect the system’s behavior (e.g., the relationship between the melting of polar ice caps and the increase in global temperatures)––and how changes in one component (e.g., the rate of melting of the polar ice caps) can cascade to other components and potentially affect the status of the entire system.

However, the multifaceted interactions within complex systems quickly outpace our ability to mentally simulate and predict system behavior, especially in systems that include time delays and feedback, common features of many complex phenomena (Cronin et al., 2009 ). For example, in the case of climate change, there is a delay between changing CO 2 emissions and the cumulative effect on CO 2 concentrations in the atmosphere (Sterman & Sweeney, 2002 ). Even highly educated people with strong mathematics backgrounds have trouble understanding and predicting the behavior of a system with these features (Cronin et al., 2009 ), and perform poorly when attempting to forecast the effect of any particular intervention (Sterman & Sweeney, 2002 ). Therefore, ST alone may not be sufficient for investigation of potential solutions to complex problems such as climate change.

To model successfully, we also need computational thinking (Wing, 2008 ). Computational thinking (CT) provides conceptual tools for finding answers to problems involving complex, multidimensional systems by applying logical and algorithmic thinking (Berland & Wilensky, 2015 ; Lee & Malyn-Smith, 2020 ). Wing ( 2006 ) views CT as thinking like a computer scientist, not like a computer, and as a competency appropriate and available to everyone, not only for computer scientists in science and engineering fields. In particular, CT helps us create algorithms by identifying patterns in phenomena to automate the transformation of data so that we can predict other phenomena in similar systems. Although CT does not require the use of a computer, a computer’s processing speed is helpful for testing solutions efficiently. For instance, scientists use computational models to test the effect of various policies on the amount of CO 2 accumulating in the atmosphere in order to reduce the trapping of solar energy in our environment. Thus, to fully explain complex phenomena or solve problems using models requires both ST and CT (National Research Council, 2012 ).

A key challenge for science, technology, engineering, and mathematics (STEM) educators and researchers is to develop learning environments that provide opportunities for learners to experience how scientists approach explaining complex phenomena and solving ill-structured problems (Krajcik & Shin, 2022 ; National Research Council, 2000 ). Because science and engineering practices (e.g., modeling, computational thinking) should be integrated with scientific ideas including disciplinary core ideas and crosscutting concepts (e.g., systems and system modeling) in meaningful contexts to promote deep learning (National Research Council, 2012 ; NGSS Lead States 2013 ), we argue that incorporating modeling, ST, and CT into existing STEM subjects supports K-12 learners in making sense of phenomena and solving problems.

We propose a theoretical framework that foregrounds modeling and highlights how both ST and CT are involved in the process of modeling phenomena or problems. Because modeling is a key science practice for learners across K-12 education (National Research Council, 2012 ; NGSS Lead States 2013 ), it would be beneficial to expand the opportunities for applying ST and CT in the context of modeling. To successfully support learners in modeling with ST and CT in various STEM disciplines in K-12 education, educators and researchers need to further develop and explore learning environments that incorporate ST and CT in the modeling practices.

We postulate that this framework can (a) guide curriculum developers and teachers in integrating ST and CT in the context of modeling in multiple STEM courses, (b) assist software developers and curriculum designers in developing effective learning tools and pedagogical supports that involve learners in modeling, ST, and CT, and (c) help researchers and teachers in measuring learners’ understanding and application of modeling, ST, and CT. In this paper, we present our framework, which is based on a literature review of ST and CT as well as examples of models to illustrate how ST and CT are integrated in and support the modeling practices. We start by defining ST, CT, and the modeling practices that form the foundation of our framework, briefly introduce our framework, then identify ST and CT aspects. Finally, we describe the framework and associated aspects of ST and CT in each modeling practice by illustrating how the ST and CT aspects support the modeling process.

Theoretical background

What is systems thinking.

Systems thinking has been emphasized in K-12 science standards in the U.S. for nearly three decades (NGSS Lead States, 2013 ; National Research Council, 2007 , 2012 ; American Association for the Advancement of Science, 1993 ). With the release of A Framework for K-12 Science Education (National Research Council, 2012 ), ST has been incorporated in the crosscutting concept of systems and system modeling . ST provides learners with a unique lens that, when combined with scientific ideas and practices, can enhance sense-making and problem-solving, and is particularly well suited for addressing the complexity found in many social and scientific phenomena—from human health and physiology to climate change. Although thinking in a systemic way about persistent problems has been around for centuries (Richardson, 1994), the term “systems thinking” (ST), as used in the literature across a wide variety of disciplines, has not been clearly defined.

From the world of business, Senge ( 1990 ) sees systems thinking as a paradigm shift toward consideration of the system as a whole, with a focus on interrelationships and change over time. Forrester ( 1961 ), Senge’s mentor and creator of the discipline known as system dynamics, speaks of “system awareness … a formal awareness of the interactions between the parts of a system” (p. 5). Meadows ( 2008 ) refers to a “system lens” and stresses that “seeing the relationship between structure and behavior we can begin to understand how a system works” (p. 1). Richmond ( 1994 ) and Sterman ( 1994 ) view ST and learning as being synergistically connected. Richmond views ST as both a paradigm and a learning method. He describes the ST paradigm as a lens through which one comes to view complex systems holistically, as well as a set of practical tools for developing and refining that lens. The “tools” that he describes closely match commonly defined modeling practices (National Research Council, 2012 ; Schwarz et al., 2009 ). More recently, systems thinking has been described as a set of skills that can be used as an aid to understanding complex systems and their behavior (Benson, 2007 ; Ben-Zvi Assaraf & Orion, 2005 ). In particular, Arnold and Wade ( 2015 ) define ST as “a set of synergistic analytic skills used to improve the capability of identifying and understanding systems” (p. 675).

For these authors ST represents a worldview, a way of thinking about the world that emerges as an individual grows in ability and willingness to see it holistically. Disciplined application of ST tools and skills supports and potentially alters one’s worldview, and one’s worldview conditions the choices one makes about the use of the tools. Building on the long tradition of ST applications in business together with more recent integration in K-12 education, ST can be defined operationally as the ability to understand a problem or phenomenon as a system of interacting elements that produces emergent behavior.

What is computational thinking?

