benefits of qualitative research in education

Qualitative research in education : Background information

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Educational Research Basics by Del Siegle

Qualitative research.

Although researchers in anthropology and sociology have used the approach known as qualitative research  for a century, the term was not used in the social sciences until the late 1960s. The term qualitative research is used as an umbrella term to refer to several research strategies. Five common types of qualitative research are grounded theory , ethnographic , narrative research , case studies , and phenomenology.

It is unfair to judge qualitative research by a quantitative research paradigm, just as it is unfair to judge quantitative research from the qualitative research paradigm .

“Qualitative researchers seek to make sense of personal stories and the ways in which they intersect” (Glesne & Peshkin, 1992). As one qualitative researcher noted, “I knew that I was not at home in the world of numbers long before I realized that I was at home in the world of words.”

The data collected in qualitative research has been termed “soft”, “that is, rich in description of people, places, and conversations, and not easily handled by statistical procedures.” Researchers do not approach their research with specific questions to answer or hypotheses to test. They are concerned with understanding behavior from the subject’s own frame of reference. Qualitative researcher believe that “multiple ways of interpreting experiences are available to each of us through interacting with others, and that it is the meaning of our experiences that constitutes reality. Reality, consequently,  is ‘socially constructed'” (Bogdan & Biklen, 1992).

Data is usually collected through sustained contact with people in the settings where they normally spend their time. Participant observations and in-depth interviewing are the two most common ways to collect data. “The researcher enters the world of the people he or she plans to study, gets to know, be known, and trusted by them, and systematically keeps a detailed written record of what is heard and observed. This material is supplemented by other data such as [artifacts], school memos and records, newspaper articles, and photographs” (Bogdan & Biklen, 1992).

Rather than test theories, qualitative researchers often inductively analyze their data and develop theories through a process that Strauss called ” developing grounded theory “. They use purposive sampling to select the people they study. Subjects are selected because of who they are and what they know, rather than by chance.

Some key terms:

Access to a group is often made possible by a gate keeper . The gate keeper is the person who helps you gain access to the people you wish to study. In a school setting it might be a principal.

Most qualitative studies involve at least one key informant . The key informant knows the inside scoop and can point you to other people who have valuable information. The “key informant” is not necessarily the same as the gate keeper. A custodian might be a good key informant to understanding faculty interactions. The process of one subject recommending that you talk with another subject is called “ snowballing .”

Qualitative researchers use rich-thick description when they write their research reports. Unlike quantitative research where the researcher wished to generalize his or her findings beyond the sample from whom the data was drawn, qualitative researcher provide rich-thick descriptions for their readers and let their readers determine if the situation described in the qualitative study applies to the reader’s situation. Qualitative researchers do not use the terms validity and reliability. Instead they are concerned about the trustworthiness of their research.

Qualitative researchers often begin their interviews with grand tour questions . Grand tour questions are open ended questions that allow the interviewee to set the direction of the interview. The interviewer then follows the leads that the interviewee provides. The interviewer can always return to his or her preplanned interview questions after the leads have been followed.

Qualitative researchers continue to collect data until they reach a point of data saturation . Data saturation occurs when the researcher is no longer hearing or seeing new information. Unlike quantitative researchers who wait until the end of the study to analyze their data, qualitative researcher analyze their data throughout their study.

Note:   It is beyond the scope of this course to provide an extensive overview of qualitative research. Our purpose is to make you aware of this research option, and hopefully help you develop an appreciation of it. Qualitative research has become a popular research procedure in education.

Del Siegle, PhD [email protected] www.delsiegle.info

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on September 5, 2024.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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The qualitative orientation in medical education research

Qualitative research is very important in educational research as it addresses the “how” and “why” research questions and enables deeper understanding of experiences, phenomena and context. Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience. Getting at the everyday realities of some social phenomenon and studying important questions as they are really practiced helps extend knowledge and understanding. To do so, you need to understand the philosophical stance of qualitative research and work from this to develop the research question, study design, data collection methods and data analysis. In this article, I provide an overview of the assumptions underlying qualitative research and the role of the researcher in the qualitative process. I then go on to discuss the type of research objectives which are common in qualitative research, then introduce the main qualitative designs, data collection tools, and finally the basics of qualitative analysis. I introduce the criteria by which you can judge the quality of qualitative research. Many classic references are cited in this article, and I urge you to seek out some of these further reading to inform your qualitative research program.

Introduction

When we speak of “quantitative” or “qualitative” methodologies, we are in the final analysis speaking about an interrelated set of assumptions about the social world which are philosophical, ideological, and epistemological. They encompass more than just data collection methodologies [ 1 ].

It is easy to assume that the differences between quantitative and qualitative research are solely about how data is collected—the randomized controlled trial versus ethnographic fieldwork, the cohort study versus the semi-structured interview. However, quantitative and qualitative approaches make different assumptions about the world [ 2 ], about how science should be conducted, and about what constitutes legitimate problems, solutions and criteria of “proof” [ 3 ].

Why is it important to understand differences in assumptions, or philosophies, of research? Why not just go ahead and do a survey or carry out some interviews? First, the assumptions behind the research tools you choose provide guidance for conducting your research. They indicate whether you should be an objective observer or whether you have a contributory role in the research process. They guide whether or not you must slavishly ask each person in a study the same questions or whether your questions can evolve as the study progresses. Second, you may wish to submit your work as a dissertation or as a research paper to be considered for publication in a journal. If so, the chances are that examiners, editors, and reviewers might have knowledge of different research philosophies from yours and may be unwilling to accept the legitimacy of your approach unless you can make its assumptions clear. Third, each research paradigm has its own norms and standards, its accepted ways of doing things. You need to “do things right”. Finally, understanding the theoretical assumptions of the research approach helps you recognize what the data collection and analysis methods you are working with do well and what they do less well, and lets you design your research to take full advantage of their strengths and compensate for their weaknesses.

In this short article, I will introduce the assumptions of qualitative research and their implications for research questions, study design, methods and tools, and analysis and interpretation. Readers who wish a comparison between qualitative and quantitative approaches may find Cleland [ 4 ] useful.

Ontology and epistemology

We start with a consideration of the ontology (assumptions about the nature of reality) and epistemology (assumptions about the nature of knowledge) of qualitative research.

Qualitative research approaches are used to understand everyday human experience in all its complexity and in all its natural settings [ 5 ]. To do this, qualitative research conforms to notions that reality is socially constructed and that inquiry is unavoidably value-laden [ 6 ]. The first of these, reality is socially constructed, means reality cannot be measured directly—it exists as perceived by people and by the observer. In other words, reality is relative and multiple, perceived through socially constructed and subjective interpretations [ 7 ]. For example, what I see as an exciting event may be seen as a threat by other people. What is considered a cultural ritual in my country may be thought of as quite bizarre elsewhere. Qualitative research is concerned with how the social world is interpreted, understood, experienced, or constructed. Mann and MacLeod [ 8 ] provide a very good overview of social constructivism which is a excellent starting point for understanding this.

The idea of people seeing things in diverse ways also holds true in research process, hence inquiry being valued-laden. Different people have different views of the same thing depending on their upbringing and other experiences, their training, and professional background. Someone who has been trained as a social scientist may “see” things differently from someone who has been medically trained. A woman may see things differently to a man. A more experienced researcher will see things differently from a novice. A qualitative researcher will have very different views of the nature of “evidence” than a quantitative researcher. All these viewpoints are valid. Moreover, different researchers can study the same topic and try to find solutions to the same challenges using different study designs—and hence come up with different interpretations and different recommendations. For example, if your position is that learning is about individual, cognitive, and acquisitive processes, then you are likely to research the use of simulation training in surgery in terms of the effectiveness and efficacy of training related to mastery of technical skills [ 9 , 10 ]. However, if your stance is that learning is inherently a social activity, one which involves interactions between people or groups of people, then you will look to see how the relationships between faculty members, participants and activities during a simulation, and the wider social and cultural context, influence learning [ 11 , 12 ].

Whether researchers are explicit about it or not, ontological and epistemological assumptions will underpin how they study aspects of teaching and learning. Differences in these assumptions shape not only study design, but also what emerges as data, how this data can be analysed and even the conclusions that can be drawn and recommendations that can be made from the study. This is referred to as worldview, defined by Creswell [ 13 ] as “a general orientation about the world and the nature of research that a researcher holds.” McMillan [ 14 ] gives a very good explanation of the importance of this phenomenon in relation to medical education research. There is increasing expectation that researchers make their worldview explicit in research papers.

The research objective

Given the underlying premise that reality is socially constructed, qualitative research focuses on answering “how” and “why” questions, of understanding a phenomena or a context. For example, “Our study aimed to answer the research question: why do assessors fail to report underperformance in medical students? [ 15 ]”, “The aim of this work was to investigate how widening participation policy is translated and interpreted for implementation at the level of the individual medical school [ 4 ].”

Common verbs in qualitative research questions are identify, explore, describe, understand, and explain. If your research question includes words like test or measure or compare in your objectives, these are more appropriate for quantitative methods, as they are better suited to these types of aims. Bezuidenhout and van Schalkwyk [ 16 ] provide a good guide to developing and refining your research question. Lingard [ 17 ]’s notion of joining the conversation and the problem-gap-hook heuristic are also very useful in terms of thinking about your question and setting it out in the introduction to a paper in such a way as to interest journal editors and readers.

Do not think formulating a research question is easy. Maxwell [ 18 ] gives a good overview of some of the potential issues including being too general, making assumptions about the nature of the issue/problem and using questions which focus the study on difference rather than process. Developing relevant, focused, answerable research questions takes time and generating good questions requires that you pay attention not just to the questions themselves but to their connections with all the other components of the study (the conceptual lens/theory, the methods) [ 18 ].

Theory can be applied to qualitative studies at different times during the research process, from the selection of the research phenomenon to the write-up of the results. The application of theory at different points can be described as follows [ 19 , 20 , 21 ]: (1) Theory frames the study questions, develops the philosophical underpinnings of the study, and makes assumptions to justify or rationalize the methodological approach. (2) Qualitative investigations relate the target phenomenon to the theory. (3) Theory provides a comparative context or framework for data analysis and interpretation. (4) Theory provides triangulation of study findings.

Schwartz-Barcott et al. [ 20 ] characterized those processes as theoretical selectivity (the linking of selected concepts with existing theories), theoretical integration (the incorporation and testing of selected concepts within a particular theoretical perspective), and theory creation (the generation of relational statements and the development of a new theory). Thus, theory can be the outcome of the research project as well as the starting point [ 22 ].

However, the emerging qualitative researcher may wish a little more direction on how to use theory in practice. I direct you to two papers: Reeves et al. [ 23 ] and Bordage [ 24 ]. These authors clearly explain the utility of theory, or conceptual frameworks, in qualitative research, how theory can give researchers different “lenses” through which to look at complicated problems and social issues, focusing their attention on different aspects of the data and providing a framework within which to conduct their analysis. Bordage [ 24 ] states that “conceptual frameworks represent ways of thinking about a problem or a study, or ways of representing how complex things work the way they do. Different frameworks will emphasise different variables and outcomes.” He presents an example in his paper and illustrates how different lens highlight or emphasise different aspects of the data. Other authors suggest that two theories are potentially better than one in exploring complex social issues [ 25 ]. There is an example of this in one of my papers, where we used the theories of Bourdieu [ 26 ] and Engestrom [ 27 , 28 ] nested within an overarching framework of complexity theory [ 29 ] to help us understand learning at a surgical bootcamp. However, I suggest that for focused studies and emerging educational researchers, one theoretical framework or lens is probably sufficient.

So how to identify an appropriate theory, and when to use it? It is crucially important to read widely, to explore lots of theories, from disciplines such as (but not only) education, psychology, sociology, and economics, to see what theory is available and what may be suitable for your study. Carefully consider any theory, check its assumptions [ 30 ] are congruent with your approach, question, and context before final selection [ 31 ] before deciding which theory to use. The time you spend exploring theory will be time well spent in terms not just of interpreting a specific data set but also to broadening your knowledge. The second question, when to use it, depends on the nature of the study, but generally the use of theory in qualitative research tends to be inductive; that is, building explanations from the ground up, based on what is discovered. This typically means that theory is brought in at the analysis stage, as a lens to interpret data.

In the qualitative approach, the activities of collecting and analyzing data, developing and modifying theory, and elaborating or refocusing the research questions, are usually going on more or less simultaneously, each influencing all of the others for a useful model of qualitative research design [ 18 ]. The researcher may need to reconsider or modify any design decision during the study in response to new developments. In this way, qualitative research design is less linear than quantitative research, which is much more step-wise and fixed.

This is not the same as no structure or plan. Most qualitative projects are pre-structured at least in terms of the equivalent of a research protocol, setting out what you are doing (aims and objectives), why (why is this important), and how (theoretical underpinning, design, methods, and analysis). I have provided a brief overview of common approaches to qualitative research design below and direct you to the numerous excellent textbooks which go into this in more detail [ 32 , 33 , 34 , 35 ].

