This is my computing education research FAQ.

I started it with the help of several computing education researchers at a Dagstuhl retreat in 2016. I consider it a community resource, so if you see something to add, fix, or improve, write me, or submit an issue🔗︎ or pull request🔗︎.

Computing education research (CER), also known as computer science education (CSEd) research, is the study of how people learn and teach computing, broadly construed. This FAQ will teach you more about the field and how you might contribute to it.

What is computing education research? 🔗︎

First, CER is not teaching. Teaching is helping people change their knowledge, skills, attitudes, beliefs, identities. Research is discovery and invention. Teachers teach computing, whereas computing education researchers make discoveries about this teaching and learning, and invent new ways for these teaching and learning to occur. CER is an example of discipline-based education research🔗︎, like math education research or science education research, all of which are part of the broader field of education and learning sciences research.

CER is also not educational technology research (EdTech). Computing education researchers often create educational technologies to support the learning and teaching of computing, but CER is not explicitly concerned with the broader use of technology in learning, teaching, and education. It's specifically concerned with the learning and teaching of computing in particular. Many computer science researchers invent learning technologies, but are not computing education researchers, because those technologies are not concerned with the learning of computing.

It's also important to note that I view "computing" broadly: it's not just about programming, or even just about computer science, but also about all of the phenomena surrounding computing, including data, information, privacy, security, ethics, software engineering, and sociocultural and sociopolitical views of computing in society. This means that computing education and CER can and does cover far more than just learning to code—it just hasn't historically.

What is the difference between 'CS' and 'computing'? 🔗︎

CS generally refers to the historically core topics in computer science research, such as theory, algorithms, data structures, programming languages, and operating systems. But other fields began to engage these ideas and identify their intersections to other fields. For example, information science, a field long concerned with data, information, and society, began to consider those topics from a computer science perspective. Science began to apply computer science ideas to data capture, storage, and analysis. Biology, as it began to see DNA as a form of biological data, and apply algorithms to analyzing it, formed bioinformatics. Communication began to explore computer-mediated communication. Behavioral and brain scientists started using computers to model decisions, knowledge, and brain activity. And social scientists of all kinds began studying and critiquing the role of computers in society.

This broadening of algorithms, data structures, and programming to all of academia put pressure on "computer science" as a phrase. Some universities with CS departments began to grow to teach and study all of these broader phenomena, leading many of them to rename themselves "Schools of Computing". The word "computing" was meant to refer not to physical hardware, but of computation itself and the many ways that it occurs and is organized in nature and human civilization. And thus, over time, CS began to connote the more narrow historical focus of CS foundations and Computing to the broader set of phenomena around the design and use of computers, including CS itself.

Given that history, "CS education" generally refers to the teaching and learning of these historically narrow topics in CS, and "CS education research" to research about this teaching and learning. In contrast, "Computing education" generally refers to the teaching and learning of any aspect of the use of computers and computational ideas, and "Computing education research" to research about that teaching and learning. Because I'm not interested in the historically narrow conception of computing, and my research considers topics outside that narrow historical conception, I use "Computing" instead of "CS".

How does computing education research compare to related fields? 🔗︎

My background isn't in these fields, though I do collaborate with people in these other communities and have learned about their differences. Here's the best characterization I can give:

• Education research is broadly concerned with formal systems of education, how to make those systems effective and just, how to prepare teachers to make them effective and just. The field is interested in general theories of learning, education, interest development, and identity, and because of its focus on formal education, is often focused on youth, who are the dominant age demographic engaged in formal education. The phrase "Computing education" uses the word "education" in this same way, but is more broadly concerned with teaching and learning in any context (in principle, but often not in practice).
• Educational psychology is focused on learning phenomena in the mind, such as learning, memory, development, intelligence, self-regulation, motivation, and self-concept. The field is also concerned with school psychologists who help students with their mental health. The field tends to be more quantitative than education research and learning sciences, following traditions of cognitive psychology. Computing education draws upon this field, especially in its history of cognitive theories of program understanding.
• Learning sciences emerged in the 1990's as a reaction to educational psychology's inattention to the setting, culture, and social context of learning. Combining perspectives from cognition, cognitive science, computer science, and design, like education research, it's much more concerned with the sociocultural factors that shape learning, and more than education and educational psychology, views design as a means to articulating theories, a way of shaping theories, and a way of testing theories. Because of the focus on context, in addition to being concerned with formal systems of education, it is also concerned with learning across the lifespan, at home, in families, and other settings.

(Shayan Doroudi provides a nice primer on learning theories🔗︎ and their disciplinary origins.)

