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DCフィールド | 値 | 言語 |
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dc.contributor.author | Dai, Yiling | en |
dc.contributor.author | Takami, Kyosuke | en |
dc.contributor.author | Flanagan, Brendan | en |
dc.contributor.author | Ogata, Hiroaki | en |
dc.contributor.alternative | 戴, 憶菱 | ja |
dc.contributor.alternative | 緒方, 広明 | ja |
dc.date.accessioned | 2023-12-13T00:00:08Z | - |
dc.date.available | 2023-12-13T00:00:08Z | - |
dc.date.issued | 2024-01-01 | - |
dc.identifier.uri | http://hdl.handle.net/2433/286393 | - |
dc.description.abstract | 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. | en |
dc.language.iso | eng | - |
dc.publisher | Asia-Pacific Society for Computers in Education | en |
dc.rights | © The Author(s). 2023 | en |
dc.rights | 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. | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | Recommender system | en |
dc.subject | Explainable recommender system | en |
dc.subject | Educational recommender system | en |
dc.subject | Learning performance | en |
dc.subject | Math learning | en |
dc.title | Beyond recommendation acceptance: explanation’s learning effects in a math recommender system | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Research and Practice in Technology Enhanced Learning | en |
dc.identifier.volume | 19 | - |
dc.relation.doi | 10.58459/rptel.2024.19020 | - |
dc.textversion | publisher | - |
dc.identifier.artnum | 020 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 20H01722 | - |
datacite.awardNumber | 23H01001 | - |
datacite.awardNumber | 21K19824 | - |
datacite.awardNumber | 23K17012 | - |
datacite.awardNumber | 23H00505 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20H01722/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23H01001/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K19824/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K17012/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23H00505/ | - |
dc.identifier.eissn | 1793-7078 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | Knowledge-Aware Learning Analytics Infrastructure to Support Smart Education and Learning | en |
jpcoar.awardTitle | Extraction and Use of Highly Explainable and Transferable Indicators for AI in Education | en |
jpcoar.awardTitle | Learning Support by Novel Modality Process Analysis of Educational Big Data | en |
jpcoar.awardTitle | 教育データAI利活用による学習者・教師の問題作成・共有支援システムの研究開発 | ja |
jpcoar.awardTitle | リアルワールド教育データからのエビデンス抽出・共有・利用のための情報基盤開発 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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