There are five basic categories of qualitative research design: ethnography, narrative, phenomenological, grounded theory, and case study [ 13 , 32 ].

2. Ethnography

In ethnography, you immerse yourself in the target participants’ environment to understand the goals, cultures, challenges, motivations, and themes that emerge. Ethnography has its roots in cultural anthropology where researchers immerse themselves within a culture, often for years. Through multiple data collection approaches—observations, interviews and documentary data, ethnographic research offers a qualitative approach with the potential to yield detailed and comprehensive accounts of different social phenomenon (actions, behavior, interactions, and beliefs). Rather than relying on interviews or surveys, you experience the environment first hand, and sometimes as a “participant observer” which gives opportunity to gather empirical insights into social practices which are normally “hidden” from the public gaze. Reeves et al. [ 36 ] give an excellent guide to ethnography in medical education which is essential reading if you are interested in using this approach.

3. Narrative

The narrative approach weaves together a sequence of events, usually from just one or two individuals to form a cohesive story. You conduct in-depth interviews, read documents, and look for themes; in other words, how does an individual story illustrate the larger life influences that created it. Often interviews are conducted over weeks, months, or even years, but the final narrative does not need to be in chronological order. Rather it can be presented as a story (or narrative) with themes, and can reconcile conflicting stories and highlight tensions and challenges which can be opportunities for innovation.

4. Phenomenology

Phenomenology is concerned with the study of experience from the perspective of the individual, “bracketing” taken-for-granted assumptions and usual ways of perceiving. Phenomenological approaches emphasise the importance of personal perspective and interpretation. As such they are powerful for understanding subjective experience, gaining insights into people’s motivations and actions, and cutting through the clutter of taken-for-granted assumptions and conventional wisdom.

Phenomenological approaches can be applied to single cases or to selected samples. A variety of methods can be used in phenomenologically-based research, including interviews, conversations, participant observation, action research, focus meetings, and analysis of personal texts. Beware though—phenomenological research generates a large quantity data for analysis.

The phenomenological approach is used in medical education research and there are some good articles which will familiarise you with this approach [ 37 , 38 ].

5. Grounded theory

Whereas a phenomenological study looks to describe the essence of an activity or event, grounded theory looks to provide an explanation or theory behind the events. Its main thrust is to generate theories regarding social phenomena: that is, to develop higher level understanding that is “grounded” in, or derived from, a systematic analysis of data [ 39 ]. Grounded theory is appropriate when the study of social interactions or experiences aims to explain a process, not to test or verify an existing theory. Rather, the theory emerges through a close and careful analysis of the data.

The key features of grounded theory are its iterative study design, theoretical (purposive) sampling, and cycles of simultaneous data collection and analysis, where analysis informs the next cycle of data collection. In keeping with this iterative design, the sample is not set at the outset but is selected purposefully as the analysis progresses; participants are chosen for their ability to confirm or challenge an emerging theory. As issues of interest are noted in the data, they are compared with other examples for similarities and differences.

Grounded theory was first proposed by Glaser and Strauss [ 40 ] in 1967 but since then there have been many interpretations of this approach, each with their own processes and norms [ 41 , 42 , 43 ].

Beware—grounded theory is often done very badly, and numerous studies are rejected by journals because they claim to use grounded theory but do not actually do so, or do so badly.

6. Case study

Researcher Yin [ 44 ] defines the case study research method as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used. The case study method enables a researcher to closely examine the data within a specific context—for example, in a small geographical area or a very limited number of individuals as the subjects of study. Case studies explore and investigate contemporary real-life phenomenon through detailed contextual analysis of a limited number of events or conditions, and their relationships. A case study involves a deep understanding through multiple types of data sources. For example, we used case study methodology recently to explore the nature of the clinical learning environment in a general surgical unit, and used both documents and interviews as data sources. Case studies can be explanatory, exploratory, or describing an event [ 44 ] and case study design can be very open or more structured [ 45 ]. Case studies are a useful approach where the focus is to explain the complexities of real life situations.

While the five methods generally use similar data collection techniques (observation, interviews, and reviewing text—see below), the purpose of the study differentiates them.

Data collection methods

The qualitative methods most commonly used for research purposes can be classified in three broad categories: (1) interviews (individual or group), (2) observation methods, and (3) document review.

The qualitative research interview seeks to describe and gain understanding of certain themes in the life world of the subjects. Interviews can be organised one-to-one or group (focus groups) depending on the topic under study, the cultural context, and the aims of the project. Observational data collection in qualitative research involves the detailed observation of people and events to learn about behaviors and interactions in natural settings [ 46 ]. Such study designs are useful when the study goal is to understand cultural aspects of a setting or phenomenon [ 47 ], when the situation of interest is hidden, (tacit), or when subjects in the setting appear to have notably different views to other groups. Written materials or documents such as institutional records, personal diaries, and historical public documents may also serve as a valuable source of secondary data, providing insight into the lives and experiences of the group under study. For example, in one of my recent studies we used document analysis to uncover the thinking behind the design of a new medical school, then carried out interviews with “users” of the new building to explore how the intentions of the planners played out in reality. However, this is only one way of incorporating document analysis into a study: see Bowen [ 48 ] for an excellent introduction to the purpose and practicalities of document review within qualitative research.

See Dicicco-Bloom and Crabtree [ 49 ] for a useful summary of the content and process of the qualitative research interview, Creswell [ 50 ] for further discussion of the many different approaches in qualitative research and their common characteristics.

1. Data management

Qualitative research may use some form of quantification, but statistical forms of analysis are not central [ 51 ]. Instead, qualitative data analysis aims to uncover emerging themes, patterns, concepts, insights, and understandings [ 52 ]. The data are allowed to “speak for themselves” by the emergence of conceptual categories and descriptive themes. Trying to squeeze narratives into boxes (like “0” and “1”) would result in the loss of contextualisation and narrative layering. The researcher must immerse themselves in the data in order to be able to see meaningful patterns and themes, making notes as they go through the processes of data collection and analysis, and then using these notes to guide the analysis strategy.

Qualitative data has to be managed before it can be analysed—you can generate a lot of data from just a few interviews or observations! You may want to use a specialist qualitative database to facilitate data management and analysis. NVivo is a well-known qualitative data analysis software package (note that qualitative software packages enable you to make and store notes, and explanations of your codes, so you do not need to juggle bits of paper and electronic data files). These and similar databases are available commercially (i.e., at a cost) and are used widely by universities. The choice of database may be dictated by the resources of your institution, your personal preference, and/or what technical support is available locally. However, if you do not have access to qualitative data management software, then use paper and pencil: read and re-read transcripts, take notes on specifics and the bigger patterns, and label different themes with different coloured pen. You do all this in a software package anyway, as data management software does not describe or analyse your data for you. See Cleland et al. [ 53 ] for comprehensive guidance on how to use qualitative databases in education research.

Data analysis

While bearing in mind that qualitative data collection and analysis are iterative rather than linear (see earlier), Miles and Huberman [ 54 ] explain the process of qualitative data analysis as (1) data reduction (extracting the essence), (2) data display (organizing for meaning), and (3) drawing conclusions (explaining the findings).

Data analysis usually follows an inductive approach where the data are allowed to “speak for themselves” by the emergence of conceptual categories and descriptive themes. The researcher must be open to multiple possibilities or ways to think about a problem, engaging in “mental excursions” using multiple stimuli, “side-tracking” or “zigzagging,” changing patterns of thinking, making linkages between the “seemingly unconnected,” and “playing at it,” all with the intention of “opening the world to us in some way” [ 52 ]. The researcher must immerse themselves in the data in order to be able to see meaningful patterns and themes, making notes as they go through the processes of data collection and analysis, and then using these notes to guide the analysis strategy and the development of a coding framework.

In this way, good qualitative research has a logical chain of reasoning, multiple sources of converging evidence to support an explanation, and rules out rival hypotheses with convincing arguments and solid data. The wider literature and theory are used to derive analytical frameworks as the process of analysis develops and different interpretations of the data are likely to be considered before the final argument is built. For example, one of our own studies aimed to explore how widening access policy is translated and implemented at the level of individual medical schools [ 4 ]. Data was collected via individual interviews with key personnel. We initially conducted a primary level thematic analysis to determine themes. After the themes emerged, and following further team discussion, we explored the literature, identified and considered various theories, in some depth, before identifying the most appropriate theory or conceptual lens for a secondary, theory-driven analysis.

There are some excellent text books which discuss qualitative data analysis in detail [ 35 , 55 ].

Judging the quality of research

There are various criteria by which you can judge the quality of qualitative research. These link to efforts by the research team to consider their findings. The most common ways of doing so are triangulation, respondent validation, reflexivity, detail and process, and fair dealing [ 56 ] (but see also Varpio et al. [ 57 ] for a detailed discussion of the limitations of some of these methods).

Triangulation compares the results from either two or more different methods of data collection (for example, interviews and observation) or, more simply, two or more data sources (for example, interviews with different people). The researcher looks for patterns of convergence to develop or corroborate an overall interpretation. This is as a way of ensuring comprehensiveness. Respondent validation, or “member checking,” includes techniques in which the investigator’s account is compared with those of the research subjects to establish the level of correspondence between the two sets. Study participants’ reactions to the analyses are then incorporated into the study findings. Providing a clear account of the process of data collection and analysis is important. By the end of the study, it should be possible to provide a clear account of how early, simple coding evolved into more sophisticated coding structures and thence into clearly defined concepts and explanations for the data collected. Reflexivity is discussed earlier but in terms of analysis reflexivity means sensitivity to the ways in which the researcher and the research process have shaped the collected data, including the role of prior assumptions and experience. These two points address credibility, whether the study has been conducted well and the findings seem reasonable. It is important to pay attention to “negative cases,” data that contradict, or seem to contradict, the emerging explanation of the phenomena under study. These can be a very useful source of information in terms of refining the analysis and thinking beyond the obvious. The final technique is to ensure that the research design explicitly incorporates a wide range of different perspectives. In practice this can mean presenting data from a wide range of diverse participants. A very practical point is worth mentioning here—any reviewer will want to see quotes labelled in some way; for example, P11FFG2 would be participant 11, female, focus group 2). This helps the reader see that your data does not just represent the view of one or two people, but that there is indeed some sort of pattern or commonality to report.

Guba and Lincoln [ 58 ] provide the following criteria for judging qualitative research: credibility, transferability, dependability, and confirmability. I direct you to the original resource and to a very good explanation of these criteria in Mann and MacLeod [ 8 ].

Qualitative research is very important in educational research as it addresses the “how” and “why” research questions and enables deeper understanding of experiences, phenomena, and context. Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience. Getting at the everyday realities of some social phenomenon and studying important questions as they are really practiced helps answer big questions. To do so, you need to understand the philosophical stance of qualitative research and work from this to develop the research question, study design, data collection methods, and data analysis.

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Article contents

Feminist theory and its use in qualitative research in education.

• Emily Freeman Emily Freeman University of North Carolina at Chapel Hill
• https://doi.org/10.1093/acrefore/9780190264093.013.1193
• Published online: 28 August 2019

Feminist theory rose in prominence in educational research during the 1980s and experienced a resurgence in popularity during the late 1990s−2010s. Standpoint epistemologies, intersectionality, and feminist poststructuralism are the most prevalent theories, but feminist researchers often work across feminist theoretical thought. Feminist qualitative research in education encompasses a myriad of methods and methodologies, but projects share a commitment to feminist ethics and theories. Among the commitments are the understanding that knowledge is situated in the subjectivities and lived experiences of both researcher and participants and research is deeply reflexive. Feminist theory informs both research questions and the methodology of a project in addition to serving as a foundation for analysis. The goals of feminist educational research include dismantling systems of oppression, highlighting gender-based disparities, and seeking new ways of constructing knowledge.

• feminist theories
• qualitative research
• educational research
• positionality
• methodology

Introduction

Feminist qualitative research begins with the understanding that all knowledge is situated in the bodies and subjectivities of people, particularly women and historically marginalized groups. Donna Haraway ( 1988 ) wrote,

I am arguing for politics and epistemologies of location, position, and situating, where partiality and not universality is the condition of being heard to make rational knowledge claims. These are claims on people’s lives I’m arguing for the view from a body, always a complex, contradictory, structuring, and structured body, versus the view from above, from nowhere, from simplicity. Only the god trick is forbidden. . . . Feminism is about a critical vision consequent upon a critical positioning in unhomogeneous gendered social space. (p. 589)

By arguing that “politics and epistemologies” are always interpretive and partial, Haraway offered feminist qualitative researchers in education a way to understand all research as potentially political and always interpretive and partial. Because all humans bring their own histories, biases, and subjectivities with them to a research space or project, it is naïve to think that the written product of research could ever be considered neutral, but what does research with a strong commitment to feminism look like in the context of education?