Computational thinking is an important skill that is related to many disciplines (e.g., mathematics, biology, chemistry, design, economics, neuroscience, statistics), as well as numerous aspects of our daily life (e.g., optimizing everyday financial decisions or navigating daily commutes to minimize time spent in traffic). However, while computer science and CT have been key drivers of scientific development and innovation for several decades, only recently has CT been emphasized as a major academic learning goal in K-12 science education.

There is a wide range of perspectives on how to define CT—from a STEM-centered approach (Berland & Wilensky, 2015 ; National Research Council, 2012 ; Weintrop et al., 2016 ) to a more generic problem-solving approach (Barr & Stephenson, 2011 ; Grover & Pea, 2018 ; Lee & Malyn-Smith, 2020 ; Wing, 2006 ). A Framework for K-12 Science Education defines CT as utilizing computational tools (e.g., programming simulations and models) grounded in mathematics to collect, generate, and analyze large data sets, identify patterns and relationships, and model complex phenomena in ways that were previously impossible (National Research Council, 2012 ). Similar to the Framework , the STEM-centered approach describes CT as connected to mathematics for supporting data collection and analysis or testing hypotheses in a productive and efficient way, but also views CT as centering on sense-making processes (Psycharis & Kallia, 2017 ; Schwarz et al., 2017 ; Weintrop et al., 2016 ). In this view, although CT is intertwined with aspects of using specific rules (with quantitative data) to program computers to build models and simulations, it is more than an algorithmic approach to problem-solving (Brennan & Resnick, 2012 ; Shute et al., 2017 ). Instead, it is a more comprehensive, scientific way to foster sense-making that encourages learners to ask, test, and refine their understandings of how phenomena occur or how to solve problems.

Many researchers suggest that CT means “thinking like a computer scientist” when confronted with a problem (Grover & Pea, 2018 ; Nardelli, 2019 ; Shute et al., 2017 ; Wing, 2008 ). These scholars elaborate on the definition of CT, focusing on computational problem-solving processes, such as breaking a complex problem into smaller problems to trace and find solutions (Grover & Pea, 2018 ; Shute et al., 2017 ; Türker & Pala, 2020 ; Wing, 2006 ). Building on the ideas put forth by Wing ( 2006 ) and Grover and Pea (2018), as well as the view of the sense-making process from the STEM approach, we define CT operationally as a way of explaining phenomena or solving problems that utilizes an iterative and quantitative approach for exploring, unpacking, synthesizing, and predicting the behavior of phenomena using computational algorithmic methods.

What are modeling practices?

Modeling enables learners to investigate questions, make sense of phenomena, and explore solutions to problems by connecting and synthesizing their knowledge from a variety of sources into a coherent and scientific view of the world (National Research Council, 2011 , 2012 ; Schwarz et al., 2017 ). Modeling includes several important practices, including building, evaluating, revising, and using models (National Research Council, 2012 ; Schwarz et al., 2017 ). Research shows that learners can deepen their understanding of scientific ideas through the development, use, evaluation, and revision of models (Schwarz & White, 2005 ; Wen et al., 2018 ; Wilkerson et al., 2018 ). Although modeling can be conducted without using computational programs (e.g., paper-pencil modeling), we are specifically interested in supporting student engagement in modeling, ST, and CT, so we are narrowing our focus to a computational approach. Therefore, we propose a Framework for Computational Systems Modeling that elucidates the synergy between modeling, ST, and CT. Building on the descriptions of the modeling process in the literature (Halloun, 2007 ; Martinez-Moyano & Richardson, 2013 ; Metcalf-Jackson et al., 2000 ; National Research Council, 2012 ; Schwarz et al., 2009 ), our framework includes five modeling practices: M1) characterize problem or phenomenon to model, M2) define the boundaries of the system, M3) design and construct model structure, M4) test, evaluate, and debug model behavior, and M5) use model to explain and predict behavior of phenomenon or design solution to a problem (Fig.  1 ).

Computational Systems Modeling Framework

In Fig.  1 , the center set of boxes describes five modeling practices and the cyclic nature of the modeling process (represented by the arrows). The process is highly iterative, with involvement in one practice influencing both future and previous practices, inviting reflection and model revision throughout. Engagement in each of these practices necessitates the employment of aspects of ST (left side of framework diagram) and CT (right side of framework diagram). To develop the Framework for Computational Systems Modeling, we conducted a literature review study to identify and define essential aspects of ST and CT. Specifically, we explored important aspects of these two types of thinking necessary for the modeling practices. Through this exploration we considered the implications of these aspects for developing curriculum, learning tools, pedagogical and scaffolding strategies for teaching and learning, and valid assessments for promoting and monitoring learner progress. We, therefore, investigated two guiding questions: (1) What are the key aspects of each type of thinking? and (2) How do aspects of ST and CT intersect with and support modeling practices? Below we explain our review process.

We employed the integrative review approach (Snyder, 2019 ) as we analyzed and synthesized literature, including experimental and non-experimental studies, as well as data from theoretical literature and opinion or position articles (e.g., books, book chapters, practitioner articles) on ST and CT. An integrative review method is appropriate for critically examining and analyzing secondary data about research topics for generating a new framework (Snyder, 2019 ; Whittemore & Knafl, 2005 ).

Literature search and selection strategies

Because our focus is on modeling as a process for making sense of phenomena, we took the view of Richmond ( 1994 ) as our starting point for ST and the view of Wing ( 2006 ) for CT, since both authors emphasize these thinking processes for learners to understand phenomena. We embarked on a literature review related to the two scholars’ research, and then extended our search using authors’ names who published studies related to the definition of ST or CT using Google Scholar. Our inclusion criteria of literature are (1) written between 1994 and 2021 with the keyword “system [and systems] thinking” and between 2006 and 2021 with the keyword “computational thinking”; and (2) directly related to the definition of ST or CT. We excluded authors who used previously defined ideas related to ST or CT and did not uniquely contribute new ideas. From these search results, we collected 80 manuscripts and narrowed our search to 55 manuscripts by selecting one representative manuscript in cases when an author had published several manuscripts (e.g., Richmond 1994 from Richmond 1993 , 1994 , 1997 ). In this way, our analysis aims to avoid misleading results that might be influenced by including the same authors’ ideas repeatedly. Our analysis included 27 of 45 manuscripts that defined ST and 28 of 35 manuscripts that defined CT.

