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

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

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

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2013 IEEE 13th International Conference on Advanced Learning Technologies
• Acceptance Date: Nov 30, 2013
• Publication Date: Jan 1, 2013
• Deposit Date: Sep 6, 2014
• Publicly Available Date: Jul 13, 2016
• 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
• Public URL: https://durham-repository.worktribe.com/output/1155608
• Additional Information: Outstanding Paper Award