{"created":"2025-01-19T01:35:05.288186+00:00","updated":"2025-01-19T10:03:17.708105+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233566","sets":["1164:6089:11568:11569"]},"path":["11569"],"owner":"44499","recid":"233566","title":["Students' Performance Prediction based on Similarity between Online Textbooks and Questions"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-16"},"_buckets":{"deposit":"c43e194e-a6b9-4330-bb17-421ecb521083"},"_deposit":{"id":"233566","pid":{"type":"depid","value":"233566","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Students' Performance Prediction based on Similarity between Online Textbooks and Questions","author_link":["634655","634654","634657","634665","634663","634658","634661","634662","634659","634660","634664","634656"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Students' Performance Prediction based on Similarity between Online Textbooks and Questions"},{"subitem_title":"Students' Performance Prediction based on Similarity between Online Textbooks and Questions","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":" 一般セッション5B","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-03-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Electrical Engineering, Kyushu University"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University"},{"subitem_text_value":"Promotion Office for Data-Driven Innovation, Kyushu University"},{"subitem_text_value":"Research Institute for Information Technology, Kyushu University"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Electrical Engineering, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Promotion Office for Data-Driven Innovation, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Research Institute for Information Technology, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Information Science and Electrical Engineering, Kyushu 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Shimada"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yongle, Ren","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Cheng, Tang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuta, Taniguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsubasa, Minematsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Fumiya, Okubo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsushi, Shimada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12496725","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8620","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In university education, predicting students' performance is important for assessing their mastery of specific knowledge areas and offering feedback. To overcome limitations in existing grade predictions that overlook students' knowledge mastery, we propose an approach to predict performance on individual exam questions. This method utilizes cosine similarity calculations to establish relationships between exam questions and their connections to online textbooks, creating a new dataset from 494 students across four undergraduate courses. We evaluated the performance of four machine-learning methods, achieving an AUC score exceeding 0.7 through 5-fold cross-validation. We applied feature weighting by multiplying cosine similarity scores to enhance correlation with the prediction target. The results show an improvement of more than 0.1 in the AUC score over the other cases. The contribution of this work is the introduction of an effective method for predicting students' exam performance.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In university education, predicting students' performance is important for assessing their mastery of specific knowledge areas and offering feedback. To overcome limitations in existing grade predictions that overlook students' knowledge mastery, we propose an approach to predict performance on individual exam questions. This method utilizes cosine similarity calculations to establish relationships between exam questions and their connections to online textbooks, creating a new dataset from 494 students across four undergraduate courses. We evaluated the performance of four machine-learning methods, achieving an AUC score exceeding 0.7 through 5-fold cross-validation. We applied feature weighting by multiplying cosine similarity scores to enhance correlation with the prediction target. The results show an improvement of more than 0.1 in the AUC score over the other cases. The contribution of this work is the introduction of an effective method for predicting students' exam performance.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告教育学習支援情報システム（CLE）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"19","bibliographicVolumeNumber":"2024-CLE-42"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233566,"links":{}}