part of speech of case study

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

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

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

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

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

How to Approach Writing a Case Analysis Paper

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

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

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

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

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

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

Structure and Writing Style

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

Introduction

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

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

Background Analysis

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

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

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

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

Identification of Problems

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

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

Alternative Solutions

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

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

Recommended Courses of Action

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

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

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

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

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

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

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

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

What Is a Case Study?

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

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

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

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

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

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

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

Types of Case Studies

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

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

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

Case Study Format

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

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

How to Write a Case Study

Let's discover how to write a case study.

Setting Up the Research

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

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

Read Also: 'CREDIBLE SOURCES: WHAT ARE THEY?'

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

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

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Case Study Outline

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

Introduction

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

Writing a Case Study Draft

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

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

Use Data to Illustrate Key Points in Your Case Study

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

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

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

Finalizing the Draft: Checklist

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

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

Problems to avoid:

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

How to Create a Title Page and Cite a Case Study

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

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

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

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

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

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

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

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

Case Study Examples

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

Eastman Kodak Case Study

Case Study Example: Audi Trains Mexican Autoworkers in Germany

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

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

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What Is A Case Study?

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

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• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Grammar: Main Parts of Speech

Definitions and examples.

The name of something, like a person, animal, place, thing, or concept. Nouns are typically used as subjects, objects, objects of prepositions, and modifiers of other nouns.

• I = subject
• the dissertation = object
• in Chapter 4 = object of a preposition
• research = modifier

This expresses what the person, animal, place, thing, or concept does. In English, verbs follow the noun.

• It takes a good deal of dedication to complete a doctoral degree.
• She studied hard for the test.
• Writing a dissertation is difficult. (The "be" verb is also sometimes referred to as a copula or a linking verb. It links the subject, in this case "writing a dissertation," to the complement or the predicate of the sentence, in this case, "hard.")

This describes a noun or pronoun. Adjectives typically come before a noun or after a stative verb, like the verb "to be."

• Diligent describes the student and appears before the noun student .
• Difficult is placed after the to be verb and describes what it is like to balance time.

Remember that adjectives in English have no plural form. The same form of the adjective is used for both singular and plural nouns.

• A different idea
• Some different ideas
• INCORRECT: some differents ideas

This gives more information about the verb and about how the action was done. Adverbs tells how, where, when, why, etc. Depending on the context, the adverb can come before or after the verb or at the beginning or end of a sentence.

• Enthusiastically describes how he completed the course and answers the how question.
• Recently modifies the verb enroll and answers the when question.
• Then describes and modifies the entire sentence. See this link on transitions for more examples of conjunctive adverbs (adverbs that join one idea to another to improve the cohesion of the writing).

This word substitutes for a noun or a noun phrase (e.g. it, she, he, they, that, those,…).

• they = applicants
• He = Smith; that = ideas; those = those ideas

This word makes the reference of the noun more specific (e.g. his, her, my, their, the, a, an, this, these, … ).

• Jones published her book in 2015.
• The book was very popular.

Preposition

This comes before a noun or a noun phrase and links it to other parts of the sentence. These are usually single words (e.g., on, at, by ,… ) but can be up to four words (e.g., as far as, in addition to, as a result of, …).

• I chose to interview teachers in the district closest to me.
• The recorder was placed next to the interviewee.
• I stopped the recording in the middle of the interview due to a low battery.

Conjunction

A word that joins two clauses. These can be coordinating (an easy way to remember this is memorizing FANBOYS = for, and, nor, but, or, yet, so) or subordinating (e.g., because, although, when, …).

• The results were not significant, so the alternative hypothesis was accepted.
• Although the results seem promising, more research must be conducted in this area.

Auxiliary Verbs

Helping verbs. They are used to build up complete verbs.

• Primary auxiliary verbs (be, have, do) show the progressive, passive, perfect, and negative verb tenses .
• Modal auxiliary verbs (can, could, may, might, must, shall, should, will, would) show a variety of meanings. They represent ability, permission, necessity, and degree of certainty. These are always followed by the simple form of the verb.
• Semimodal auxiliary verbs (e.g., be going to, ought to, have to, had better, used to, be able to,…). These are always followed by the simple form of the verb.
• primary: have investigated = present perfect tense; has not been determined = passive, perfect, negative form
• The modal could shows ability, and the verb conduct stays in its simple form; the modal may shows degree of certainty, and the verb lead stays in its simple form.
• These semimodals are followed by the simple form of the verb.

Common Endings

Nouns, verbs, adjectives, and adverbs often have unique word endings, called suffixes . Looking at the suffix can help to distinguish the word from other parts of speech and help identify the function of the word in the sentence. It is important to use the correct word form in written sentences so that readers can clearly follow the intended meaning.

Here are some common endings for the basic parts of speech. If ever in doubt, consult the dictionary for the correct word form.

Common Noun Endings

Common verb endings, common adjective endings, common adverb endings, placement and position of adjectives and adverbs, order of adjectives.

If more than one adjective is used in a sentence, they tend to occur in a certain order. In English, two or three adjectives modifying a noun tend to be the limit. However, when writing in APA, not many adjectives should be used (since APA is objective, scientific writing). If adjectives are used, the framework below can be used as guidance in adjective placement.

• Determiner (e.g., this, that, these, those, my, mine, your, yours, him, his, hers they, their, some, our, several,…) or article (a, an, the)
• Opinion, quality, or observation adjective (e.g., lovely, useful, cute, difficult, comfortable)
• Physical description
• (a) size (big, little, tall, short)
• (b) shape (circular,  irregular, triangular)
• (c) age (old, new, young, adolescent)
• (d) color (red, green, yellow)
• Origin (e.g., English, Mexican, Japanese)
• Material (e.g., cotton, metal, plastic)
• Qualifier (noun used as an adjective to modify the noun that follows; i.e., campus activities, rocking chair, business suit)
• Head noun that the adjectives are describing (e.g., activities, chair, suit)

For example:

• This (1) lovely (2) new (3) wooden (4) Italian (5) rocking (6) chair (7) is in my office.
• Your (1) beautiful (2) green (3) French (4) silk (5) business (6) suit (7) has a hole in it.

Commas With Multiple Adjectives

A comma is used between two adjectives only if the adjectives belong to the same category (for example, if there are two adjectives describing color or two adjectives describing material). To test this, ask these two questions:

• Does the sentence make sense if the adjectives are written in reverse order?
• Does the sentence make sense if the word “and” is written between them?

If the answer is yes to the above questions, the adjectives are separated with a comma. Also keep in mind a comma is never used before the noun that it modifies.

• This useful big round old green English leather rocking chair is comfortable . (Note that there are no commas here because there is only one adjective from each category.)
• A lovely large yellow, red, and green oil painting was hung on the wall. (Note the commas between yellow, red, and green since these are all in the same category of color.)

Position of Adverbs

Adverbs can appear in different positions in a sentence.

• At the beginning of a sentence: Generally , teachers work more than 40 hours a week.
• After the subject, before the verb: Teachers generally work more than 40 hours a week.
• At the end of a sentence: Teachers work more than 40 hours a week, generally .
• However, an adverb is not placed between a verb and a direct object. INCORRECT: Teachers work generally more than 40 hours a week.

More Detailed Rules for the Position of Adverbs

• Adverbs that modify the whole sentence can move to different positions, such as certainly, recently, fortunately, actually, and obviously.
• Recently , I started a new job.
• I recently started a new job.
• I started a new job recently .
• Many adverbs of frequency modify the entire sentence and not just the verb, such as frequently, usually, always, sometimes, often , and seldom . These adverbs appear in the middle of the sentence, after the subject.
• INCORRECT: Frequently she gets time to herself.
• INCORRECT: She gets time to herself frequently .
• She has frequently exercised during her lunch hour. (The adverb appears after the first auxiliary verb.)
• She is frequently hanging out with old friends. (The adverb appears after the to be verb.)
• Adverbial phrases work best at the end of a sentence.
• He greeted us in a very friendly way .
• I collected data for 2 months .

Main Parts of Speech Video Playlist

Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

• Mastering the Mechanics: Nouns (video transcript)
• Mastering the Mechanics: Introduction to Verbs (video transcript)
• Mastering the Mechanics: Articles (video transcript)
• Mastering the Mechanics: Introduction to Pronouns (video transcript)
• Mastering the Mechanics: Modifiers (video transcript)

Writing Tools: Dictionary and Thesaurus Refresher Video

Note that this video was created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

• Writing Tools: Dictionary and Thesaurus Refresher (video transcript)

Related Resources

Knowledge Check: Main Parts of Speech

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• Knowledge Base
• Parts of speech

The 8 Parts of Speech | Chart, Definition & Examples

A part of speech (also called a word class ) is a category that describes the role a word plays in a sentence. Understanding the different parts of speech can help you analyze how words function in a sentence and improve your writing.

The parts of speech are classified differently in different grammars, but most traditional grammars list eight parts of speech in English: nouns , pronouns , verbs , adjectives , adverbs , prepositions , conjunctions , and interjections . Some modern grammars add others, such as determiners and articles .

Many words can function as different parts of speech depending on how they are used. For example, “laugh” can be a noun (e.g., “I like your laugh”) or a verb (e.g., “don’t laugh”).

Table of contents

• Prepositions
• Conjunctions
• Interjections

Other parts of speech

Interesting language articles, frequently asked questions.

A noun is a word that refers to a person, concept, place, or thing. Nouns can act as the subject of a sentence (i.e., the person or thing performing the action) or as the object of a verb (i.e., the person or thing affected by the action).

There are numerous types of nouns, including common nouns (used to refer to nonspecific people, concepts, places, or things), proper nouns (used to refer to specific people, concepts, places, or things), and collective nouns (used to refer to a group of people or things).

Ella lives in France .

Other types of nouns include countable and uncountable nouns , concrete nouns , abstract nouns , and gerunds .

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A pronoun is a word used in place of a noun. Pronouns typically refer back to an antecedent (a previously mentioned noun) and must demonstrate correct pronoun-antecedent agreement . Like nouns, pronouns can refer to people, places, concepts, and things.

There are numerous types of pronouns, including personal pronouns (used in place of the proper name of a person), demonstrative pronouns (used to refer to specific things and indicate their relative position), and interrogative pronouns (used to introduce questions about things, people, and ownership).

That is a horrible painting!

A verb is a word that describes an action (e.g., “jump”), occurrence (e.g., “become”), or state of being (e.g., “exist”). Verbs indicate what the subject of a sentence is doing. Every complete sentence must contain at least one verb.

Verbs can change form depending on subject (e.g., first person singular), tense (e.g., simple past), mood (e.g., interrogative), and voice (e.g., passive voice ).

Regular verbs are verbs whose simple past and past participle are formed by adding“-ed” to the end of the word (or “-d” if the word already ends in “e”). Irregular verbs are verbs whose simple past and past participles are formed in some other way.

“I’ve already checked twice.”

“I heard that you used to sing .”

Other types of verbs include auxiliary verbs , linking verbs , modal verbs , and phrasal verbs .

An adjective is a word that describes a noun or pronoun. Adjectives can be attributive , appearing before a noun (e.g., “a red hat”), or predicative , appearing after a noun with the use of a linking verb like “to be” (e.g., “the hat is red ”).

Adjectives can also have a comparative function. Comparative adjectives compare two or more things. Superlative adjectives describe something as having the most or least of a specific characteristic.

Other types of adjectives include coordinate adjectives , participial adjectives , and denominal adjectives .

An adverb is a word that can modify a verb, adjective, adverb, or sentence. Adverbs are often formed by adding “-ly” to the end of an adjective (e.g., “slow” becomes “slowly”), although not all adverbs have this ending, and not all words with this ending are adverbs.

There are numerous types of adverbs, including adverbs of manner (used to describe how something occurs), adverbs of degree (used to indicate extent or degree), and adverbs of place (used to describe the location of an action or event).

Talia writes quite quickly.

Other types of adverbs include adverbs of frequency , adverbs of purpose , focusing adverbs , and adverbial phrases .

A preposition is a word (e.g., “at”) or phrase (e.g., “on top of”) used to show the relationship between the different parts of a sentence. Prepositions can be used to indicate aspects such as time , place , and direction .

I left the cup on the kitchen counter.

A conjunction is a word used to connect different parts of a sentence (e.g., words, phrases, or clauses).

The main types of conjunctions are coordinating conjunctions (used to connect items that are grammatically equal), subordinating conjunctions (used to introduce a dependent clause), and correlative conjunctions (used in pairs to join grammatically equal parts of a sentence).

You can choose what movie we watch because I chose the last time.

An interjection is a word or phrase used to express a feeling, give a command, or greet someone. Interjections are a grammatically independent part of speech, so they can often be excluded from a sentence without affecting the meaning.

Types of interjections include volitive interjections (used to make a demand or request), emotive interjections (used to express a feeling or reaction), cognitive interjections (used to indicate thoughts), and greetings and parting words (used at the beginning and end of a conversation).

Ouch ! I hurt my arm.

I’m, um , not sure.

The traditional classification of English words into eight parts of speech is by no means the only one or the objective truth. Grammarians have often divided them into more or fewer classes. Other commonly mentioned parts of speech include determiners and articles.

• Determiners

A determiner is a word that describes a noun by indicating quantity, possession, or relative position.

Common types of determiners include demonstrative determiners (used to indicate the relative position of a noun), possessive determiners (used to describe ownership), and quantifiers (used to indicate the quantity of a noun).

