Introduction to the Special Issue on EDM in Computer Science Education (CSEDM)

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Published Mar 15, 2023
Thomas W. Price Sharon Hsiao Bita Akram Peter Brusilovsky Juho Leinonen

Abstract

Educational Data Mining in Computer Science Education (CSEDM) is an interdisciplinary research community that combines discipline-based computing education research (CER) with educational data-mining (EDM) to advance knowledge in ways that go beyond what either research community could do on its own.

The JEDM Special Issue on CSEDM received a total of 12 submissions. Each submission was reviewed by at least three reviewers, who brought expertise from both the EDM and CER communities, as well as one of special issue editors. Ultimately, three papers were accepted, for an acceptance rate of 25%.


These three papers cover a variety of important topics in CSEDM research. Edwards et al. discuss the challenges of collecting, sharing and analyzing programming data, and contribute two high-quality CS datasets. Gitinabard et al. contribute new approaches for analyzing data from pairs of students working on programs together, and show how such data can inform classroom instruction. Finally, Zhang et al. contribute a novel model for predicting students' programming performance based on their past performance. Together, these papers showcase the complexities of data, analytics and modeling in the domain of CS, and contribute to our understanding of how students learn in CS classrooms.

How to Cite

Price, T. W., Hsiao, S., Akram, B., Brusilovsky, P., & Leinonen, J. (2023). Introduction to the Special Issue on EDM in Computer Science Education (CSEDM). Journal of Educational Data Mining, 15(1). https://doi.org/10.5281/zenodo.7728208
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Section
Editorial Acknowledgment