How does computing education fit in to all of this? Like other discipline-based education research (DBER)🔗︎ such as math and physics education, it draws upon all three of the fields above, using theories and ideas from those fields. However, because it is focused on a discipline, it is specifically concerned with the content of the discipline, specific methods of learning and teaching that content. In this sense, it is more applied, bridging foundational ideas that span any human learning to applied ideas specific to the learning of specific ideas and skills.

What are major research questions in CER? 🔗︎

As with any research discipline, research questions can and should be specific. However, there are some major overarching questions in this field that researchers have begun to investigate, including:

• What is computing?
• What does it mean to know computing?
• How do people learn computing?
• How do teachers teach and assess computing?
• How does identity interact with people's learning of computing?
• How can people learn computing more effectively?
• How can teachers teach computing more effectively?
• How can access to computing education be improved?
• How can computing education be delivered equitably to all?
• How can computing education be reimagined to serve goals other than profit and disruption?
• How do systems of oppression such as racism, sexism, and ablism shape learning, teaching, and curricula?
• How can we implement anti-racist CS education?
• How can learning technologies teach computing?
• How does computing education affect people's lives?
• What are the societal costs of computing illiteracy?
• What can be taught about computing to learners of different ages?

While the "people" in the questions above could be anyone (youth, teens, college students, adults, and even teachers), the history of CER has primarily focused on teaching students in post-secondary settings, because the faculty conducting research have found it easier to study the students they are teaching. This is changing as countries around the world begin to incorporate computing into all levels of school, and as private industry begins to create technologies and services that teach computing to all ages. For example, my research has investigated new ways to teach youth from age 8-18, as well as adults.

What are some exciting CER discoveries? 🔗︎

There are so many! Examples include:

• The field discovered that diversity in computing education is low because of the narrow, exclusionary nature of computing cultures, not because of inherent disinterest or inability on the part of diverse learners (e.g., Fisher & Margolis 2002🔗︎, Margolis 2010🔗︎).
• The field invented contextualized computing ed pedagogy (e.g., Mark Guzdial's media computation🔗︎), which has greatly increased the diversity of computer science graduates, and spread to many universities.
• The field built upon the earliest structured editors like the Cornell Program Synthesizer🔗︎, eventually maturing them into block-based editing environments like Alice🔗︎, Scratch🔗︎ and Blockly🔗︎. These editors greatly increased engagement in computing education, and greatly reduced barriers to learning programming languages.
• Seymour Papert, who was broadly concerned with learning, but also the learning of computing, contributed constructionism, a new theory of learning (Papert 1980🔗︎).
• Alan Kay, one of the earliest researchers to investigate the learning of computing, helped build upon ideas of object-orientation from Simula🔗︎, which inspired Smalltalk, which along with other languages such as C++, inspired the modern object-oriented programming languages and IDEs we use today.

The field's recent efforts to transform STEM education through computing, invent rapid new forms of learning online, and devise more equitable ways to teach should be equally, if not more impactful.

What jobs can computing education researchers get? 🔗︎

Most computing education researchers are faculty in universities. Many of these faculty are tenure-track faculty like myself, which means a substantial portion of our time (~50%) is spent on scholarship. However, there are also many full-time instructors who find additional time to do research on top of their teaching. Many of the original authors at ICER were once members of the Bootstrapping or Scaffolding groups (led by Fincher, Petre, and Tenenberg), who were CS teachers that started to do research in their own classrooms.

Not all computing education researchers are college faculty. Some work in industry creating educational technologies for teaching computing, applying their expertise to the research and design of educational software. Some work in non-profits, using their expertise to advocate for computing education in schools, while conducting research on factors that affect policy. Some work in school districts, helping to implement computing education curricula in schools, while studying and evaluating the effectiveness of the implementation. Others work in government, facilitating research funding. Others still become teachers themselves, both at universities and other schools.

Tenure-track faculty are in the best position to make advances in the field because a substantial portion of their time is dedicated to research, but the research contributions by teaching-track faculty are critical, as they often bring more richly informed perspectives on the practice of teaching. It is possible to do research in other positions, but it is often outside the scope of a job. Because of this, many non-tenure track faculty focus their research on settings that their job gives them access to, which can restrict which research questions they can answer.

How do I become a CER researcher? 🔗︎

The most effective route is to get a Ph.D. in computing education research at one of the many Ph.D. granting universities in the world. Ph.D. students learn to conduct research over the course of multiple years (generally 4 to 6) under the supervision of an advisor.