My brother is selling his old car.

Other types of determiners include distributive determiners , determiners of difference , and numbers .

An article is a word that modifies a noun by indicating whether it is specific or general.

• The definite article the is used to refer to a specific version of a noun. The can be used with all countable and uncountable nouns (e.g., “the door,” “the energy,” “the mountains”).
• The indefinite articles a and an refer to general or unspecific nouns. The indefinite articles can only be used with singular countable nouns (e.g., “a poster,” “an engine”).

There’s a concert this weekend.

If you want to know more about nouns , pronouns , verbs , and other parts of speech, make sure to check out some of our language articles with explanations and examples.

Nouns & pronouns

• Common nouns
• Proper nouns
• Collective nouns
• Personal pronouns
• Uncountable and countable nouns
• Verb tenses
• Phrasal verbs
• Types of verbs
• Active vs passive voice
• Subject-verb agreement

A is an indefinite article (along with an ). While articles can be classed as their own part of speech, they’re also considered a type of determiner .

The indefinite articles are used to introduce nonspecific countable nouns (e.g., “a dog,” “an island”).

In is primarily classed as a preposition, but it can be classed as various other parts of speech, depending on how it is used:

• Preposition (e.g., “ in the field”)
• Noun (e.g., “I have an in with that company”)
• Adjective (e.g., “Tim is part of the in crowd”)
• Adverb (e.g., “Will you be in this evening?”)

As a part of speech, and is classed as a conjunction . Specifically, it’s a coordinating conjunction .

And can be used to connect grammatically equal parts of a sentence, such as two nouns (e.g., “a cup and plate”), or two adjectives (e.g., “strong and smart”). And can also be used to connect phrases and clauses.

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Part of speech tagging: a systematic review of deep learning and machine learning approaches

• Alebachew Chiche   ORCID: orcid.org/0000-0003-2668-6509 1 &
• Betselot Yitagesu 2  

Journal of Big Data volume  9 , Article number:  10 ( 2022 ) Cite this article

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Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

Introduction

Natural language processing (NLP) has become a part of daily life and a crucial tool today. It aids people in many areas, such as information retrieval, information extraction, machine translation, question-answering speech synthesis and recognition, and so on. In particular, NLP is an automatic approach to analyzing texts using a different set of technologies and theories with the help of a computer. It is also defined as a computerized approach to process and understand natural language. Thus, it improves human-to-human communication, enables human-to-machine communication by doing useful processing of texts or speeches. Part-of-speech (POS) tagging is one of the most important addressed areas and main building block and application in the natural language processing discipline [ 1 , 2 , 3 ]. So, Part of Speech (POS) Tagging is a notable NLP topic that aims in assigning each word of a text the proper syntactic tag in its context of appearance [ 4 , 5 , 6 , 7 , 8 ]. Part-of-speech (POS) tagging, also called grammatical tagging, is the automatic assignment of part-of-speech tags to words in a sentence [ 9 , 10 , 11 ]. A POS is a grammatical classification that commonly includes verbs, adjectives, adverbs, nouns, etc. POS tagging is an important natural language processing application used in machine translation, word sense disambiguation, question answering parsing, and so on. The genesis of POS tagging is based on the ambiguity of many words in terms of their part of speech in a context.

Manually tagging part-of-speech to words is a tedious, laborious, expensive, and time-consuming task; therefore, widespread interest is becoming in automating the tagging process [ 12 ]. As stated by Pisceldo et al. [ 4 ], the main issue that must be addressed in part of speech tagging is that of ambiguity: words behave differently given different contexts in most languages, and thus the difficulty is to identify the correct tag of a word appearing in a particular sentence. Several approaches have been deployed to automatic POS tagging, like transformational-based, rule-based and probabilistic approaches. Rule-based part of speech taggers assign a tag to a word based on manually created linguistic rules; for instance, a word that follows adjectives is tagged as a noun [ 12 ]. And probabilistic approaches [ 12 ] determine the frequent tag of a word in a given context based on probability values calculated from a tagged corpus which is tagged manually. On the other hand, a combination of probabilistic and rule-based approaches is the transformational-based approach to automatically calculate symbolic rules from a corpus.

To accomplish the requirements of an efficient POS tagger, the researchers have explored the possibility of using Deep learning (DL) and Machine learning (ML) techniques. Under the big umbrella of artificial intelligence, both ML and DL aim to learn meaningful information from the given big language resources [ 13 , 14 ]. Because of the growth of powerful graphics processor units (GPUs), these techniques have gained widespread recognition and appeal in the field of natural language processing, notably part of speech tagging (POST), throughout the previous decade. [ 13 , 15 ]. Both ML and DL are powerful tools for extracting valuable and hidden features from the given corpus and assigning the correct POS tags to words based on the patterns discovered. To learn valuable information from the corpus, the ML-based POS tagger relies mostly on feature engineering [ 16 ]. On the other hand, DL-based POS taggers are better at learning complicated features from raw data without relying on feature engineering because of their deep structure [ 17 ].

Different researchers forwarded numerous ML and DL-based solutions to make POS taggers effective in tagging part of speech of words in their context. However, the extensive use of POS tagging and the resulting complications have generated several challenges for POS tagging systems to appropriately tag the word class. The research on using the DL methods for POS tagging is currently in its early stage, and there is still a gap to further explore this approach within POS tagging to effectively assign part of speech within the sentence.

The main contributions of this paper are addressed in three phases. Phase I; we selected recent journal articles focusing on DL- and ML-based POS tagging (published between 2017 and February 2021). Phase II; we extensively reviewed and discussed each article from various parameters such as proposed methods and techniques, weakness, strength, and evaluation metrics. Phase III; in this phase, recent trends in POS tagging using AI methods are provided, challenges in DL/ML-based POS tagging are highlighted, and we provided future research directions in this domain. This review paper is explored based on three aspects: (i) Systematic article selection process is followed to obtain more related research articles on POS tagging implementation using Artificial Intelligence methods, while others reviewed without using the systematic approach. (ii) Our study emphasized the research articles published between 2017 and July 2021 to provide a piece of updated information in the design of AI-oriented POST. (iii) A recent POS tagging model based on the DL and ML approach is reviewed according to their methods and techniques, and evaluation metrics. The intent is to provide new researchers with more updated knowledge on AI-oriented POS tagging in one place.

Therefore, this paper aims to review Artificial Intelligence oriented POS tagging and related studies published from 2017 to 2021 by examining what methods and techniques have been used, what experiments have been conducted, and what performance metrics have been used for evaluation. The research paper provides a comprehensive overview of the advancement and recent trends in DL- and ML-based solutions for POS tagger Systems. The key idea is to provide up-to-date information on recent DL-based and ML-based POS taggers that provide a ground for the new researchers who want to start exploring this research domain.

The rest of the paper is organized as follows: “ Methodology ” section describes the research methodology deployed for the study. “ POS tagging approaches ” section presents the basic POS tagging approaches. “ Artificial Intelligence methods for POS tagging ” section describes the ML and DL methodologies used. The details about the evaluation metrics are shown in “ Evaluation metrics ” section. Recent observations in POS implementation, research challenges, and future research directions are also presented in “ Remarks, challenges, and future trends ” section. Finally, the Conclusion of the review article is presented in “ Conclusion ” section.

Methodology

This study explores a systematic literature review of various DL and ML-based POS tagging and examines the research articles published from 2017 to 2021. A systematic article review is a research methodology conducted to identify, extract, and examine useful literature related to a particular research area. We followed two stages process in this systematic review.

Stage-1 identifies the information resource and keywords to execute query related to "POST" and obtain an initial list of articles. Stage-2 applies certain criteria on the initial list to select the most related and core articles and store them into a final list reviewed in this paper. The main aim of this review paper is to answer some of the following questions: (i) What is state-of-the-art in the design of AI-oriented POS tagging? (ii) What are the current ML and DL methodologies deployed for designing POS tagging? (iii) What are the strengths and weaknesses of deployed methods and techniques? (iv)? What are the most common evaluation metrics used for testing? And (v) What are the future research trends in AI-oriented POS tagging?

In the first phase, keywords and search engines are selected for searching articles. As a potential search engine, Scopus document search is selected due to searching all well-known databases. The search query is executed using the initial keyword like "Part of speech tagging" and filter the publication duration that showed between 2017 and 2021. The initial query search results from articles that proposed POS tagging using different methods like AI-oriented, rule-based stochastic etc., for different applications. Then the query keyword is redefined by combining the keyword deep learning or machine learning to get more important research articles. Accordingly, important articles from query search based on the defined keywords were taken and stored as an initial list of articles. The process of stage-1 is presented in Fig.  1 .

Stage one methodology

Whereas in stage-2, we defined criteria to get a more focused article from the initial list used for analysis. As a result, articles were selected that proposed new ML and DL methods written in English. In this review, we did not include papers with keywords like survey, review, and analysis. Based on these criteria, we selected articles for this review and stored them in the final article list, then used them for analysis. All selected articles which are stored in the final list were analyzed based on the DL or ML methodology proposed and the strengths and weaknesses of the proposed methodology. And also analyzed performance metrics used for evaluation and testing purposes. At last, future research directions and challenges in the design of effective and efficient AI-based POS tagging are identified. The complete process used in stage-1 and stage-2 is summarized in Figs.  1 and 2 , respectively.

Stage two methodology

POS tagging approaches

This section first describes the details about approaches of POS tagging based on its methods and techniques deployed for tagging the given the word. Several POS tagging approaches have been proposed to automatically tag words with part-of-speech tags in a sentence. The most familiar approaches are rule-based [ 18 , 19 ], artificial neural network [ 20 ], stochastic [ 21 , 22 ] and hybrid approaches [ 22 , 23 , 24 ]. The most commonly used part of speech tagging approaches is presented as follows.

A rule-based approach for POS tagging uses hand-crafted rules to assign tags to words in a sentence. According to [ 19 , 25 ], the rules generated mostly depend on linguistic features of the language, such as lexical, morphological, and syntactical information. Linguistic experts may construct these rules or use machine learning on an annotated corpus [ 10 , 11 ]. The first way of getting rules is tedious, prone to error, and time-consuming. Besides, it needs highly a language expert on the language being tagged. For the second process, a model built using experts then learns and stores a sequence of rules using a training corpus without expert rule [ 19 ].

Artificial neural network

Artificial Neural Network is an algorithm inspired by biological neurons and is used to estimate functions that can depend on a large number of inputs, and they are generally unknown [ 29 , 30 ]. It is presented as interconnected systems of "neurons" that are used to exchange messages. The associations between neurons have numeric loads that can be changed dependent on experience, making neural organizations versatile to sources of info and ready to learn. It is an assortment of an enormous number of interconnected handling neurons cooperating to tackle given issues (Fig. 3 ).

ML/DL Based POS Tagging Model

Like other approaches, an ANN approach that can be used for POS tagger developments requires a pre-processing activity before working on the actual ANN-based tagger [ 11 , 14 ]. The output from the pre-processing task would be taken as an input for the input layer of the neural network. From this pre-processed input, the neural network trains itself by adopting the value of the numeric weights of the connection between input layers until the correct POS tag is provided.

Hidden Markov Model

The hidden Markov model is the most widely implemented POS tagging method under the stochastic approach [ 6 , 23 , 31 ]. It follows a factual Markov model in which the tagger framework being demonstrated is thought to be explored from one state to another with an inconspicuous state. Unlike the Markov model, in HMM, the state is not directly observable to the observer, but the output that depends on the hidden state is visible. As stated in [ 23 , 32 , 33 ], Hidden Markov Model is a familiar statistical model that is used to find the most frequent tag sequence T = {t1, t2, t3… tn} for a word sequence in sentence W = {w1, w2, w3…wn} [ 33 ]. The Viterbi algorithm is a well-known method for tagging the most likely tag sequence for each word in a sentence when using a hidden Markov model.

Maximum Entropy Markov Model

Maximum Entropy Markov is a conditional probabilistic sequence model [ 12 , 34 , 35 ]. Maximum entropy modeling aims to take the probabilistic lexical distribution that scores maximum entropy out of the distributions to complement a certain set of constraints. The constraints limit the model to perform as per a set of measurements collected from the training corpus.

The most commonly deployed statistics for POS tagging are: how often a word was annotated in a certain way and how often labels showed up in a sequence. On the other hand, unlike HMM in the maximum entropy approach, it is likely to effortlessly characterize and include much more complex measurements, which are not confined to n-gram sequences [ 36 ]. Also, the problem of HMM is solved by the Maximum Entropy Markov model (MEMM) because it is possible to include random features sets. However, the MEMM approach has a business problem in labeling because it normalizes not the whole sequence; rather, it normalizes per state [ 35 ].

Artificial intelligence methods for POS tagging

This section provides a general methodology of the AI-based POS tagging along with the details of the most commonly deployed DL and ML algorithms used to implement an effective POS tagging. Both DL and ML are broadly classified into supervised and unsupervised algorithms [ 22 , 32 , 37 , 38 ]. In supervised learning algorithms, the hidden information is extracted from the labeled data. In contrast, unsupervised learning algorithms find useful features and information from the unlabeled data.