Data analysis

We used the following filters sequentially, as we extracted a list of aspects of both ST and CT to create a usable framework: (a) the ST or CT aspect is described widely across the ST or CT literature in multiple scholars’ works, (b) the ST or CT aspect is not overly broad or generic; we excluded aspects that are ubiquitous across fields but not specific to ST or CT, and (c) the ST or CT aspect is operationalizable through observable behaviors (e.g., tangible artifacts or discussion).

We first created an initial list of aspects based on Richmond’s ( 1994 ) definition of ST and Wing’s ( 2006 ) definition of CT, respectively, to extract and sort information from each selected literature. For example, the aspects include causal reasoning, identifying interconnections, and predicting system behavior for ST (Fig.  1 left side), and problem decomposition, artifact creation, and debugging for CT (Fig.  1 right side). Second, each manuscript was reviewed by two of the authors independently, sorting texts based on which aspects were described. Third, the two authors confirmed their sorting of the texts by discussing each manuscript. Then, five of the authors reviewed the aggregated texts associated with each aspect presented from 27 ST and 28 CT manuscripts. As we reviewed the literature pertaining to definitions of ST and CT, we revised the aspects, expanding or dividing them as we gained new information and identified associated sub-aspects. All authors discussed the aspects and finalized them by resolving discrepancies and clarifying ambiguities. A shared spreadsheet was used to store and analyze all data.

Findings and discussion of literature review

Aspects and sub-aspects of systems thinking.

From the reviews of ST literature in both business and education, and using our filters, we identified five aspects of systems thinking: (ST1) defining a system (boundaries and structure), (ST2) engaging in causal reasoning, (ST3) identifying interconnections and feedback, (ST4) framing problems or phenomena in terms of behavior over time, and (ST5) predicting system behavior based on system structure (Fig.  1 left side). Figure  2 shows the distribution of ST aspects that emerged from our review of 27 manuscripts.

Distribution of systems thinking aspects. ( Note: ST1. Defining a system [boundaries and structure]; ST2. Engaging in causal reasoning; ST3. Identifying interconnections and feedback; ST4. Framing problems or phenomena in terms of behavior over time; ST5. Predicting system behavior based on system structure)

ST1, defining a system (boundaries and structure) , requires an individual to clearly identify a system’s function and be as specific as possible about the phenomenon to be understood or the problem to be addressed. Table  1 presents a summary of the various ways 15 manuscripts have described defining a system .

Defining a system focuses attention on internal system structure (relevant elements and interactions among them to produce system behaviors) and limits the tendency to link extraneous outside factors to behavior. This focus allows learners to more clearly define the spatial and temporal limits of the system they wish to explain or understand (Hopper & Stave, 2008 ) Meadows ( 2008 ) discusses the importance of “system boundaries” and the need to clearly define a goal, problem, or purpose when attempting to think systemically. Considering scale is critical when deciding what content is necessary to explain the system behavior of interest (Arnold & Wade, 2017 ; Yoon et al., 2018 ).

To operationalize defining a system three sub-aspects are necessary: (ST1a) identifying relevant elements within the system’s defined boundaries (Arnold & Wade, 2015 ), (ST1b) evaluating the appropriateness of elements to see if their elimination significantly impacts the overall behavior of the system in relation to the question being explored (Ben-Zvi Assaraf & Orion, 2005 ; Weintrop et al., 2016 ), and (ST1c) determining the inputs and outputs of the system (Yoon et al., 2018 ).

ST2, engaging in causal reasoning , includes examination of the relationships and interactions of system elements. Causal reasoning is described as a key aspect of ST by most researchers, appearing in 25 manuscripts (Fig.  2 ). Table  1 presents a summary of the descriptions of causal reasoning. In addition, two other manuscripts imply the importance of causal reasoning as they state that causal reasoning serves as a foundation for recognizing interconnections and feedback loops in a system (ST3). For example, identifying interconnections among elements in a system (e.g., events, entities, or processes) requires understanding of one-to-one causal relationships (Forrester, 1994 ; Ossimitz, 2000 ).

For deep understanding of phenomena, learners should be able to describe both direct (impact of one element upon another) and indirect (the effects of multiple causal connections acting together in extended chains) relationships among various elements in a system of the phenomenon (Kim, 1999 ; Jacobson & Wilensky, 2006 ). Based on our review of causal reasoning, three sub-aspects are operationalizable: (ST2a) recognizing cause and effect relationships among elements (Arnold & Wade, 2015 ), (ST2b) quantitatively (or semi-quantitatively) defining proximal causal relationships between elements (Grotzer et al., 2017 ), and (ST2c) identifying (or predicting) the behavioral impacts of multiple causal relationships (Sterman, 2002 ; Levy & Wilensky, 2011 ).

ST3, identifying interconnections and feedback , involves analyzing causal chains that result in circular structural patterns (feedback structures) within a system. This aspect of ST was proposed by all 27 manuscripts (Fig.  2 ). Many studies in ST have focused on helping learners identify the relationships of interdependencies of system elements (Jacobson & Wilensky, 2006 ; Levy & Wilensky, 2011 ; Samon & Levy 2020 ; Yoon et al., 2018 ) (Table  1 ). Feedback structures are created when chains loop back upon themselves, creating closed loops of cause and effect (Jacobson et al., 2011 ; Pallant & Lee, 2017 ; Richmond, 1994 ). There are two basic types of feedback loops: balancing (or negative) feedback that tends to stabilize system behavior and reinforcing (or positive) feedback that causes behavior to diverge away from equilibrium (Booth-Sweeney & Sterman, 2000 ; Meadows, 2008 ).

This aspect provides learners an expansion of perspective, from one that focuses primarily on the analysis of individual elements and interactions to one that includes consideration of how the system and its constituent parts interact and relate to one another as a whole (Ben-Zvi Assaraf & Orion, 2005 ). Based on our literature review, it can be operationalized in two sub-aspects: (ST3a) identifying circular structures of causal relationships (Danish et al., 2017 ; Grotzer et al., 2017 ; Ossimitz, 2000 ) and (ST3b) recognizing balancing and reinforcing feedback structures and their relationship to the stability and growth within a system (Fisher, 2018 ; Meadows, 2008 ).