Writing specifically about the ways researchers of both genders can use feminist ethnographic methods while conducting research on schools and schooling, Levinson ( 1998 ) stated, “I define feminist ethnography as intensive qualitative research, aimed toward the description and analysis of the gendered construction and representation of experience, which is informed by a political and intellectual commitment to the empowerment of women and the creation of more equitable arrangements between and among specific, culturally defined genders” (p. 339). The core of Levinson’s definition is helpful for understanding the ways that feminist educational anthropologists engage with schools as gendered and political constructs and the larger questions of feminist qualitative research in education. His message also extends to other forms of feminist qualitative research. By focusing on description, analysis, and representation of gendered constructs, educational researchers can move beyond simple binary analyses to more nuanced understandings of the myriad ways gender operates within educational contexts.

Feminist qualitative research spans the range of qualitative methodologies, but much early research emerged out of the feminist postmodern turn in anthropology (Behar & Gordon, 1995 ), which was a response to male anthropologists who ignored the gendered implications of ethnographic research (e.g., Clifford & Marcus, 1986 ). Historically, most of the work on feminist education was conducted in the 1980s and 1990s, with a resurgence in the late 2010s (Culley & Portuges, 1985 ; DuBois, Kelly, Kennedy, Korsmeyer, & Robinson, 1985 ; Gottesman, 2016 ; Maher & Tetreault, 1994 ; Thayer-Bacon, Stone, & Sprecher, 2013 ). Within this body of research, the majority focuses on higher education (Coffey & Delamont, 2000 ; Digiovanni & Liston, 2005 ; Diller, Houston, Morgan, & Ayim, 1996 ; Gabriel & Smithson, 1990 ; Mayberry & Rose, 1999 ). Even leading journals, such as Feminist Teacher ( 1984 −present), focus mostly on the challenges of teaching about and to women in higher education, although more scholarship on P–12 education has emerged in recent issues.

There is also a large collection of work on the links between gender, achievement, and self-esteem (American Association of University Women, 1992 , 1999 ; Digiovanni & Liston, 2005 ; Gilligan, 1982 ; Hancock, 1989 ; Jackson, Paechter, & Renold, 2010 ; National Coalition for Women and Girls in Education, 2002 ; Orenstein, 1994 ; Pipher, 1994 ; Sadker & Sadker, 1994 ). However, just because research examines gender does not mean that it is feminist. Simply using gender as a category of analysis does not mean the research project is informed by feminist theory, ethics, or methods, but it is often a starting point for researchers who are interested in the complex ways gender is constructed and the ways it operates in education.

This article examines the histories and theories of U.S.–based feminism, the tenets of feminist qualitative research and methodologies, examples of feminist qualitative studies, and the possibilities for feminist qualitative research in education to provide feminist educational researchers context and methods for engaging in transformative and subversive research. Each section provides a brief overview of the major concepts and conversations, along with examples from educational research to highlight the ways feminist theory has informed educational scholarship. Some examples are given limited attention and serve as entry points into a more detailed analysis of a few key examples. While there is a large body of non-Western feminist theory (e.g., the works of Lila Abu-Lughod, Sara Ahmed, Raewyn Connell, Saba Mahmood, Chandra Mohanty, and Gayatri Spivak), much of the educational research using feminist theory draws on Western feminist theory. This article focuses on U.S.–based research to show the ways that the utilization of feminist theory has changed since the 1980s.

Histories, Origins, and Theories of U.S.–Based Feminism

The normative historiography of feminist theory and activism in the United States is broken into three waves. First-wave feminism (1830s−1920s) primarily focused on women’s suffrage and women’s rights to legally exist in public spaces. During this time period, there were major schisms between feminist groups concerning abolition, rights for African American women, and the erasure of marginalized voices from larger feminist debates. The second wave (1960s and 1980s) worked to extend some of the rights won during the first wave. Activists of this time period focused on women’s rights to enter the workforce, sexual harassment, educational equality, and abortion rights. During this wave, colleges and universities started creating women’s studies departments and those scholars provided much of the theoretical work that informs feminist research and activism today. While there were major feminist victories during second-wave feminism, notably Title IX and Roe v. Wade , issues concerning the marginalization of race, sexual orientation, and gender identity led many feminists of color to separate from mainstream white feminist groups. The third wave (1990s to the present) is often characterized as the intersectional wave, as some feminist groups began utilizing Kimberlé Crenshaw’s concept of intersectionality ( 1991 ) to understand that oppression operates via multiple categories (e.g., gender, race, class, age, ability) and that intersecting oppressions lead to different lived experiences.

Historians and scholars of feminism argue that dividing feminist activism into three waves flattens and erases the major contributions of women of color and gender-nonconforming people. Thompson ( 2002 ) called this history a history of hegemonic feminism and proposed that we look at the contributions of multiracial feminism when discussing history. Her work, along with that of Allen ( 1984 ) about the indigenous roots of U.S. feminism, raised many questions about the ways that feminism operates within the public and academic spheres. For those who wish to engage in feminist research, it is vital to spend time understanding the historical, theoretical, and political ways that feminism(s) can both liberate and oppress, depending on the scholar’s understandings of, and orientations to, feminist projects.

Standpoint Epistemology

Much of the theoretical work that informs feminist qualitative research today emerged out of second-wave feminist scholarship. Standpoint epistemology, according to Harding ( 1991 , 2004 ), posits that knowledge comes from one’s particular social location, that it is subjective, and the further one is from the hegemonic norm, the clearer one can see oppression. This was a major challenge to androcentric and Enlightenment theories of knowledge because standpoint theory acknowledges that there is no universal understanding of the world. This theory aligns with the second-wave feminist slogan, “The personal is political,” and advocates for a view of knowledge that is produced from the body.

Greene ( 1994 ) wrote from a feminist postmodernist epistemology and attacked Enlightenment thinking by using standpoint theory as her starting point. Her work serves as an example of one way that educational scholars can use standpoint theory in their work. She theorized encounters with “imaginative literature” to help educators conceptualize new ways of using reading and writing in the classroom and called for teachers to think of literature as “a harbinger of the possible.” (Greene, 1994 , p. 218). Greene wrote from an explicitly feminist perspective and moved beyond simple analyses of gender to a larger critique of the ways that knowledge is constructed in classrooms.

Intersectionality

Crenshaw ( 1991 ) and Collins ( 2000 ) challenged and expanded standpoint theory to move it beyond an individual understanding of knowledge to a group-based theory of oppression. Their work, and that of other black and womanist feminists, opened up multiple spaces of possibility for feminist scholars and researchers because it challenged hegemonic feminist thought. For those interested in conducting feminist research in educational settings, their work is especially pertinent because they advocate for feminists to attend to all aspects of oppression rather than flattening them to one of simple gender-based oppression.

Haddix, McArthur, Muhammad, Price-Dennis, and Sealey-Ruiz ( 2016 ), all women-of-color feminist educators, wrote a provocateur piece in a special issue of English Education on black girls’ literacy. The four authors drew on black feminist thought and conducted a virtual kitchen-table conversation. By symbolically representing their conversations as one from the kitchen, this article pays homage to women-of-color feminism and pushes educators who read English Education to reconsider elements of their own subjectivities. Third-wave feminism and black feminism emphasize intersectionality, in that different demographic details like race, class, and gender are inextricably linked in power structures. Intersectionality is an important frame for educational research because identifying the unique experiences, realities, and narratives of those involved in educational systems can highlight the ways that power and oppression operate in society.

Feminist Poststructural Theory

Feminist poststructural theory has greatly informed many feminist projects in educational research. Deconstruction is

a critical practice that aims to ‘dismantle [ déconstruire ] the metaphysical and rhetorical structures that are at work, not in order to reject or discard them, but to reinscribe them in another way,’ (Derrida, quoted in Spivak, 1974 , p. lxxv). Thus, deconstruction is not about tearing down, but about looking at how a structure has been constructed, what holds it together, and what it produces. (St. Pierre, 2000 , p. 482)

Reality, subjectivity, knowledge, and truth are constructed through language and discourse (cultural practices, power relations, etc.), so truth is local and diverse, rather than a universal experience (St. Pierre, 2000 ). Feminist poststructuralist theory may be used to question structural inequality that is maintained in education through dominant discourses.

In Go Be a Writer! Expanding the Curricular Boundaries of Literacy Learning with Children , Kuby and Rucker ( 2016 ) explored early elementary literacy practices using poststructural and posthumanist theories. Their book drew on hours of classroom observations, student interviews and work, and their own musings on ways to de-standardize literacy instruction and curriculum. Through the process of pedagogical documentation, Kuby and Rucker drew on the works of Barad, Deleuze and Guattari, and Derrida to explore the ways they saw children engaging in what they call “literacy desiring(s).” One aim of the book is to find practical and applicable ways to “Disrupt literacy in ways that rewrite the curriculum, the interactions, and the power dynamics of the classroom even begetting a new kind of energy that spirals and bounces and explodes” (Kuby & Rucker, 2016 , p. 5). The second goal of their book is not only to understand what happened in Rucker’s classroom using the theories, but also to unbound the links between “teaching↔learning” (p. 202) and to write with the theories, rather than separating theory from the methodology and classroom enactments (p. 45) because “knowing/being/doing were not separate” (p. 28). This work engages with key tenets of feminist poststructuralist theory and adds to both the theoretical and pedagogical conversations about what counts as a literacy practice.

While the discussion in this section provides an overview of the histories and major feminist theories, it is by no means exhaustive. Scholars who wish to engage in feminist educational research need to spend time doing the work of understanding the various theories and trajectories that constitute feminist work so they are able to ground their projects and theories in a particular tradition that will inform the ethics and methods of research.

Tenets of Feminist Qualitative Research

Why engage in feminist qualitative research.

Evans and Spivak ( 2016 ) stated, “The only real and effective way you can sabotage something this way is when you are working intimately within it.” Feminist researchers are in the classroom and the academy, working intimately within curricular, pedagogical, and methodological constraints that serve neoliberal ideologies, so it is vital to better understand the ways that we can engage in affirmative sabotage to build a more just and equitable world. Spivak’s ( 2014 ) notion of affirmative sabotage has become a cornerstone for understanding feminist qualitative research and teaching. She borrowed and built on Gramsci’s role of the organic intellectual and stated that they/we need to engage in affirmative sabotage to transform the humanities.

I used the term “affirmative sabotage” to gloss on the usual meaning of sabotage: the deliberate ruining of the master’s machine from the inside. Affirmative sabotage doesn’t just ruin; the idea is of entering the discourse that you are criticizing fully, so that you can turn it around from inside. The only real and effective way you can sabotage something this way is when you are working intimately within it. (Evans & Spivak, 2016 )

While Spivak has been mostly concerned with literary education, her writings provide teachers and researchers numerous lines of inquiry into projects that can explode androcentric universal notions of knowledge and resist reproductive heteronormativity.

Spivak’s pedagogical musings center on deconstruction, primarily Derridean notions of deconstruction (Derrida, 2016 ; Jackson & Mazzei, 2012 ; Spivak, 2006 , 2009 , 2012 ) that seek to destabilize existing categories and to call into question previously unquestioned beliefs about the goals of education. Her works provide an excellent starting point for examining the links between feminism and educational research. The desire to create new worlds within classrooms, worlds that are fluid, interpretive, and inclusive in order to interrogate power structures, lies at the core of what it means to be a feminist education researcher. As researchers, we must seriously engage with feminist theory and include it in our research so that feminism is not seen as a dirty word, but as a movement/pedagogy/methodology that seeks the liberation of all (Davis, 2016 ).

Feminist research and feminist teaching are intrinsically linked. As Kerkhoff ( 2015 ) wrote, “Feminist pedagogy requires students to challenge the norms and to question whether existing practices privilege certain groups and marginalize others” (p. 444), and this is exactly what feminist educational research should do. Bailey ( 2001 ) called on teachers, particularly those who identify as feminists, to be activists, “The values of one’s teaching should not be separated sharply from the values one expresses outside the classroom, because teaching is not inherently pure or laboratory practice” (p. 126); however, we have to be careful not to glorify teachers as activists because that leads to the risk of misinterpreting actions. Bailey argued that teaching critical thinking is not enough if it is not coupled with curriculums and pedagogies that are antiheteronormative, antisexist, and antiracist. As Bailey warned, just using feminist theory or identifying as a feminist is not enough. It is very easy to use the language and theories of feminism without being actively feminist in one’s research. There are ethical and methodological issues that feminist scholars must consider when conducting research.

Feminist research requires one to discuss ethics, not as a bureaucratic move, but as a reflexive move that shows the researchers understand that, no matter how much they wish it didn’t, power always plays a role in the process. According to Davies ( 2014 ), “Ethics, as Barad defines it, is a matter of questioning what is being made to matter and how that mattering affects what it is possible to do and to think” (p. 11). In other words, ethics is what is made to matter in a particular time and place.

Davies ( 2016 ) extended her definition of ethics to the interactions one has with others.

