Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks

Adaptive textbooks use student interaction data to infer the current state of student knowledge and recommend most relevant learning materials. A challenge of student mod- eling for adaptive textbooks is that conventional student models are constructed based on performance data (quiz or problem-solving), however, students' interactions with on- line textbooks may produce a large volume of student read- ing 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 stu- dent reading data combined with few available quiz activi- ties to infer the students' current state of knowledge. The evaluation shows that proposed model learns more accurate students' knowledge state than Knowledge Tracing.

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