Mining Discriminative Sequential Patterns of Self-regulated Learners

This research explores the links between self-regulation behaviors and indicators of learning performance. A data mining approach coupled with appropriate qualitative measures is proposed to extract behavioral sequences that are representative of learning success. Applied on an online programming platform, obtained results allowed to highlight important self-regulation behaviors during the planning and engagement phases. It e.g. appears that successful self-regulated learners are those who analyze their tasks before working on them. This work brings methodological contributions in the field of self-regulation learning measurement and is a first step towards the design of intelligent tutoring systems.