Machine Learning Algorithms

Machine Learning could be a set of AI that has all the strategies and algorithms that enable the machines to learn automatically by using mathematical models to extract relevant knowledge from the given datasets [ 15 , 38 , 39 , 40 , 41 , 42 ]. The most common ML algorithms used for POS taggers are Neural Network, Naïve Bayes, HMM, Support Vector Machine (SVM), ANN, Conditional Random Field (CRF), Brill, and TnT.

Naive Bayes

In some circumstances, statistical dependencies between system variables exist. Notwithstanding, it may be hard to definitively communicate the probabilistic connections among these factors [ 43 ]. A probabilistic graph model can be used to exploit these casual dependencies or relationships between the variables of a problem, which is called Naïve Bayesian Networks (NB). The probabilistic model provides an answer for "What is the probability of a given word occurrence before the other words in a given sentence?" by following conditional probability [ 44 ].

Hirpassa et al. [ 39 ] proposed an automatic prediction of POS tags of words in the Amharic language to address the POS tagging problem. The statistical-based POS taggers are compared. The performances of all these taggers, which are Conditional Random Field (CRF), Naive Bays (NB), Trigrams'n'Tags (TnT) Tagger, and an HMM-based tagger, are compared with the same training and testing datasets. The empirical result shows that CRF-based tagger has outperformed the performance of others. The CRF-based tagger has achieved the best accuracy of 94.08% during the experiment.

Support vector machine

Support vector machines (SVM) is first proposed by Vapnik (1998). SVM is a machine learning algorithm used in applications that need binary classification, adopted for various kinds of domain problems, including NLP [ 16 , 45 ]. Basically, an SVM algorithm learns a linear hyperplane that splits the set of positive collections from the set of negative collections with the highest boundary. Surahio and Maha [ 45 ] have tried to develop a prediction System for Sindhi Parts of Speech Tags using the Support Vector Machine learning algorithm. Rule-Based Approach (RBA) and SVM experiment on the same dataset. Based on the experiments, SVM has achieved better detection accuracy when compared to RBA tagging techniques.

Conditional random field (CRF)

A conditional random field (CRF) is a method used for building discriminative probabilistic models that segment and label a given sequential data [ 12 , 33 , 46 , 47 , 48 ]. A conditional random field is an undirected x, y graphical model in which each yi vertex represents a random variable whose distribution is dependent on some observation variable X, and each margin characterizes a dependency between xi and yi random variables. The dependency of Yi on Xi is defined in a set of functions of f(Yi-1,Yi,X,i). Khan et al. [ 22 ] proposed a conditional random field (CRF)-based Urdu POS tagger with both language dependent and independent feature sets.

It used both deep learning and machine learning approaches with the language-dependent feature set using two datasets to compare the effectiveness of ML and DL approaches. Also, Hirpassa et al. [ 39 ] proposed an automatic prediction of POS tags of words in the Amharic language to address the POS tagging problem. The statistical-based POS taggers are compared. The performances of all these taggers, which are Conditional Random Field (CRF), Naive Bays (NB), Trigrams'n'Tags (TnT) Tagger, and an HMM-based tagger, are compared with the same training and testing datasets. The empirical result shows that CRF-based tagger has outperformed the performance of others. The CRF-based tagger has achieved the best accuracy of 94.08% during the experiment.

Hidden Markov model (HMM)

The Hidden Markov model is the most commonly used model for part of speech tagging appropriate [ 49 , 50 , 51 , 52 ]. HMM is appropriate in cases where something is hidden while another is observed. In this case, the observed ones are words, and the hidden one is tagged. Demilie [ 53 ] proposed an Awngi language part of speech tagger using the Hidden Markov Model. They created 23 hand-crafted tag sets and collected 94,000 sentences. A tenfold cross-validation mechanism was used to evaluate the performance of the Awngi HMM POS tagger. The empirical result shows that uni-gram and bi-gram taggers achieve 93.64% and 94.77% tagging accuracy, respectively. The other author, Hirpassa et al. [ 39 ], proposed an automatic prediction of POS tags of words in the Amharic language to address the POS tagging problem. The statistical-based POS taggers are compared. The performances of all these taggers, which are Conditional Random Field (CRF), Naive Bays (NB), Trigrams'n'Tags (TnT) Tagger, and an HMM-based tagger, are compared with the same training and testing datasets. As the empirical result shows, CRF-based tagger has outperformed the performance of others. The CRF-based tagger has achieved the highest accuracy of 94.08% during the experiment.

Deep learning algorithms

Currently, deep learning methods are the most common word in machine learning to automatically extract complex data representation at a high level of abstraction, especially used for extremely complex problems. It is a data-intensive approach to come with a better result than traditional methods (Naïve Bayes, SVM, HMM, etc.). During the text-based corpora, deep learning sequential models are better than feed-forward methods. In this paper, some of the common sequential deep learning methods such as FNN, MLP, GRU, CNN, RNN, LSTM, and BLSTM are discussed.

Multilayer perceptron (MLP)

The neural network (NN) is a machine learning algorithm that mimics the neurons of the human brain for processing information (Haykin, 1999). One of the widely deployed neural network techniques is Multilayer perceptron (MLP) in many NLP and other pattern recognition problems. An MLP neural network consists of three layers: an input layer as input nodes, one or more hidden layers, and an output layer of computation nodes. Besides, the backpropagation learning algorithm is often used to train an MLP neural network, which is also called backpropagation NN. In the beginning, randomly assigned weights are set at the beginning of algorithm training. Then, the MLP algorithm automatically performs weight changing to define the hidden layer unit representation is mostly good at minimizing the misclassification [ 54 , 55 , 56 ]. Besharati et al. [ 54 ] proposed a POS tagging model for the Persian language using word vectors as the input for MLP and LSTM neural networks. Then the proposed model is compared with the results of the other neural network models and with a second-order HMM tagger, which is used as a benchmark.

Long short-term memory

A Long Short-Term Memory (LSTM) is a special kind of RNN network architecture, which has the capability of learning long-term dependencies. An LSTM can also learn to fill the gap in time intervals in more than1000 steps [ 14 , 57 , 58 ].

Bidirectional long short-term memory

Bidirectional LSTM contains two separate hidden layers to process information in both directions. The first hidden layer processes the forward input sequences, while the other hidden layer processes it backward; both are then connected to the same output layer, which provides access to the future and past context of every point in the sequence. Hence BLSTM beat both standard LSTMs and RNNs, and it also significantly provides a faster and more accurate model [ 14 , 58 ].

Gate recurrent unit

Gated recurrent unit (GRU) is an extension of recurrent neural network which aims to process memories of sequence of data by storing prior input state of the network, which they plan to target vectors based on the prior input [ 14 , 58 ].

Feed-forward neural network

A feed-forward neural network (FNN) is one artificial neural network in which connections between the neuron units do not form a cycle. Also, in Feedforward neural networks, information processing is passed through the network input layers to output layers [ 59 ].

Recurrent neural network (RNN)

On the other hand, a recurrent neural network (RNN) is among an artificial neural network model where connections between the processing units form cyclic paths. It is recurrent since they receive inputs, update the hidden layers depending on the prior computations, and that make predictions for all elements of a sequence [ 33 , 46 , 60 , 61 , 62 ].

Deep neural network

In a normal Recurrent Neural Network (RNN), the information pipes through only one layer to the output layer before processing. But Deep Neural Networks (DNN) is a combination of both deep neural networks (DNN) and RNNs concepts [ 33 , 63 ].

Convolutional neural network

A convolutional neural network (CNN) is a deep learning network structure that is more suitable for the information stored in the array's data structure. Like other neural network structures, CNN comprises an input layer, the memory stack of pooling and convolutional layers for extracting feature sets, and then a fully connected layer with a softmax classifier in the classification layer [ 64 , 65 , 66 , 67 , 68 ].

Evaluation metrics

This section describes the most commonly deployed performance metrics for validating the performance of ML and DL methods for POS tagging. All the evaluation metrics are based on the different metrics used in the Confusion Matrix, which is a confusion matrix providing information about the Actual and Predicted class which are; True Positive (TP)—assigns correct tags to the given words, false positive (FP)—assigns incorrect tags to the given words, false negative (FN)—not assign any tags to given words [ 14 , 55 , 72 ].

True Positive (TP): The word correctly tagged as labelled by experts

False Negative (FN): The given word is not tagged to any of the tag sets.

False Positive (FP): The given word tagged wrongly.

True Negative (TN): The occurrences correctly categorized as normal instances.

In addition to these, the various evaluation metrics used in the previous works are,

Precision: The ratio of correctly tagged part of speech to all the samples tagged words:

Recall: The ratio of all samples correctly tagged as tagged to all the samples that are tagged by expert (aka a Detection Rate).

False alarm rate: the false positive rate is defined as the ratio of wrongly tagged word samples to all the samples.

True negative rate: The ratio of the number of correctly tagged samples to all the samples.

Accuracy: The ratio of correctly tagged part of speech to the total number of instances (aka Detection accuracy).

F-Measure: It is the harmonic mean of the Precision and Recall.

Remarks, challenges, and future trends

This section first presents the researcher's observation in POS tagging based on their proposed methodology and performance criteria. It also highlights the potential research gap and challenges and lastly forwards the future trends for the researchers to come up with a robust, efficient, and effective POS tagger.

Observations and state of art

The effectiveness of AI-oriented POS tagging depends on the learning phase using appropriate corpora. For classical machine Learning techniques, the algorithms could be trained under a small corpus to come with better results. But in the presence of a larger corpus size, deep learning methods are preferable compared to the classical machine learning techniques. These methods learn and uncover useful knowledge from given raw datasets. To make POS tagging efficient in tagging unknown words, it needs to be trained with known corpus. In nature, deep learning algorithms are resource hungry in terms of computational resources and time consumption, so the large corpus and deep nature of the algorithms make the learning process difficult.

Table 1 highlights the summary of the strengths and weaknesses of the reviewed articles. It is observed that deep learning-oriented POS tagging methodologies are preferred by researchers nowadays over the machine learning methods because of their efficiency in learning from the large-size corpus in an unlabeled text.

The introduction of GPUs and cloud-based platforms nowadays has eased the implementation of the deep learning method due to the need for extensive computational resources by Deep Learning (DL).

Based on the reviewed article, we observed that for the past three years, the majority of the researchers preferred Deep Learning (DL) tools for developing the POS tagging model, as depicted in Fig.  4 . It is observed that 68% of the proposed approaches are based on the deep learning approaches, 12% of proposed solutions use a hybrid approach by combining machine learning with deep learning algorithms, and the remaining 20% of proposed POS tagger models are implemented based on machine learning methods.

Methods distribution

Besides, Table 2 shows the frequency of Deep Learning and Machine Learning algorithms deployed by different researchers to design an effective POS tagger model. It is shown that the three most frequent deep learning algorithms used are LSTM, RNN, and BiLSTM, respectively. Then the machine learning approaches like CRF and HMM come into the list and are most commonly deployed in the hybrid approach to improve deep learning algorithms. Also, machine learning algorithms like KNN, MLP, and SVM are less frequently used algorithms during this period.

The analysis of the evaluation metrics used in various researches for evaluating the performance of the methodology is presented in Fig.  5 . It is well known that the most commonly deployed performance metrics are Accuracy and Recall (Detection rate). For efficient POS tagging, the model needs a higher Accuracy and Recall. It is observed that the most widely used metrics are accuracy, recall, precision, and F-measure. So, to examine the effectiveness and efficiency of the proposed methodology, these four-evaluation metrics should be taken as performance metrics. For a typical POS tagger developed using machine learning and deep learning algorithms, Accuracy, Recall, F-measure, and Precision should be the compulsory metric to evaluate the methodology (Table 3 ).

Research challenges

This subsection presents the research challenges that existed in the field of POS tagging.

Lack of Enough and standard dataset: Most recent research studies indicated the unavailability of enough standard corpus for building better POS taggers for a particular language. The proposed methodologies faced difficulties in getting a balanced corpus size for some part of speech within the corpus. To come up with a better POS tagger, it needs to be trained and tested using a balanced and verified corpus. By incorporating a balanced and maximum number of tokens within a corpus, it should enable the DL and ML-based POS tagger to learn more patterns. Then the POS tagger could label words with an appropriate part of speech. But preparing a suitable language corpus is a tedious process that needs plenty of language resources and language experts' knowledge to verify. Therefore, the research challenge for developing an efficient POS tagging model is the preparation of enough and standard corpus with enough tokens of almost all balanced parts of speech. The corpus should be released publicly to help reduce the resource scarcity of the research community.

Lower detection accuracy: It is observed that most of the proposed POS tagging methodologies reveal lower detection accuracy of the POS tagging model as a whole, for some parts of speech tags in particular. This low detection accuracy problem is faced because of the imbalanced nature of the corpus. The ML/DL-based POS tagger trained with less frequent part of speech tags provides low detection accuracy than part of speech with more part of speech. To overcome these problems, it should come up with a balanced corpus and also an efficient technique like Synthetic Minority Over-sampling Technique (SMOTE), RandomOverSampler; which are techniques used to balance unbalanced classes of the corpus. These techniques can be used to increase the number of minority parts of speech tag instances to come up with a balanced corpus. But there is still a research gap to improve accuracy and demands more research effort in this arena.

