An Exploratory Study to Identify Learners' Programming Behavior Interactions

As the number of tools and platforms that have been developed to support learning programming demonstrates, learning programming is becoming more and more ubiquitous in all curricula. In this paper, we present an exploratory study that aims to identify students' programming behaviors. The analysis is based on unsupervised classification algorithms, and features have been selected from prior works on educational data mining. Six students' behaviors were identified using the k-means algorithm. ANCOVA, an extension of analysis of variance (ANOVA), was used to test the main and interaction effects of students' behaviors on their final course scores.