Undergraduate research is a key part of creating pathways to Ph.D. programs. Undergraduates can help accelerate research projects, and even lead their own projects, helping with admission to Ph.D. programs (especially if you publish, which demonstrates your interest and ability in conducting research). See the CRA-E best practices guide on undergraduate CS research🔗︎ for a glimpse into how effective undergraduate research experiences should work.

Where can I get a Ph.D. in CER? 🔗︎

You need to find a university that grants Ph.D.s and has tenure-track faculty who do research in CER on a topic that you're interested in. The alphabetical list below contains some of the many faculty who advise Ph.D. students on computing education research, or who can serve on PhD committees. Find them online and see what kind of research they're doing. (This list may be out of date, as faculty sometimes move universities, retire, go to industry, or change research areas, so be sure to check their website for the latest information).

One common question is whether to get a Ph.D. in a CS department, a College of Education, or some other kind of computing or learning related department, such as information schools, which are often concerned with computing and data literacy. Ultimately, the doctoral program you choose is going to shape a few things: 1) the classes you're required to take in the first year or two, 2) the peers you might sit near and talk to, 3) the faculty who might serve on your committee and what expertise and values they have, and 4) what resources you have to get particular kinds of jobs. For example, if you go to a CS Ph.D. program, you're going to learn about the latest research in various areas of CS, be surrounded by people interested in computing, but possibly not many interested in computing education; you'll have resources for getting faculty jobs in CS departments, but not really Colleges of Education. In contrast, if you go to a College or School of Education Ph.D. program, you're going to learn about the latest knowledge in education and learning sciences, and be surrounded by people passionate about learning, equity, and justice, but possibly not many people interested in computing. And if you go to a place like an information school, you'll gain new perspectives about data and computing, be surrounded by a radical diversity of people with interests that span many disciplines, but possibly one of only a few people interested in computing education.

Because of the tradeoffs above, the best places to go are ones where there are advisors that share your interests, a critical mass of people interested in computing education, and healthy interchange between academic units interested in computing, learning, and data. This is because computing education is inherently interdisciplinary; you want peers and faculty that appreciate that, value that, and support that.

One note about selecting advisors: their disciplinary affiliation is just one indicator of the nature of the contributions they might make (people in CS departments might built learning technologies, people in colleges of education might focus on teacher training and pedagogy), but this is not a perfect indicator. Look closely at researchers' recent publications; and if their websites seem out of date, write them to ask what they're working on.

Another caveat: some of the faculty below have chosen their expertise descriptions, but others I had to extract from faculty websites. I've put a * next to expertise that hasn't been chosen or agreed to by the researcher being described. These expertise tags are also likely to be perpetualy out of date, as researchers pursue new topics. The best thing to do is click on their name to visit their website and see what kinds of research they have published. That's the most direct indicator of their interests, the methods they use, and the types of contributions they want to make (other than just writing them and asking, which you can also do).

For doctoral admissions, how important is it to focus on a single research area? 🔗︎

Advisors differ on the criteria they use to select candidates. Personally, I look for 1) experience with research, 2) passion in the subject of computing education, 3) the requisite skills to pursue that passion, and 4) an overlap with my interests. You can get experience by working with faculty at your own institution. That can be hard if you don't have faculty doing work in this area. The requisite skills depend a lot on the contributions you want to make. If you want to envision and build new learning technologies, can you code well enough to build them? If you want to investigate new teacher training methods, do you have teaching experience? If you want to do more theoretical work, how strong are your writing and analytical skills? All of these skills end up being important in some way to participating in CER discourse, just to varying degrees.

Working specifically in computing education isn't necessary to achieve the above. Perhaps you have undergraduate research experience in HCI, software engineering, or programming languages. That can be fine, as long as your passion is clear and the skills you have align with the questions you want to answer. Researchers are always investigating new questions, so it's perfectly normal to have experience from other related areas of computing and information science.

Of course, even if you meet all of the criteria above (or other criteria that other advisors might have), you might not get accepted. That's because doctoral advising is extremely time-intensive: we commit to advise people for anywhere from 3-6 years or more, and so we can only take on so many students at a time. There might be a dozen people who apply to work with one of us, but we only have capacity to admit one or two at most.

Where can I find a CER job? 🔗︎

There are many places where global CS education-related jobs are posted:

• The #jobpostings channel on the CSforAll Slack🔗︎:
• The CRA Jobs website🔗︎
• The SIGCSE-jobs mailing list🔗︎
• The Higher Ed Jobs🔗︎
• EdJoin🔗︎
• Evan Peck maintains a list of CS faculty postings from "PUIs"🔗︎ (primarily undergraduate institutions).
• Many organizations, including non-universities, hire postdocs (e.g., Quinn Burke at Digital Promise🔗︎, has hired postdocs and consultants)

Monitor those closely for opportunities. The field is growing, but in unconventional ways: there are tenure-track positions, teaching-track positions, professor of practice positions, postdocs, research and development positions in not-for-profits, and much, much more.

