Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

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Author(s): Kyosuke Takami (Education Data Science Center, National Institute for Educational Policy Research, Tokyo, Japan), Brendan Flanagan (Center for Innovative Research and Education in Data Science, Kyoto University, Kyoto, Japan), Yiling Dai (Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan)and Hiroaki Ogata (Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan)
Copyright: 2024
Volume: 22
Issue: 1
Pages: 23
Source title: International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.337600

Keywords: Distance Education / Education / Educational Technologies / Information Science Reference

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Abstract

Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.

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Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

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