{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225615","sets":["934:10195:11103:11245"]},"path":["11245"],"owner":"44499","recid":"225615","title":["機械学習を用いた退学予測に基づくエンロールメントマネジメントシステムの構築"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-04-15"},"_buckets":{"deposit":"4b313016-1943-40f6-b7cb-175be808d26a"},"_deposit":{"id":"225615","pid":{"type":"depid","value":"225615","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いた退学予測に基づくエンロールメントマネジメントシステムの構築","author_link":["597380","597383","597382","597381"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた退学予測に基づくエンロールメントマネジメントシステムの構築"},{"subitem_title":"Development of an Enrollment Management System Based on Dropout Prediction Using Machine Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集号投稿論文] 機械学習, エンロールメントマネジメント, 退学防止, IR","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2023-04-15","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"札幌学院大学"},{"subitem_text_value":"北海道大学大学院情報科学院"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Sapporo Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Hokkaido University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/225615/files/IPSJ-TDP0402002.pdf","label":"IPSJ-TDP0402002.pdf"},"date":[{"dateType":"Available","dateValue":"2023-04-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TDP0402002.pdf","filesize":[{"value":"2.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7e7b4ddc-bf32-481c-9904-8e65c41a5fc2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石川, 千温"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"石本, 翔真"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Chiharu, Ishikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shoma, Ishimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12894091","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2435-6484","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"大学におけるIR分析を発展させ，エンロールメントマネジメントに不可欠な学生の学修状況の把握と分析，とりわけ，退学へと至る問題状況を早期に察知し，予測するためのシステムを機械学習の技術を用いて構築した．機械学習に親和性の高いPython言語とExcelを用いて開発した予測システムでは，卒業年が2022年である学生の退学予測において，実践上有効と思われる精度を出すことができ，本システムの可能性が実証された．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We developed a system using machine learning to expand IR analysis in universities, and to grasp and analyze students' academic progress, which is indispensable for enrollment management, especially to detect and predict problematic situations that may lead to dropout from school at an early stage. The prediction system developed using the Python language, which has a high affinity for machine learning, and Excel was able to achieve an accuracy that was considered effective in practice in predicting the dropout of students whose graduation year is 2022, demonstrating the potential of this system.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌デジタルプラクティス（TDP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-04-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"4"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:25:07.225134+00:00","updated":"2025-01-19T12:44:35.711733+00:00","id":225615}