Computing Education Research

Computing Education Research (CER) is the scholarship of teaching and learning in Computing related fields like computer science, software engineering, data science, information technology, and information sciences. CER studies are conducted on computing students and educators at all levels including K-12, undergraduate, graduate, teachers, educators, and researchers. CER utilizes theories and practices from other educational research areas like psychology, sociology, cognitive science, education, and engineering. With the emergence of computing as an important competency for the next generation of learners, CER is critical for building on existing theory and developing new theories to support the learners of tomorrow.

CER Opportunities

As a growing field, computing educators face many challenges in supporting learners in their classrooms, bootcamps, workshops, and summer camps. Some broad questions that we need to consider as a community are:

  • How to best teach computing (at scale)?
  • What technologies will best support computing education goals?
  • How do we support and retain learners from a variety of backgrounds?
  • How do we ensure equity for learners?

Computing educators have the potential to contribute to the body of CER knowledge and support our learners through research that evaluates and adoption of evidence-based findings as classroom practice. Additional community level questions are:

  • How can we train computing educators in CER?
  • How can we enable educators to conduct CER?

The 2016 white paper on the Importance of Computing Education Research by Steve Cooper, Jeff Forbes, Armando Fox, Susanne Hambrusch, Amy Ko, and Beth Simon provides some additional areas of opportunity for CER.

CER Considerations

There are challenges and constraints with CER.


CER studies take time to design and run. Because most CER involves human subjects, ethics considerations, which may involve some type of approval process like IRB in the United States, will take time and should be considered when creating a study time line. Once the study is designed, the time to conduct the study may vary from a short classroom intervention to multiple semesters or years of data collection.

Some considerations for researchers:

  • Do you need a baseline or treatment to compare with some intervention?
  • Can you compare students from different cohorts? For example, are students in the Fall offering of your course the same as students in your Spring offering of your course? What about Fall to Fall comparisons or Spring to Spring?
  • Can data collection and/or analysis occur throughout the study or should that wait until after the semester of instruction is complete?
  • Who is available to help with the study intervention, data collection, data analysis, and reporting? A single researcher may have different constraints than a research team.
  • What other activities are the researcher(s) participating in during the timeframe?


The scale of the research activities should be appropriate to answer the research questions within the constraints of the institutional context and the research team. A single study should focus on one intervention to better understand the impact of that intervention. Too many changes at once makes it difficult to know if any (or all) of the interventions worked and which (if any) were most effective. While you may be motivated to make all the planned changes at once, take the time to study each intervention and build a research agenda over the course of several studies.


CER is a rapidly growing field and funding is available from governments (e.g., National Science Foundation in the United States), organizations (e.g., NCWIT, CSforAll, etc.), and private companies (e.g., Google, Microsoft, IBM, etc.). Educational institutions may have internal grants to support SoTL projects. Keep an eye out for opportunities to pay yourself and cover expenses for CER work in your classroom.


While CER studies can be run solo, it is helpful to include and mentor undergraduate or graduate researchers in the study. Collaboration with colleagues may also be important to handle things like soliciting participation in a research study and holding on to informed consents during a semester of instruction. As you’re designing your study, consider the different collaborators on the project and create processes for how each collaborator will contribute. For example, an undergraduate researcher probably shouldn’t see data with identifiers from a class they were enrolled in.


Where possible, CER should be tied to evaluated expectations for your position and complement realms of responsibility. For example, a research study could also be used to demonstrate continuous improvement for a departmental accreditation visit. Or an intervention that increases the retention of populations historically underrepresented in computing could support departmental diversity, equity, and inclusion efforts.