{"updated":"2025-01-19T21:48:11.232837+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00199052","sets":["6164:6165:8377:9886"]},"path":["9886"],"owner":"44499","recid":"199052","title":["Encoder-Decoder DKTモデルによるeラーニング推薦システム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-09-01"},"_buckets":{"deposit":"f074588e-b2c8-468a-979a-9e00bc50abe8"},"_deposit":{"id":"199052","pid":{"type":"depid","value":"199052","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Encoder-Decoder DKTモデルによるeラーニング推薦システム","author_link":["481347","481349","481348"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Encoder-Decoder DKTモデルによるeラーニング推薦システム"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報検索・情報推薦","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2019-09-01","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大学"},{"subitem_text_value":"筑波大学"},{"subitem_text_value":"筑波大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"University of Tsukuba","subitem_text_language":"en"}]},"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":44499,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/199052/files/IPSJ-WebDBF2019007.pdf","label":"IPSJ-WebDBF2019007.pdf"},"date":[{"dateType":"Available","dateValue":"2021-09-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-WebDBF2019007.pdf","filesize":[{"value":"1.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"330","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"08ebd06a-e238-4aea-b2db-1e2bd0b808e7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"潘, 秋実"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"冨平, 準喜"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"手塚, 太郎"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"eラーニングシステムは世界的に急速な普及を続けている．その学習ログに機械学習による推薦機能を加え，生徒の特性に合わせて最適な学習プログラムを提供するための様々な試みが近年活発に行われてきた．eラーニングにおいて機械学習が利用される代表的なタスクとして，生徒が次に解く問題の正解確率を予測する Knowledge Tracing （KT）がある．このタスクに対する手法としては現在，Bayesian Knowledge Tracing （BKT）と Deep Knowledge Tracing （DKT）が活発に研究されている．しかし既存の KT モデルとその実用的な利用の間にはギャップがあるのが現状である．DKT が抱える限界の一つは学習者が過去に学習した概念が現在に与える影響をうまく考慮できないことである．この問題に対処するため，本研究では encoder-decoder モデルを用いることでより適切に学習パスを予測する手法を提案する．これにより，生徒はeラーニングシステムにおいてより良い学習効果を得られるような問題を選択できるようになることが期待される．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"28","bibliographic_titles":[{"bibliographic_title":"WebDB Forum 2019論文集"}],"bibliographicPageStart":"25","bibliographicIssueDates":{"bibliographicIssueDate":"2019-09-01","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:03:10.375425+00:00","id":199052,"links":{}}