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Found 9 papers, 7 papers with code
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
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.
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners.
Multidimensional Item Response Theory in the Style of Collaborative Filtering
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.
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
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.
Privacy-Preserving Synthetic Educational Data Generation
Institutions collect massive learning traces but they may not disclose it for privacy issues.
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
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.
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
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.
Deep Factorization Machines for Knowledge Tracing
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well.