This is not ethics as a matter of separate individuals following a set of rules. Ethical practice, as both Barad and Deleuze define it, requires thinking beyond the already known, being open in the moment of the encounter, pausing at the threshold and crossing over. Ethical practice is emergent in encounters with others, in emergent listening with others. It is a matter of questioning what is being made to matter and how that mattering affects what it is possible to do and to think. Ethics is emergent in the intra-active encounters in which knowing, being, and doing (epistemology, ontology, and ethics) are inextricably linked. (Barad, 2007 , p. 83)

The ethics of any project must be negotiated and contested before, during, and after the process of conducting research in conjunction with the participants. Feminist research is highly reflexive and should be conducted in ways that challenge power dynamics between individuals and social institutions. Educational researchers must heed the warning to avoid the “god-trick” (Haraway, 1988 ) and to continually question and re-question the ways we seek to define and present subjugated knowledge (Hesse-Biber, 2012 ).

Positionalities and Reflexivity

According to feminist ethnographer Noelle Stout, “Positionality isn’t meant to be a few sentences at the beginning of a work” (personal communication, April 5, 2016 ). In order to move to new ways of experiencing and studying the world, it is vital that scholars examine the ways that reflexivity and positionality are constructed. In a glorious footnote, Margery Wolf ( 1992 ) related reflexivity in anthropological writing to a bureaucratic procedure (p. 136), and that resonates with how positionality often operates in the field of education.

The current trend in educational research is to include a positionality statement that fixes the identity of the author in a particular place and time and is derived from feminist standpoint theory. Researchers should make their biases and the identities of the authors clear in a text, but there are serious issues with the way that positionality functions as a boundary around the authors. Examining how the researchers exert authority within a text allows the reader the opportunity to determine the intent and philosophy behind the text. If positionality were used in an embedded and reflexive manner, then educational research would be much richer and allow more nuanced views of schools, in addition to being more feminist in nature. The rest of this section briefly discussrs articles that engage with feminist ethics regarding researcher subjectivities and positionality, and two articles are examined in greater depth.

When looking for examples of research that includes deeply reflexive and embedded positionality, one finds that they mostly deal with issues of race, equity, and diversity. The highlighted articles provide examples of positionality statements that are deeply reflexive and represent the ways that feminist researchers can attend to the ethics of being part of a research project. These examples all come from feminist ethnographic projects, but they are applicable to a wide variety of feminist qualitative projects.

Martinez ( 2016 ) examined how research methods are or are not appropriate for specific contexts. Calderon ( 2016 ) examined autoethnography and the reproduction of “settler colonial understandings of marginalized communities” (p. 5). Similarly, Wissman, Staples, Vasudevan, and Nichols ( 2015 ) discussed how to research with adolescents through engaged participation and collaborative inquiry, and Ceglowski and Makovsky ( 2012 ) discussed the ways researchers can engage in duoethnography with young children.

Abajian ( 2016 ) uncovered the ways military recruiters operate in high schools and paid particular attention to the politics of remaining neutral while also working to subvert school militarization. She wrote,

Because of the sensitive and also controversial nature of my research, it was not possible to have a collaborative process with students, teachers, and parents. Purposefully intervening would have made documentation impossible because that would have (rightfully) aligned me with anti-war and counter-recruitment activists who were usually not welcomed on school campuses (Abajian & Guzman, 2013 ). It was difficult enough to find an administrator who gave me consent to conduct my research within her school, as I had explicitly stated in my participant recruitment letters and consent forms that I was going to research the promotion of post-secondary paths including the military. Hence, any purposeful intervention on my part would have resulted in the termination of my research project. At the same time, my documentation was, in essence, an intervention. I hoped that my presence as an observer positively shaped the context of my observation and also contributed to the larger struggle against the militarization of schools. (p. 26)

Her positionality played a vital role in the creation, implementation, and analysis of military recruitment, but it also forced her into unexpected silences in order to carry out her research. Abajian’s positionality statement brings up many questions about the ways researchers have to use or silence their positionality to further their research, especially if they are working in ostensibly “neutral” and “politically free” zones, such as schools. Her work drew on engaged anthropology (Low & Merry, 2010 ) and critical reflexivity (Duncan-Andrade & Morrell, 2008 ) to highlight how researchers’ subjectivities shape ethnographic projects. Questions of subjectivity and positionality in her work reflect the larger discourses around these topics in feminist theory and qualitative research.

Brown ( 2011 ) provided another example of embedded and reflexive positionality of the articles surveyed. Her entire study engaged with questions about how her positionality influenced the study during the field-work portion of her ethnography on how race and racism operate in ethnographic field-work. This excerpt from her study highlights how she conceived of positionality and how it informed her work and her process.

Next, I provide a brief overview of the research study from which this paper emerged and I follow this with a presentation of four, first-person narratives from key encounters I experienced while doing ethnographic field research. Each of these stories centres the role race played as I negotiated my multiple, complex positionality vis-á-vis different informants and participants in my study. These stories highlight the emotional pressures that race work has on the researcher and the research process, thus reaffirming why one needs to recognise the role race plays, and may play, in research prior to, during, and after conducting one’s study (Milner, 2007 ). I conclude by discussing the implications these insights have on preparing researchers of color to conduct cross-racial qualitative research. (Brown, 2011 , p. 98)

Brown centered the roles of race and subjectivity, both hers and her participants, by focusing her analysis on the four narratives. The researchers highlighted in this section thought deeply about the ethics of their projects and the ways that their positionality informed their choice of methods.

Methods and Challenges

Feminist qualitative research can take many forms, but the most common data collection methods include interviews, observations, and narrative or discourse analysis. For the purposes of this article, methods refer to the tools and techniques researchers use, while methodology refers to the larger philosophical and epistemological approaches to conducting research. It is also important to note that these are not fixed terms, and that there continues to be much debate about what constitutes feminist theory and feminist research methods among feminist qualitative researchers. This section discusses some of the tensions and constraints of using feminist theory in educational research.

Jackson and Mazzei ( 2012 ) called on researchers to think through their data with theory at all stages of the collection and analysis process. They also reminded us that all data collection is partial and informed by the researcher’s own beliefs (Koro-Ljungberg, Löytönen, & Tesar, 2017 ). Interviews are sites of power and critiques because they show the power of stories and serve as a method of worlding, the process of “making a world, turning insight into instrument, through and into a possible act of freedom” (Spivak, 2014 , p. xiii). Interviews allow researchers and participants ways to engage in new ways of understanding past experiences and connecting them to feminist theories. The narratives serve as data, but it is worth noting that the data collected from interviews are “partial, incomplete, and always being re-told and re-membered” (Jackson & Mazzei, 2012 , p. 3), much like the lived experiences of both researcher and participant.

Research, data collection, and interpretation are not neutral endeavors, particularly with interviews (Jackson & Mazzei, 2009 ; Mazzei, 2007 , 2013 ). Since education research emerged out of educational psychology (Lather, 1991 ; St. Pierre, 2016 ), historically there has been an emphasis on generalizability and positivist data collection methods. Most feminist research makes no claims of generalizability or truth; indeed, to do so would negate the hyperpersonal and particular nature of this type of research (Love, 2017 ). St. Pierre ( 2016 ) viewed the lack of generalizability as an asset of feminist and poststructural research, rather than a limitation, because it creates a space of resistance against positivist research methodologies.

Denzin and Giardina ( 2016 ) urged researchers to “consider an alternative mode of thinking about the critical turn in qualitative inquiry and posit the following suggestion: perhaps it is time we turned away from ‘methodology’ altogether ” (p. 5, italics original). Despite the contention over the term critical among some feminist scholars (e.g. Ellsworth, 1989 ), their suggestion is valid and has been picked up by feminist and poststructural scholars who examine the tensions between following a strict research method/ology and the theoretical systems out of which they operate because precision in method obscures the messy and human nature of research (Koro-Ljungberg, 2016 ; Koro-Ljungberg et al., 2017 ; Love, 2017 ; St. Pierre & Pillow, 2000 ). Feminist qualitative researchers should seek to complicate the question of what method and methodology mean when conducting feminist research (Lather, 1991 ), due to the feminist emphasis on reflexive and situated research methods (Hesse-Biber, 2012 ).

Examples of Feminist Qualitative Research in Education

A complete overview of the literature is not possible here, due to considerations of length, but the articles and books selected represent the various debates within feminist educational research. They also show how research preoccupations have changed over the course of feminist work in education. The literature review is divided into three broad categories: Power, canons, and gender; feminist pedagogies, curriculums, and classrooms; and teacher education, identities, and knowledge. Each section provides a broad overview of the literature to demonstrate the breadth of work using feminist theory, with some examples more deeply explicated to describe how feminist theories inform the scholarship.

Power, Canons, and Gender

The literature in this category contests disciplinary practices that are androcentric in both content and form, while asserting the value of using feminist knowledge to construct knowledge. The majority of the work was written in the 1980s and supported the creation of feminist ways of knowing, particularly via the creation of women’s studies programs or courses in existing departments that centered female voices and experiences.

Questioning the canon has long been a focus of feminist scholarship, as has the attempt to subvert its power in the disciplines. Bezucha ( 1985 ) focused on the ways that departments of history resist the inclusion of both women and feminism in the historical canon. Similarly, Miller ( 1985 ) discussed feminism as subversion when seeking to expand the canon of French literature in higher education.

Lauter and Dieterich ( 1972 ) examined a report by ERIC, “Women’s Place in Academe,” a collection of articles about the discrepancies by gender in jobs and tenure-track positions and the lack of inclusion of women authors in literature classes. They also found that women were relegated to “softer” disciplines and that feminist knowledge was not acknowledged as valid work. Culley and Portuges ( 1985 ) expanded the focus beyond disciplines to the larger structures of higher education and noted the varies ways that professors subvert from within their disciplines. DuBois et al. ( 1985 ) chronicled the development of feminist scholarship in the disciplines of anthropology, education, history, literature, and philosophy. They explained that the institutions of higher education often prevent feminist scholars from working across disciplines in an attempt to keep them separate. Raymond ( 1985 ) also critiqued the academy for not encouraging relationships across disciplines and offered the development of women’s, gender, and feminist studies as one solution to greater interdisciplinary work.

Parson ( 2016 ) examined the ways that STEM syllabi reinforce gendered norms in higher education. She specifically looked at eight syllabi from math, chemistry, biology, physics, and geology classes to determine how modal verbs showing stance, pronouns, intertextuality, interdiscursivity, and gender showed power relations in higher education. She framed the study through poststructuralist feminist critical discourse analysis to uncover “the ways that gendered practices that favor men are represented and replicated in the syllabus” (p. 103). She found that all the syllabi positioned knowledge as something that is, rather than something that can be co-constructed. Additionally, the syllabi also favored individual and masculine notions of what it means to learn by stressing the competitive and difficult nature of the classroom and content.

When reading newer work on feminism in higher education and the construction of knowledge, it is easy to feel that, while the conversations might have shifted somewhat, the challenge of conducting interdisciplinary feminist work in institutions of higher education remains as present as it was during the creation of women’s and gender studies departments. The articles all point to the fact that simply including women’s and marginalized voices in the academy does not erase or mitigate the larger issues of gender discrimination and androcentricity within the silos of the academy.

Feminist Pedagogies, Curricula, and Classrooms

This category of literature has many similarities to the previous one, but all the works focus more specifically on questions of curriculum and pedagogy. A review of the literature shows that the earliest conversations were about the role of women in academia and knowledge construction, and this selection builds on that work to emphasize the ways that feminism can influence the events within classes and expands the focus to more levels of education.

Rich ( 1985 ) explained that curriculum in higher education courses needs to validate gender identities while resisting patriarchal canons. Maher ( 1985 ) narrowed the focus to a critique of the lecture as a pedagogical technique that reinforces androcentric ways of learning and knowing. She called for classes in higher education to be “collaborative, cooperative, and interactive” (p. 30), a cry that still echoes across many college campuses today, especially from students in large lecture-based courses. Maher and Tetreault ( 1994 ) provided a collection of essays that are rooted in feminist classroom practice and moved from the classroom into theoretical possibilities for feminist education. Warren ( 1998 ) recommended using Peggy McIntosh’s five phases of curriculum development ( 1990 ) and extending it to include feminist pedagogies that challenge patriarchal ways of teaching. Exploring the relational encounters that exist in feminist classrooms, Sánchez-Pardo ( 2017 ) discussed the ethics of pedagogy as a politics of visibility and investigated the ways that democratic classrooms relate to feminist classrooms.

While all of the previously cited literature is U.S.–based, the next two works focus on the ways that feminist pedagogies and curriculum operate in a European context. Weiner ( 1994 ) used autobiography and narrative methodologies to provide an introduction to how feminism has influenced educational research and pedagogy in Britain. Revelles-Benavente and Ramos ( 2017 ) collected a series of studies about how situated feminist knowledge challenges the problems of neoliberal education across Europe. These two, among many European feminist works, demonstrate the range of scholarship and show the trans-Atlantic links between how feminism has been received in educational settings. However, much more work needs to be done in looking at the broader global context, and particularly by feminist scholars who come from non-Western contexts.

