Related papers: A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability

A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability

URL: http://arxiv.org/abs/2302.08673v1
Date: Fri, 17 Feb 2023 03:50:49 GMT
Title: A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability
Authors: Hengyu Liu, Tiancheng Zhang, Fan Li, Minghe Yu and Ge Yu
Abstract summary: We propose an inTerpretable pRobAbilistiC gEnerative moDel (TRACED) which can track students' numerous knowledge concepts mastery probabilities over time. We conduct experiments with four real-world datasets in three knowledge-driven tasks. The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students' future performance.
Score: 8.920928164556171
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Knowledge tracing aims to track students' knowledge status over time to predict students' future performance accurately. Markov chain-based knowledge tracking (MCKT) models can track knowledge concept mastery probability over time. However, as the number of tracked knowledge concepts increases, the time complexity of MCKT predicting student performance increases exponentially (also called explaining away problem. In addition, the existing MCKT models only consider the relationship between students' knowledge status and problems when modeling students' responses but ignore the relationship between knowledge concepts in the same problem. To address these challenges, we propose an inTerpretable pRobAbilistiC gEnerative moDel (TRACED), which can track students' numerous knowledge concepts mastery probabilities over time. To solve \emph{explain away problem}, we design Long and Short-Term Memory (LSTM)-based networks to approximate the posterior distribution, predict students' future performance, and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly. To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems. We conduct experiments with four real-world datasets in three knowledge-driven tasks. The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students' future performance and can learn the relationship among students, knowledge concepts, and problems from students' exercise sequences. We also conduct several case studies. The case studies show that TRACED exhibits excellent interpretability and thus has the potential for personalized automatic feedback in the real-world educational environment.