ST4, framing problems or phenomena in terms of behavior over time , requires that learners distinguish between phenomena that are best described as evolving over time and those that are not. Twenty-seven manuscripts reported that framing problems in terms of behavior over time is an important aspect of ST (Fig.  2 ), as shown in Table  1 . Many advocates of ST refer to the recognition of the link between structure and behavior as “dynamic thinking” and acknowledge proficiency with it as difficult to obtain without the use of systems thinking tools (Booth-Sweeney & Sterman, 2000 ; Grotzer et al., 2017 ; Plate & Monroe, 2014 ).

This aspect is especially important when change over time is crucial for thoroughly understanding a system’s behavior. Some phenomena are best investigated without consideration of change over time, for instance, in open systems where change to a system input affects all of the internally connected system aspects. Other phenomena are better described using the cumulative patterns of change observed in a system’s state over time. Phenomena that evolve in this way are not significantly impacted by external factors but have an internal feedback structure that dictates how change will occur as time passes (Booth-Sweeney & Sterman, 2000 , 2007 ). This aspect is operationalizable in two sub-aspects: (ST4a) determining the time frame necessary to describe a problem or phenomenon (Sterman, 1994 , 2002 ) and (ST4b) recognizing time-related behavioral patterns that are common both within and across systems (Nguyen & Santagata, 2021 ; Pallant & Lee, 2017 ; Riess & Mischo, 2010 ; Tripto et al., 2018 ).

ST5, predicting system behavior based on system structure , necessitates an understanding of how both direct causal relationships and more comprehensive substructures (e.g., feedback loops, accumulating variables, and interactions among them) influence behaviors common to all systems (Forrester, 1994 ). This aspect was proposed in 18 of 27 manuscripts we reviewed (Table  1 ). Although a subset of manuscripts discussed this aspect specifically, those that do not imply that predicting system behavior based on system structure is important to systems thinking (e.g., describe learning activities such as predicting behaviors based on graphs or data sets) (Hmelo-Silver et al., 2017 ).

This aspect offers learners help in characterizing complex systems by identifying common structures that allows one to generalize about the connection between system structure and behavior and develop guidelines that can be applied to systems of different types (Laszlo, 1996 ). There are three operationalizable sub-aspects: (ST5a) identifying how individual cause and effect relationships impact the broader system behavior (Barth-Cohen, 2018 ), (ST5b) recognizing how the various substructures of a system (specific types and combinations of variables within systems) influence its behavior (Danish et al., 2017 ), and (ST5c) predicting how specific structural modifications will change the dynamics of a system (Richmond, 1994 ).

Through our analysis of the literature, we synthesized 13 sub-aspects associated with five ST aspects. Aspects that are vague or difficult to measure, such as using experiential evidence from the real world together with simulations to “challenge the boundaries of mental (and formal) models” (Booth-Sweeney & Sterman, 2000 , p. 250) were not included, nor were aspects that are not commonly included in the literature. In addition, aspects related to CT and modeling practices, such as Richmond’s ( 1994 ) “quantitative thinking,” Hopper and Stave’s (2008) “using conceptual models and creating simulation models,” and Arnold and Wade’s (2017) “reducing complexity by modeling systems conceptually” were not listed in ST, but are included in CT or modeling.

Aspects and sub-aspects of computational thinking

From the review of the literature, we identified five key CT aspects in the context of modeling using the aforementioned filters: (CT1) decomposing problems such that they are computationally solvable, (CT2) creating artifacts using algorithmic thinking, (CT3) generating, organizing, and interpreting data, (CT4) testing and debugging, and (CT5) making iterative refinements (Fig.  1 right side). Figure  3 shows the distribution of CT aspects represented in the 28 manuscripts.

Distribution of computational thinking aspects. ( Note: CT1. Decomposing problems such that they are computationally solvable; CT2. Creating artifacts using algorithmic thinking; CT3. Generating, organizing, and interpreting data; CT4. Testing and debugging; CT5. Making iterative refinements)

CT1, decomposing problems such that they are computationally solvable , consists of identifying elements and relationships observable in problems or phenomena and characterizing them in a way that is quantifiable and thus calculable. Problem decomposition deconstructs a problem into its constituent parts to make it computationally solvable and more manageable (Grover & Pea, 2018 ). All 28 manuscripts we reviewed specify this aspect (Fig.  3 ) in various ways as an essential characteristic of CT for understanding and representing problems to readily solve, as shown in Table  2 . Based on the literature, this aspect is operationalizable in three sub-aspects: (CT1a) describing a clear goal or question that can be answered, as well as an approach to answering the question using computational tools (Irgens et al., 2020 ; Shute et al., 2017 ; Wang et al., 2021 ), (CT1b) identifying the essential elements of a phenomenon or problem (Anderson, 2016 ; Türker & Pala, 2020 ), and (CT1c) describing elements in such a way that they are calculable for use in a computational representation of the phenomenon or problem (Brennan & Resnick, 2012 ; Chen et al., 2017 ; Hutchins et al., 2020 ; Lee & Malyn-Smith, 2020 ).

CT2, creating artifacts using algorithmic thinking , refers to developing a computational representation so that the output can explain and predict real-world phenomena. This is the essential aspect of CT, as proposed in all manuscripts with extensive descriptions as shown in Table  2 . This is a unique aspect of CT in that algorithmic thinking provides precise step-by-step procedures to generate problem solutions and involves defining a set of operations for manipulating variables to produce an output as a result of those manipulations (Ogegbo & Ramnarain, 2021 ; Sengupta et al., 2013 ; Shute et al., 2017 ). Weintrop and colleagues ( 2016 ) described CT in the form of a taxonomy focusing on creating computational artifacts (e.g., programming, algorithm development, and creating computational abstractions). This aspect is operationalizable in two sub-aspects: (CT2a) parameterizing relevant elements and defining relationships among elements so that a machine or human can interpret them (Anderson, 2016 ; Chen et al., 2017 ; Nardelli, 2019 ; Yadav et al., 2014 ) and (CT2b) encoding elements and relationships into an algorithmic form that can be executed (Aho, 2012 ; Hadad et al., 2020 ; Ogegbo & Ramnarain, 2021 ; Sengupta et al., 2013 ; Shute et al., 2017 ).