The following literature moves us into P–12 classrooms. DiGiovanni and Liston ( 2005 ) called for a new research agenda in K–5 education that explores the hidden curriculums surrounding gender and gender identity. One source of the hidden curriculum is classroom literature, which both Davies ( 2003 ) and Vandergrift ( 1995 ) discussed in their works. Davies ( 2003 ) used feminist ethnography to understand how children who were exposed to feminist picture books talked about gender and gender roles. Vandergrift ( 1995 ) presented a theoretical piece that explored the ways picture books reinforce or resist canons. She laid out a future research agenda using reader response theory to better comprehend how young children question gender in literature. Willinsky ( 1987 ) explored the ways that dictionary definitions reinforced constructions of gender. He looked at the definitions of the words clitoris, penis , and vagina in six school dictionaries and then compared them with A Feminist Dictionary to see how the definitions varied across texts. He found a stark difference in the treatment of the words vagina and penis ; definitions of the word vagina were treated as medical or anatomical and devoid of sexuality, while definitions of the term penis were linked to sex (p. 151).

Weisner ( 2004 ) addressed middle school classrooms and highlighted the various ways her school discouraged unconventional and feminist ways of teaching. She also brought up issues of silence, on the part of both teachers and students, regarding sexuality. By including students in the curriculum planning process, Weisner provided more possibilities for challenging power in classrooms. Wallace ( 1999 ) returned to the realm of higher education and pushed literature professors to expand pedagogy to be about more than just the texts that are read. She challenged the metaphoric dichotomy of classrooms as places of love or battlefields; in doing so, she “advocate[d] active ignorance and attention to resistances” (p. 194) as a method of subverting transference from students to teachers.

The works discussed in this section cover topics ranging from the place of women in curriculum to the gendered encounters teachers and students have with curriculums and pedagogies. They offer current feminist scholars many directions for future research, particularly in the arena of P–12 education.

Teacher Education, Identities, and Knowledge

The third subset of literature examines the ways that teachers exist in classrooms and some possibilities for feminist teacher education. The majority of the literature in this section starts from the premise that the teachers are engaged in feminist projects. The selections concerning teacher education offer critiques of existing heteropatriarchal normative teacher education and include possibilities for weaving feminism and feminist pedagogies into the education of preservice teachers.

Holzman ( 1986 ) explored the role of multicultural teaching and how it can challenge systematic oppression; however, she complicated the process with her personal narrative of being a lesbian and working to find a place within the school for her sexual identity. She questioned how teachers can protect their identities while also engaging in the fight for justice and equity. Hoffman ( 1985 ) discussed the ways teacher power operates in the classroom and how to balance the personal and political while still engaging in disciplinary curriculums. She contended that teachers can work from personal knowledge and connect it to the larger curricular concerns of their discipline. Golden ( 1998 ) used teacher narratives to unpack how teachers can become radicalized in the higher education classroom when faced with unrelenting patriarchal and heteronormative messages.

Extending this work, Bailey ( 2001 ) discussed teachers as activists within the classroom. She focused on three aspects of teaching: integrity with regard to relationships, course content, and teaching strategies. She concluded that teachers cannot separate their values from their profession. Simon ( 2007 ) conducted a case study of a secondary teacher and communities of inquiry to see how they impacted her work in the classroom. The teacher, Laura, explicitly tied her inquiry activities to activist teacher education and critical pedagogy, “For this study, inquiry is fundamental to critical pedagogy, shaped by power and ideology, relationships within and outside of the classroom, as well as teachers’ and students’ autochthonous histories and epistemologies” (Simon, 2007 , p. 47). Laura’s experiences during her teacher education program continued during her years in the classroom, leading her to create a larger activism-oriented teacher organization.

Collecting educational autobiographies from 17 college-level feminist professors, Maher and Tetreault ( 1994 ) worried that educators often conflated “the experience and values of white middle-class women like ourselves for gendered universals” (p. 15). They complicated the idea of a democratic feminist teacher, raised issues regarding the problematic ways hegemonic feminism flattens experience to that of just white women, and pushed feminist professors to pay particular attention to the intersections of race, class, gender, and sexuality when teaching.

Cheira ( 2017 ) called for gender-conscious teaching and literature-based teaching to confront the gender stereotypes she encountered in Portuguese secondary schools. Papoulis and Smith ( 1992 ) conducted summer sessions where teachers experienced writing activities they could teach their students. Conceptualized as an experiential professional development course, the article revolved around an incident where the seminar was reading Emily Dickinson and the men in the course asked the two female instructors why they had to read feminist literature and the conversations that arose. The stories the women told tie into Papoulis and Smith’s call for teacher educators to interrogate their underlying beliefs and ideologies about gender, race, and class, so they are able to foster communities of study that can purposefully and consciously address feminist inquiry.

McWilliam ( 1994 ) collected stories of preservice teachers in Australia to understand how feminism can influence teacher education. She explored how textual practices affect how preservice teachers understand teaching and their role. Robertson ( 1994 ) tackled the issue of teacher education and challenged teachers to move beyond the two metaphors of banking and midwifery when discussing feminist ways of teaching. She called for teacher educators to use feminist pedagogies within schools of education so that preservice teachers experience a feminist education. Maher and Rathbone ( 1986 ) explored the scholarship on women’s and girls’ educational experiences and used their findings to call for changes in teacher education. They argued that schools reinforce the notion that female qualities are inferior due to androcentric curriculums and ways of showing knowledge. Justice-oriented teacher education is a more recent iteration of this debate, and Jones and Hughes ( 2016 ) called for community-based practices to expand the traditional definitions of schooling and education. They called for preservice teachers to be conversant with, and open to, feminist storylines that defy existing gendered, raced, and classed stereotypes.

Bieler ( 2010 ) drew on feminist and critical definitions of dialogue (e.g., those by Bakhtin, Freire, Ellsworth, hooks, and Burbules) to reframe mentoring discourse in university supervision and dialogic praxis. She concluded by calling on university supervisors to change their methods of working with preservice teachers to “Explicitly and transparently cultivat[e] dialogic praxis-oriented mentoring relationships so that the newest members of our field can ‘feel their own strength at last,’ as Homer’s Telemachus aspired to do” (Bieler, 2010 , p. 422).

Johnson ( 2004 ) also examined the role of teacher educators, but she focused on the bodies and sexualities of preservice teachers. She explored the dynamics of sexual tension in secondary classrooms, the role of the body in teaching, and concerns about clothing when teaching. She explicitly worked to resist and undermine Cartesian dualities and, instead, explored the erotic power of teaching and seducing students into a love of subject matter. “But empowered women threaten the patriarchal structure of this society. Therefore, women have been acculturated to distrust erotic power” (Johnson, 2004 , p. 7). Like Bieler ( 2010 ), Johnson ( 2004 ) concluded that, “Teacher educators could play a role in creating a space within the larger framework of teacher education discourse such that bodily knowledge is considered along with pedagogical and content knowledge as a necessary component of teacher training and professional development” (p. 24). The articles about teacher education all sought to provoke questions about how we engage in the preparation and continuing development of educators.

Teacher identity and teacher education constitute how teachers construct knowledge, as both students and teachers. The works in this section raise issues of what identities are “acceptable” in the classroom, ways teachers and teacher educators can disrupt oppressive storylines and practices, and the challenges of utilizing feminist pedagogies without falling into hegemonic feminist practices.

Possibilities for Feminist Qualitative Research

Spivak ( 2012 ) believed that “gender is our first instrument of abstraction” (p. 30) and is often overlooked in a desire to understand political, curricular, or cultural moments. More work needs to be done to center gender and intersecting identities in educational research. One way is by using feminist qualitative methods. Classrooms and educational systems need to be examined through their gendered components, and the ways students operate within and negotiate systems of power and oppression need to be explored. We need to see if and how teachers are actively challenging patriarchal and heteronormative curriculums and to learn new methods for engaging in affirmative sabotage (Spivak, 2014 ). Given the historical emphasis on higher education, more work is needed regarding P–12 education, because it is in P–12 classrooms that affirmative sabotage may be the most necessary to subvert systems of oppression.

In order to engage in affirmative sabotage, it is vital that qualitative researchers who wish to use feminist theory spend time grappling with the complexity and multiplicity of feminist theory. It is only by doing this thought work that researchers will be able to understand the ongoing debates within feminist theory and to use it in a way that leads to a more equitable and just world. Simply using feminist theory because it may be trendy ignores the very real political nature of feminist activism. Researchers need to consider which theories they draw on and why they use those theories in their projects. One way of doing this is to explicitly think with theory (Jackson & Mazzei, 2012 ) at all stages of the research project and to consider which voices are being heard and which are being silenced (Gilligan, 2011 ; Spivak, 1988 ) in educational research. More consideration also needs to be given to non-U.S. and non-Western feminist theories and research to expand our understanding of education and schooling.

Paying close attention to feminist debates about method and methodology provides another possibility for qualitative research. The very process of challenging positivist research methods opens up new spaces and places for feminist qualitative research in education. It also allows researchers room to explore subjectivities that are often marginalized. When researchers engage in the deeply reflexive work that feminist research requires, it leads to acts of affirmative sabotage within the academy. These discussions create the spaces that lead to new visions and new worlds. Spivak ( 2006 ) once declared, “I am helpless before the fact that all my essays these days seem to end with projects for future work” (p. 35), but this is precisely the beauty of feminist qualitative research. We are setting ourselves and other feminist researchers up for future work, future questions, and actively changing the nature of qualitative research.

Acknowledgements

Dr. George Noblit provided the author with the opportunity to think deeply about qualitative methods and to write this article, for which the author is extremely grateful. Dr. Lynda Stone and Dr. Tanya Shields are thanked for encouraging the author’s passion for feminist theory and for providing many hours of fruitful conversation and book lists. A final thank you is owed to the author’s partner, Ben Skelton, for hours of listening to her talk about feminist methods, for always being a first reader, and for taking care of their infant while the author finished writing this article.

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Computational thinking with game design: An action research study with middle school students

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• Lorien Cafarella 1 &
• Lucas Vasconcelos   ORCID: orcid.org/0000-0001-9074-203X 2  

Middle school students often enter Computer Science (CS) classes without previous CS or Computational Thinking (CT) instruction. This study evaluated how Code.org’s block-based programming curriculum affects middle school students’ CT skills and attitudes toward CT and CS. Sixteen students participated in the study. This was a mixed methods action research study that used pre- and post-tests, surveys, artifacts, and interviews as data sources. Descriptive statistics, paired samples t-tests, and inductive thematic analysis were administered. Findings showed a statistically significant increase in participants’ algorithmic thinking, debugging, and pattern recognition skills but not in abstraction skills. Attitudes toward CT and CS improved but the difference was not statistically significant. Qualitative themes revealed benefits of game-based learning to promote CT skills, collaboration to promote successful error debugging, and enjoyment of programming resulting from a balance between structured guidance and creative freedom. Findings emphasize the importance of low-threshold and engaging strategies to introduce novice learners to CT and CS.

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1 Introduction

Students have limited exposure to Computer Science (CS) education in K-12 schools (Brown et al., 2014 ; Google & Gallup, 2020 ), which hampers their ability to develop Computational Thinking (CT) skills. CT includes problem-solving skills that students need to acquire in order to flourish in a digital era in which computer software drives several aspects of our lives (Román-González et al., 2017 ; Wing, 2006 ). As such, CT is an essential part of learning for all ages and should be incorporated into K-12 curricula (Runciman, 2011 ) so that students can not only learn how to use computers and consume technology, but also create technologies and use them for innovative problem solving (Kafai, 2016 ; Runciman, 2011 ).

Students’ limited access to CT and CS education is partly due to the decline in the number of qualified CS teachers in the past two decades (Kafai, 2016 ; Runciman, 2011 ). Several recommendations to address the CS teacher shortage include the creation of pathways for teachers to become CS endorsed and using funds for CS professional development (Computer Science Teachers Association, 2019 ). Lack of teacher preparation leads to inadequate CS instruction, which may prevent students from developing positive attitudes toward CT and CS. Very often, K-12 students think CS is unattainable, difficult to learn, and not enjoyable. It is imperative to teach CS to students in ways that integrate positive learning opportunities that not only promote CT skills but also improve their attitudes toward CT and CS.

One way to foster positive student attitudes toward CS education is by adopting fun, engaging, and lower-threshold computational activities that are part of an already established curriculum. Specifically, we propose a combination of game design and block-based programming to foster CT skills among young learners. Using block-based programming to design games (Akcaoglu, 2014 ; Akcaoglu & Kale, 2016 ) is a promising approach that allows students to practice CT skills while constructing a personally relevant artifact (Kafai & Burke, 2015 ; Ketenci et al., 2019 ). This study focuses on Code.org’s block-based programming curriculum for game design. To our knowledge, empirical studies seeking to investigate the impact of that curriculum on students’ CT skills and attitudes toward CT and CS are lacking. The present study addresses this literature gap.

2 Related literature

2.1 computational thinking.

