Abstract
Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.
Highlights
Information technology enhanced education makes the continuous improvement of teaching and learning over years
A concept-based recommender system for recommending learning materials is proposed to meet the pedagogical goals of instructors during the creation of an online programming course, and decrease the time that the instructors spend on authoring task and keep the coherence of the sequential structure of the course as stated in [7]
Bayesian inference module predicts the probability of unmastered knowledge components with the academic capacity of student and the Bayesian network and recommends the questions in descending order of probabilities of the unmastered knowledge components for the student, who practices the questions in a personalized sequence
Summary
Information technology enhanced education makes the continuous improvement of teaching and learning over years. A concept-based recommender system for recommending learning materials is proposed to meet the pedagogical goals of instructors during the creation of an online programming course, and decrease the time that the instructors spend on authoring task and keep the coherence of the sequential structure of the course as stated in [7] In above these educational recommender systems, the student peers and courses as item recommendation are similar to those e-commerce recommender systems termed as neighborhood-based.
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