Resource requirement: Most recent POS tagging methodologies proposed are based on very complex models that need high computing resources and time for processing. These can be solved by using a multi-core high-performance GPU to fasten the computation process and reduce time, but it will incur a high amount of money. The deployment of these complex models may experience an extra processing overhead that will affect the performance of the POS tagger. Besides alleviating the overhead of processing units and computational processes, the most important features must be selected to speed up the processing by using an efficient feature selection algorithm. Although various research works have been explored to come up with the best feature selection algorithm, there is still room for improvement in this direction.

Future directions

This part of the article provides the area which needs further improvement in ML/ DL-oriented POS tagging research.

Efficient POS Tagging Model: As stated, POS tagging is one of the most important and groundwork for any other natural language processing tools like information extraction, information retrieval, machine translation. Recent research works show that there is a constraint in automatically tagging "Unknown" words with a high false positive rate. To this end, the performance of the POS tagger can be improved by using a balanced, up-to-date systematic dataset. An attempt to propose an efficient and complete POS tagging model for most under resource languages using ML/DL methodologies is almost null. So, research can be explored in this area to come up with an efficient POS tagging model that can automatically label parts of speech to words. The POS tagging model should incorporate sentences from different domains in a corpus and repeatedly train the model with the updated corpus to enable the model to learn the new features. This mechanism will ultimately improve the POS tagging model in identifying UNKNOWN words and then minimize false positive rates. Despite the fact that several research studies are being conducted in order to develop an efficient and successful POS tagging strategy, there is still room for improvement.

Way forwards to complex models: Recently, like other domains, ML/DL-oriented POS tagging has been popular because of the ability to learn the feature deeply so as to generate excellent patterns in identifying parts of speech to words. Obviously, the DL-oriented POS tagging models are too complex that need high storage capacity, computational power, and time. This complex nature of the DL-based POS tagging implementation challenges the real-world scenario. The solution to address this problem is to use GPU-based high-performance computers, but GPU-based devices are costly. So, to reduce computational costs, the model can be trained and explored on cloud-based GPU platforms. The second solution forwarded is to use efficient and intelligent feature selection algorithms for reducing the complex nature of deep learning algorithms. This will use less computing resources by selecting the main features while the same detection accuracy is achieved using the whole set of features.

This review paper presents a comprehensive assessment of the part of speech tagging approaches based on the deep learning (DL) and machine learning (ML) methods to provide interested and new researchers with up-to-date knowledge, recent researcher's inclinations, and advancement of the arena. As a research methodology, a systematic approach is followed to prioritize and select important research articles in the field of artificial intelligence-based POS tagging. At the outset, the theoretical concept of NLP and POS tagging and its various POS tagging approaches are explained comprehensively based on the reviewed research articles. Then the methodology that is followed by each article is presented, and strong points and weak points of each article are provided in terms of the capability and difficulty of the POS tagging model. Based on this review, the recent development of research shows the use of deep learning (DL) oriented methodologies improves the efficiency and effectiveness of POS tagging in terms of accuracy and reduction in false-positive rate. Almost 68% of the proposed POS tagging solutions were deep learning (DL) based methods, with LSTM, RNN, and BiLSTM being the three topmost frequently used DL algorithms. The remaining 20% and 12% of proposed POS tagging models are machine learning (ML) and Hybrid approaches, respectively. However, deep learning methods have shown much better tagging performance than the machine learning-oriented methods in terms of learning features by themselves. But these methods are more complex and need high computing resources. So, these difficulties should be solved to improve POS tagging performance. Given the increasing application of DL and ML techniques in POS tagging, this paper can provide a valuable reference and a baseline for researches in both ML and DL fields that want to pull the potential of these techniques in the POS tagging arena. Proposing an efficient POS tagging model by adopting less complex deep learning algorithms and an effective POS tagging in terms of detection mechanism is a potential future research area. Further, the researcher will use this knowledge to propose a new and efficient deep learning-based POS tagging which will effectively identify a part of the speech of words within the sentences.

Availability of data and materials

Not applicable.

Abbreviations

Autoencoder

Artificial Intelligence

Artificial Neural Network

Bidirectional Long Short-Term Memory

Convolutional Neural Network

Conditional Random Field

Deep Belief Network

Deep Learning

Deep Neural Network

False Alarm Rate

False Negative

Feedforward Neural Network

False Positive

Gated Recurrent Unit

Synthetic Minority Over-sampling Technique

K-Nearest Neighbor

Long Short-Term Memory

Machine Learning

Multilayer Perceptron

Naïve Bayes

Natural Language Processing

Part of Speech

Part of Speech Tagging

Recurrent Neural Network

Support Vector Machine

True Negative

True Positive

Alharbi R, Magdy W, Darwish K, AbdelAli A, Mubarak H. Part-of-speech tagging for Arabic Gulf dialect using Bi-LSTM. Int Conf Lang Resour Eval. 2018;3925–3932:2019.

Google Scholar  

Demilie WB. Analysis of implemented part of speech tagger approaches: the case of Ethiopian languages. Indian J Sci Technol. 2020;13(48):4661–71.

Article   Google Scholar  

Sánchez-Martínez F, Pérez-Ortiz JA, Forcada ML. Using target-language information to train part-of-speech taggers for machine translation. Mach Transl. 2008;22(1–2):29–66.

Singh J, Joshi N, Mathur I. Part of speech tagging of marathi text using trigram method. Int J Adv Inf Technol. 2013;3(2):35–41.

Marques NC, Lopes GP. Using Neural Nets for Portuguese Part-of-Speech Tagging. In: Proc. Fifth Int. Conf. Cogn. Sci. Nat. Lang. Process., no. August, 1996.

Kumawat D, Jain V. POS tagging approaches: a comparison. Int J Comput Appl. 2015;118(6):32–8.

Chungku C, Rabgay J, Faaß G. Building NLP resources for Dzongkha: a tagset and a tagged corpus. in: Proceedings of the 8th Workshop on Asian Language Resources, pp. 103–110. 2010.

Singh J, Joshi N, Mathur I. Development of Marathi part of speech tagger using statistical approach. In: Proc. 2013 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2013, no. October 2013, pp. 1554–1559, 2013.

Cutting D. A Practical Part-of-Speech Tagger Doug Cutting and Julian Kupiec and Jan Pedersen and Penelope Sibun Xerox Palo Alto Research Center 3333 Coyote. In: Proc. Conf., pp. 133–140, 1992.

Lv C, Liu H, Dong Y, Chen Y. Corpus based part-of-speech tagging. Int J Speech Technol. 2016;19(3):647–54.

Divyapushpalakshmi M, Ramalakshmi R. An efficient sentimental analysis using hybrid deep learning and optimization technique for Twitter using parts of speech (POS) tagging. Int J Speech Technol. 2021;24(2):329–39.

Pisceldo F, Adriani M, and R. Manurung R. Probabilistic Part of Speech Tagging for Bahasa Indonesia. In: Proc. 3rd Int. MALINDO Work. Coloca. event ACL-IJCNLP. 2009.

Alzubaidi L, et al. Review of deep learning: concepts, CNN architectures, challenges, applications. Fut Direct. 2021;8:1.

Deshmukh RD, Kiwelekar A. Deep Learning Techniques for Part of Speech Tagging by Natural Language Processing. In: 2nd Int. Conf. Innov. Mech. Ind. Appl. ICIMIA 2020 - Conf. Proc., no. Icimia, pp. 76–81, 2020.

Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Al Najada H. Survey of review spam detection using machine learning techniques. J Big Data. 2015;2:1.

Antony PJ, Mohan SP, Soman KP. SVM based part of speech tagger for Malayalam. In: ITC 2010 - 2010 Int. Conf. Recent Trends Information Telecommunication Computer. p. 339–341, 2010.

Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. J Big Data. 2015;2(1):1–21.

Brill E. Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput Linguist. 1995;21(4):543–66.

Brill E. Rule-Based Part of Speech. In: Proc. third Conf. Appl. Nat. Lang. Process. (ANLC ’92), pp. 152–155; 1992.

Brill E. A Simple Rule-Based Part Of Speech Tagger. In: Proceedings of the Third Conference on Applied Computational Linguistics (ACL), Trento, Italy, 1992, pp. 1–14; 1992.

Mamo G, Meshesha M. Parts of speech tagging for Afaan Oromo. Int J Adv Comput Sci Appl. 2011;1(3):1–5.

Hall J. A Probabilistic Part-of-Speech Tagger with Suffix Probabilities A Probabilistic Part-of-Speech Tagger with Suffix Probabilities. MSc: Thesis, Växjö University; 2003.

Zin KK. Hidden markov model with rule based approach for part of speech tagging of Myanmar language. In: Proc. 3rd Int. Conf. Commun. Inf. Technol. CIT’09 , pp. 123–128; 2009.

Altunyurt L, Orhan Z, Güngör T. A composite approach for part of speech tagging in Turkish. InProceeding of International Scientific Conference on Computer Science, Istanbul, Turkey 2006.

Pham B. Parts of Speech Tagging : Rule-Based. https://digitalcommons.harrisburgu.edu/cisc_student-coursework/2 , February, 2020.

Mekuria Z. Design and development of part-of-speech tagger for Kafi-noonoo Language. MSc: Thesis, Addis Ababa University, Ethiopia; 2013.

Farhat NH. Photonit neural networks and learning mathines the role of electron-trapping materials. IEEE Expert Syst their Appl. 1992;7(5):63–72.

Chen CLP, Zhang CY, Chen L, Gan M. Fuzzy restricted boltzmann machine for the enhancement of deep learning. IEEE Trans Fuzzy Syst. 2015;23(6):2163–73.

Chen T. An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment. J Ambient Intell Humaniz Comput. 2018;9(4):1013–25.

Lu BL, Ma Q, Ichikawa M, Isahara H. Efficient part-of-speech tagging with a min-max modular neural-network model. Appl Intell. 2003;19(1–2):65–81.

Article   MATH   Google Scholar  

Nisheeth J, Hemant D, Iti M. HMM based POS tagger for Hindi. In: Proceeding of 2013 International Conference on Artificial Intelligence and Soft Computing. pp. 341–349, 2013. http://doi: https://doi.org/10.5121/csit.2013.3639

Getinet Y. Unsupervised Part Of Speech Tagging For Amharic. MSc: Thesis, University of Gondar Ethiopia; 2015.

Khan W, et al. Part of speech tagging in urdu: comparison of machine and deep learning approaches. IEEE Access. 2019;7:38918–36.

Silfverberg M, Ruokolainen T, Kurimo M, Linden K. PVS A, Karthik G. Part-of-speech tagging and chunking using conditional random fields and transformation based learning. Shallow Parsing for South Asian Languages. 2007; pp. 259–264.

Wang G, Sun J, Ma J, Xu K, Gu J. Sentiment classification: the contribution of ensemble learning. Decis Support Syst. 2014;57(1):77–93.

Xia R, Zong C, Li S. Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci (Ny). 2011;181(6):1138–52.

Biemann C. Unsupervised part-of-speech tagging in the large. Res Lang Comput. 2009;7(2):101–35.

Moraboena S, Ketepalli G, Ragam P. A deep learning approach to network intrusion detection using deep autoencoder. Rev d’Intelligence Artif. 2020;34(4):457–63.

Hirpssa S, Lehal GS. POS tagging for amharic text: a machine learning approach. INFOCOMP. 2020;19(1):1–8.

Gupta V, Singh VK, Mukhija P, Ghose U. Aspect-based sentiment analysis of mobile reviews. J Intell Fuzzy Syst. 2019;36(5):4721–30.

Mansour RF, Escorcia-Gutierrez J, Gamarra M, Gupta D, Castillo O, Kumar S. Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification. Pattern Recognit Lett. 2021;151:267–74.

Jacob SS, Vijayakumar R. Sentimental analysis over twitter data using clustering based machine learning algorithm. J Ambient Intelligence Humanized Computing. 2021;4:1–2.

Tseng C, Patel N, Paranjape H, Lin TY, Teoh S. Classifying Twitter Data with Naive Bayes Classifier. In: 2012 IEEE International Conference on Granular Computing Classifying , 2012; pp. 1–6.

Kumar S, Nezhurina MI. An ensemble classification approach for prediction of user’s next location based on Twitter data. J Ambient Intell Humaniz Comput. 2019;10(11):4503–13.

Surahio FA, Mahar JA. Prediction system for sindhi parts of speech tags by using support vector machine. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2018; pp. 1-6.

Gashaw I, Shashirekha H. Machine Learning Approaches for Amharic Parts-of-speech Tagging,” in Proc. of ICON-2018, Patiala, India, pp.69–74, December 2018.

Suraksha NM, Reshma K, Kumar KS. “Part-Of-Speech Tagging And Parsing Of Kannada Text Using Conditional Random Fields ( CRFs ),” 2017 International Conference on Intelligent Computing and Control (I2C2) , 2017.

Sutton C, McCallum A. An introduction to conditional random fields. Found Trends Mach Learn. 2011;4(4):267–373.

Khorjuvenkar DN, Ainapurkar M, Chagas S. Parts of speech tagging for Konkani language. In: Proc. 2nd Int. Conf. Comput. Methodol. Commun. ICCMC 2018, no. ICCMC, pp. 605–607, 2018.

Ankita, Abdul Nazeer KA. Part-of-speech tagging and named entity recognition using improved hidden markov model and bloom filter. In: 2018 Int. Conf. Comput. Power Commun. Technol. GUCON 2018, pp. 1072–1077, 2019.

Mohammed S. Using machine learning to build POS tagger for under-resourced language: the case of Somali. Int J Inf Technol. 2020;12(3):717–29.