The term computational thinking (CT) has received various definitions over the years. Wing ( 2006 ) explained that CT “involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to CS” (p. 33). CT was further defined by Dagli and Tokmak ( 2022 ) as a problem-solving process aimed to understand how computers work. Brennan and Resnick ( 2012 ) proposed a CT framework that includes three dimensions: computational and programming concepts, computational practices, and computational perspectives. The computational and programming concepts focused on sequences, loops, events, conditionals, operators, and data; computational practices included incremental and iterative programming, testing and debugging, reusing and remixing, and abstracting and modularizing; and computational perspectives emerge regarding the world and oneself through programming (Brennan & Resnick, 2012 ). CT has also been described as the cognitive processes needed to frame problems so that the solutions manifest as computational and algorithmic steps (Aho, 2012 ). For this study, we adopt Shute et al.’s ( 2017 ) definition of CT “as the conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts” (p. 1). This definition was chosen because it emphasizes the concepts of problem solving and creation of solutions which are central to CS education.

The literature on CT includes several skills that are associated with CT. Wing ( 2006 ) described the skills as problem-solving, recursive thinking, abstraction and decomposition, preparing for error correction, and heuristic reasoning. Shute et al. ( 2017 ) believed that the skills included in CT were decomposition, abstraction, algorithms, debugging, iteration, and generalization. Dagli and Tokmak ( 2022 ) defined CT skills as “interpreting and understanding the digital data, algorithmic thinking, critical thinking, and decision making” (p. 513). There is significant overlap in definitions of CT skills. For this study, CT is divided into four skills: algorithmic thinking, abstraction, debugging, and pattern recognition. These four skills are the basis for understanding computational activities whether these involve programming or not.

2.1.1 Algorithmic thinking

An algorithm is a sequence of steps (Peel & Friedrichsen, 2018 ). Algorithmic thinking is a “logical, organized way of thinking used to break down a complicated goal into a series of (ordered) steps using available tools” (Lockwood et al., 2016 , p. 1591). Algorithmic thinking entails creating a sequential order of actions that are logically organized and can be used to guide a machine or a human through the process of solving a complex problem (Chuechote et al., 2020 ). The processes of creating a recipe, designing a flowchart to guide decision making, and writing code that triggers an alarm when a home door is broken into are real applications of algorithms and algorithmic thinking.

2.1.2 Abstraction

Abstraction is the process of simplifying complex content or conveying only the important information that is needed for a given context or audience (Peel & Friedrichsen, 2018 ; Taub et al., 2014 ). In other words, abstraction involves gathering relevant information, discarding unrelated data to develop patterns, and discovering commonalities across different scenarios (Shute et al., 2017 ). In CS, practicing abstraction entails handling complexity by hiding unnecessary details (Cetin & Dubinsky, 2017 ) to hide chunks of an algorithmic sequence that can be accessed later if needed.

2.1.3 Debugging

Debugging is the process of error identification and correction when a solution does not work as expected (Dagli & Tokmak, 2022 ; Kim et al., 2022 ; Shute et al., 2017 ). Programs and other algorithmic sequences rarely work on a first attempt (Brennan & Resnick, 2012 ; Vasconcelos & Kim, 2020 ). When an error or bug is identified, one needs to read the program lines, locate the error, test a hypothesized solution, and evaluate the outcome. Hence, it is crucial for one to develop systematic strategies for dealing with problems and persisting through iterative rounds of debugging until the problem is addressed (Peel & Friedrichsen, 2018 ).

2.1.4 Pattern recognition

Pattern recognition is the ability to “identify patterns/rules underlying the data/information structure” (Shute et al., 2017 , p. 153). These patterns include specific programming concepts that are linked to events and interactions in the algorithmic sequence. To use pattern recognition, one must recognize patterns or sequences in previously written algorithms and then effectively apply them to a situation (Chalmers, 2018 ) to solve a different problem. So, the ability to identify patterns in a program and reuse or remix them is what pattern recognition entails (Prextová et al., 2018 ).

3 Attitudes toward CS and programming

Students often hold stereotypical beliefs about CS. These stereotypes include that CS is just about coding, it is only for smart people, it is boring, or requires a tremendous amount of work (Carter, 2006 ; Lewis et al., 2010 ; Taub et al., 2012 ; Vasconcelos et al., 2022 , 2023 ). Students also hold misconceptions about careers in the computing industry. They assume that programming is the foundation of computing jobs when in reality the foundation of CS is problem solving (Grover et al., 2015 ; Taub et al., 2012 ) regardless of whether it involves programming or not. Inaccurate perceptions about CS may lead to negative attitudes and lowered self-efficacy, which is a predicting factor of academic performance and future involvement in CS (Gunbatar & Karalar, 2018 ). Inaccurate perceptions of CS and the level of challenge in computational tasks lead to negative attitudes toward CS education and careers.

Another factor that may lead to negative attitudes is the nature of CT activities themselves (Gunbatar & Karalar, 2018 ). A study that introduced middle schoolers to unplugged CT activities without programming tasks found no improvement on students’ attitudes and intentions toward studying CS (Taub et al., 2012 ). Alternatively, students who perceive the programming task as too challenging or far beyond their abilities are more likely to disengage and give up (Durak, 2020 ). This may happen with text-based programming environments, which are too complex for novice learners who would need to type up commands and follow syntax rules. Students often perceive text-based programming as time consuming and not fun (Carter, 2006 ; Weintrop & Wilensky, 2018 ). Negative attitudes due to a high level of difficulty in programming may cause disengagement in CT activities, which in turn hinders development of CT skills (Zhao & Shute, 2019 ).

4 Game design to teach CT

4.1 game-based learning and digital games.

Game-based learning is broadly defined as an approach that uses games to promote playful and fun learning (Barman & Kjällander, 2022 ; Homer et al., 2020 ) while also promoting interaction with target educational content (Banihashem et al., 2023 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ; Lamb et al., 2018 ; Noroozi et al., 2020 ). This approach has been extensively adopted to improve learners’ motivation and engagement (Barreto et al., 2018 ; Leonard et al., 2016 ; Partovi & Razavi, 2019 ) across a variety of contexts. Game-based learning often features digital games, which are interactive and complex systems that immerse one in a scenario or virtual world as one uses gameplay mechanics, follows pre-determined rules, overcomes challenges, and interacts with other characters or environmental elements to achieve certain goals (Akcaoglu & Green, 2019 ; Barreto et al., 2018 ; Chen et al., 2020 ). Digital games may be designed purely for leisure and entertainment, and these are known as commercial games, or for educational purposes by exposing players to content that is embedded within their structure, and these are called educational or serious games (Dahalan et al., 2024 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ). While digital games can be played with mobile devices, computers, and consoles, this study focuses on browser-based games that can be designed and played with block-based programming languages.

4.2 Game design

Game design is a specific type of game-based learning in which students lead the process of designing and developing digital games using a set of tools and/or online platforms (Akcaoglu, 2014 ; Cheng et al., 2023 ; Kafai & Burke, 2015 ). Game design is inherently a constructionist approach (Kafai & Burke, 2015 ; Papert, 1980 ) as it involves construction of knowledge through designing, developing, and playing a digital game. In CS education, game design offers an open-ended opportunity to apply CT skills through creation of unique, interactive, and functional user interfaces (Jiang et al., 2022 ; Kafai & Burke, 2015 ; Repenning et al., 2010 ; Wang et al., 2023 ) using a block-based programming language. Game design grounds abstract CT concepts into concrete game play practices (Lu et al., 2023 ; Wang et al., 2023 ). For instance, designing a game engages learners in various levels of CT that involve identifying a problem and scenario to serve as the game foundation, breaking down the game problem into smaller parts that can be represented on the screen, abstracting and generalizing algorithmic sequences to control similar behaviors across multiple game elements, creating an algorithmic sequence of steps that encapsulate target concepts/commands to be used as the problem solution (that is, to win the game), and practicing analytical reasoning that goes with the iterative process of testing and debugging algorithms that control a game. Research has shown that game design can promote higher academic achievement in programming courses (Topalli & Cagiltay, 2018 ), greater motivation to use and understand CT (Ouahbi et al., 2015 ), and improved CT skills to apply concepts such as variables, loops, and if–then statements (Hsu & Tsai, 2023 ; Kafai & Burke, 2015 ; Mladenović et al., 2018 ).

Game design offers fun and engaging opportunities to manipulate game elements (e.g., a storyline, rewards) (Filippou et al., 2018 ; Noroozi et al., 2020 ) and develop CT skills, which may positively influence students’ attitudes toward CS. Moreover, reducing the challenge in programming tasks by adopting a block-based programming language can boost confidence and promote positive attitudes (Grover et al., 2015 ; Gunbatar & Karalar, 2018 ). Rather than typing up text to create commands, which requires knowledge of programming syntax, students just need to stack blocks to sequentially combine a series of commands that lead to a desired output (Vasconcelos & Kim, 2020 , 2022 ). Further, students argue that block-based programming environments are fun and more interactive than text-based programming (Weintrop, 2019 ; Weintrop & Wilensky, 2017 ). One study showed encouraging effects of video games and block-based programming through boosted confidence toward programming and participants’ developed self-identification as a programmer (Zhao & Shute, 2019 ). In addition, studies show promising results from using block-based programming through improved understanding of CT concepts and enhanced CT skills (Hsu & Tsai, 2023 ; Mladenović et al., 2018 ; Zur-Bargury et al. ( 2013 ). The literature about attitudes toward CS and CT (e.g., Choi, 2022 ; Mason & Rich, 2019 , 2020 ) and about Code.org’s block-based programming curriculum (e.g., Choi, 2022 ; Kale & Yuan, 2021 ; Kale et al., 2023 ; Lahullier, 2019 ) is prolific, but no study has assessed the impact of Code.org’s block-based programming curriculum on middle schooler’s CT skills and attitudes toward CT and CS. This is the literature gap that the present study addresses.

5 Purpose statement

The purpose of this action research study was to assess how Code.org’s block-based programming curriculum in game design affects middle school students’ CT skills and their attitudes toward CT and CS. The following research questions guided the study:

How and to what degree does a block-based programming curriculum in game design affect middle school students’ CT skills?

How and to what degree does a block-based programming curriculum in game design affect middle school students’ attitudes toward CT and CS?

6 The code.org CS discoveries curriculum

Code.org’s CS Discoveries curriculum is engaging, challenging, and developmentally appropriate for middle school students. Code.org is a platform that contains lessons on how to design and create games with block-based programming and discover CT and CS concepts without the burden of memorizing syntax (Kalelioglu, 2015 ). The lessons target the four CT skills focused on this study: algorithmic thinking, abstraction, debugging, and pattern recognition. Lesson activities rely on guided discovery to promote “a level of freedom for learners so that they explore the problem, identify patterns, and discover the underlying principles on the problem” (Kale & Yuan, 2021 , p. 622). Guided discovery is an approach to teaching small ideas or program structures that students can gradually connect to prior knowledge, which allows CS teachers to scaffold content learning. Activities are organized with an increasing level of challenge. Teacher lesson plans provide detailed notes about the content to be taught, suggested scripting to start class discussions, and answer keys.

The Code.org platform allows teachers to monitor student progress through a dashboard, provide feedback, and grade student-created programs (Kalelioglu, 2015 ). The teacher can see errors students make, identify the concepts students struggle with, and provide targeted feedback for program improvement. Furthermore, the platform has a keep working tag that shows students outstanding tasks and errors to be addressed.

6.1 Implementation timeline

This was a 9-week module that used Code.org’s Animations & Games Unit to teach CT. This class met every day face-to-face, Monday through Friday, for 45 min for the entire school year. All assignments were completed individually on students’ assigned Chromebooks. Participants were allowed to collaborate with each other, provide insights into other participants’ programs, help find errors, and encourage others to create fun and interactive games. The curriculum has a game design unit that is divided into two chapters. Chapter one contained lessons 1 through 17 where students learn to use sprites, variables, the draw-loop, conditionals, and user input. For example, participants learned how to order the code to create different elements in a scene (Fig.  1 ). Chapter two contains lessons 18 through 27 where students learn velocity, sprite collisions, functions, and game design process. For instance, participants learned how to program user input to create an interactive character and generate a visual outcome (Fig.  2 ). Each lesson has multiple steps where participants completed tasks focused on content, skill building, assessments, practice, and challenges. An overview of CS topics, duration, target blocks, and deliverables is provided in Table  1 .

Lesson 9 instructional example

Mini project lesson 17 student example

7.1 Research design

This was an action research study, which consists of a “systematic inquiry conducted by educators for the purpose of gathering information about how their particular schools operate, how they teach, and how their students learn” (Mertler, 2020 , p. 29). Action research seeks to address a problem of practice within the scope of an educator’s professional practice (Anderson et al., 2001 ; Arslan-Ari et al., 2020 ; Johnson, 2008 ). Findings of action research lead to evidence-based changes to improve processes of teaching and learning. Action research was an appropriate design for this study, which aimed to address a problem of practice related to CS education within the first author’s instructional setting. Specifically, this action research study was designed to collect standardized data from middle school students to investigate the impact of Code.org’s curriculum on their CT skills and their attitudes toward CT and CS. The ultimate goal was to make data-driven decisions to enhance CS education for these students based on study findings. This action research used a triangulation mixed methods design by combining qualitative and quantitative data sources. The integration of both types of data produces insights beyond the information provided by one type of data alone (Creswell & Creswell, 2018 ; Mertler, 2020 ).