Is there funding for CER? 🔗︎

Yes! At least in the U.S., Ph.D. students are generally funded by the research grants their advisors obtain, and can also receive NSF Graduate Research Fellowships, which cover three years of tuition and stipend. Undergraduates can participate in NSF-sponsored Research Experience for Undergraduate projects that faculty sponsor. CER faculty can also apply for NSF CAREER grants on computing education research, or an NSF Research Initiation Initiative🔗︎ for new faculty. Most Ph.D. granting institutions also offer teaching assistantships. In the United States, there are also regularly programs that fund CER. This changes frequently, but here is a current snapshot as of 2016:

• NSF CS for All🔗︎. Funds basic research on CS education as well as researcher-practitioner partnerships focused on building K-12 CS education capacity, access, participation, and engagement.
• NSF IUSE🔗︎. Funds programs that improve the quality of and access to STEM education in undergraduate programs. Does not directly fund basic research.
• NSF DUE🔗︎. Funds innovations in STEM education at 2- and 4-year colleges.
• NSF ITEST🔗︎. Funds programs that broaden participation in STEM. Does not directly fund basic research.
• NSF DRK-12🔗︎. Funds projects that enhance the quality of and access to STEM education in K-12, including basic research.
• NSF RETTL🔗︎. Funds projects on Emerging Technologies for Teaching and Learning, including intelligent tutors, computer-based instruction, computational tools for learning, etc.
• NSF EHR CORE Research🔗︎. Funds basic education research. Not CS specific, but it has separate tracks within its reviewing structure for CS and engineering.

What do I need to know to be an effective researcher? 🔗︎

First, you need to know some computing yourself. That doesn't mean you need an entire computer science degree, but it helps to have learned to code a bit, and to understand what an algorithm and a data structure is. It can also help to understand the culture of computer science as an academic discipline. Taking the first few introductory courses in a CS department is usually enough to provide this content knowledge foundation, unless you want to do research on the teaching of more advanced topics in CS.

Another thing to know is what makes good computing education research. One guide is to read peer review criteria. For example, ACM TOCE maintains and evolves a list of nuanced and pluralist peer review criteria🔗︎ that cover many kinds of research.

Beyond that, there is a substantial prior work to learn before you can make original discoveries. I've organized some of the major works into categories below, to focus your reading.

Education research foundations

As computing education research is a discipline-based kind of education research, foundations in education research are key. Below are essential works for conducting research on learning and teaching:

• How People Learn: Brain, Mind, Experience, and School🔗︎ and How People Learn II: Learners, Contexts, and Cultures🔗︎ provide an essential foundation in the major discoveries and theories of learning sciences and education research. Anyone doing research on learning should know everything in these books.
• Power and Privilege in the Learning Sciences: Critical and Socialcultural Theories of Learning🔗︎ presents foundational theories of learning that powerfully shape who learns.
• Research Methods for Social Justice and Equity in Education🔗︎ presents key methods for conducting education research in equitable ways, but also about equity and justice.
• Successful qualitative research🔗︎ discusses foundational methods for qualitative methods and thematic analysis in particular, a key approach to qualitative data anlysis frequently used in computing education. A good companion to this is Padiyath and Nelson-Fromm's reflection on thematic analysis🔗︎, which dispells some common misuses and misunderstandings about interpretative epistemic frames.
• There are also several notable commentaries on tensions between qualitative and quantitative data, including Hammer and Berland's arguments about qualitative coding as generating claims🔗︎, McDonald et al's guidelines for inter-rater reliability practices🔗︎, Soden, et al's guide for how to evaluate interpretive research🔗︎, and Chi's classic guide for quantifying verbal data🔗︎.
• For any scholars marginalized in CS or society more broadly, and studying marginalization, a must read is A Methodology for the Marginalized🔗︎. It deconstructs the complex dynamics of managing secondary trauma.

Race and Technology

While not specifically about computing education, these books critically examine the role of computing in justice. The ideas in these books are key to understanding the social implications of computing on society. I focus on race in particular because race, at least in the United States, has structured injustice more heavily than all other social categories, making it critical to understanding the effects of computing.

