Conference paper Green Open Access 2022

A Classification Approach to Recognize On-Task Student’s Behavior for Context Aware Recommendations

RL
Roux, Lisa
 NT
Nodenot, Thierry
 EP
Etcheverry, Patrick
 DP
Dagorret, Pantxika
 MC
Marquesuzaá, Christophe
 LP
Lopistéguy, Philippe
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference · Vol. 13284 LNCS · pp. 161-170
Abstract

The increasing development of e-learning systems has raised the necessity to apply recommender systems with the aim of guiding learners through the various courses, activities, etc. at their disposal. The learner-oriented approaches allow the recommendations to fit the user’s needs as precisely as possible. Nevertheless, due to the multiplicity of possible educational situations and individual particularities, offering adaptive recommendations and diversity is still a major challenge. In order to improve this aspect and provide the learner with recommendations appropriate to both their current specific needs and general profile, we focus on an hybrid system whose knowledge will be augmented through the learner’s activity and results. This system will base its analyses and future recommendations according to the evolving student’s profile and behaviour during the task. For that purpose, a first step is to categorize the on-task student’s behaviour. This paper focuses on this problem and proposes a model, provided by educational sciences, on which the recognition process could be based. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Author Keywords

E-learning Recommender Systems Learner behaviour model Supervised classification

Index Keywords

E-learning Learning systems Education computing Students 'current Recommender systems E - learning E-learning systems Hybrid systems User profile Behaviour models Classification approach Context-aware recommendations Educational science Learner behavior model Recognition process Supervised classification
Author Affiliations
Université de Pau et des Pays de l'Adour, Pau, Nouvelle-Aquitaine, France
Funding & Acknowledgements
No funding information
References 10 References
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