7.2 Setting and participants

This study was conducted in an urban Title 1 middle school in the Southeast of the United States. Participants were recruited from a CS course taught to 25 middle school students enrolled in 8th grade and attending the CS course in the spring of 2022. This was an advanced course that offered high school credit to middle schoolers. A total of 16 students agreed to join the study. They were 13.75 years old on average. Nine were female and seven were male. Eight identified as African American, four as White, one as Asian, and three as multiracial. One was an English as Second Language student. A total of eight participants were considered gifted students in the course. Eight participants had previously enrolled in a CS course.

7.3 Data collection

Institutional Review Board approval, school district approval, parental consent, and participant assent were obtained prior to data collection. Four different data sources were adopted to assess participants’ CT skills and attitudes toward CT and CS: an attitudinal survey, pre- and post-tests, participant artifacts, and participant interviews.

7.3.1 Pre- and post-survey

To assess participants’ attitudes toward CT and CS before the intervention, a 5-point Likert scale attitudinal survey with 19 items was administered (Appendix A). Eight items were borrowed from three of the six subscales in Korkmaz et al.’s ( 2017 ) Computational Thinking Scale: creativity (8 items), algorithmic thinking (6 items), and critical thinking (5 items). Sample items include I like Computer Science and It is fun to try to solve complex problems . Minor adjustments were performed to adapt survey items to lower grade students. For example, the item I believe that I can easily catch the relation between the two pictures was adapted to I believe that I can easily catch the relation between the two pictures or two programs. The added excerpt was for improved clarity. Korkmaz et al. ( 2017 ) confirmed that the items had adequate or good reliability ranging from 0.79 to 0.87.

7.3.2 Pre- and post-tests

The pre-test was administered to 16 participants to establish their baseline CT skills prior to the intervention. The test had 29 questions (Appendix A). Three questions were selected from Rachmatullah et al.’s ( 2020 ) Middle Grades Computer Science Concept Inventory, and all questions presented an internal consistency higher than 0.80. Minor changes were made to these questions: a screenshot of a block-based program was added to the question so participants could visualize the target programming concepts. Seventeen questions were designed by the CS teacher. Construct validity and reliability for the teacher-created questions were established by piloting them with a similar population of middle school students enrolled in the same course prior to data collection. The questions were also reviewed by an expert on CS education and research methods. Minor changes were performed to the questions. Moreover, nine questions were adopted from the Code.org curriculum (see Table  2 ), and these have been created and widely used by CS educators. Twenty-one questions were multiple-choice, two were true/false, and six were short answer. The test assessed algorithmic thinking (14 questions), abstraction (five questions), debugging (five questions), and pattern recognition (five questions). The test was delivered via Google Forms. An identical post-test was administered after the intervention.

7.3.3 Participant artifacts

Participants used block-based programming to create games in Code.org’s online platform at the end of each lesson throughout the intervention. Artifacts, or any type of performance assessment or student projects, are used to systematically evaluate the attainment of learning targets (McMillan, 2013 ). These block-based programming games served as a tool to assess participants’ CT skills based on the extent to which they were able to apply such skills into game design.

7.3.4 Participant interviews

A semi-structured interview protocol (Appendix B) was designed to collect qualitative data on participants’ CT skills and attitudes toward CT and CS to allow triangulation of findings with quantitative data sources. Four participants were randomly chosen for a 20-min, in-person, one-on-one interview after the intervention. Sample questions include When you encountered a problem in your code without an obvious answer, what steps did you take to solve it? (debugging skill) and Do you see yourself as a computer scientist? (attitudes toward CS). The code that participants used to create games was demonstrated during the interview to prompt descriptions about their “strategy on designing video games using block-based programming” (Tang et al., 2020 , p. 4). Follow-up prompts were included to encourage elaboration on responses (Chalmers, 2018 ). Interviews were audio recorded.

8 Data analysis

This study included quantitative and qualitative data analysis methods to develop a better understanding of the phenomenon being investigated (Creswell & Creswell, 2018 ). These methods consisted of descriptive statistics, paired samples t-tests, inductive and thematic analysis as displayed in the research matrix (Table  3 ).

8.1 Descriptive statistics

Data from pre- and post-tests as well as pre- and post-surveys was descriptively analyzed with JASP, a free computer-based statistics software. Measures of central tendency (mean) were used to summarize the central position of the quantitative data set distribution, and measures of dispersion (standard deviation) were used to assess the variability within the same data set (Hanneman et al., 2012 ; Mertler, 2020 ). Descriptive statistics were important to synthesize a large amount of quantitative data and facilitate interpretations about trends and patterns in participants’ CT skills and attitudes towards CT and CS.

8.2 Paired-samples T-tests

Pre- and post-survey data was first computed with Microsoft Excel, then JASP to analyze composite scores for participant attitudes. All reliability coefficients for the composite subscales fall within the range of 0.74 to 0.86. According to DeVellis ( 2016 ), reliability coefficients of 0.70 and above have acceptable reliability. Subsequently, the Shapiro–Wilk test for normality revealed that all subscales from the pre- and post-tests as well as pre- and post-surveys were normally distributed. Thus, the parametric paired samples t-tests (Hanneman et al., 2012 ) were performed to test the hypothesis of a statistically significant increase in CT skills and attitudes towards CT and CS.

8.3 Participant artifacts

A rubric created by Code.org was used to assess the four CT skills (algorithmic thinking, debugging, abstraction, and pattern recognition) using participants’ artifacts, that is, the games created with block-based programs. The rubric contains 7 criteria: program development, program readability, use of functions, background and variables, interactions and controls, position and movement, and variables. Each criterion is assessed based on four levels of achievement ranging from no evidence (0 points) to extensive evidence (7–10 possible points) as presented in Table  4 . The first author, who individually coded participant artifacts, had used the rubric previously to assess student artifacts with a similar population attending the same class. Results from artifact scoring were entered into an Excel spreadsheet and then analyzed with JASP to generate descriptive statistics about participants’ performance. The full rubric with descriptions about each level of achievement is provided in Appendix C.

8.4 Inductive thematic analysis

Inductive thematic analysis was used to identify and organize data into codes and categories to construct a framework to present qualitative findings (Mertler, 2020 ). Open coding and in vivo coding were administered to identify patterns or similarities in the data set (Saldaña, 2016 ). Iterative rounds of coding were performed using the computer-based qualitative data analysis tool Delve. The first author read the coded data multiple times and used Delve’s retrieval features to select and visualize all excerpts assigned the same code. This facilitated visualization of patterns across participants. Then authors held peer debriefing meetings to assign codes into categories and jointly craft qualitative themes that describe participants’ experiences (Braun & Clarke, 2006 ; Clarke & Braun, 2017 ). Themes were probed against coded excerpts for relevance. Thick and rich descriptions with quotes were used to support themes (Mertler, 2020 ).

9.1 Quantitative results

9.1.1 computational thinking skills.

Results showed a noticeable increase in participants’ CT skills between the overall pre-test ( M  = 11.38, SD  = 3.32) and post-test scores ( M  = 18.69, SD  = 4.81) (see Table  5 ). The largest improvement was in algorithmic thinking ( M  = 9.25, SD  = 2.38) followed by pattern recognition ( M  = 3.31, SD  = 1.40) and debugging ( M  = 3.06, SD  = 0.77). The smallest increase was in abstraction ( M  = 3.38, SD  = 0.96). The average artifact score was ( M  = 75.06, SD  = 22.63), with subscale scores for algorithmic thinking ( M  = 24.88, SD  = 8.84), debugging ( M  = 14.75, SD  = 3.43), abstraction ( M  = 20.31, SD  = 6.48) and pattern recognition ( M  = 15.38, SD  = 4.78). Roughly 87% of participants scored 60 or higher in their game designs (see Fig.  3 ).

Artifact scores

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. Paired samples t-tests revealed a statistically significant improvement in three CT skills: algorithmic thinking ( M  = 6.20, SD  = 2.65, t (14) = -3.11, p  = 0.004), debugging ( M  = 3.06, SD  = 0.77, t (14) = -4.22, p  < 0.001), and pattern recognition ( M  = 1.40, SD  = 0.74, t (14) = -4.50, p  < 0.001) (see Table  6 ). Participants’ abstraction skills were not statistically significantly different ( M  = 3.38, SD  = 0.96, t (14) = -1.20, p  = 0.035). A large effect size was found for algorithmic thinking ( d  = 0.80), debugging ( d  = 1.09), pattern recognition ( d  = 1.16), and the overall test ( d  = 1.20), while a medium effect size was found for abstraction skills ( d  = 0.51).

9.1.2 Participant attitudes

There was a marginal increase in participants’ attitudes toward CT and CS from the pre-survey ( M  = 25.31, SD  = 4.90) to the post-survey ( M  = 25.75, SD  = 5.46) (Table  7 ). Moreover, there was a small positive increase in participants’ CT beliefs from the pre-survey ( M  = 24.25, SD  = 3.98) to the post-survey ( M  = 25.95, SD  = 6.01). The overall survey scores showed a similar increase from the pre-survey ( M  = 49.56, SD  = 8.51) to the post-survey ( M  = 51.69, SD  = 11.23).

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. It was determined that paired-samples t-tests would be the most appropriate method to analyze the data inferentially (Gibbons & Chakraborti, 2021 ). Paired samples t-tests revealed no statistically significant differences between the pre- and the post-surveys for CS attitudes ( M  = 25.75, SD  = 5.46, t (15) = -0.43, p  = 0.34), CT beliefs ( M  = 25.94, SD  = 6.01, t (15) = -1.41, p  = 0.09), and the overall survey ( M  = 51.69, SD  = 11.23, t (15) = -1.05, p  = 0.16) (see Table  7 ). A small effect size was found for the attitudes ( d  = 0.11), CT beliefs ( d  = 0.35), and overall survey ( d  = 0.26).

9.2 Qualitative results

9.2.1 configuration of game elements as the foundation to understand and apply ct.

Designing and programming games exposed participants to basic programming concepts which in turn supported learning of CT skills. Participants in this study grounded application of CT skills on their experience with video game elements. Particularly, participants used configuration of game elements such as rewards and points, character movement around the scene, player-character interactions, character-character interactions, and games rules as the foundation to understand and apply CT skills.

Participants who were interviewed mentioned several elements of games to explain programming concepts. For example, when Joe was asked about what he wanted to happen if the score was below zero, he said “I wanted the game to end because if somebody has something like negative one and if they continue playing, then the game will never stop”. Similarly, when May was asked why she looped her sprites she stated, “the loop I used is that I wanted when the player touches the sprites, the sprites had to go in a different place.” Participants also referred to game scores to discuss variables, counter pattern, and character interactions. When asked about how variables work, participants mentioned scores as a variable which can be added or subtracted to for winning or losing a game. Mary stated that a “variable is a number that is subject to change. So, um, in scoring, you could say every time your sprite touches an object, you will gain a point”. May similarly said, “if the player touches the enemy, the [player’s] health would go down, but if you [the player] touch[es] like a candy, your scoring will go up.”

When prompted to define conditional statements, several participants described them as a tool that allows the game designer to create situations and outcomes. For instance, Connie said “if the score is higher than 10”, or “if the right arrow is pressed.” Along these lines, Mary added that “a conditional statement asks if a certain, uh, aspect is true or false and based off of whether that aspect is true or false, it will perform a certain action”. Participants connected game characters with sprites, which are two-dimensional images that represent a character and/or background element. Participants liked the word sprite, thought it was funny, and used it to describe all images adopted in their programs. Further, participants learned how to move sprites with the arrow keys using if statements, the counter pattern, and loops. John stated, “the loop will allow the fish sprite to reset back to the left of the screen”. In summary, most interviewed participants relied on game elements to articulate an understanding of CT concepts and explain how they applied CT for game design.

9.2.2 Collaboration promoted debugging of participants’ own programs

Collaboration during block-based programming involves working with peers to plan, revise, and complete a program. Participants were encouraged to collaborate with their peers if they were stuck and did not know what to do before asking the teacher for help. This allowed participants to take ownership of their work and understand that collaboration is not cheating. During interviews, participants emphasized that collaboration was beneficial. May stated, “I asked one of my friends, because she's very helpful for me. She helped me a bit for the velocity stuff. And then the rest I was able to figure out on my own.” Along these lines, Mary stated that “it was very helpful” collaborating with a classmate. Being able to work with peers and review others’ programs helped participants feel at ease, identify errors in the program, and understand CT concepts more independently without the instructor.

Throughout the module, participants were given faulty programs in which they had to identify and fix the bug. When asked about the debugging process, Joe stated that “I try to look back at the code and make sure that there's no spelling errors.” This is a first step in the debugging process to find the bug location. However, when participants were not successful, they were encouraged to work with peers. When prompted to talk about peer collaboration, May explained, “what she did is she showed me a bit of hers and that showed me how to deal with mine.” These statements indicate that reviewing a peer’s program and comparing it against their own program helped participants create insights about where the bug was without asking the teacher for help.

