ABSTRACT
Is quantitative data collected by CS education researchers objective? If we combine data from a set of studies that measure the same type of intervention, will that really show us the strength of that intervention? Are qualitative studies really less rigorous than quantitative because the number of participants may be as low as one?
In this panel, we will first present the different types of studies that are most common in CS education research and provide a working definition for what we mean by various types of research methodologies (e.g., quantitative, qualitative, mixed methods). Drawing upon our experiences in the field of studying computing education, we will then explore some of the myths surrounding data, highlighting where evidence presented through research data is rigorous and, when not, how we (as researchers) can mitigate the risks of collecting and sharing data that is unsound in publications.
We encourage you to attend. After all, this panel has been recommended by four out of five computer science education researchers.
- Sanne F Akkerman, Arthur Bakker, and William R Penuel. 2021. Relevance of Educational Research: An Ontological Conceptualization. Educational Researcher (2021), 0013189X211028239.Google Scholar
- Darrell Huff. 1954. How to Lie with Statistics .W.W. Norton & Company.Google ScholarDigital Library
- Monica M McGill, Tom McKlin, and Errol Kaylor. 2019. Defining what empirically works best: Dynamic generation of meta-analysis for computer science education. In Proceedings of the 2019 ACM Conference on ICER . 199--207.Google ScholarDigital Library
Index Terms
The Case for Acknowledging Subjectivity in CS Education Research Data
Recommendations
CS Education: Catching the Wave
SIGCSE '16: Proceedings of the 47th ACM Technical Symposium on Computing Science EducationComputer Science (CS) education has caught a wave -- of media attention, public support, public/private commitments, broad-based participation by educators, and a surge in student enrollments at the undergraduate level. It is a startling change over ...
Graduate Programs in CS Education: Why 2020 is the Right Time
SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science EducationOpportunities for training CS K-12 pre-service and in-service teachers, research in CS Education, and career pathways for PhDs/EdDs in CS education are happening, but often in an uncoordinated way. We advocate that now is the right time for CS and ...
Undergraduate Computational Science and Engineering Education
It is widely acknowledged that computational science and engineering (CSE) will play a critical role in the future of the scientific discovery process and engineering design. However, in recent years computational skills have been deemphasized in the ...
Comments