{"created":"2023-06-28T15:00:19.669166+00:00","id":505,"links":{},"metadata":{"_buckets":{"deposit":"e94bf12e-e6a2-436f-b46c-99f092747aa8"},"_deposit":{"created_by":16,"id":"505","owners":[16],"pid":{"revision_id":0,"type":"depid","value":"505"},"status":"published"},"_oai":{"id":"oai:seisen-jc.repo.nii.ac.jp:00000505","sets":["1:104"]},"author_link":["651","650"],"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-03-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"37","bibliographicPageEnd":"10","bibliographicPageStart":"1","bibliographic_titles":[{"bibliographic_title":"清泉女学院短期大学研究紀要"},{"bibliographic_title":"Bulletin of Seisen Jogakuin College","bibliographic_titleLang":"en"}]}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"本研究では、ラーニング・アナリティクス、性格特性、教学IRデータを活用し、１年春学期末のGPAと学校生活満足度を予測するモデルを開発した。モデル開発手法として、k-means法によるクラスター分析、線形重回帰分析及びニューラルネットワークを採用した。予測精度を計算した結果、線形重回帰モデルの最大決定係数は、（GPA，学校生活満足度）=（0.279，0.305）であり、ニューラルネットワークモデルの最大決定係数は、（GPA，学校生活満足度）=（0.831，0.479）となった。","subitem_description_type":"Abstract"}]},"item_10002_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"651","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Katase, Takuya"}]}]},"item_10002_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"清泉女学院短期大学"}]},"item_10002_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00293551","subitem_source_identifier_type":"NCID"}]},"item_10002_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0289-6761","subitem_source_identifier_type":"ISSN"}]},"item_10002_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"片瀬, 拓弥"}],"nameIdentifiers":[{"nameIdentifier":"650","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-05-23"}],"displaytype":"detail","filename":"01_katase.pdf","filesize":[{"value":"6.7 MB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"seisen-kiyo_37-1","url":"https://seisen-jc.repo.nii.ac.jp/record/505/files/01_katase.pdf"},"version_id":"634e4172-fb6c-45ed-ad07-7b7d5234bc39"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ラーニング・アナリティクス","subitem_subject_scheme":"Other"},{"subitem_subject_scheme":"Other"},{"subitem_subject":"教学IR","subitem_subject_scheme":"Other"},{"subitem_subject_scheme":"Other"},{"subitem_subject":"GPA","subitem_subject_scheme":"Other"},{"subitem_subject_scheme":"Other"},{"subitem_subject":"学校生活満足度","subitem_subject_scheme":"Other"},{"subitem_subject_scheme":"Other"},{"subitem_subject":"予測モデル","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ラーニング・アナリティクス、性格特性、教学IRデータを活用したGPAと学校生活満足度の予測モデルの開発","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ラーニング・アナリティクス、性格特性、教学IRデータを活用したGPAと学校生活満足度の予測モデルの開発"},{"subitem_title":"Development of prediction model of GPA and school life satisfaction by Learning Analytics, personality traits and data of Institutional Research for education","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"16","path":["104"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-05-23"},"publish_date":"2019-05-23","publish_status":"0","recid":"505","relation_version_is_last":true,"title":["ラーニング・アナリティクス、性格特性、教学IRデータを活用したGPAと学校生活満足度の予測モデルの開発"],"weko_creator_id":"16","weko_shared_id":-1},"updated":"2023-06-28T15:08:52.375021+00:00"}