Supervision : Vanda LUENGO

Co-supervision : YESSAD Amel

Adapting the Prerequisite Structure to the Learner in Student Modeling

Data-driven learner models aim to represent and understand students’ knowledge and other meta-cognitive characteristics to support their learning by making predictions about their future performance. Learner modeling can be approached using various complex system models, each providing a different perspective on the student and the learning process. Knowledge-enhanced machine learning techniques, such as Bayesian networks, are particularly well suited for incorporating domain knowledge into the learner model, making them a valuable tool in student modeling. This work explores the modeling and the potential applications of a new framework called Embedding Prerequisite Relationships In Student Modeling (E-PRISM), which includes a learner model based on Dynamic Bayesian Networks (DBNs). It uses a new architecture for Bayesian networks that rely on the clause of Independence of Causal Influences (ICI), which reduces the number of parameters in the network and allows enhanced interpretability. The study examines the strengths of E-PRISM, including its ability to consider the prerequisite structure between knowledge components, its limited number of parameters, and its enhanced interpretability. The study also introduces a novel approach for approximate inference in large ICI-based Bayesian networks, as well as a performant parameter learning algorithm in ICI-based Bayesian networks. Overall, the study demonstrates the potential of E-PRISM as a promising tool for discovering the prerequisite structure of domain knowledge that may be adapted to the learner with the perspective of improving the outer-loop adaptivity.

2021-2023 Publications

• 2023
  • O. Allègre : “Adapting the Prerequisite Structure to the Learner in Student Modeling”, thesis, phd defence 05/17/2023, supervision Luengo, Vanda, co-supervision : Yessad, Amel (2023)
  • O. Allègre, A. Yessad, V. Luengo : “Discovering prerequisite relationships between knowledge components from an interpretable learner model”, EDM 2023, Bengaluru, India, pp. 490-496, (ISBN: 978-1-7336736-4-8) (2023)
• 2021
  • O. Allègre, A. Yessad, V. Luengo : “Amélioration de l’interprétabilité du diagnostic cognitif de l’apprenant par catégorisation des composantes de connaissance”, 10^e Conférence sur les Environnements Informatiques pour l’Apprentissage Humain, Transformations dans le domaine des EIAH : innovations technologiques et d’usage(s), Fribourg, Switzerland, pp. 34-45 (2021)