Deep learning based knowledge tracing in intelligent tutoring systems.

The emergence of online education, e.g., intelligent tutoring system (ITS), complements or partially replaces conventional offline education, especially during the COVID-19 pandemic. Knowledge tracing (KT) plays a pivotal role in the intelligent tutoring system in capturing the knowledge states of students. By analyzing a series of students' interaction records of questions and answers in ITS, KT is able to provide personalized feedbacks to students. Recent advances in deep learning techniques, such as deep knowledge tracing, apply recurrent neural networks over students' interaction records for knowledge state modeling and achieve great improvement in the prediction of performance on future tasks and assessment questions. However, in practice, KT is often in lack of sufficient student interaction records to accurately model and predict students' knowledge states, the so-called data sparsity issue. Meanwhile, the data sparsity issue is generally overlooked in the existing knowledge tracing systems. In this paper, we propose a quality-aware deep learning framework for knowledge tracing, based on the sparse attention techniques and generative decoding. Extensive experiments are conducted over a series of real datasets showing that our proposal accurately captures students' knowledge states.