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Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior

Watson, Christopher; Li, Frederick W.B.; Godwin, Jamie L.

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Authors

Christopher Watson

Jamie L. Godwin



Abstract

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress over the duration of a course. In this paper we present a new approach for predicting a student's performance in a programming course, based upon analyzing directly logged data, describing various aspects of their ordinary programming behavior. An evaluation using data logged from a sample of 45 programming students at our University, showed that our approach was an excellent early predictor of performance, explaining 42.49% of the variance in coursework marks - double the explanatory power when compared to the closest related technique in the literature.

Citation

Watson, C., Li, F. W., & Godwin, J. L. (2013). Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. In Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013) (319-323). https://doi.org/10.1109/icalt.2013.99

Conference Name 2013 IEEE 13th International Conference on Advanced Learning Technologies
Conference Location Beijing
Acceptance Date Nov 30, 2013
Publication Date Jan 1, 2013
Deposit Date Sep 6, 2014
Publicly Available Date Mar 29, 2024
Pages 319-323
Series ISSN 2161-3761
Book Title Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013).
DOI https://doi.org/10.1109/icalt.2013.99
Additional Information Outstanding Paper Award

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Accepted Conference Proceeding (467 Kb)
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