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Cited by 332

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies

Open access
Ihantola, Petri, Vihavainen, Arto, Ahadi, Alireza, Butler, Matthew, Börstler, Jürgen, Edwards, Stephen H., Isohanni, Essi, Korhonen, Ari, Petersen, Andrew, Rivers, Kelly, Rubio, Miguel Ángel, Sheard, Judy, Skupas, Bronius, Spacco, Jaime, Szabo, Claudia, Toll, Daniel·2015·Proceedings of the 2015 ITiCSE on Working Group Reports
DOI 10.1145/2858796.2858798
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Abstract

Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.

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Metadata

Authors
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. Á., Sheard, J., Skupas, B., Spacco, J., Szabo, C., & Toll, D.
Venue
Proceedings of the 2015 ITiCSE on Working Group Reports
Publisher
ACM
Published
2015-07-04
Pages
41-63
DOI
10.1145/2858796.2858798
OpenAlex
W2264893711
Cited by
332
References
89
FWCI
14.36
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. https://app.cochat.ai/papers/10.1145/2858796.2858798
The paper
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. Á., Sheard, J., Skupas, B., Spacco, J., Szabo, C., & Toll, D. (2015). Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. Proceedings of the 2015 ITiCSE on Working Group Reports. https://doi.org/10.1145/2858796.2858798

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.