Research Progress on Deep Learning Knowledge Tracing for Intelligent Education

With the continuous advancement of education digitalization, intelligent education has developed rapidly. As a core research task in the field of intelligent education, Knowledge Tracing (KT) aims to capture students' mastery of knowledge concepts based on their historical learning data to provide personalized learning paths and resources to meet the objectives of Artificial Intelligence (AI)-assisted education. Traditional KT methods primarily rely mainly on Bayesian and logic models, which have good scientific explanatory properties but exhibit limited performance when processing massive amounts of educational data. Because of its excellent feature extraction ability and performance, deep learning technology is more suitable than traditional KT methods for capturing learners' knowledge status from massive data. Therefore, a comprehensive review of research on deep learning-based KT in the field of intelligent education is conducted. First, the relevant concepts, research backgrounds, and current development status of KT in intelligent education scenarios are introduced. The KT methods based on deep learning in recent years are then analyzed and divided into four categories: Recurrent Neural Network (RNN), self-attention network, memory-enhancing neural network, and Graph Neural Network (GNN). The basic ideas and algorithm processes of these four classical and mainstream methods are systematically classified and sorted in terms of learner and exercise characteristics. Subsequently, the public education datasets currently available to researchers are introduced, and the performance of different methods on these datasets is compared. Finally, this paper summarizes deep learning KT in intelligent education and discusses possible future research directions in this field.