Mathew W, Raposo R, Martins B. Predicting future locations with hidden Markov models. In: Proceedings of the 2012 ACM conference on ubiquitous computing; 2012, p. 911–18.

Demilie WB. Parts of Speech Tagger for Awngi Language. Int J Eng Sci Comput. 2019;9:1.

Besharati S, Veisi H, Darzi A, Saravani SHH. A hybrid statistical and deep learning based technique for Persian part of speech tagging. Iran J Comput Sci. 2021;4(1):35–43.

Argaw M. Amharic Parts-of-Speech Tagger using Neural Word Embeddings as Features Amharic Parts-of-Speech Tagger using Neural Word Embeddings as Features. MSc.Thesis: Addis Ababa University, Ethiopia; 2019.

Singh A, Verma C, Seal S, Singh V. Development of part of speech tagger using deep learning. Int J Eng Adv Technol. 2019;9(1):3384–91.

Bahcevan CA, Kutlu E, Yildiz T. Deep Neural Network Architecture for Part-of-Speech Tagging for Turkish Language. UBMK 2018 - 3rd Int. Conf. Comput. Sci. Eng. , pp. 235–238, 2018.

Gopalakrishnan A, Soman KP, Premjith B. Part-of-speech tagger for biomedical domain using deep neural network architecture. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2019, pp. 1-5.

Anastasyev D, Gusev I, Indenbom E. Improving part-of-speech tagging via multi-task learning and character-level word representations. Komp’juternaja Lingvistika i Intellektual’nye Tehnol. , vol. 2018-May, no. 17, pp. 14–27, 2018.

Prabha G, Jyothsna PV, Shahina KK, Premjith B, Soman KP. “A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language,” 2018 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2018 , pp. 1132–1136, 2018.

Sayami S, Shakya S. Nepali POS Tagging Using Deep Learning Approaches. Int J Sci. 2020;17:69–84.

Attia M, Samih Y, Elkahky A, Mubarak H, Abdelali A, Darwish K. POS Tagging for Improving Code-Switching Identification in Arabic. no. August, pp. 18–29, 2019.

Srivastava P, Chauhan K, Aggarwal D, Shukla A, Dhar J, Jain VP. Deep learning based unsupervised POS tagging for Sanskrit. In: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence 2018; pp. 1-6.

Pasupa K, Ayutthaya TS. Thai sentiment analysis with deep learning techniques: a comparative study based on word embedding, POS-tag, and sentic features. Sustain Cities Soc. 2019;50:101615.

Meftah S, Semmar N, Sadat F, Hx KA. A neural network model for part-of-speech tagging of social media texts. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).pdf,” pp. 2821–2828, 2018.

Mishra P. Building a Kannada POS Tagger Using Machine Learning and Neural Network Models. arXiv:1808.03175.

Gupta V, Jain N, Shubham S, Madan A, Chaudhary A, Xin Q. “Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language—Hindi”, ACM Trans . Asian Low-Resource Lang Inf Process. 2021;20(5):1–23.

Gupta V, Juyal S, Singh GP, Killa C, Gupta N. Emotion recognition of audio/speech data using deep learning approaches. J Inf Optim Sci. 2020;41(6):1309–17.

Kumar S, Kumar MA, Soman KP. Deep learning based part-of-speech tagging for Malayalam Twitter data (Special issue: deep learning techniques for natural language processing). J Intelligent Syst. 2019;28(3):423–35.

Baig A, Rahman MU, Kazi H, Baloch A. Developing a pos tagged corpus of urdu tweets. Computers. 2020;9(4):1–13.

Bonchanoski M, Zdravkova K. Machine learning-based approach to automatic POS tagging of macedonian language. In: ACM Int. Conf. Proceeding Ser. , vol. Part F1309, 2017.

Kumar S, Kumar MA, Soman KP. Deep learning based part-of-speech tagging for Malayalam twitter data (Special issue: Deep learning techniques for natural language processing). J Intell Syst. 2019;28(3):423–35.

Kabir MF, Abdullah-Al-Mamun K, Huda MN. Deep learning based parts of speech tagger for Bengali. In: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) 2016; pp. 26-29.

Patoary AH, Kibria MJ, Kaium A. Implementation of Automated Bengali Parts of Speech Tagger: An Approach Using Deep Learning Algorithm. In: 2020 IEEE Region 10 Symposium (TENSYMP) 2020; pp. 308-311.

Akhil KK, Rajimol R, Anoop VS. Parts-of-Speech tagging for Malayalam using deep learning techniques. Int J Inf Technol. 2020;12(3):741–8.

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Alebachew Chiche

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Betselot Yitagesu

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Chiche, A., Yitagesu, B. Part of speech tagging: a systematic review of deep learning and machine learning approaches. J Big Data 9 , 10 (2022). https://doi.org/10.1186/s40537-022-00561-y

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Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

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[Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging](https://aclanthology.org/J95-4004) (Brill, CL 1995)

• Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging (Brill, CL 1995)
• Eric Brill. 1995. Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging . Computational Linguistics , 21(4):543–565.

Parts of Speech: A Guide to Learning English Grammar

By: Author English Study Online

Posted on Last updated: December 27, 2023

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In this page, we will break down each part of speech and provide examples to help you understand their usage. We will also discuss how to identify the different parts of speech in a sentence and provide tips on how to use them correctly. Whether you are a beginner or an advanced English learner, this article will provide valuable insights into the parts of speech and improve your language skills. Let’s get started!

Table of Contents

Overview of Parts of Speech

In this section, we will provide a brief overview of the eight parts of speech in English. Understanding the parts of speech is essential for anyone learning the English language, as it enables them to construct meaningful sentences and communicate effectively.

The eight parts of speech are:

Prepositions

Conjunctions, interjections.

Each part of speech has a specific function in a sentence. For example, nouns are used to name people, places, things, or ideas, while verbs are used to describe an action or state of being. Adjectives are used to describe nouns, while adverbs are used to describe verbs, adjectives, or other adverbs.

Pronouns are used to replace nouns in a sentence, while prepositions are used to indicate the relationship between a noun or pronoun and other words in a sentence. Conjunctions are used to connect words, phrases, or clauses, while interjections are used to express emotions or feelings.

Nouns are words that represent people, places, things, or ideas. They are one of the most important parts of speech in English and are used in nearly every sentence. In this section, we will explore the different types of nouns and their functions.

Common Nouns

Common nouns are general names for people, places, or things. They are not capitalized unless they appear at the beginning of a sentence.

• Examples of common nouns include “book,” “city,” and “teacher.”

Proper Nouns

Proper nouns are specific names for people, places, or things. They are always capitalized.

• Examples of proper nouns include “Harry Potter,” “New York City,” and “Ms. Johnson.”

Abstract Nouns

Abstract nouns are names for ideas, concepts, or emotions. They are intangible and cannot be seen, heard, or touched.

• Examples of abstract nouns include “love,” “happiness,” and “freedom.”

Collective Nouns

Collective nouns are names for groups of people or things. They can be singular or plural, depending on the context.

• Examples of collective nouns include “team,” “family,” and “herd.”

In this section, we will discuss the different types of pronouns used in English grammar. Pronouns are words that replace nouns in a sentence. They help to avoid repetition and make sentences more concise.

Personal Pronouns

Personal pronouns refer to specific people or things. They can be used as the subject or object of a sentence. Here are the personal pronouns in English:

Demonstrative Pronouns

Demonstrative pronouns are used to point to specific people or things. They can be used to indicate distance or location. Here are the demonstrative pronouns in English:

Interrogative Pronouns

Interrogative pronouns are used to ask questions. They are typically used at the beginning of a sentence. Here are the interrogative pronouns in English:

Indefinite Pronouns

Indefinite pronouns refer to non-specific people or things. They can be used as the subject or object of a sentence. Here are the indefinite pronouns in English:

Verbs are one of the most important parts of speech in English. They are used to describe an action, state, or occurrence. In this section, we will cover the three types of verbs: action verbs, linking verbs, and helping verbs.

Action Verbs

Action verbs are used to describe an action that is being performed by the subject of the sentence. They can be used in the present, past, or future tense. Here are a few examples of action verbs:

Linking Verbs

Linking verbs are used to connect the subject of the sentence to a noun, pronoun, or adjective that describes it. They do not show action. Here are a few examples of linking verbs:

Helping Verbs

Helping verbs are used in conjunction with the main verb to express tense, voice, or mood. They do not have a meaning on their own. Here are a few examples of helping verbs:

In conclusion, verbs are an essential part of English grammar. Understanding the different types of verbs and how they are used in a sentence can help you communicate more effectively in both written and spoken English.

In this section, we will discuss adjectives, which are an important part of speech in English. Adjectives are words that describe or modify nouns or pronouns. They provide more information about the noun or pronoun, such as its size, shape, color, or quality.

Descriptive Adjectives

Descriptive adjectives are the most common type of adjectives. They describe the physical or observable characteristics of a noun or pronoun. For example, in the sentence “The red car is fast,” “red” is a descriptive adjective that describes the color of the car, and “fast” is another descriptive adjective that describes its speed.

Here are some examples of descriptive adjectives:

Quantitative Adjectives

Quantitative adjectives are used to describe the quantity or amount of a noun or pronoun. They answer the question “how much” or “how many.” For example, in the sentence “I have two apples,” “two” is a quantitative adjective that describes the number of apples.

Here are some examples of quantitative adjectives:

Demonstrative Adjectives

Demonstrative adjectives are used to point out or indicate a specific noun or pronoun. They answer the question “which one” or “whose.” For example, in the sentence “This book is mine,” “this” is a demonstrative adjective that indicates the specific book that belongs to the speaker.

Here are some examples of demonstrative adjectives:

In conclusion, adjectives are an important part of speech in English. They provide more information about nouns and pronouns, and they help to make our language more descriptive and precise. By understanding the different types of adjectives, we can use them effectively in our speaking and writing.

In this section, we will discuss adverbs, which are words that modify or describe verbs, adjectives, or other adverbs. Adverbs give more information about the action, manner, place, time, frequency, degree, or intensity of a verb.

Adverbs of Manner

Adverbs of manner describe how an action is performed. They answer the question “how?” and usually end in “-ly”, but not always. Here are some examples:

• She sings beautifully.
• He speaks softly.
• They ran quickly.
• The dog barked loudly.

Adverbs of manner can also be formed by adding “-ly” to some adjectives. For example:

• She is a quick learner. (adjective: quick)
• He is a careful driver. (adjective: careful)

Adverbs of Place

Adverbs of place describe where an action takes place. They answer the question “where?” and usually come after the verb or object. Here are some examples:

• She looked everywhere.
• He lives nearby.
• They went outside.
• The cat hid underneath the bed.

Adverbs of Time

Adverbs of time describe when an action takes place. They answer the question “when?” and can come at the beginning, middle, or end of a sentence. Here are some examples:

• She wakes up early every day.
• He arrived yesterday.
• They will leave soon.
• The concert starts tonight.

Adverbs of time can also be used to show the duration of an action. For example:

• She studied for hours.
• He worked all day.
• They talked for a long time.

In this section, we will discuss prepositions and their usage in English. Prepositions are words that show the relationship between a noun or pronoun and other words in a sentence. They usually indicate the position or direction of the noun or pronoun in relation to other elements in the sentence.

Prepositions of Time

Prepositions of time are used to indicate when an action took place. They include words such as “at,” “in,” and “on.”

• “At” is used for specific times, such as “at 2 pm” or “at midnight.”
• “In” is used for longer periods of time, such as “in the morning” or “in October.”
• “On” is used for dates, such as “on Monday” or “on July 4th.”

Prepositions of Place

Prepositions of place are used to indicate where something is located. They include words such as “in,” “on,” and “at.”

• “In” is used for enclosed spaces, such as “in the house” or “in the car.”
• “On” is used for surfaces, such as “on the table” or “on the floor.”
• “At” is used for specific locations, such as “at the park” or “at the beach.”

Prepositions of Direction

Prepositions of direction are used to indicate movement. They include words such as “to,” “from,” and “towards.”

• “To” is used to indicate movement towards a specific destination, such as “I am going to the store.”
• “From” is used to indicate movement away from a specific location, such as “I am coming from the park.”
• “Towards” is used to indicate movement in the direction of a specific location, such as “I am walking towards the museum.”

In this section, we will discuss the different types of conjunctions and their functions in English grammar. Conjunctions are words that connect words, phrases, or clauses in a sentence. They are essential in creating complex sentences and conveying relationships between ideas.

Coordinating Conjunctions

Coordinating conjunctions join words, phrases, or clauses that are of equal importance. They are easy to remember using the mnemonic device FANBOYS: for, and, nor, but, or, yet, so. Here are some examples:

• I like pizza and pasta.
• She is neither tall nor short.
• He wanted to go to the beach, but it was raining.

Subordinating Conjunctions

Subordinating conjunctions connect dependent clauses to independent clauses and establish a relationship between them. They are used to show cause and effect, time, condition, and contrast. Some examples of subordinating conjunctions are:

Here are some examples:

• Because it was raining, we stayed inside.
• Although she was tired, she stayed up to finish her work.
• While I was studying, my roommate was watching TV.

Correlative Conjunctions

Correlative conjunctions are pairs of words that work together to connect words, phrases, or clauses. They are used to show a relationship between two elements. Here are some examples:

• both…and
• either…or
• neither…nor
• not only…but also
• Both my sister and I like to read.
• Either you come with us or you stay here.
• Not only was he late, but he also forgot his homework.