CT3, generating, organizing, and interpreting data , involves identifying meaningful patterns from a rich set of data to answer a question (ISTE & CSTA, 2011; National Research Council, 2010 , 2012 ; Weintrop et al., 2016 ). This aspect has gained more attention as a unique characteristic of CT recently with the realization of the importance of data mining, data analytics, and machine learning (Lee & Malyn-Smith, 2020 ). All manuscripts described this aspect as pattern recognition using abstract thinking (Anderson, 2016 ; Shute et al., 2017 ), data practices (Türker & Pala, 2020 ), or data management (Lee & Malyn-Smith, 2020 ). Weintrop and colleagues ( 2016 ) considered CT in terms of data practices that involve mathematical reasoning skills such as collecting, creating, manipulating, analyzing, and visualizing data. During this process, it is critical to find distinctive patterns and correlations in data, make claims, and draw conclusions (Grover & Pea, 2018 ). This aspect is operationalizable in two sub-aspects based on our review: (CT3a) planning for, generating, and organizing data using visual representations (e.g., tables, graphs, or maps) (Basu et al., 2016 ; Hutchins et al., 2020 ) and (CT3b) analyzing and interpreting data to identify relationships and trends (Ogegbo & Ramnarain, 2021 ; Shute et al., 2017 ).

CT4, testing and debugging , refers to evaluating the appropriateness of a computational solution based on the goal as well as the available supporting evidence (Grover & Pea, 2018 ; ISTE & CSTA, 2011; Weintrop et al., 2016 ). All manuscripts described the importance of this aspect as an evaluation or verification of the solution (Anderson, 2016 ; Basu et al., 2016 ), or in terms of fixing behavior, troubleshooting, or systematic trial and error processes (Aho, 2012 ; Brennan & Resnick, 2012 ; Sullivan & Heffernan 2016 ) (Table  2 ). It involves comparing a solution with real-world data or expected outcomes to refine the solution and analyze whether the solution behaves as expected (Grover & Pea, 2013 ; Weintrop et al., 2016 ). Through analyzing the manuscripts, this aspect is operationalizable in three sub-aspects: (CT4a) detecting issues in an inappropriate solution (Basu et al., 2016 ; Sullivan & Heffernan, 2016 ), (CT4b) fixing issues based on the behavior of the artifact (Aho, 2012 ; Brennan & Resnick, 2012 ), and (CT4c) confirming the solution using a range of inputs (Kolikant, 2011 ; Sengupta et al., 2013 ).

CT5, making iterative refinements , is a process of repeatedly making gradual modifications to account for new evidence and new insights collected through observations (of the phenomenon and the output of a computational artifact), readings, and discussions (Grover & Pea, 2018 ; ISTE & CSTA, 2011; Shute et al., 2017 ; Weintrop et al., 2016 ). Seventeen authors refer to this aspect in terms of iterative and incremental refinement or development (Brennan & Resnick, 2012 ; Hutchins et al., 2020 ; Ogegbo & Ramnarain, 2021 ; Shute et al., 2017 ; Tang et al., 2020 ) (Fig.  3 ; Table  2 ). The authors we reviewed imply that iterative revision or refinement processes are essential for CT. Those who do not specify this aspect seem to include it in the process of CT2, developing computational artifacts, as they expect learners to revise their artifacts multiple times as they gain more knowledge about the phenomenon (Nardelli, 2019 ; Selby & Woollard, 2013 ). To refine a solution learners articulate the differences between their solution and the underlying phenomenon and reflect on the limitations of their solution. Through the review of CT literature, this aspect is operationalizable in three sub-aspects: (CT5a) making changes based on new conceptual understandings (Barr & Stephenson, 2011 ), (CT5b) making changes based on a comparison between computational outputs and validating data sources (Chen et al., 2017 ), and (CT5c) making changes due to an unexpected algorithmic behavior (Brennan & Resnick, 2012 ; Sengupta et al., 2013 ; Shute et al., 2017 ).

The analysis of our CT literature review synthesizes 13 sub-aspects associated with the five CT aspects. We did not include (1) generic features (e.g., generation, creativity, collaboration, critical thinking) although multiple authors listed them as CT, (2) perception or disposition features (e.g., confidence in dealing with complexity, persistence in working with difficult problems, tolerance for ambiguity, or the ability to deal with open-ended problems) (ISTE & CSTA, 2011), and (3) overly broad features (e.g., abstraction, problem-solving processes), which were often contextualized into more specific aspects of CT. For example, some authors unpack “abstraction” into the selection of essential steps by reducing repeated steps (Grover & Pea, 2018 ), which is covered by CT2 , creating artifacts using algorithmic thinking , in our framework.

Integration of ST and CT in modeling

Integration of st and ct in st literature.

Some researchers view ST as related to CT through quantitative thinking (Booth Sweeney & Sterman, 2000; Richmond 1994 ) and creating simulation models (Arnold & Wade, 2017 ; Barth-Cohen, 2018 ; Dickes et al., 2016 ; Forrester, 1971 ; Stave & Hopper, 2007 ). Forrester regarded systems thinking as “a method for analyzing complex systems that uses computer simulation models to reveal how known structures and policies often produce unexpected and troublesome behavior” (1971, p. 115). Because of the reference to computer simulation models, we interpret this description as combining ST and CT through modeling. Computational modeling thus provides new ways to explore, understand, and represent interconnections among system elements, as well as to observe the output of system behaviors (Wilkerson et al., 2018 ).

Integration of ST and CT in CT literature

Researchers in CT (Berland & Wilensky, 2015 ; Brennan & Resnick, 2012 ; Lee & Malyn-Smith 2020 ; Sengupta et al., 2013 ; Shute et al., 2017 ; Weintrop et al., 2016 ; Wing, 2011 , 2017 ) claim that CT and ST are intertwined and support each other for successfully managing and solving complex problems across STEM disciplines. Wing ( 2017 ) contends that CT is “using abstraction and decomposition when designing a large complex system” (p. 8). CT supports representing the interrelationships among sub-parts in a system that are computational in nature and which form larger complex systems (Berland & Wilensky, 2015 ; Brennan & Resnick, 2012 ; Lee & Malyn-Smith 2020 ). For example, while ST supports learners to conceptualize a problem as a system of interacting elements, they use CT to make the relationships tractable through algorithms. This results in learners understanding larger and more complex systems (using ST) and finding solutions efficiently (using CT).

