Review on Research of Knowledge Tracking

Abstract

Knowledge tracking, which aims to model students’ changing knowledge states over learning time based on their historical answer records and then predict students’ answer performance, is a core module supporting smart education systems and has received increasing attention from researchers. This paper comprehensively compares the research progress in this field, analyzes the basic theoretical research related to knowledge tracking, and analyzes the knowledge tracking models from probabilistic models, logical models, and deep learning-based models according to different research methods. Probabilistic models assume that learning follows Markov processes, logical models are a class of logic function-based models, and deep learning-based knowledge tracking models relying on the powerful feature extraction ability of deep learning have become a hot research topic in recent years. The improvement methods proposed for the problems faced by deep learning-based knowledge tracking models such as interpretability and lack of learning features are presented. The public datasets currently available to researchers are given as well as a comparison of the performance of different models. Finally, this paper concludes with a summary of this rapidly growing field on knowledge tracking, suggesting some possible future research directions for the problems of research in this area.

Keywords

• wisdom education; online learning; knowledge state; knowledge tracking model; deep learning