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Predicting performance in an introductory programming course by logging and analyzing student programming behaviour.

Watson, Christopher and Li, Frederick W. B. and Godwin, Jamie L. (2013) 'Predicting performance in an introductory programming course by logging and analyzing student programming behaviour.', in Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013). Piscataway, NJ: IEEE Computer Society, pp. 319-323.

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.

Item Type:Book chapter
Additional Information:Outstanding Paper Award.
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1109/ICALT.2013.99
Publisher statement:© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:30 November 2013
Date deposited:13 July 2016
Date of first online publication:2013
Date first made open access:No date available

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