• Race After Technology🔗︎ examines how racism is embedded in software and the role of computer scientists and the software industry in reinforcing racism.
• Black Software: The Internet and Racial Justice, from the AfroNet to Black Lives Matter🔗︎ explores the long history of racial justice movements organized online and how the impact of their innovations have been erased with false narratives social media company innovation.

CER literature reviews

These works summarize bodies of knowledge in computing education research, helping you to more quickly learn what the field has discovered. All of these are essential reading.

• The Cambridge Handbook of Computing Education Research🔗︎ is a carefully edited synthesis of all of the major discoveries in computing education research since its beginning as a field 50 years ago up until 2018. I authored several chapters along with more than a dozen other leading researchers with the goal of creating the definitive introduction to the field. It is reflective of prior work, for better or worse, going deeply into pedagogy, but only briefly (but elegantly) addressing issues of racism, sexism, and inclusion. Therefore, it should not be viewed as a vision for the future of the field, but rather a reflection of its past.
• Learner-Centered Design of Computing Education: Research on Computing for Everyone🔗︎ is a wonderful synthesis of computing education research, with a focus on pedagogy for anyone learning computing, rather than just computer science students.
• Computational Thinking in K-12 A Review of the State of the Field🔗︎ examines the state of discourse about "Computational Thinking", a contested idea that has spread broadly throughout K-12 CS education, despite its questionable soundness as an idea.
• Introductory programming: a systematic literature review🔗︎. The result of an ITiCSE working group, analyzes the literature along many facets: students, teachings, the curriculum, and assessment, and surfacing directions for future research.
• A survey of literature on the teaching of introductory programming🔗︎ summarizes papers on classroom instruction in introductory programming courses in higher education.
• Constructing a core literature for computing education research🔗︎ presents an appendix of impactful papers published before 2005.
• Misconceptions in programming🔗︎ is a great review of the broad literature on misconceptions that people form about programming languages.
• Lowering the Barriers to Programming: A Taxonomy of Programming Environments and Languages for Novice Programmers🔗︎ surveys hundreds of programming languages and environments intended to support learning to code. There have been many more since its publishing in 2005, but before ever inventing one of your own, it's important to know what's been invented already.
• The State of the Art in End-User Software Engineering🔗︎ summarizes the literature on end-user programming, which is related, but not the same as novice programming. It synthesizes of all of the programming languages, environments, and tools that have helped people learn to code while automating a task.

Notable works

Everyone working in CER should have read these books and understand their implications for research and practice.

• 'Underrepresented Minority' Considered Harmful, Racist Language🔗︎ is a short blog post that discusses the terminology we use when we discuss diversity in computer science.

• Mindstorms: Children, Computers, and Powerful Ideas🔗︎ is a classic book that envisions a theory of learning grounded in the construction of knowledge through personally meaningful tinkering and creation, especially with computers. I summarized the book🔗︎ in a blog post.

• Stuck in the Shallow End: Education, Race, and Computing🔗︎ illustrates the numerous racist structures, beliefs, and practices in K-12 education that systematically exclude students of color from CS education.

• Unlocking the Clubhouse: Women in Computing🔗︎ examines how the culture of higher education CS systematically excludes and deters women from participating in CS education, and explores promising practices for changing this culture.

• Epistemological Pluralism: Styles and Voices Within the Computer Culture🔗︎ presents a critique of academic computing culture for is exclusion of diverse interests and ways of knowing.

• When Twice as Good Isn't Enough: The Case for Cultural Competence in Computing🔗︎ critiques CS departments for being uncritical of themselves, their curricula, and the software industry, advocating for cultural competence amongst faculty and students.

• The Intersection of Gender, Race and Cultural Boundaries, or Why is Computer Science in Malaysia Dominated by Women?🔗︎ examines the inherent intersectional complexity of race, gender, and culture that shapes participation in computing education.

• They can't find us: the search for informal CS education🔗︎ demonstrates how search engines, CS education terminology, and culture interact to connect educated White families to informal CS learning opportunities, while obscuring them from less privileged families.

• Visions of Computer Science Education: Unpacking Arguments for and Projected Impacts of CS4All Initiatives🔗︎ analyzes the abundance of arguments for K-12 CS for All efforts, and how they intersect with varying political ideologies.

• On Theory Use in Computing Education Research🔗︎ examines the use of theory in computing education and how it is often weaponized to prevent the publication of new ideas.

• Ethics, Identity, and Political Vision: Toward a Justice-Centered Approach to Equity in Computer Science Education🔗︎ advocates for CS education researchers and teachers to more directly engage the sociopolitical context of CS education curricula and teaching.

