{"created":"2025-01-19T01:31:06.063890+00:00","updated":"2025-01-19T10:55:02.332231+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230998","sets":["6504:11436:11444"]},"path":["11444"],"owner":"44499","recid":"230998","title":["学生モデリングにおける説明可能性向上のためのDeep Factorization Machines"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"09f0314d-f038-4237-a656-a84d522fd825"},"_deposit":{"id":"230998","pid":{"type":"depid","value":"230998","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"学生モデリングにおける説明可能性向上のためのDeep Factorization Machines","author_link":["622943","622942"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"学生モデリングにおける説明可能性向上のためのDeep Factorization Machines"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータと人間社会","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"木更津高専"},{"subitem_text_value":"木更津高専"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/230998/files/IPSJ-Z85-6ZL-09.pdf","label":"IPSJ-Z85-6ZL-09.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6ZL-09.pdf","filesize":[{"value":"315.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"bef650f3-27b7-44bd-8424-9e11c458203e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"柿崎, 透真"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大枝, 真一"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"教育現場においてITS(Intelligent Tutoring System)を効果的に活用するためには，学習者のスキル状態を把握し，それに見合った設問を推薦する必要がある．そのため学習者のスキル状態の推定が可能な学生モデリングを用いることで，ITSの性能を向上させる研究が行われている．学生モデリング手法はKnowledge Tracingが主流となっており，Deep Knowledge TracingやSelf-Attentive Knowledge Tracingなどのディープラーニングアプローチが盛んに研究されている．しかし，これらのモデルは学生が解いた問題の番号と，その問題に対する回答の正誤のみを入力としており，その他の特徴量を用いることを想定していない．本研究では，特徴量の相互作用を考慮できる手法のFM（Factorizaton Machines）とDeep Learningを組み合わせたモデルであるDeepFMとFiBiNETを用いて学生モデリングを行い，特徴量重要度をSHAPやLIMEのような手法で可視化することで，実世界への機械学習手法の応用を目指す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"934","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"933","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230998,"links":{}}