Exercise Semantic Embedding for Knowledge Tracking in Open Domain

As one of the foundational techniques of data-driven intelligent education systems, knowledge tracing (KT) tracks students’ mastery of specific knowledge points by analyzing historical student–exercise interaction data. Since most student–exercise interaction data are open-domain data in real-world applications, new exercises and knowledge are never modeled, which causes the knowledge tracing performance to degrade. Based on this situation, the primary goal of this study is to address the problem of knowledge tracing in open-domain data. To address this, this paper proposes a two-stage knowledge tracing framework, namely Exercise Semantic embedding for Knowledge Tracing (ESKT). In the first stage of ESKT, the exercise semantic information is embedded in a pre-trained language model (PLM). In the second stage, to capture the semantic answer information, this paper proposes a Knowledge Tracing with an Answer Encoder and Multiple Questions Attention Mechanism (KTAM). To verify the performance of the framework, it is compared with the State-Of-The-Art (SOTA) methodology AKT, SAINT, on the English reading comprehension dataset, and the results prove that ESKT can achieve, at most, a 7% AUC boost in open-domain datasets. In conclusion, this paper innovatively uses a pre-trained language model for exercise semantic embedding to solve the problem of knowledge-tracking tasks in open-domain data.