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Communication Dans Un Congrès Année : 2024

Mining Discriminative Sequential Patterns of Self-regulated Learners

Extraction de motifs séquentiels discriminants chez les apprenants auto-régulés

Amine Boulahmel
Fahima Djelil
Gregory Smits

Résumé

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.
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Dates et versions

hal-04617383 , version 1 (04-07-2024)

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  • HAL Id : hal-04617383 , version 1

Citer

Amine Boulahmel, Fahima Djelil, Jean-Marie Gilliot, Philippe Leray, Gregory Smits. Mining Discriminative Sequential Patterns of Self-regulated Learners. International Conference on Intelligent Tutoring Systems, Jun 2024, Thessalonique, Greece. pp.137-149. ⟨hal-04617383⟩
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