While CT overlaps with ST, Shute and colleagues ( 2017 ) distinguish CT from ST in that CT aims to design efficient solutions to problems through computation while ST focuses on constructing and analyzing various relationships among elements in a system for explaining and generalizing them to other similar systems. Although there are relationships between the two ways of thinking, we view CT and ST as co-equal in the context of modeling because of their unique characteristics. Our framework thus defines CT and ST as separate entities (Fig.  1 ).

Integration of ST and CT in computational modeling

Our review of ST and CT literature shows that computational modeling is a promising context for learners to engage in ST and CT (Arnold & Wade, 2017 ; Barr & Stephenson, 2011 ; Fisher, 2018 ; Hopper & Stave, 2008 ; Kolikant, 2011 ). Since ST and CT are intrinsically linked to computational modeling, they support learners’ modeling practices (Fisher, 2018 ). Computational models are non-static representations of phenomena that can be simulated by a computer or a human and differ from static model representations (e.g., paper-pencil models) because they produce output values.

Efforts to bridge CT and STEM in K-12 science have centered prominently on building and using computational models (Ogegbo & Ramnarain, 2021 ; Sengupta et al., 2013 ; Shute et al., 2017 ; Sullivan & Heffernan, 2016 ). Computational models provide useful teaching and learning tools for integrating CT into STEM to make scientific ideas accessible to learners and enhance student understanding of phenomena (Nguyen & Santagata, 2021 ). As learners begin to build models they can define the components and structural features so that a computer can interpret model behavior. CT aids learners in modeling for investigating, representing, and understanding a phenomenon or a system (Irgens et al., 2020 ; Sullivan & Heffernan, 2016 ).

Scholars have also developed computational modeling tools to promote ST (Levy & Wilensky, 2011 ; Richmond, 1994 ; Samon & Levy, 2019; Wilensky & Resnick 1999 ), and argue that the ability to effectively use computer simulations is an important aspect of ST (National Research Council, 2011 ). ST supports learners in modeling to define the boundaries of the system (Dickes et al., 2016 ) and reduce the complexity of a system conceptually (Arnold & Wade, 2017 ). Their research shows that learners develop proficiency for scientific ideas and ST while building computational models (e.g., Stella [Richmond, 1994 ], NetLogo [Levy & Wilensky, 2011 ; Samon & Levy, 2019; Yoon et al., 2017 ]). Below is a description of our framework, which encapsulates how modeling can integrate ST and CT, and how ST and CT can support learners’ modeling practices.

Framework for computational systems modeling

The framework illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems (Fig.  1 ). We use example models created using SageModeler to show how ST and CT aspects are manifested in modeling. SageModeler is a free, web-based, open-source computational modeling tool with several affordances ( https://sagemodeler.concord.org ). Learners have: (1) multiple ways of building models (system diagrams, static equilibrium models Footnote 1 , and dynamic time-based models), (2) multiple forms (visual and textual) of representing variables and relationships that are customizable by the learner, (3) multiple ways of defining functional relationships between variables without having to write equations or computer code, and (4) multiple pathways for generating visualizations of model output. In order to better illustrate our approach to integrating ST and CT in the context of modeling, we describe how these features of SageModeler can be used to support ST and CT in each modeling practice.

M1. Characterize a problem or phenomenon to model

The ability to characterize a problem or phenomenon to model supports learners in gaining a firm conceptual understanding of the phenomenon and helps to facilitate the modeling process by narrowing the scope of the phenomenon and determining the best modeling approach to apply (Dickes et al., 2016 ; Hutchins et al., 2020 ). Models are often built as aids for understanding a problem or perplexing observation. When learners face a problem or encounter a phenomenon to be understood or explained, they need to clearly define the problem or ask a “central question” to be investigated (Meadows, 2008 ). This allows the learner to delineate model boundaries (see M2 below), choosing only those elements and connections that are deemed relevant to the question. At this stage, these elements reflect learners’ general observations of the phenomenon or problem and may initially lack the specificity needed to design a computational model. For example, when learners consider anthropogenic climate change, they are likely to first identify the elements as “carbon dioxide,” “ocean,” “ice caps,” “agriculture,” “human activity,” etc. This step helps foster learners’ initial sense-making.

To fully engage in this practice, learners need to use ST1, defining a system, ST4, framing problems or phenomena in terms of behavior over time when appropriate, and CT1, decomposing problems such that they are computationally solvable . ST1 and CT1 are important in this modeling practice because they help learners focus on the question that needs to be answered computationally (Brennan & Resnick, 2012 ; Lee & Malyn-Smith 2020 ). Science educators propose that “an explicit model of a system under study” (National Research Council, 2012 , p. 90) can be a potential learning tool for deep understanding. In SageModeler, learners can create a text box for writing their questions, and select to use a number of different modeling strategies, which supports ST1, ST4, and CT1 (Fig.  4 ).

Dynamic time-based model. ( Note. Learners build a dynamic time-based model with drag-and-drop features and a research question by converting the elements into measurable variables and setting relationships between them.)

ST4, framing problems or phenomena in terms of behavior over time , can be helpful if the phenomenon being studied displays dynamic behavior. The understanding of how different aspects of the phenomenon evolve can aid in determining which elements of the system should be included in the model to be built (see M2 below). Such understanding also helps learners choose an approach for building a model (e.g., dynamic or static equilibrium). One key feature in SageModeler is that it allows for the development of dynamic models containing feedback structures that can more accurately portray the behaviors of real-world phenomena over time and directly support learners in ST4 (Fig.  4 ). However, this type of modeling may not be appropriate for all phenomena and should only be used when it is necessary to explain how the behavior of the system changes over time. When characterizing a problem or phenomenon to model, learners need to consider whether a static or dynamic structure will better suit their purpose. Although specifics of that structure will likely emerge as the model is being constructed, the type of model chosen and its purposes may influence the choice of system boundaries.

M2. Define the boundaries of the system and M3. Design and construct model structure

These two practices are often connected (hence a box surrounds them in the framework) and tend to occur in a synchronous fashion as learners build and revise models.

