Improve Learner-based Recommender System with Learner’s Mood in Online Learning Platform

Qing Tang (1) , Marie-Hélène Abel (1) , Elsa Negre (2)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

Learning with huge amount of online educational resources is challenging, especially when variety resources come from different online systems. Recommender systems are used to help learners obtain appropriate resources efficiently in online learning. To improve the performance of recommender system, more and more learner’s attributes (e.g. learning style, learning ability, knowledge level, etc.) have been considered. We are committed to proposing a learner-based recommender system, not just consider learner’s physical features, but also learner’s mood while learning. This recommender system can make recommendations according to the links between learners, and can change the recommendation strategy as learner’s mood changes, which will have a certain improvement in recommendation accuracy and makes recommended results more reasonable and interpretable.

Mots clés: online collaborative learning; learner’s activity; learner’s mood; learner model; recommender system; learner's activity; learner's mood

Domaines: Environnements Informatiques pour l'Apprentissage Humain; Recherche d'information [cs.IR]

Qing Tang, Marie-Hélène Abel, Elsa Negre. Improve Learner-based Recommender System with Learner’s Mood in Online Learning Platform. 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021), Dec 2021, Pasadena, CA, United States. pp.1704-1709, ⟨10.1109/ICMLA52953.2021.00271⟩. ⟨hal-03529869⟩