• Halving fail rates using peer instruction: a study of four computer science courses🔗︎ presents one of the few rigorously examined teaching methods that promotes improved learning, especially for students marginalized by CS education cultures.

• African American men constructing computing identity🔗︎ examines how race, culture, and stigma can warp genuine interests in computing, and how informal learning interventions can counter these forces.

• COMPUGIRLS’ Standpoint: Culturally Responsive Computing and Its Effect on Girls of Color🔗︎ illustrates the impact of culturally repsonsive computing on girls of color.

• Digital Youth Divas: Exploring Narrative-Driven Curriculum to Spark Middle School Girls’ Interest in Computational Activities🔗︎ explores how to engage girls of color by centering their stories.

• Becoming Technosocial Change Agents: Intersectionality and Culturally Responsive Pedagogies as Vital Resources for Increasing Girls’ Participation in Computing🔗︎ explores the importanc of intersectional views on culturally responsive pedagogy.

If you've read all of the above and are looking for more literature, be sure to follow all of the SIGCSE conferences, and other relevant education and learning science journals, monitoring the ACM Digital Library🔗︎ and the NSF funded website CSEdResearch.org🔗︎, which surveys the broad expanse of CS education research, including article summaries and evaluation instruments.

What books provide guidance on CS teaching? 🔗︎

While there are many books that provide guidance on teaching in general (e.g., Tools for teaching (Davis, 2009), How learning works: Seven research-based principles for smart teaching (Ambrose et al., 2010), Teaching what you don’t know (Huston, 2009), What Works Clearinghouse🔗︎), there are only a handful of books written to guide CS educators (alphabetically):

• The Big Book of Computing Pedagogy🔗︎ offers a collection of CS teaching practices and methods written by CS educators and CS education researchers.
• Coding as a playground: Programming and computational thinking in the early childhood classroom🔗︎ (Bers, 2017). A review of the opportunities in teaching younger children to code.
• Computational Thinking🔗︎ (Denning & Tedre, 2019). A historical introduction to computational thinking.
• Computational thinking and coding for every student🔗︎ (Krauss and Prottsman, 2016). Includes strategies and activities for teaching computational thinking, with several lessons and annotated resources.
• Computer Science K-12: Imagining the Possibilities!🔗︎ (Bergman, 2018). Full of rich case studies, activities, projects, and practical guidance on organizing and managing CS classrooms.
• Computer Science in K-12: An A-Z Handbook on Teaching Programming🔗︎ (Grover, 2020) includes 26 chapters featuring foundational programming concepts and practices, as well as well-researched pedagogies for teaching introductory programming. With chapter contributions from researchers and classroom teachers, the book shares concrete examples as well as abstract principles distilled from research studies and classroom practice along with many illustrative examples (in block- and text-based programming) that can be used in classrooms.
• Computer Science Teacher: Insight Into the Computing Classroom🔗︎ (Clark, 2017). Focuses on secondary CS teaching and what the role entails, providing a rich set of case studies and quotes.
• Computational Thinking in Education: A Pedagogical Perspective🔗︎ (Yadav and Berthelsen, 2022), explores the relevance of computational thinking in primary and secondary education, giving an overview of what computational thinking is and how to integrate it into learning, instruction, and assessment.
• Computer Science Education: Perspectives on teaching and learning in school🔗︎ (Sentance et al. 2018). An edited book full of rich summaries about CS education research, but written less for teachers and more for those interested in research.
• Connected Code: Why Children Need to Learn Programming🔗︎ (Kafai et al., 2016). Argues for moving beyond computational thinking to computational participation, leveraging social networks and digital making. Discusses examples of youth participation with programmable toys, tools, and textiles and the ethical challenges that emerge in these social contexts.
• Critically Conscious Computing: Methods for Secondary Education🔗︎ (Ko et al., 2021). A critical survey of CS and CS education foundations and a collection of dialogic teaching methods for promoting student critical consciousnes about computing and society.
• Guide to teaching computer science: An activity-based approach🔗︎ (Hazzan et al. 2015). Includes detailed learning activities, curriculum reviews, CS education research, lesson planning, and course design. Some pre-service CS teachers find the concrete examples help; others find it jargony and overly complex.
• Invent to learn: Making, tinkering, and engineering in the classroom🔗︎ (Martinez and Stager, 2013). A practical guide to bringing tinkering into the classroom via project-based learning.
• Preparing Pre-Service Teachers to Teach Computer Science: Models, Pracitces, and Policies🔗︎ (Mouza, Yadav, Ottenbreit-Leftwich, 2021). An edited volume surveying research on CS teacher education, offering examples for how to design and deliver effective teacher preparation.
• Teaching Computing in Secondary Schools🔗︎ (Lau, 2017). Offers a framework for planning and delivering CS curricula.
• Teaching Computing: A Practitioner's Perspective🔗︎ (Walker, 2018). Full of teaching tips for higher education faculty teaching CS.
• Teaching tech together🔗︎. This is an informal survey of research useful for teaching programming. Greg put it together to help others become better teachers of computing.
• Your First year teaching computer science🔗︎ (Gregg, 2021).
• Code in Every Class🔗︎ (Brookhouser and Megnin, 2017).
• Integrting Computer Science Across the Core: Strategies for K-12 Districts🔗︎(Lynch and Ardito, 2020).
• Creative Coding: Lessons and Strategies to Integrate Computer Science Across the 6-8 Curriculum🔗︎ (Caldwell, 2018)

