Recurrent knowledge tracing machine based on the knowledge state of students

Abstract Knowledge tracing (KT) aims to closely trace the knowledge level of students during their learning. KT is often implemented by intelligent tutoring systems (ITS) to predict the student performance, and schedule the individual learning plan for each student. However, most existing KT models cannot predict the knowledge forgetting well due to the lack of temporal information and some KT models for knowledge forgetting prediction are not accurate enough. To resolve these issues, this paper proposes a recurrent knowledge tracing machine (RKTM), which temporally enriches the encoding of knowledge tracing machine (KTM) and difficulty, student ability, skill, and student skill practice history (DAS3H) with the knowledge state of students. RKTM consists of two major components, including the tracing component and the predicting component. The tracing component temporally traces the knowledge state of a student, while the predicting component captures the interaction between the current learning scenario and the current knowledge state of that student and provides accurate prediction of student performance. Experiments show that the proposed RKTM well outperforms KTM, DAS3H, and some other models.