M2. Define the boundaries of the system

When learners define the boundaries of the system, they break down the system into specific elements that better suit the aims of their question and facilitate modeling. Within this practice it is essential to consider the size and scope of the question under study by reviewing the elements, selecting those essential to understanding the behavior of the system (Anderson, 2016 ; Türker & Pala, 2020 ), and ignoring irrelevant elements. ST1, defining a system, CT1, decomposing problems such that they are computationally solvable , and CT2, creating artifacts using algorithmic thinking , support learners as they define the boundaries of the system.

ST1, defining a system , guides learners to examine the system and consider what is included and what is excluded in the model, in other words, specifying the boundary of the system being modeled (Arnold & Wade, 2017 ). ST1 also encompasses considerations of how the model components are linked to each other to form model structures that will impact the emergent behavior of the system. CT1, decomposing problems such that they are computationally solvable , is vital to this modeling practice because it breaks down complex phenomena into logical sequences of cause and effect that can be described computationally (Aho, 2012 ; Basu et al., 2016 ; Berland & Wilensky, 2015 ; Sengupta et al., 2013 ; Shute et al., 2017 ; Türker & Pala, 2020 ). Learners also use CT2, creating artifacts using algorithmic thinking , at this stage to redefine and encode the elements as measurable variables (Cansu & Cansu, 2019 ; Kolikant, 2011 ). Learners must determine how the elements they have chosen are causally connected and transform their abstract conceptual understanding of the system into concrete language that can be encoded meaningfully and can be computed. For example, an element previously identified as “human activity” could be redefined as variables, such as “amount of fossil fuels burned in power plants,” “amount of greenhouse gases,” and “amount of forest fires” (Fig.  4 ).

M3. Design and construct model structure

After defining measurable variables, learners can begin to design and construct model structure. When designing and constructing model structure, learners are actively involved in defining relationships among variables within the model. In a model of climate change, for example, learners set a relationship between “temperature of the Earth” and the “# of ice caps melting” variables by defining functional relationships, as shown in Fig.  4 . This modeling practice encourages learners to carefully examine cause and effect relationships within the system in a model (Levy & Wilensky, 2008 , 2009 , 2011 ; Wilensky & Resnick, 1999 ). ST2, engaging in causal reasoning , supports learners to describe both direct and indirect relationships among various components of a system model (Grotzer, 2003 , 2017).

As learners continue to build and revise their models, the goal is to move their attention from simple relationships between two adjacent variables towards observing the cumulative behavior of longer causal chains (such as the relationship between the “amount of fossil fuels burned” and the “# of ice caps melting”), as well as broader structural patterns, such as feedback loops (Dickes et al., 2016 ; Fisher, 2018 ; Grotzer, 2003 , 2017; Jacobson et al., 2011 ). Knowledge of the connections between model structure and behavior support learners in designing and building the model appropriately (Meadows, 2008 ; Perkins & Grotzer, 2005 ). Learners engaging in this modeling practice have an opportunity to use ST3, identifying interconnections and feedback . In turn, familiarity with ST4, framing problems or phenomena in terms of behavior over time , helps learners, when appropriate, to determine which variables represent accumulations in a system and how other variables interact with those accumulations over time. ST4 is also important when considering the length of time over which a model is to be simulated. A time frame that is too short may not reveal important behaviors in the model while one that is too long may hide important behavioral detail.

The aspect of CT2, creating artifacts using algorithmic thinking , is important in this modeling practice, particularly in building computational models, which encode variables and relationships such that a computer can utilize this encoding to run a simulation (Nardelli, 2019 ; Ogegbo & Ramnarain, 2021 ; Sengupta et al., 2013 ). SageModeler takes a semiquantitative approach to defining how one variable affects another, and how accumulations and flows change over time in dynamic models. Initial values of the variables are set using a slider that goes from “low” to “high” (Fig.  5 ) and learners use words with associated graphs to define the links between variables (Fig.  4 ). The links between variables also change visually to show how those relationships are defined. These features support learners in ST2, engaging in causal reasoning , ST3, identifying interconnections and feedback , and CT2, creating computational artifacts .

M4. Test, evaluate, and debug model behavior

As learners construct computational models, they are constantly revising those models based on new evidence, incorporating new variables to match their growing understanding of the system or removing irrelevant variables because they are outside the scope and scale of the question (Basu et al., 2016 ; Brennan & Resnick, 2012 ). During revisions learners consider relationships they have set among variables and whether or not they result in accurate or expected behaviors when the model is simulated (Hadad et al., 2020 ; Lee et al., 2020 ). This iterative testing and evaluation continues until the learner is satisfied that the created artifact sufficiently represents the phenomenon or system under consideration. This modeling practice combines ST5, predicting system behavior based on system structure, CT3, generating, organizing, and interpreting data, CT4, testing and debugging , and CT5, making iterative refinements .

A major advantage of computational models is the opportunity to run a simulation, allowing learners to generate output from the model and test if their model matches their conceptual understanding of the phenomenon. Simulation encourages learners to use ST5, predicting system behavior based on system structure , as they anticipate the model’s output based on the visual representation of the model’s structure. If learners’ computational models do not behave as expected by comparing their conceptual understanding with the model’s output using CT3, generating, organizing, and interpreting data (Aho, 2012 ; Selby & Woollard, 2013 ; Türker & Pala 2020 ) and CT4, testing and debugging (Barr & Stephenson, 2011 ; Sengupta et al., 2013 ; Sullivan & Heffernan, 2016 ; Yadav et al., 2014 ), they can use CT5, make iterative refinements , and re-engage in M2 and M3 by redefining the system under study and revising their models.

In addition to utilizing simulation outputs, learners also make use of data from real-world measurements or experiments to help evaluate and make iterative changes to their models. Such external validation helps learners recognize how their model structures do or do not reflect the system they are modeling and helps guide their subsequent model revisions. Generating model output supports learner involvement in CT3, generating, organizing, and interpreting data , and CT4, testing and debugging . Pattern recognition and identifying relationships in data are important in the creation of an abstract model because they support learners in evaluating the behaviors of a model (Lee & Malyn-Smith, 2020 ; Shute et al., 2017 ). The entire M4 practice is supported by CT5, making iterative refinements , to lead learners when revising their models systematically based on evidence.

