Early identification of novice programmers' challenges in coding using machine learning techniques

Publication Type:
Conference Proceeding
ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research, 2016, pp. 263 - 264
Issue Date:
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It is well known that many first year undergraduate university students struggle with learning to program. Educational Data Mining (EDM) applies machine learning and statistics to information generated from educational settings. In this PhD project, EDM is used to study first semester novice programmers, using data collected from students as they work on computers to complete their normal weekly laboratory exercises. Analysis of the generated snapshots has shown the potential for early identification of students who later struggle in the course. The aim of this study is to propose a method for early identification of "at risk" students while providing suggestions on how they can improve their coding style. This PhD project is within its final year.
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