Is one missing from this list? Let me know and I'll add it.

What conferences and journals publish CER? 🔗︎

Most academic fields have exclusively academic venues for publication, with few practitioners participating in or reading the research that researchers produce. The CER community is unique (and I believe quite fortunate) in that practitioners are deeply involved in the academic research community (partly because most faculty conducting research are teachers themselves). Below I note several conferences and journals where you can publish computing education research (see SIGCSE for a broader list🔗︎). Note that I separate the pure research venues from the venues that combine both research and practice since the combined venues are often dominated by practioners, which can make it hard to have focused research conversations and rigorous peer review.

Research only venues

• ICER🔗︎ (the ACM International Computing Education Research conference) is the only academic conference that strictly publishes research. All of the reviewers who peer review submissions are trained researchers with Ph.D.s. ICER tends to focus on theoretically, methodologically, and empirically-rich work, advancing the science of computing education. It is held around the world but is generally in North America every other year.

• TOCE🔗︎ (the ACM Transactions on Computing Education) publishes research, and is similar in scope to ICER, but in a journal format. Like ICER, the editorial board and reviewers are all trained researchers.

• CSE🔗︎ (the Journal of Computer Science Education) publishes research and is similar to TOCE and ICER in its reviewing community and similar in research rigor and prestige. However, unlike TOCE and ICER, publications in CSE are generally expected to have more direct implications for teachers.

• ICLS🔗︎ (the International Conference on Learning Sciences) does not strictly focus on computing education, but publishes high quality research on learning sciences. Accepts both qualitative and quantitative work, especially of mixed methods. Also tends to focus more on K-12 than the venues focusing strictly on CER.

• JLS🔗︎ (the Journal of Learning Sciences) is one of the top education research journals and expects a strong connection to learning theory and mostly wants empirical work. It is not a journal that publishes HCI, so work must be connected to cognition, sociocultural context, or other theory, and not system design.

• CSCL🔗︎ (the International Conference on Computer-Supported Collaborative Learning) focuses on issues related to learning through collaboration and promoting productive collaborative discourse with the help of the computer and other communications technologies.

• IJCSCL🔗︎ (the International Journal of Computer-Supported Collaborative Learning), like CSCL, focuses on learning through collaboration.

• L@S🔗︎ (the ACM Conference on Learning at Scale) is a computer science conference that focuses on techniques for scaling instruction. Some of the work published here concerns computing education, but many other domains are represented as well. Often focuses on MOOCs and other forms of online learning.

• RESPECT🔗︎ (the IEEE Conference on Research on Equity and Sustained Participation in Engineering, Computing, and Technology) is a conference focused on engagement, participation, and equity in STEM fields. It has research and experience report tracks, and expects empirical papers grounded in theory.

• IDC🔗︎ (ACM SIGCHI Interaction Design and Children) is an HCI conference with a focus on children, focusing on design artifacts for kids and enabling kids to be designers, with a special focus on participatory design as a methodology.

• CHI🔗︎ (ACM SIGCHI Conference on Human Factors in Computing) is an HCI conference with a focus on any aspect of interactions between people and computers, including programming. As one of the largest and broadest ACM conferences, it's easy for research on learning to get lost here, but so does every other topic!

• AERA🔗︎ (the American Education Research Association conference) has a division for engineering and computing education that publishes papers on computational thinking.

• JEE🔗︎ (the Journal of Engineering Education). High-quality but with few international collaborations (like the MIMN studies in CER). Occasionally has papers related to computing.

• IEEE Transactions on Education🔗︎. I know little about this journal. Feel free to share opinions!

• EDM🔗︎ (the International Conference on Educational Data Mining). Explores using educational data to understand student learning.

