26 citations · 64 across the 19 of their papers we have counts for
6 papers · 1 filter
RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer
Samuel Girard, Juan D. Pinto, Jill-Jênn Vie +1
As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced interpretability, which is cru…
Counterfactual learning of new adaptive instructional policies using logged data
Samuel Girard, Sein Minn, Amel Bouzeghoub +1
Optimizing instructional policies in Intelligent Tutoring Systems (ITS) typically requires costly online experimentation or student simulators that may fail to capture real-world d…
Reinforcement Learning Foundation Models Should Already Be A Thing
Abdelrahman Zighem, Jill-Jênn Vie
Foundation models for language and vision are powered by internet-scale data, while structured domains such as tabular prediction are powered by synthetic data. This substitute shi…
Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models
Mounir Lbath, Alexandre Parésy, Abdelkayoum Kaddouri +2
Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and…
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger
Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfi…
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
Jill-Jênn Vie, Tomas Rigaux, Hisashi Kashima
Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filter…