no code implementations • 18 Aug 2023 • 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 assessment and learning.
1 code implementation • 10 May 2023 • Jean Vassoyan, Jill-Jênn Vie, Pirmin Lemberger
Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners.
no code implementations • 3 Jan 2023 • 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 performance from observed assessment data.
1 code implementation • 20 Dec 2022 • 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 filtering, especially when side information over users or items is available.
1 code implementation • 7 Jul 2022 • Jill-Jênn Vie, Tomas Rigaux, Sein Minn
Institutions collect massive learning traces but they may not disclose it for privacy issues.
3 code implementations • 14 May 2019 • Benoît Choffin, Fabrice Popineau, Yolaine Bourda, Jill-Jênn Vie
In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills.
2 code implementations • 8 Nov 2018 • 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.
1 code implementation • 1 May 2018 • Jill-Jênn Vie
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
2 code implementations • 3 Sep 2017 • Jill-Jênn Vie, Florian Yger, Ryan Lahfa, Basile Clement, Kévin Cocchi, Thomas Chalumeau, Hisashi Kashima
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well.