In conclusion, conjunctions are important in creating complex sentences and conveying relationships between ideas. By understanding the different types of conjunctions and their functions, you can improve your writing and communication skills.

In English grammar, interjections are words or phrases that express strong emotions or feelings. They are also known as exclamations and are one of the eight parts of speech in English. Interjections are grammatically independent from the words around them, and they can often be removed from a sentence or context without affecting its basic meaning.

Interjections can be used to express a wide range of emotions, including surprise, joy, anger, frustration, and pain. Some common examples of interjections include “ wow ,” “ ouch ,” “ yay ,” “ oh no ,” and “ oops .” They can be used to add emphasis to a sentence or to convey a particular tone or mood.

It is important to note that interjections do not have any grammatical function in a sentence. They are not nouns, verbs, adjectives, or any other part of speech. Instead, they simply stand alone as a way to express emotion.

When using interjections in writing, it is important to consider the context in which they are being used. While they can be a useful tool for adding emphasis or conveying emotion, they can also be overused or misused, which can detract from the overall effectiveness of the writing.

Articles/Determiners

In English grammar, articles and determiners are words that are used with nouns to provide more information about them. They help us to understand the context and meaning of a sentence.

There are three articles in the English language: “ the ,” “ a, ” and “ an. ” “The” is known as the definite article because it refers to a specific noun that has already been mentioned or is known to the reader. For example, “The cat is sleeping on the sofa.” In this sentence, “the” refers to a specific cat that has already been mentioned or is known to the reader.

“A” and “an” are known as indefinite articles because they refer to any member of a group or class of nouns. “A” is used before words that begin with a consonant sound, while “an” is used before words that begin with a vowel sound. For example, “I need a pen” and “She ate an apple.”

Determiners

Determiners are words that come before a noun to provide more information about it. They can include articles, as well as words like “ this ,” “ that ,” “ these ,” and “ those .”

In addition to these, there are other types of determiners such as possessive determiners (e.g. “my,” “your,” “his,” “her,” “its,” “our,” and “their”), demonstrative determiners (e.g. “this,” “that,” “these,” and “those”), and quantifying determiners (e.g. “some,” “any,” “many,” “few,” “several,” etc.).

Determiners can also be used with adjectives to provide more information about a noun. For example, “She ate the delicious apple” and “I saw that beautiful sunset.”

Understanding articles and determiners is crucial for mastering English grammar. By using them correctly, you can convey your thoughts and ideas more clearly and effectively.

Frequently Asked Questions

What are the 8 parts of speech in English?

In English, there are eight parts of speech: nouns, pronouns, verbs, adjectives, adverbs, prepositions, conjunctions, and interjections. Each part of speech serves a different function in a sentence and helps to convey meaning.

What are some examples of different parts of speech?

Here are a few examples of different parts of speech:

• Noun: dog, cat, book, table
• Pronoun: he, she, it, they
• Verb: run, jump, sing, dance
• Adjective: happy, sad, tall, short
• Adverb: quickly, slowly, loudly, softly
• Preposition: in, on, at, under
• Conjunction: and, but, or, so
• Interjection: wow, oh, ouch, hooray

What is the difference between a noun and a verb?

A noun is a word that represents a person, place, thing, or idea. A verb is a word that represents an action, occurrence, or state of being. In other words, a noun is a subject or object in a sentence, while a verb is the action or occurrence that takes place.

What are the different types of nouns?

There are several different types of nouns, including:

• Common nouns: refer to general, non-specific people, places, things, or ideas (e.g. dog, city, book)
• Proper nouns: refer to specific people, places, things, or ideas and are always capitalized (e.g. John, Paris, The Great Gatsby )
• Concrete nouns: refer to tangible, physical objects (e.g. table, chair, car)
• Abstract nouns: refer to intangible concepts or ideas (e.g. love, happiness, freedom)
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Language is More than Speech: A Case Study

Morton ann gernsbacher.

Contact Information: Morton Ann Gernsbacher, Ph.D., 1202 W. Johnson Street, University of Wisconsin-Madison, Madison, WI 53562, ude.csiw@bsnreGAM

Some individuals face severe challenges with producing oral language (i.e., speech). In this article a case study of a child who experienced severe challenges with speech development is presented. Medical records, historical home videos, audio recordings, and photographs, in conjunction with an extensive journal maintained by the child’s mother provide the basis for this report, which profiles the child’s development from birth to age 8;0. This child’s development demonstrates the necessity of distinguishing between language—the mental representation of concepts and their relations—and speech—one means for communicating mental representations.

That the word language derives from lingua (“tongue”) betrays the common confusion about the relation between speech and language. ( Schein & Stewart , p. viii)

Some individuals face severe challenges with producing oral language (i.e., speech). In this article a case study of a child who experienced severe challenges with speech development is presented. Medical records, historical home videos, audio recordings, and photographs, in conjunction with an extensive journal maintained by the child’s mother provide the basis for this report, which profiles the child’s development from birth to age 8;0. This child’s development demonstrates the necessity of distinguishing between language—the mental representation of concepts and their relations — and speech — one means for communicating mental representations.

Neonatal Development

RH was the first and only child born to middle-age, professional parents. He was the product of an uncomplicated pregnancy, followed by a Caesarian delivery prompted by failure of labor to progress. During labor, RH’s heart rate was observed to decelerate rapidly in response to larger uterine contractions. A Caesarian delivery was prepared for, but not executed, earlier in labor because of this precipitous heart-rate deceleration. An eventual Caesarian delivery was uncomplicated, and RH’s birth weight was 7 lbs, 7 oz, with Apgar scores of 9/10 (1 min/5 min). RH’s immediate postnatal behavior was remarkable, according to his mother, for demonstrating a calm, soothed demeanor, very early and natural nursing (i.e., within moments of being handed to his mother), and striking visual attentiveness. His mother recounts that he fixated the ceiling mounted television while being held in his father’s lap in the hospital bed to watch several minutes of a collegiate basketball game (the Final Four championship).

RH’s first months of development were characterized by a calm demeanor, positive affect, and high visual attentiveness. RH began producing a social smile at 5 weeks, 5 days. RH greatly enjoyed leg extension activities, such as infant “kick gyms” (i.e., attractive stimuli dangling within kicking range, when RH was supine) and “Johnny Jump Ups” (i.e., a cloth saddle attachable to door openings with springs that allow an infant to bounce vertically using his legs). RH’s mother reported that RH would remain exuberant about jumping in a baby jumper for nearly an hour. RH was also reported to enjoy watching commercial video tapes, beginning as young as two months of age when he primarily watched videos of other baby’s faces (producing a range of emotional expressions), and later (beginning in the third month) when he began watching videos of children’s educational shows, such as Barney . RH was able to sit unaided by his fifth month of life and began crawling at the beginning of his seventh month. Immediately upon beginning to crawl, RH was reported by his mother to cease enjoying the baby jumper.

RH was characterized by his mother and by other adults as a “very quiet baby.” He rarely cried and had very limited babble, even by eight or nine months of age. He remained quite happy, playful, and curious, by his mother’s report, but did not produce typical amounts of or a typical diversity of vocalizations. According to both his mother’s report and recorded audiotape, RH produced little gurgling or cooing, and the sparse babbling produced was primarily vocalic (i.e., produced with vowels, not consonants). RH was reported to experience the onset of “stranger anxiety” at a developmentally typical point (around seven months) and passed the standard “a-not-b” object permanence task developmentally early ( Smith, Thelen, Titzer, & McLin, 1999 ). RH could stand unaided by nine months of age, and shortly before his one-year birthday he began to walk. According to historic home video tape, RH’s first succession of more than two steps unaided comprised 16 steps and a half-turn. According to medical records, RH’s ‘well baby visits’ with his pediatrician were annotated with the phrases, “highly social” and “very active” at 6, 9, and 12 months.

Toddler Development

RH’s mother reported that by the end of his first year of life, RH was particularly interested in numbers, letters, and colors—all interests that would maintain and strengthen through his second and third years of life. Indeed, prior to his second birthday, he arranged a set of large (8”×4”×2”), plastic alphabet blocks in perfect alphabetical order; he arranged correctly the letters in his name using large (12” high) foam letters, and he arranged in numerical order large, plastic numbers 1 through 20. He virtually always alerted to numerical or alphabetical stimuli, and he enjoyed watching videos about counting or spelling, in addition to videos portraying other toddlers or preschoolers playing. He was introduced, by one of his babysitters, to videos starring the twin celebrities, Mary-Kate and Ashley Olsen, filmed when they were preschoolers, which he greatly enjoyed watching.

As a toddler, RH continued to be characterized by his parents and other care providers as cheerful and highly active, and an additional trait—an inordinate sense of physical balance—became apparent. RH’s mother reported that he very rarely fell, despite his interest in walking in precarious environments (e.g., cobble stone streets) and his frequent climbing on furniture and other scalable structures. At 14 months of age, he began a toddler gymnastics program and was distinguished from the other same-aged toddlers by his agility in running quickly, without falling, down a ‘tumble track’ (a 40’ by 10’ trampoline). Despite RH’s active mobility and agility, RH’s mother reported in retrospect that his reaching and grasping were rare. She did not feel the need to remove, for example, fine china displays that were in his arm’s reach because he never approached these objects or others with his hands. Materials on kitchen counters, his parents’ work desks, or other surfaces that were within his toddler reach were never disturbed. Even earlier, during his first year of life, he rarely reached for anything (a desired toy, his mother’s hair, or his father’s or other care providers’ eye glasses) with his hands. Because manual (e.g., index finger or flat palm) pointing is a developmental outgrowth of manual reaching ( Hammes & Langdell, 1981 ), it is not surprising that RH did not develop or use any pointing behavior during the second year of life.

RH’s vocal production remained severely limited during his second year of life; his mother reported, and historic home video suggested, that he was even more “quiet” (i.e., non-vocal) after his 13 th month than he had been during the last half of his first year of life. RH communicated primarily with facial and other whole-body nonverbal expressions (predominantly those of positive affect, e.g., joy, satisfaction, curiosity, attention, and on rare occasions those of negative affect, e.g., distress or frustration). RH frequently led an adult to a desired item (e.g., a video) by either taking the adult’s hand after RH began walking, or previously, when RH was only crawling, taking the adult’s shirt bottom (as RH crawled along). RH’s mother viewed this form of communication as highly adaptive for a child whose volitional vocalizations and distal arm and hand control were so limited, and this style of communication remained a core part of his communicative repertoire for several years, becoming more fine grained (e.g., leading an adult by the hand to a door, and then placing the adult’s hand on the round door knob that RH was unable to open).

During his second year of life, RH enjoyed viewing visual stimuli upside down and would do so by facing backward to the stimuli, bending at the waist, and looking back at the stimuli between his legs. For example, often when watching familiar videos, RH would face away from the television, bend at the waist, and watch the video inverted by looking back toward the television between his legs. When a small, portable television was placed on the ground, RH stood behind the television and bent over it so that his head rested on the ground, only a couple of feet from the screen, and the image was inverted. RH’s mother reported that RH resisted having books read to him, by grabbing the book out of the reader’s hands and then studying intensely the bar code of the ISBN on the backside of the book. (His mother reported that by 30 months of age, he was proficient in bar code; i.e., he could distinguish altered bar code from authentic bar code.)

RH appeared to be fascinated by looking through sheets of colored acetate, and indeed, according to his mother, a sheet of red colored acetate was the first object that he demonstratively shared with his mother, encouraging her (nonverbally) to also look through the acetate. RH’s mother reported that RH was also very interested in door hinges, automatic doors, escalators, and the non-right angle caused by the family’s vaulted living room ceiling. To this latter stimulus RH would lead his mother and indicate nonverbally for her to observe the unusual angle. RH’s mother interpreted this act (RH’s leading his mother by the hand to the floor underneath the non-right angle and directing with gaze his mother’s own gaze to the angle) as an act of initiating joint attention. However, the communicative act, which RH’s mother reported as quite successful, was accomplished without index finger pointing or verbalization.

Given RH’s appreciation of visual stimulation during his toddler years, RH’s mother reported introducing him to his first computer game when he was 19 months old. RH’s parents purchased a child-sized trackball, which used a 4” wide surface and a slow tracking speed. With the child-sized trackball, RH needed to move only his arm, rather than more fine-grained movements of the wrist or fingers, to control the cursor’s movement. RH experienced great success with the computer game; RH’s parents report that adults who observed him playing this computer-based game would “stand in awe.” One game involved a computerized version of a form board for which the child needed to bring the cursor to a puzzle piece and then drag the puzzle piece to the appropriate outline shape. Although at this time RH was completely unsuccessful at putting together even the simplest of physical form boards, he mastered the computerized version instantaneously. Another game was akin to a child’s version of a conceptual slot machine. The goal was to click through several different options to select three of a kind. RH mastered that game without any adult guidance. RH’s expertise with computer games kept his mother from assuming that his lack of speech was due primarily to cognitive limitations.