9.2.3 Balancing creative freedom and structure led to enjoyment of programming

The block-based programming module offered an optimal combination of structure and creative freedom so participants could design their own game. The structure comes in the form of guided planning for using required programs and CT skills while the freedom comes in the form of making decisions about the game environment, characters, and rules in ways that were personally relevant. This balance ensured participants mastered target CT skills but also enjoyed taking ownership over the design of game content or characters. As May said, “I liked choosing my sprites and then deciding how they would interact”. Similarly, John showed excitement when asked about his game sprites. He said “you see, I made that sprite, I drew it and it looks so good.”

The balance between creative freedom and structure made programming a fun activity, which appears to have promoted positive attitudes. Participants seemed to enjoy programming their own games which led them to being very engaged and working hard on game design. May was curious about creating games prior to the study, and then game design helped her perceive programming as a fun activity. May stated “I'd just say creating your own games and websites is very fun. I've always wanted to learn how to create those (…) I thought it wasn't gonna be, but it's very, I'm having a lot of fun doing it.” Along these lines, Joe confirmed that he likes CS and that the experience helped him understand the mechanisms that make programmed artifacts function. Joe said “I just like coding in general. It helps me understand how like video games and stuff like that work.” Understanding the functionality of games was also something Mary enjoyed. She stated, “I like to be able to create code and then watch it actually work”, and “I enjoy creating games once I know how to make them.”

10 Discussion

CT is a fundamental skill for the twenty-first century workforce within and beyond the computing industry (Wing, 2019 ). And yet, middle schoolers in the U.S. have limited exposure to CS education (Google & Gallup, 2020 ), which prevents them from developing CT skills (Brown et al., 2014 ). CS education made the leap from teaching students to consume technology to teaching students to be technology creators within the past 10 years (Kafai, 2016 ; Runciman, 2011 ). But despite the development of many new initiatives and curricula, students still think CS concepts are difficult to learn (Mladenović et al., 2018 ). One way to develop middle school students’ CT skills is to use Code.org’s CS Discoveries game design curriculum. The first author, who is a CS school teacher, led this action research study by collecting standardized data in the form of pre- and post-tests, interviews, and participant artifacts to assess the impact of the curriculum on middle school students’ CT skills and attitudes towards CT and CS. A discussion of results follows.

10.1 CT skills

Quantitative results showed a statistically significant improvement in three of the four CT skills: algorithmic thinking, debugging, and pattern recognition. A total of 14 out of 16 participants had higher overall post-test scores. Participants’ games also showed development of CT skills. These results align with previous research that found the use of game design to benefit learning to program and development of CT skills (Ouahbi et al., 2015 ; Scherer et al., 2020 ).

Algorithmic thinking is the ability to think in steps to solve problems (Chuechote et al., 2020 ). Both quantitative and qualitative results showed improved algorithmic thinking skills. Participants were able to “solve tasks demanding thinking, not only using the rules and algorithms they had learned” (Harangus & Kátai, 2018 , p. 1037) but also creating solutions for complex problems (Durak, 2020 ) related to their own game design. Previous studies that adopted the Code.org curriculum also showed an improvement in participants’ algorithmic thinking skills (Chuechote et al., 2020 ; Lockwood et al., 2016 ; Oluk & Çakir, 2021 ; Peel & Friedrichsen, 2018 ; Tonbuloğlu & Tonbuloğlu, 2019 ). Block-based programming in Code.org allows one to build a plan and think algorithmically as part of their CT learning experience (Kale et al., 2023 ). Further, block-based programming is an effective tool to prepare students for future CS courses as it helps students focus on the process of learning CS concepts without the syntax from text-based programming (Weintrop, 2019 ). The use of block-based programs to control game mechanics offered an “interactive learning environment centered on problem-solving” (Wang et al., 2023 , p. 1506) which encouraged participants to break down character behaviors and game animations into smaller, more manageable tasks. Hence, the game design approach was conducive of algorithmic thinking throughout the multiple lessons implemented in this study.

Debugging is very much part of problem solving within and beyond CS. In this study, finding and fixing program errors was critical for successful game design. Practicing error debugging can lead to improved understanding of programming concepts (Kim et al., 2018 ). Both quantitative and qualitative data showed that participants improved debugging skills. The CS teacher who taught the module encouraged participants to engage in collaborative debugging by reviewing each other’s code and helping each other find and fix errors, which has been found beneficial in the CS education literature. In Papavlasopoulou et al.’ ( 2019 ) study, participants who collaborated more on programming tasks “had a higher level of shared understanding and could communicate better during the coding activity” (p. 421). Kim et al. ( 2022 ) reported that students who were given scaffolds to help debug errors and students who worked with collaborators were more successful at programming. Collaborative debugging seems to promote increased persistence and engagement in programming and problem solving, which in turn contributes to the development of CT skills (Margulieux et al., 2020 ; Tonbuloğlu & Tonbuloğlu, 2019 ; Turchi et al., 2019 ). From a game design standpoint, participants in this study improved their debugging skills due to the required creation and testing of algorithmic sequences to refine the actions to occur within their game and based on visual feedback. In fact, visual feedback has been identified as one of the most effective factors leading to positive learning outcomes in game-based learning (Dehghanzadeh et al., 2024 ; Noroozi et al., 2016 , 2023 ; Sailer & Sailer, 2021 ). In this study, visual feedback helped participants identify a discrepancy between the intended and the actual performance (Carless, 2006 ) of game characters on the screen, which prompted debugging.

In pattern recognition, one sees similarities between program elements that can be matched with previous tasks (Qian & Choi, 2023 ) and then apply those patterns from one problem to the next (Barrón-Estrada et al., 2022 ; Yasin & Nusantara, 2023 ). Results from this study showed an improvement in participants’ pattern recognition. The Code.org curriculum repeatedly referred participants back to previous tasks so they could identify similarities and develop a plan for reusing parts of a program. For example, after learning to program a game score, participants reused that program chunk in every lesson afterwards in increasingly challenging activities. Improved pattern recognition through block-based programming was also found in other studies in which participants successfully synthesized multiple repeated blocks into a more efficient use of loops (Hernández-Zavaleta et al., 2021 ). We argue that the game design curriculum supported study pattern recognition as “participants transferred their learning of block-based programming from previous coding challenges to the new ones when they located any similarities (…) across the challenges” (Umutlu, 2022 , p. 761). The game design approach was crucial in engaging participants in programming similar patterns to control game character behaviors.

Abstraction, or the process of simplifying information, is applicable through the use of functions to hide parts of the program that are reused and reactivated when needed. For example, it is not necessary to display the actual code that controls the addition of points to the game score. This function can be found when needed, but it was often hidden in the code. Results of this study showed an improvement in abstraction skills, but the difference was not statistically significant. One possible explanation for the lack of statistical significance is that the tasks may have been too advanced for participants at this age. In another study with children enrolled in first through sixth grade, “older students were found to do better on the abstraction task than students in the youngest age group” (Rijke et al., 2018 , p. 86). Jean Piaget’s theory of cognitive development states that children are still forming schemas until the age of 12, which makes abstract reasoning difficult for them (Piaget & Cook, 1952 ). Another possibility is that abstraction is not an explicit focus of the Code.org curriculum and perhaps it should be integrated earlier into those lessons. Kale and Yuan’s ( 2021 ) study implemented the same curriculum and found that improvement in participants’ abstraction was lower than in pattern recognition and algorithmic thinking, which aligns with findings from this study. While it was expected that game design with block-based programming would be beneficial for abstraction skills, perhaps participants in this study needed more support. One possibility for future research is the inclusion of causal maps so participants can produce visual illustrations that show relationships among variables of interest (Akcaoglu & Green, 2019 ; Öllinger et al., 2015 ) and relationships between planned programming concepts and game character behaviors.

10.2 Attitudes toward CT and CS

Participants’ attitudes toward CT and CS were moderately high after the study, but they did not statistically significantly differ. We argue that these results could be due to several factors. First, these participants were taking an elective CS course, so perhaps they already had positive attitudes toward CT and CS prior to the study. Second, some programming concepts, such as conditionals, are quite challenging for middle schoolers. In Brennan and Resnick’s ( 2012 ) study, not every student could correctly describe how a conditional statement works or why it was used in the program. One of the interviewees in this study had difficulty answering questions about conditionals even though they knew how to complete the task. Challenges with more advanced programming concepts such as conditionals may have contributed to lowered self-efficacy. Limited ability to apply CT skills can be the reason for less positive attitudes toward programming and CT. Similar to this study, Lambić et al. ( 2021 ) found no significant difference in participants’ attitudes after implementing Code.org’s curriculum while Hsu and Tsai’s ( 2023 ) study found no significant difference in participants’ attitudes after an intervention that combined block-based programming, physical computing, and game design.

Despite the lack of statistical significance, qualitative data suggests improvements in CT skills through game design and revealed that participants enjoyed block-based programming. This aligns with Kalelioglu’s ( 2015 ) study, in which participants reported that they liked using the Code.org site and desired to learn more about programming. Other studies have found that block-based programming contributed to positive attitudes such as interest in taking more programming courses (Weintrop & Wilensky, 2017 ), and positive attitudes towards CS (Bastug & Kircaburun, 2017 ; Kalelioglu, 2015 ). As Gunbatar and Karalar ( 2018 ) stated, “visual programming environments can increase students’ self-efficacy perceptions and attitudes toward programming” (p. 931) because those environments offer a lower level of challenge but also the possibility to increase the level of complexity in the program. Previous research has found that integrating block-based programming into game design yielded positive attitudes such as higher interest in programming (Hromkovic & Staub, 2019 ) and enhanced self-efficacy (Tsai et al., 2023 ).

Participants’ moderately positive attitudes may also be due to the use of games for educational purposes. Game-based learning is an approach through which students learn either by playing games or creating them (Denner et al., 2012 ; Kafai & Burke, 2015 ; Topalli & Cagiltay, 2018 ; Turchi et al., 2019 ). Research has identified the positive effects of game-based learning on student motivation and engagement (e.g., Barreto et al., 2018 ; Breien & Wasson, 2021 ; Hwang et al., 2014 ; Partovi & Razavi, 2019 ; Sharma et al., 2021 ). In this study, participants not only played games, but they were also engaged in game design. We argue that two key components of the constructionist game design experience, personalization and collaboration, contributed to participants’ enjoyment of block-based programming as well as positive attitudes toward CT and CS. Specifically, participants were free to design a game that was personally relevant and they were encouraged to help each other during programming, and these are critical to foster student motivation and engagement in CT (Barman & Kjällander, 2022 ; Hava & Ünlü, 2021 ; Sharma et al., 2021 ; Turchi et al., 2019 ).

11 Limitations

Study limitations should be considered. First, a larger sample size would have been beneficial to achieve more robust quantitative results. Future research should consider implementing the study across multiple middle school classrooms to increase the sample size. Second, the study did not provide insights on fluctuations in participants’ attitudes over time. Adding more timepoints of data collection about attitudes toward CT and CS throughout such a long intervention is recommended. Third, relying exclusively on self-reported data from surveys and interviews with young participants presents issues related to response accuracy and social desirability bias because participants’ self-reported perceptions may not accurately reflect their actual learning experience (Noroozi et al., 2024 ). Strategies to minimize bias from self-reported data should be included in future research. For example, alternative data sources such as participant observations, user data from the game design environment (Banihashem et al., 2023 ), and alternative approaches such as a longitudinal study of attitudes and engagement over time. Finally, only the first author scored the participant artifacts. In the future, inviting another CS teacher to individually score the artifacts and compare results would enhance the validity of results.

12 Conclusion and implications

K-12 students need meaningful CS learning opportunities that foster development of CT skills and positive attitudes toward CT and CS. Significant gains were found in participants’ algorithmic thinking, pattern recognition, and debugging skills but not in abstraction. These results are partly explained by the use of a block-based programming language that lowers that difficulty to learn programming concepts, the use of game design as a strategy to promote freedom of expression and creativity, and the incentive for collaborative problem solving. These elements of the module should be taken into consideration for CS education that aims to teach CT skills.

Student attitudes towards CT and CS did not improve as expected. And yet, students stated that they enjoyed designing and programming their own game. Strategies for motivational support that extend beyond the adopted curriculum and the adopted programming software should be included. Specifically, future initiatives can normalize struggle as part of problem solving and CT, expose students to career paths and tasks that do not necessarily involve programing, and introduce them to successful role models in the field that they can identify with.

The design of this study focused on middle school students, so it is important to consider the results with caution before generalizing them to other contexts and populations. The intervention, which combines game design with block-based programming to foster CT, can and should be implemented and evaluated across other contexts of CS education (e.g., informal learning environments) and other levels of education (e.g., high school students, preservice teacher preparation).

Data availability

Data collected from this study are available from the corresponding author on reasonable request.

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Cafarella, L., Vasconcelos, L. Computational thinking with game design: An action research study with middle school students. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13010-5

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