Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Do Common Educational Datasets Contain Static Information? A Statistical Study

Abstract : In Intelligent Tutoring Systems (ITS), methods to choose the next exercise for a student are inspired from generic recommender systems, used, for instance, in online shopping or multimedia recommendation. As such, collaborative filtering, especially matrix factorization, is often included as apart of recommendation algorithms in ITS. One notable difference in ITS is the rapid evolution of users, who improve their performance, as opposed to multimedia recommendation where preferences are more static. This raises the following question: how reliably can we use matrix factorization, a tool tried and tested in a static environment, in a context where timelines seem to be of importance. In this article we tried to quantify empirically how much information can be extracted statically from datasets in education versus datasets in multimedia, as the quality of such information is critical to be able to accurately make predictions and recommendations. We found that educational datasets contain less static information compared to multi-media datasets, to the extent that vectors of higher dimensions only marginally increase the precision of the matrix factorization compared to a 1-dimensional characterization.These results show that educational datasets must be used with time information, and warn against the dangers of directly trying to use existing algorithms developed for static datasets.
Type de document :
Communication dans un congrès
Liste complète des métadonnées
Contributeur : Théo Barollet Connectez-vous pour contacter le contributeur
Soumis le : vendredi 14 janvier 2022 - 13:35:01
Dernière modification le : jeudi 20 janvier 2022 - 14:08:25


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-03526276, version 1


Théo Barollet, Florent Bouchez-Tichadou, Fabrice Rastello. Do Common Educational Datasets Contain Static Information? A Statistical Study. EDM 2021 - Conference on Educational Data Mining, Jun 2021, Paris / Virtual, France. pp.1-7. ⟨hal-03526276⟩



Les métriques sont temporairement indisponibles