{"links":{},"id":241381,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241381","sets":["1164:4842:11543:11860"]},"path":["11860"],"owner":"44499","recid":"241381","title":["間主観的な適応型自己評価に向けた機械学習による評価予測モデルに関する初期研究"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-30"},"_buckets":{"deposit":"882fcbd0-46a5-4c71-9047-779a36908913"},"_deposit":{"id":"241381","pid":{"type":"depid","value":"241381","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"間主観的な適応型自己評価に向けた機械学習による評価予測モデルに関する初期研究","author_link":["664432","664433","664431","664430"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"間主観的な適応型自己評価に向けた機械学習による評価予測モデルに関する初期研究"},{"subitem_title":"Initial Study on a Machine Learning-Based Evaluation Prediction Model for Inter-Subjective Adaptive Self-Assessment","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"CE学生セッション(1)","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-11-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"帝京大学"},{"subitem_text_value":"帝京大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"The University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"Teikyo University","subitem_text_language":"en"},{"subitem_text_value":"Teikyo 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/241381/files/IPSJ-CE24177001.pdf","label":"IPSJ-CE24177001.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-30"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CE24177001.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"19"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f452f1c7-afe8-414b-a00c-a3c3b4bd015c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"和久井, 優大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡辺, 博芳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"眞坂, 美江子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮崎, 誠"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096193","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":"2188-8930","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"汎用的能力は，職業的および社会的な成功において重要な役割を担うが，自己評価には多くの評価項目が必要になり，評価者に負担がかかりやすい．そのため，評価者の回答に基づいて適応的に評価項目を提示する「適応型自己評価」の研究を進めている．その中で本研究では，下位の評価項目であるチェックリストの自己評価結果から総合評価となるルーブリック評価を予測する機械学習モデルの構築に関する初期的検討を行った．具体的には，複数の機械学習アルゴリズムを用いてモデルの精度を比較した．その結果，ロジスティック回帰およびサポートベクターマシンが最も高い精度を示した．また，プライバシ保護のため，実データと同一の統計的性質を持つ「合成データ」を用いたが，このデータによる学習モデルが実データにも適用可能であることが示唆された．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Generic skills are essential for success in professional and social environments. However, self-assessment of these skills requires many assessment items, placing a significant burden on students. To address this, we are developing an ”adaptive self-assessment” approach, which adaptively presents assessment items based on the student's responses. In this initial study, we explore a machine learning model that predicts rubric-based comprehensive assessments using self-assessment results from checklists of specific skills. We compared the accuracy of several machine learning algorithms and found that logistic regression and support vector machines achieved the highest accuracy. Additionally, to protect privacy, we used synthetic data that maintains the same statistical properties as real data for model training. The results show that models trained on synthetic data can be effectively applied to real data.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"10","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータと教育（CE）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024-CE-177"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:45:57.075456+00:00","updated":"2025-01-19T07:41:15.211683+00:00"}