Authors:
Anaëlle Badier
;
Mathieu Lefort
and
Marie Lefevre
Affiliation:
Univ Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France
Keyword(s):
Adaptive Learning, Recommendation System, Educational Mobile Application, After-School Learning.
Abstract:
In this article we present a learning resources recommendation system for an after-school educational mobile application. The goal of our system is to recommend relevant content among the learning resources available in the application to fit student needs and to encourage autonomous learning. The system is based on a graph of key notions to structure the application learning resources. We use the Item Response Theory method to evaluate the student knowledge and filter the most relevant resources to study depending on three learning strategies: revision, continuation and deepening. The resources filtered by the selected strategy, are ranked mainly based on a pedagogical score. The system has been implemented for the Mathematics subject and analysed for middle and high-school students in real-life conditions. In Fall 2022, we recorded the learning traces of 4 670 students that interacted with the system. By analysing experts opinions, logs and students feedback, we can conclude that o
ur system is pedagogically relevant, appreciated and used by students.
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