Recommending Remedial Readings Using Student Knowledge State

Assessment plays a vital role in learning, as it provides both instructors and students with feedback on the overall effectiveness of their teaching or learning. However, when a student fails to correctly answer certain questions in an assessment (such as a quiz), the student needs specific recommendations that are tailored to their learning needs and to the knowledge deficiency exposed by the assessment outcomes. In this paper, we explore the methods for automatically identifying the recommended textbook materials that are most relevant and suitable to the student. In particular, we conducted experiments on how to incorporate students' current knowledge state on domain concepts associated with the activity to recommend personalized remedial sections to each student. The results show that incorporating student knowledge states can significantly improve the quality of recommendations as compared to traditional content-based recommendations. [For the full proceedings, see ED607784.]