Once variables are chosen and linked together by relationships that have been defined semi-quantitatively, SageModeler can simulate the model and generate model output that can be compared with expected behavior and external validating data sources (Fig.  5 ). Additionally, learners can create multivariate graphs using simulation output or external data to show the effect of any variable on any other variable in the model and to validate model output. For example, in Fig.  5 , learners test their models by running simulations and changing the starting values of input variables (e.g., “# of cars burning gasoline”) to explore how downstream variables (e.g., “global temperature”) change, thus examining if the simulation outputs met their expectations. Learners also generate a graph between two key variables (“# of cars burning gasoline” and “global temperature”) to test the model. Research findings on learners who build, revise, and test computational models with SageModeler show promising impacts on student learning while they engage in ST and CT (Eidin et al., 2020 ).

Testing, evaluating and debugging a model. ( Testing, evaluating and debugging a model . Note. Learners examine a model through simulation and data generation)

M5. Use model to explain and predict behavior of phenomenon or design solution to a problem

Once a model reaches a level of functionality where it appropriately and consistently illustrates the behavior of the system under exploration, learners engage in the modeling practice of using the model to explain and predict behavior of phenomenon or design solution to a problem. This requires that learners utilize ST2, engaging in causal reasoning , and ST5, predicting system behavior based on system structure . Learners must read and interpret the model as a series of interconnected relationships among variables and make sense of the model output before they can use their model to facilitate a verbal or written explanation of the phenomenon or anticipate the outcome of an internal or external intervention on system behavior. Because the model serves as a tool to explain or predict a phenomenon or solve a problem, examining the usability of the model is critical (Schwarz & White, 2005 ). Therefore, learners should be able to articulate the differences between their model and the underlying real-world phenomenon, reflecting on both the limitations and usability of their model.

Further, CT3, the practice of generating, organizing, and interpreting data , supports learners as they compare model output to data collected from the real-world phenomenon and assess the similarities and differences between them. By engaging in the construction of a computational model that mimics reality and considering the limits of the model to produce accurate behavior, learners gain an understanding about the power of modeling to leverage learning and increase intuition about complex systems.

Table  3 summarizes how learners are engaged in 5 ST aspects with 13 associated sub-aspects and 5 CT aspects with 13 associated sub-aspects through modeling.

Implications and future directions

This framework serves as a foundation for developing curriculum, teacher and learner supports, assessments, and research instruments to promote, monitor, and explore how learners engage in ST and CT through model building, testing, evaluating, and revising. Specific aspects of ST and CT can guide the design of supports to help learners participate in knowledge construction through modeling, and can help researchers and practitioners develop indicators (evidence) that can clearly describe measurable behaviors that show whether learners use the desired ST and CT aspects. This approach provides a direction for designing activities to produce specific learner-generated knowledge products that can support modeling practices and their corresponding ST and CT aspects in K-12 STEM curricula.

Further studies in the context of well-developed curricula aligned with the Framework for Computational Systems Modeling are required to (1) explore additional ST and CT aspects learners use within the context of modeling, (2) confirm that these aspects can be observed through associated sub-aspects in modeling contexts, and (3) describe how and when learners use the ST and CT aspects through the five modeling practices defined in the framework.

Limitations

As is typical in an integrative review approach (Snyder, 2019 ), our literature review might be biased based on our conceptual understanding of modeling, ST, and CT because we limited it to scholars within our defined set of search criteria. Due to the broad conceptualizations of CT and ST and a wide range of fields where these are applicable, our literature collection may have missed relevant studies. Given the breadth of these fields, it is difficult to condense all of the literature into one coherent manuscript. As such we emphasized aspects of modeling, ST, and CT that synergized and supported each other.

Conclusions

Modeling, systems thinking, and computational thinking are important for an educated STEM workforce and the general public to explain and predict scientific phenomena and to solve pressing global and local problems (National Research Council, 2012 ). ST and CT in the context of modeling are critical for professionals in science and engineering to advance knowledge about the natural world and for civic engagement by the public to understand and evaluate proposed solutions to local and global problems. We suggest that schools provide learners with more opportunities to develop, test, and revise computational models and thus use aspects of both systems thinking and computational thinking.

Additional opportunities exist for learning scientists to carry out an integrated and comprehensive research and development program in a range of learning contexts for exploring the relationship between modeling, ST, CT, and student learning. Such a program of research should aim to integrate ST and CT through modeling to create pedagogically appropriate teaching and learning materials, and to develop and collect evidence to confirm learner engagement in modeling, ST, and CT. Our efforts in developing a framework contribute to this mission to educate learners as science-literate citizens who are proficient in building and using models that utilize a systems thinking perspective while taking advantage of the computational power of algorithms to explain and predict phenomena or seek answers and develop a range of potential solutions to problems that plague our society and world.

Availability of data and material

Not applicable. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Code Availability

Not applicable.

A static equilibrium model consists of a set of variables linked by relationships that define how one variable influences another. Any change to an input variable is immediately reflected in new values calculated for each variable in the system. There is no time component to this type of system model. Any change to the input instantaneously results in a new model state.

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All authors contributed to the conceptualization of the framework. The first draft of the manuscript was written by Namsoo Shin, Jonathan Bowers, Steve Roderick, and Cynthia McIntyre and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Namsoo Shin conducted literature reviews and led the direction of the manuscript. Jonathan Bowers co-wrote the manuscript throughout the entire writing process of this manuscript. Steve Roderick conducted initial literature reviews and wrote the first draft of the systems thinking section, and selected students’ models based on the framework as examples. Cynthia McIntyre co-wrote and reviewed the description of the framework. Lynn Stephens searched, conducted, and co-wrote extensive literature reviews of systems thinking. Emanuel Eidin provided necessary manuscripts and reviewed the analysis of the literature. Joseph Krajcik oversaw the direction of the manuscript, and iteratively reviewed and revised the manuscript. Daniel Damelin led the entire development of the framework.

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Shin, N., Bowers, J., Roderick, S. et al. A framework for supporting systems thinking and computational thinking through constructing models. Instr Sci 50 , 933–960 (2022). https://doi.org/10.1007/s11251-022-09590-9

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