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

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