26 citations · 64 across the 19 of their papers we have counts for
22 papers
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…
Estimating Learners' Skill Acquisition Without Temporal Information
Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi +2
Recent research in educational data mining, especially knowledge tracing, has focused on predicting learners' future knowledge states to support adaptive instruction. However, in m…
Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
Clémence Réda, Tomas Rigaux, Hiba Bederina +3
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also mi…
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…
Large Language Models as Students Who Think Aloud: Overly Coherent, Verbose, and Confident
Conrad Borchers, Jill-Jênn Vie, Roger Azevedo
Large language models (LLMs) are increasingly embedded in AI-based tutoring systems. Can they faithfully model novice reasoning and metacognitive judgments? Existing evaluations em…
Diversified recommendations of cultural activities with personalized determinantal point processes
Carole Ibrahim, Hiba Bederina, Daniel Cuesta +3
While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metr…
Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
Hiba Bederina, Jill-Jênn Vie
The challenge of balancing user relevance and content diversity in recommender systems is increasingly critical amid growing concerns about content homogeneity and reduced user eng…
A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
Jean Vassoyan, Anan Schütt, Jill-Jênn Vie +3
Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to ad…
Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students Training Data
Erva Nihan Kandemir, Jill-Jenn Vie, Adam Sanchez-Ayte +2
Accurate estimation of question difficulty and prediction of student performance play key roles in optimizing educational instruction and enhancing learning outcomes within digital…
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
Jill-Jênn Vie, Hisashi Kashima
Knowledge tracing consists in predicting the performance of some students on new questions given their performance on previous questions, and can be a prior step to optimizing asse…
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…
Multidimensional Item Response Theory in the Style of Collaborative Filtering
Yoav Bergner, Peter F. Halpin, Jill-Jênn Vie
This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student perfo…
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…
Privacy-Preserving Synthetic Educational Data Generation
Jill-Jênn Vie, Tomas Rigaux, Sein Minn
Institutions collect massive learning traces but they may not disclose it for privacy issues. Synthetic data generation opens new opportunities for research in education. In this p…
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
Sein Minn, Jill-Jenn Vie, Koh Takeuchi +2
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the sk…
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
Benoît Choffin, Fabrice Popineau, Yolaine Bourda +1
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to impr…
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Jill-Jênn Vie, Hisashi Kashima
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By trackin…
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
Sein Minn, Yi Yu, Michel C. Desmarais +2
In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instru…
Deep Factorization Machines for Knowledge Tracing
Jill-Jênn Vie
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning mo…
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Jill-Jênn Vie, Florian Yger, Ryan Lahfa +4
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a us…