RH’s stranger anxiety remained during his second year of life, although by all formal measures (e.g., Ainsworth, Blehar, Waters, & Wall, 1978 ) and informal assessments, RH maintained a secure attachment with his primary care provider. RH’s mother notes that he was “less likely to make eye contact” with novel adults than other children his age and that he rarely oriented when his name was called (i.e., made the controlled movement to look up and orient to the direction from which the person was calling). RH’s mother reported (and historic home video demonstrates) that RH was unable to follow with controlled vision a directional prompt, such as an adult pointing an index finger to a distal or even proximal stimulus. Thus, RH appeared to lack the traditional markers of receiving joint attention ( Tomasello & Farrar, 1986 ).

After reviewing family photos and historic video tapes, RH’s mother observed, in retrospect, that RH must have had extreme tactile sensitivity on the palms of his hands and in and around his mouth. In many photographs he was shown using fisted hands to grab seemingly innocuous objects, such as a soft, rubber therapy ball. In many contexts, he appeared highly reluctant to use his hands for exploration (such as with novel toys and novel food). In one family photograph he was shown retching after being encouraged to touch a “koosh” ball. RH’s mother reported that he was at this point in development highly resistant to having this teeth brushed, wearing hats and gloves (even in the winter), tasting novel foods, and trying on new shoes.

At RH’s 18 month ‘well baby’ visit, RH’s mother expressed concern to the pediatrician about RH’s speech delay. Records indicate that RH’s mother’s concern was not because she and her child could not communicate quite effectively, or that he was unable to communicate with others, but because comparing his expressive language development with typical milestones indicated a delay. RH’s pediatrician recommended an audiology examination, the first of which was conducted when RH was 19 months. According to records, the first behavioral audiology exam was completely unsuccessful with RH failing to alert to any of the auditory probes. Another behavioral audiology examination conducted at 20 months indicated that RH alerted slightly to one or two of the auditory probes; however, the test was far from conclusive. A third behavioral audiology examination conducted at 21 months was equally inconclusive with the exception of RH orienting rather strikingly to the audio track of a Barney videotape, which his mother had brought to the examination and which was presented auditorily at the conclusion of the examination.

At 22 months RH was evaluated via Brain Stem Auditory Evoked Response (also known as Auditory Brainstem Response), while RH was sedated as an outpatient at a hospital. The evaluation indicated no evidence of abnormal neurologic conduction through the brainstem auditory pathways. At 23 months, RH was evaluated by a multi-disciplinary team at a national clinic for developmental disabilities. With the exception of the Bayley Scales of Infant Development ( Bayley, 1969 ), very few standardized tests could be administered, and even the Bayley was an approximation. A highly experienced developmental pediatrician observed and interacted with RH and his mother during a two-hour session. The result of the multi-hour evaluation was a diagnosis of pervasive developmental disorder.

Following this evaluation and diagnosis, RH was enrolled in occupational therapy and speech/language therapy. RH’s parents used as a guide to their interactions the “Communicating Partners” curriculum (e.g., MacDonald, 1987 ). They reported placing great emphasis on following their child’s lead, reciprocating his interaction, enhancing his strengths, encouraging all of his efforts toward communication (even those assumed by other programs to be ‘unconventional’ or ‘inappropriate’), and sharing mutual affect. In addition, RH began attending an integrated toddler program for two hours a day during the week. A speech-language therapist and occupational therapist were assigned to RH at the integrated toddler program, in addition to those professionals whom he saw in the community; however, after a few sessions with the occupational therapist assigned by the toddler program the parents declined her further services because she used ‘pull out’ sessions with tasks that were too frustrating for RH.

The speech-language therapist at the toddler program suggested developing sign-language, a decision, which in retrospect for RH’s mother, seemed ill-conceived. RH’s fine motor control was not developed well enough to promote even the simplest of signs. Nonetheless, the speech-language therapist worked for eight weeks with RH on the ASL sign for “more.” When RH was unable to produce this sign independently after eight weeks, it was suggested to RH’s mother that RH lacked the symbolic understanding needed for “developing language.” RH’s mother reported that she disagreed strongly with this assessment and asked the staff if they had any evidence that RH was able to produce the component motor plans for the sign (e.g., bring hands to the midline). They did not (e.g., RH had never clapped).

RH’s mother later wrote in her journal the following entry related to this topic.

What a bias we as a society have against children who can’t talk. This week RH was transitioning to a different classroom with different teachers in a different building at a different time of day. Before he left the house on Monday morning I asked RH if he wanted to take something special with him to school to serve as a transitional object, though I didn’t use that term. RH chose two small dolls: one of his buddy, Bert, and the other of his buddy, Ernie [characters from Sesame Street]. As it turns out the teachers took the dolls away from RH, shortly after RH’s father left for the day, because the dolls were “commercial.” After looking around for them for 10 or so minutes, RH went to the art table and picked up two markers: one yellow and one orange. Because he then carried these two markers around with him the rest of the morning, always setting them down when he was playing with something else, but making sure that they remained with him, I was told on Tuesday during the first parent-teacher conference of the term, that we already had a problem. When I asked what the problem was with carrying around two markers, not even knowing the colors or the fact that the teachers had taken RH’s dolls away, I was told that the behavior was ‘weird.’ Had RH been able to muster even just a “ehhee” or “buh buh” as he made the markers dance in his lap during music time, the teachers most likely would have figured out that RH was demonstrating the highest level of representational play ( Ungerer & Sigman, 1981 ).

Preschool Age Development

According to RH’s mother, during RH’s preschool years he remained a delightful child, whose mood was almost always “off the charts” in positive affect. He sometimes seemed other worldly and frequently marched to his own drummer; however, he remained affectionate and engaging with persons he knew well, including his immediate family, his other care providers, and the speech-language and occupational therapists in the community with whom he worked after leaving the toddler program. He remained physically active, and he frequently sought out opportunities for proprioceptive feedback (such as jumping on beds and trampolines). According to RH’s speech therapist, it was primarily while jumping on a trampoline that RH was able (during much of his preschool age years) to produce the phonation required for any vocalization, which remained quite primitive during this time.

RH developed a relationship with a surrogate sister, a neighbor, who was three years older than he, and with whom he spent one full day a week during the summer and occasional days during the academic year. For over a year, when RH was 5 years old, he had a same-aged best friend (DW), a typically developing boy with whom RH played one-on-one for about six or more hours a week, always with support. By all observable measures, DW enjoyed RH’s company as much as RH enjoyed his. RH taught DW as much about sand physics, water physics, and weather stripping, which was one of RH’s fascinations during that period of his life, as DW taught RH about more typical 5-year old boy interests, such as water gun fights and rough housing. RH’s and DW’s very close bond of friendship appeared to require little speech. Unfortunately, according to RH’s mother, the relationship ended abruptly the day that DW—with no malevolence or seeming premeditation—suggested to RH when they were dividing up who would play what that RH play a particular character, because—like RH—that character “would never talk.” RH appeared to be immediately heart broken and despondent, and the bond was never reparable.

RH typically avoided all mutual eye contact with strangers, although for a short period RH adopted the habit of squinting after he made brief eye contact with novel people. RH’s fine motor skills remained severely impaired, including the bimanual coordination needed for sign language and conventional gestures, as was his eye-hand coordination. Because RH’s manual motor skills were so severely challenged that he struggled to produce common gestures and conventional sign language, RH appeared to create his own gesture system, which drew on motions that he could perform. According to his mother and his speech therapist, RH had a repertoire of a dozen frequently used idiosyncratic gestures and was sometimes able to spontaneously generate novel gestures, which were typically iconic of motions or spatial relations about which RH was attempting to communicate. All gestures at this point in RH’s development were produced bimanually. RH’s mother reported that most persons not familiar with RH’s gesture system interpreted his movements as being repetitive or erratic.

RH was unable to volitionally produce facial expressions, but his repertoire of spontaneous facial expressions was moderately sized. All of RH’s vocalizations at this point in development were primarily vocalic; his consonant repertoire was limited to /m/ and occasionally /b/. Many of RH’s vocalizations were produced in what his mother referred to as “squeal mode;” however, audio tape analysis demonstrated that many of these “squeals” carried the intonation of well-formed utterances. For example, during one session with his speech therapist, RH vocalized the intonational pattern of “I’m not yawning,” after his speech therapist teased him about looking a bit tired. As with RH’s facial expressions and manual gestures, RH’s vocal expressions were all spontaneous (i.e., he was unable to produce vocalizations on command or in volitional imitation).

Grade-school Age Development

When RH was 5;5, his mother watched a British Broadcast Company documentary (BBC, 2001) about an Indian mother and son who had worked together to enable the son, minimally verbal, to develop handwriting as a communication medium; RH’s mother then had the opportunity to visit with the mother and son in the United States ( Mukhopadhyay, 2000 ). Although RH’s mother was unwilling to go to the extreme measures that the Indian mother had used with her son, RH’s mother was very motivated to explore the possibilities of RH using even a gross style of handwriting for augmentative communication. Realizing that RH had less control over the smaller muscles (such as those used during typical-sized handwriting) than he did for larger muscle groups, RH’s mother designed a system so that RH could begin by using larger muscles, such as his shoulder girdle. She placed large sheets of easel sized pages on the wall at RH’s shoulder height, and RH practiced marking (with a slash) using a wide felt-tip marker in large, designated regions. RH began with considerable physical support (hand-over-hand), which was slowly faded over the course of several weeks.

Once RH mastered the ability to mark independently within a several inch region of a designated target on the large easel-sized paper, RH was able to use this gross style of handwriting to demonstrate his literacy. For example, one of the first exercises accomplished by RH is shown in Figure 1 ; the goal was to mark through words in a list for which the vowel digraph ‘oo’ was pronounced /u/ as in “tooth.” RH’s success on this task demonstrated not only his self-taught literacy, but also his finely tuned phonemic awareness. Another task required identifying the correct verb tense, as shown in Figure 2 , and another, as shown in Figure 3 , required identifying the correct contraction (and verb tense). RH’s mother reported being a bit surprised to observe RH’s knowledge of prefixes and suffixes, as shown in Figure 4 , in which only one prefix or suffix fits each stem word. RH scored perfectly on each of these activities and many more, all taken from a 3 rd grade Language Arts workbook and all completed during the first week after RH mastered a marker, when RH was 5;10.

Handwriting by RH’s mother with over marks by RH.

Upon RH’s mastery of using a marker, this ability was used as a communication medium. For example, RH’s mother reported that if RH woke up with a fever, she would make a list of body parts that possibly could be in pain (e.g., head, throat, ears). For presumed “yes” or “no” statements RH’s mother originally offered only a “yes” and “no” response placed beneath the statement (e.g., “I am hungry. YES NO”). However, RH began sometimes to mark through both answer choices, as shown in Figure 5 , and another time RH marked through both answer choices—and made a marking in between, as shown in Figure 6 . Then he made only the marking in between the two answer choices, as shown in Figure 7 , and RH’s mother reported finally understanding his intention: RH wanted a maybe option, which he used in many politic situations, as shown in Figure 8 . RH’s mother reported that the “maybe” option was quite useful; she recounts an incident in which she was about to become angry at RH for pouring out a container of water she had asked him not to pour, and prior to scolding RH, she decided to find out if rather than RH doing this forbidden task on purpose, it was an accident. His answer was “maybe.”

Handwriting by RH’s mother with over marks by RH

RH continued to use this gross style of handwriting (i.e., marking through options) as a medium of communication for over two years. During that time, RH was able to scale down from the easel-sized pages to more standard 8.5×11 typing paper. When paper was not available (or necessary for recording academic work), RH used a magnetic writing toy (e.g., “magnadoodle”), which he carried in his backpack. RH was able to communicate about wants and needs, and to have extended conversations about abstract and complex topics, such as religion, death, and the societal versus medical definition of disability.

Only a few months after RH mastered holding a marker, he was administered the state-wide assessment of academic skills for fourth grade. This standardized multiple-choice test assessed skill in writing, mathematics, and reading, using a multiple-choice format. With the only modification being spacing the answer choices about four inches apart, rather than the mere millimeters that typically separate bubbles on computer-scored answer sheets, RH scored perfectly on the 150-item standardized test. A month later, he scored perfectly on the state-wide assessment for fifth grade. He was 5; 11.

When RH was 6; 4, he was tested on the Peabody Picture Vocabulary Test ( Dunn & Dunn, 1997 ), a commonly used verbal IQ test. RH would have been untestable with the standard requirement to point to the correct picture, because he still did not have a reliable proximal (or distal) point at that age; however, the picture plates were scanned into a computer, and RH was allowed to use his large, child-sized trackball to scroll to the correct answer. RH achieved a raw score of 181, which translated to a standardized score of 160, at the 99.9 th percentile, with an age equivalence of 22 years. Similarly, RH’s performance on the standardized Test of Receptive Grammar ( Bishop, 1983 ), in which children select the picture that best represents the sentence, and the sentences vary in their grammatical complexity, was at the 95 th percentile. At this point, RH’s mean length of utterance (commonly known as MLU) was 1.5, and the intelligibility of his utterances was less than 20% to familiar listeners. When RH was 7; 5, he developed a reliable index finger point. His mother reported that this development was a highly celebrated accomplishment; it followed development of his trunk strength and stability, coincident with development of his shoulder girdle strength and stability, and involved a range of finger motility and isolation that RH had not been able to achieve before. Indeed, the isolated index finger point was RH’s first uni-manual—as opposed to bimanual—gesture. With his newly developed ability to point, RH and his mother considered whether RH might be aided by using a keyboard type augmentative communication device. They had considered a keyboard type augmentative device three years earlier, before they began the modified handwriting, but RH’s body was not ready to support an index finger point at that time.

