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タイトル: | Beyond recommendation acceptance: explanation’s learning effects in a math recommender system |
著者: | Dai, Yiling Takami, Kyosuke Flanagan, Brendan Ogata, Hiroaki |
著者名の別形: | 戴, 憶菱 緒方, 広明 |
キーワード: | Recommender system Explainable recommender system Educational recommender system Learning performance Math learning |
発行日: | 1-Jan-2024 |
出版者: | Asia-Pacific Society for Computers in Education |
誌名: | Research and Practice in Technology Enhanced Learning |
巻: | 19 |
論文番号: | 020 |
抄録: | Recommender systems can provide personalized advice on learning for individual students. Providing explanations of those recommendations are expected to increase the transparency and persuasiveness of the system, thus improve students’ adoption of the recommendation. Little research has explored the explanations’ practical effects on learning performance except for the acceptance of recommended learning activities. The recommendation explanations can improve the learning performance if the explanations are designed to contribute to relevant learning skills. This study conducted a comparative experiment (N = 276) in high school classrooms, aiming to investigate whether the use of an explainable math recommender system improves students’ learning performance. We found that the presence of the explanations had positive effects on students’ learning improvement and perceptions of the systems, but not the number of solved quizzes during the learning task. These results imply the possibility that the recommendation explanations may affect students’ meta-cognitive skills and their perceptions, which further contribute to students’ learning improvement. When separating the students based on their prior math abilities, we found a significant correlation between the number of viewed recommendations and the final learning improvement for the students with lower math abilities. This indicates that the students with lower math abilities may benefit from reading their learning progress indicated in the explanations. For students with higher math abilities, their learning improvement was more related to the behavior to select and solve recommended quizzes, which indicates a necessity of more sophisticated and interactive recommender system. |
著作権等: | © The Author(s). 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
URI: | http://hdl.handle.net/2433/286393 |
DOI(出版社版): | 10.58459/rptel.2024.19020 |
出現コレクション: | 学術雑誌掲載論文等 |
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