• JEDM🔗︎ (the Journal of Educational Data Mining). Publishes research on the use of data mining in education.

Research and practice venues

• SIGCSE🔗︎ (the SIGCSE Technical Symposium on Computer Science Education) publishes both research and practice papers in a short format, bringing together researchers and teachers. This is the largest conference on computer science education and generally attracts teachers. There is a dedicated research track separate from experience reports, though the research track has a 6-page limit, making it unsuitable for many forms of research, such as qualitative work or more substantial quantitative work. Generally held in North America.
• ITiCSE🔗︎ (the Annual Conference on Innovation and Technology in Computer Science Education) publishes both research and practice papers, with a focus on practice. Generally held in Europe.
• Koli Calling🔗︎ (International Conference on Computing Education Research), held in Finland every year, publishes research and practice papers with a focus on qualitative research. A small but dedicated community.
• WiPSCE🔗︎ (Workshop in Primary and Secondary Computing Education) aims to bring together researchers and practitioners, and publishes both research and practice papers. It is generally held in Europe.
• ACE🔗︎ (the Australasian Computing Education Conference) is a regional conference with a mix of research and practice papers, bringing together education researchers and practitioners. Held in Australia or New Zealand, but welcomes attendees from anywhere.
• LaTiCE🔗︎ (the International Conference on Learning and Teaching in Computing and Engineering) publishes both research and practice papers. Held primarily in Asia.
• FIE🔗︎ (the ASEE Frontiers in Education conference) is more broad and more practitioner focused than SIGCSE and occasionally has CER work.

What is SIGCSE? 🔗︎

SIGCSE🔗︎, like other ACM Special Interest Groups (SIGs), is an organization that focuses on a particular topic within ACM, namely computer science education. It sponsors ACM conferences (e.g., the SIGCSE Technical Symposium and ICER) and influences their structure and focus. Note that SIGCSE the group organizes SIGCSE the conference. I know, it's confusing, but aren't you glad you read this?

What's the difference between a research paper and an experience report? 🔗︎

This is an important question, since many of the conference venues in the computing education community publish both. Unfortunately, the community hasn't developed much clarity about the differences between these. The result is that many papers published in the SIGCSE experience report track look like research papers, and many of the papers published in the SIGCSE research track look like experience reports. What's the essential difference?

In my opinion, the key distinction between research and an experience report is your audience, which implies your goals: are you writing to researchers, who aspire to build upon everything we know to advance theories about what we know about CS teaching and learning? In contrast, if you're writing to teachers, you're likely sharing practical knowledge, such as an interesting method you tried, a surprising experience, or a teaching method others might experiment with. The critical difference is that in research, we're trying to be certain that we know something, but it's okay if we don't know how to put that knowledge into action yet, whereas in practice, we're trying to learn how to teach something, even if we're not certain it will work. Another way to characterize the difference are some of the evaluation criteria. Research papers should be novel with respect to everything we know and sound, but not necessarily immediately useful. Experience report papers should be novel with respect to common knowledge (but not necessarily novel with respect to all knowledge), useful and interesting, but not necessarily sound.

I believe that both are valuable in their own ways. Research allows us to build confidence in what we know, whereas sharing experience allows us to teach each other. We need both for a thriving practice of CS teaching and a thriving body of knowledge to inform that practice.

How can I keep up with the latest research, practice, and policy? 🔗︎

There are a few excellent blogs (in alphabetical order):

• Mark Guzdial's Computing Education Research🔗︎ blog has been active since 2009 and contains thousands of posts that explain computing education research to a broader community.
• Felienne Hermans has a blog about programming and inclusion🔗︎.
• Amy Ko's Bits & Behavior🔗︎ publication at Medium covers CER, software engineering, HCI, and broader issues in academia.
• Shriram Krishnamurthi's Parenthetically Speaking🔗︎ discussions a range of topics on academia, programming languages, and computing education.
• William Lau's blog on CS education and teaching more broadly🔗︎.
• Lauren Margulieux's blog🔗︎ discusses learning sciences, discipline-based education reseach, and computing education.
• Alfred Thompson's Computer Science Teacher🔗︎ blog covers a range of computing education, research, and policy issues.
• The CACM blog🔗︎ often has posts related to computing education.

Is this list missing you? Let me know!

How can I connect with the community? 🔗︎

Post-pandemic, many online communities have frayed. Here are a few that remain:

• The CS for All Consortium maintains a Slack team🔗︎. Join as a member🔗︎ to access it.
• There's a private group on Facebook called Computer Science Education: Researchers and Practitioners🔗︎.