{"created":"2023-05-15T08:43:47.644600+00:00","id":8880,"links":{},"metadata":{"_buckets":{"deposit":"da479489-10f8-4d92-9d7c-f3b7355ec3ca"},"_deposit":{"created_by":13,"id":"8880","owners":[13],"pid":{"revision_id":0,"type":"depid","value":"8880"},"status":"published"},"_oai":{"id":"oai:uec.repo.nii.ac.jp:00008880","sets":["6"]},"author_link":["24164","24165","24166"],"control_number":"8880","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2018-02-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"92","bibliographicPageStart":"79","bibliographicVolumeNumber":"J102-D","bibliographic_titles":[{"bibliographic_title":"電子情報通信学会論文誌. D, 情報・システム","bibliographic_titleLang":"ja"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"教育の最も難しい問題は，教師は学習者に教えすぎても，教えなさすぎても学習者の十分な発達は望めないということである．そのために，教師は個々の学習者の理解度や最適な支援の度合いを予測することが重要な課題となっている．足場がけによる学習者のパフォーマンスを予測するために，項目反応理論を用いて最適な予測正答確率になるようにヒントを提示する足場がけシステムが開発されている．しかし，従来の項目反応理論では，学習者の能力変化がモデルに考慮されておらず，正確な正答確率を予測できないために，最適なヒント数を予測できていない可能性がある．本研究では，学習者の能力が時間変化していくプロセスを項目反応理論に組み込み，能力が隠れマルコフ過程に従って変化すると仮定した新しい項目反応モデルを提案する．提案モデルでは，能力値が継続する時間(課題数) を表すウィンドウサイズと能力の変動の程度を反映する変動パラメータをもち，これらの最適値がデータから推定されるために，学習者の真の能力変化を反映でき，予測精度を向上させることが期待される．実データを用いて，本提案の有効性を示す．","subitem_description_type":"Abstract"},{"subitem_description":"To scaffold a learner efficiently, a teacher should predict the optimal degree of assistance to support learner's development. However, conventional Item Response Theory (IRT) model does not consider learner's ability changes during his/her studying, therefore the IRT model might cause over-assistance or lack of assistance. We propose a new IRT model that incorporates learner's ability change according to a Hidden Markov process. The proposed model has the following two new parameters: the degree of the ability changes and the period of time that the learner's ability does not change. The experiments result shows that the proposed model improves the prediction accuracy of learner's performances.","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"電子情報通信学会"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.14923/transinfj.2018JDP7022","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://search.ieice.org/index.html","subitem_relation_type_select":"URI"}}]},"item_10001_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright © 2018 IEICE"}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1881-0225","subitem_source_identifier_type":"ISSN"}]},"item_10001_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":"堤, 瑛美子","creatorNameLang":"ja"},{"creatorName":"ツツミ, エミコ","creatorNameLang":"ja-Kana"},{"creatorName":"TSUTSUMI, Emiko","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宇都, 雅輝","creatorNameLang":"ja"},{"creatorName":"ウト, マサキ","creatorNameLang":"ja-Kana"},{"creatorName":"UTO, Masaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"植野, 真臣","creatorNameLang":"ja"},{"creatorName":"ウエノ, マオミ","creatorNameLang":"ja-Kana"},{"creatorName":"UENO, Maomi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-02-08"}],"displaytype":"detail","filename":"j102-d_2_79.pdf","filesize":[{"value":"1.2 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"j102-d_2_79","url":"https://uec.repo.nii.ac.jp/record/8880/files/j102-d_2_79.pdf"},"version_id":"93d67e1a-a1a1-4da8-8964-5a3f807ca6b3"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"アダプティブ・ラーニング","subitem_subject_scheme":"Other"},{"subitem_subject":"ダイナミックアセスメント","subitem_subject_scheme":"Other"},{"subitem_subject":"項目反応理論","subitem_subject_scheme":"Other"},{"subitem_subject":"隠れマルコフモデル","subitem_subject_scheme":"Other"},{"subitem_subject":"adaptive learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"dynamic assessment","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"item response theory","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"hidden Markov model","subitem_subject_language":"en","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":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ダイナミックアセスメントのための隠れマルコフIRTモデル","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ダイナミックアセスメントのための隠れマルコフIRTモデル","subitem_title_language":"ja"},{"subitem_title":"Item Responce Theory for dynamic assesment","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"13","path":["6"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2019-01-15"},"publish_date":"2019-01-15","publish_status":"0","recid":"8880","relation_version_is_last":true,"title":["ダイナミックアセスメントのための隠れマルコフIRTモデル"],"weko_creator_id":"13","weko_shared_id":-1},"updated":"2024-03-05T05:44:24.482346+00:00"}