Dynamic knowledge modeling with heterogeneous activities for adaptive textbooks

Abstract

Adaptive textbooks use student interaction data to infer the current state of student knowledge and recommend most relevant learning materials. A challenge of student modeling for adaptive textbooks is that conventional student models are constructed based on performance data (quiz or problem-solving). However, students’ interactions with online textbooks may produce a large volume of student reading data but a limited amount of performance data. In this work, we propose a dynamic student knowledge modeling framework for online adaptive textbooks, which utilizes student reading data combined with few available quiz activities to infer the students’ current state of knowledge. The evaluation shows that proposed model learns more accurate students’ knowledge state than Knowledge Tracing.