RH’s mother began with the same strategy that she had used with the modified handwriting, namely, starting with an ample-sized target so that RH could use larger muscle groups while practicing to use smaller muscle groups. Thus, she produced a cardboard replica of a QWERTY keyboard with .5 inch-high letters spaced 1.5 inches apart horizontally and vertically. RH began with physical support at the wrist while seated in a person’s lap (for further proprioceptive input and support). The wrist support was faded to support at the elbow, and the lap support was faded to sitting beside the person providing support. The elbow support was then faded to a light touch on the shoulder, and then physical support was faded completely. With RH’s approval, the size of the keyboard replica was reduced two additional times, with the last adjustment approximating the size of a standard computer keyboard. RH and his mother reported appreciating the cardboard keyboard (i.e., the keyboard replica) because it was more portable and more durable; for example, it could be used when RH was in various physical positions, rather than seated with the keyboard on a flat desk-like surface.

As with the gross style of handwriting, which RH had mastered a couple of years earlier, RH also used the modified typing (i.e., index finger pointing to letters on the replica keyboard) both for academic work and for general communication (including email and postal mail, which was RH recorded manually by an observer while RH composed on the replica keyboard and then transcribed to other media). The modified typing demonstrated that RH’s language skills included highly advanced expressive language, in addition to his previously demonstrated highly advanced receptive language. The modified typing also demonstrated how naturally and fluently RH could converse interpersonally when the output did not require vocalization.

For example, RH’s mother wrote the following entry in her journal.

Just a week after RH began typing, we had the following exchange. The context was that we were talking about how mad it was making me that RH was at risk of ruining some of his videos because he wanted to flip the “lip” of the video off to see the actual tape (the thing I am calling the lip is the part that the video player does lift up, but people are not supposed to). RH had already broken two videos by too energetically lifting the lip up to see the tape. So I was having a pretty motherly moment in nagging him to not do this anymore. Actually I was telling him in no uncertain terms that I wouldn’t do it for him because that’s his clever way of getting something done if he knows he’ll get into trouble for doing it—he coaxes someone else into doing it for him. So I was pretty steamed about this . RH typed, “BUT THEY ARE MINE.” I replied (in speech), “Yes, I know that they are yours, but I’m the one who spends my time and my money buying them.” A note here is that RH’s video collection, which is quite extensive, is also quite esoteric. I have to really search far and wide for each one; it’s not like going to ToysRUs and picking up what every other child is watching that week . RH rebutted by typing, “BUT THEY CAN BE REPLACED,” to which I replied, “Yes, I know they can be replaced, but that’s more of my time and my money to replace them when I don’t like your doing it in the first place.” Realizing that I wasn’t getting very far with my reasoning, I decided to try an analogy. I asked RH if he remembered the beautiful diamond earrings that he and his father had bought me for my birthday, and he typed, “YES.” Then I asked him how it would be if I just flushed those diamond earrings down the toilet because, after all, they are mine and they can be replaced, so how would it be? RH typed, “LAMENTABLE.” At this point I was laughing too hard to be mad. And I confess I didn’t really know that lamentable was a word until I looked it up later that night in a dictionary . Later that night I was telling RH that it was definitely time for him to calm down and start trying to fall asleep, but he was still being a bit too animated. I had reminded him several times to calm down. Then I asked him, “Do you know why it’s now time to start calming down and trying to fall asleep?” RH typed, “BECAUSE I AM JUST ABOUT TO PISS YOU OFF.” So I then asked, “Do you want to piss me off?” And RH typed, “BETTER YOU THAN ME.” RH’s use of the slang term, “piss off” prompted a discussion the next morning of slang and curse words, the bottom line of which is that I learned that RH was highly knowledgeable of an entire lexicon of slang and curse words. Indeed, his lexicon surpassed mine. He was also fully cognizant of which words were more slang-like compared with which words were downright verboten in formal company, and he could scale between those two extremes. I found this compelling, because my naïve conception was that children learn which words are taboo and how taboo they are by producing them—often without accurate knowledge of their full taboo status—and being reprimanded. At least that’s how I remembered learning where on that sliding scale a few verboten words resided according to my own parents. However, RH had not only acquired an extensive vocabulary of slang and curse words, as he had with non-slang/curse words, he had extrapolated from what was likely very rare instances of each word’s occurrence to know the word’s shock value.

In addition to using modified typing for direct communication, such as conversations and email, RH also used modified typing for creative expression. At 7; 11, he completed a book of 30 poems. The first poem he typed was the following:

When winter comes, And snow has fallen, Trees are barren no more. Find me at your door.

RH also used modified typing to clarify the words he articulated with his speech. An audio recording contained a repeated production of the utterance /KOO ki ki/ (‘COO key key’), which RH’s mother reported was produced while RH was playing with one of his troll dolls (referred to by his family as a “trollie,” pronounced /troli/, rhyming with “holy”). RH typed that the target for this utterance, /KOO ki ki/ (‘COO key key’), was “cool trollie.” Another audio recording contained the production, /ga GA ga ga KI k^l/ (‘gah GAH gah gah KEY kuhl’), which RH translated through typing to be “[I] got the one that’s critical” (said in response to his mother asking if he wanted her to print out any more photographs after she had printed what seemed to be his one favorite). As a final example, an audio recording contained the production, /æ æ æ I i/ (‘aa aa aa EE ee), for which the target utterance, revealed through RH’s typing, was “that one is so neat.”

RH’s mother reported that RH’s ability to type also facilitated other people’s understanding of some of his other “atypical” behaviors. For example, even though through much of his toddler and preschool years, RH greatly enjoyed placing items and objects in linear arrangements (typically by color wavelength or other dimensions of importance to him), during his eighth year of life, he enjoyed making large piles or “nests” of favored possessions (such as CD insets, DVD covers, video cases, and books). As his mother reported, this free-flowing style was completely at odds with her own penchant for neatness and order. Thus, one morning when his mother was approaching one of his larger “nests,” she began uttering, “you know, RH, what about …” She reported not getting any further in articulating her question when RH began giggling. To an outsider, one might think that RH was simply emitting some random outburst of laughter. However, when asked by his mother the basis of his laughter, RH typed, “I THINK IT’S FUNNY HOW YOU’RE NOW TRYING TO THINK OF WAYS TO ORGANIZE MY STUFF. GIVE IT UP MOM. IT’S FUTILE.”

Finally, RH’s modified typing provided a mechanism for him to share insights to the origin of his severe speech impairment. For example, when RH was 7;7 and his mother suggested that he try some oral motor imitation exercises, the following conversation ensued (with RH’s contributions being through modified typing and his mother’s, signified by “M” through her speech):

M: How about we try some imitation?

RH: [looks at his mother quizzically]

M: You know what imitation is, right?

RH: YES, IT IS THE HIGHEST FORM OF FLATTERY.

M: Funny. No, seriously, how would you define imitation?

RH: PURPOSEFULLY MIMICKING ANOTHER PERSON’S GESTURES OR BEHAVIORS.

M: Right. So, let’s try some.

RH: BUT IT MAKES ME SAD.

RH: BECAUSE IT’S SO HARD FOR ME TO DO. I CAN BARELY DO IT.

At another point, also during his attempts at oral motor exercises, RH expressed the following frustration, through typing: IT’S AS THOUGH MY MOUTH HAS A MIND OF ITS OWN.

Conclusions

Over 20 years ago, a document prepared for the federal Office of Technology Assessment, stated that “people of all levels of intelligence are found in the population with the inability to speak, which is one of several neurological or neuromuscular impairments. But, only rarely have distinctions been drawn between those incapable of thinking or comprehending and those who simply cannot express themselves. Lack of speech has been confused with lack of language and often been automatically equated with lack of intelligence” ( OTA, 1983 ). The case study presented in this article has presented a profile of an individual whose struggle with speech should neither be confused with a lack of a language nor be equated with a lack of intelligence. Moreover, this case study has identified other challenges to well-accepted equations such as that between traditional manifestations of joint attention (e.g., pointing and following a point) and language development.

RH is clearly not the first individual to demonstrate the folly of equating language with speech. Others in the lay autism literature (e.g., Blackman, 2001 ; Eastham & Eastham, 1990 ) have done so before him, and it is very likely that others will continue to do so. These individuals and their lives demand distinguishing between language—the mental representation of concepts and their relations—and speech—one means for communicating mental representations.

• Ainsworth MDS, Blehar MC, Waters E, Wall S. Patterns of attachment: A study of the strange situation. Hillsdale, NJ: Erlbaum; 1978. [ Google Scholar ]
• Bayley N. Bayley Scales of Infant Development. Second Edition. San Antonio, TX: Psychcorp; 1969. [ Google Scholar ]
• Bishop DVM. Test of Reception of Grammar (TROG) University of Cambridge, England: Medical Research Council Applied Psychology Unit; 1983. [ Google Scholar ]
• Blackman L. Lucy’s story: Autism and other adventures. London: Jessica Kingsley Press; 2001. [ Google Scholar ]
• British Broadcasting Company. Inside story: Tito’s story. 2000. May, [ Google Scholar ]
• Dunn LM, Dunn LA. Peabody Picture Vocabulary Test—Third Edition. Circle Pines, MN: American Guidance Service; 1997. [ Google Scholar ]
• Eastham D. Understand: Fifty memowriter poems. Ottawa: Oliver-Pate; 1985. [ Google Scholar ]
• Eastham M, David E. Silent words - Forever friends. Ottawa: Oliver-Pate; 1990. [ Google Scholar ]
• Hammes JG, Langdell T. Precursors of symbol formation and childhood autism. Journal of Autism and Developmental Disorders. 1981; 11 :331–346. [ PubMed ] [ Google Scholar ]
• Health Technology Case Study 26: Assistive Devices for Severe Speech Impairments. Washington, DC: U.S. Congress, Office of Technology Assessment. OTA-HCS-26; 1983. [ Google Scholar ]
• MacDonald JD. Before your child talks: Practical guides for parents and professionals. Columbus, OH: Communicating Partners Center; 1997. [ Google Scholar ]
• Mukhopadhyay TR. Beyond the silence: My life, the world and autism. London: NAS Publications; 2000. [ Google Scholar ]
• Schein JD, Stewart DA. Language in motion: Exploring the nature of sign. Washington, DC: Gallaudet University Press; 1995. [ Google Scholar ]
• Smith LB, Thelen E, Titzer R, McLin D. Knowing in the context of acting: The task dynamics of the A-not-B error. Psychological Review. 1999; 106 :235–260. [ PubMed ] [ Google Scholar ]
• Tomasello M, Farrar MJ. Joint attention and early language. Child Development. 1986; 57 :1454–1463. [ PubMed ] [ Google Scholar ]
• Ungerer JA, Sigman M. Symbolic play and language comprehension in autistic children. Journal of The Academy of Child Psychiatry. 1981; 20 :318–337. [ PubMed ] [ Google Scholar ]

ORIGINAL RESEARCH article

This article is part of the research topic.

Integration-Focused Approaches of Educational Systems Across the EU

Navigating the Peer-to-Peer Workflow in Non-Formal Education Through an Innovative E-learning Platform: A Case Study of the KIDS4ALLL Educational Project in Hungary and Italy Provisionally Accepted

• 1 University of Turin, Italy
• 2 TÁRKI Social Research Institute, Hungary

The final, formatted version of the article will be published soon.

The digital revolution is affecting all aspects of life, radically transforming everyday tasks and routines. The ability to cope with new challenges in life, including new forms of learning are key skills in the 21st-century, however, education systems often struggle with tackling digital inequalities. A digital learning platform developed by the KIDS4ALLL educational project, implemented in face-to-face student interactions, aims to mitigate the divide and the resulting social disadvantages among children with and without migration/ethnic minority background. Analysing data collected during the pilot phase of the project in two of the participating countries, Italy and Hungary, this paper examines how students and teaching staff adapt to a newly introduced digital learning tool based on peer-to-peer workflows. Firstly, it examines the role of educators' interpersonal competences in navigating the innovative learning activities and delves into how they use them and how they manage resources. Secondly, the study explores what attitudes and behaviours are observed among students engaged in the proposed peer-led activities, in particular in terms of their ability to cope with uncertainty and complexity. The analytical framework of the paper is based on two cultural dimensions offered by Hofstede (2001), the index of uncertainty avoidance (UAI) and power distance (PDI), and it utilizes the personal, social and learning-to-learn competence of the 8 LLL Key Competences as defined by the European Commission to conceptualize the skills of educators and students. Interpreting data from Italy and Hungary in their respective social and educational contexts, the study finds that the most important features that proved to be effective and useful during the pilot phase were the democratic power-relations between students and educators, the peer-to-peer scheme and its further development to the peer-for-peer approach. The child-friendly and real-life-related new curriculum and its appealing digital learning platform, embedded into a flexible, playful and child-centred pedagogical approach, were also successful. These are all complementing the traditional, formal school environment and pedagogy which, despite all developments in formal education in the past decades, can be characterized as teacher-centred and frontal.

Keywords: peer-to-peer learning, Educational inclusion, LLL Key competences, uncertainty avoidance, Power distance

Received: 10 Jan 2024; Accepted: 08 Apr 2024.

Copyright: © 2024 Schroot, Lőrincz and Bernát. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Tanja Schroot, University of Turin, Turin, Italy

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