{"created":"2021-03-01T06:01:00.185729+00:00","id":8674,"links":{},"metadata":{"_buckets":{"deposit":"af0e7b94-f1e3-46e8-95dd-483a9d03bb7c"},"_deposit":{"id":"8674","owners":[],"pid":{"revision_id":0,"type":"depid","value":"8674"},"status":"published"},"_oai":{"id":"oai:kitami-it.repo.nii.ac.jp:00008674","sets":["1:86"]},"author_link":["90060","90061","273","89671"],"item_1646810750418":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_3_biblio_info_186":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2015","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"57","bibliographicPageStart":"51","bibliographicVolumeNumber":"17","bibliographic_titles":[{"bibliographic_title":"バイオメディカル・ファジィ・システム学会誌"}]}]},"item_3_description_184":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"本研究では，マルコフ決定過程を用いて講義をモデル化し，統計的決定理論に基づく2つの教授戦略算出方法を提案する．従来研究で算出される教授戦略は1回の講義の効果を最大化するのに対して，本研究で算出する教授戦略は複数の講義で構成される科目全体での効果を最大化する．提案方法では動的計画法を利用している．1つ目の提案方法は個別指導用の方法で， 2つ目の提案方法は複数指導用の方法である．教育目的はマルコフ決定過程の利得関数によって表現される．また，教授戦略はマルコフ決定過程の政策によって表現される．提案方法によって算出される最適な教授戦略はマルコフ決定過程の期待利得を最大化する．提案方法の有効性を示すために数値計算例を紹介する．","subitem_description_type":"Abstract"},{"subitem_description":"In this research Markov decision processes are used in order to represent lectures. The best teaching strategy computed in previous research maximizes the effectiveness of a lecture. The best teaching strategy computed in this research maximizes the effectiveness of a subject which is composed of multiple lectures. We propose two computation methods based on statisticαl decision theory for teaching strategies. Dynamic programming algorithm is used in the proposed methods. The first proposed method selects teaching materials for α learner. The second proposed method selects teaching materials for multiple learners. A purpose of education is represented by the reward function of Markov decision processes. A teaching strategy is represented by the policy of Markov decision processes. The best teaching strategies computed by the proposed methods maximize the expected reward of Markov decision processes. We show the effectiveness of the proposed methods by some numerical calculation examρles.","subitem_description_type":"Abstract"}]},"item_3_full_name_183":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"90060","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Maeda, Yasunari","nameLang":"en"}]},{"nameIdentifiers":[{"nameIdentifier":"90061","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Suzuki, Masakiyo","nameLang":"en"}]}]},"item_3_publisher_212":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"バイオメディカル・ファジィ・システム学会"}]},"item_3_relation_191":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_text":"https://doi.org/10.24466/jbfsa.17.2_51"}],"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.24466/jbfsa.17.2_51","subitem_relation_type_select":"DOI"}}]},"item_3_rights_192":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"c 2015 Biomedicai Fuzzy Systems Association"}]},"item_3_select_195":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_3_source_id_187":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2424-2578","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"前田, 康成","creatorNameLang":"ja"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"鈴木, 正清","creatorNameLang":"ja"}],"nameIdentifiers":[{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2018-12-11"}],"displaytype":"detail","filename":"マルコフ決定過程を用いた教授戦略.pdf","filesize":[{"value":"3.2 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"マルコフ決定過程を用いた教授戦略","url":"https://kitami-it.repo.nii.ac.jp/record/8674/files/マルコフ決定過程を用いた教授戦略.pdf"},"version_id":"c3564c9c-063b-4b24-bf5c-5a70bc41442e"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"講義","subitem_subject_scheme":"Other"},{"subitem_subject":"教授戦略","subitem_subject_scheme":"Other"},{"subitem_subject":"マルコフ決定過程","subitem_subject_scheme":"Other"},{"subitem_subject":"動的計画法","subitem_subject_scheme":"Other"},{"subitem_subject":"lecture","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"teαching strategy","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Markov decision processes","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"dynamic programming","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":"マルコフ決定過程を用いた教授戦略","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マルコフ決定過程を用いた教授戦略","subitem_title_language":"ja"},{"subitem_title":"Teaching Strategies Using Markov Decision Processes","subitem_title_language":"en"}]},"item_type_id":"3","owner":"1","path":["86"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2018-12-11"},"publish_date":"2018-12-11","publish_status":"0","recid":"8674","relation_version_is_last":true,"title":["マルコフ決定過程を用いた教授戦略"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-12-13T02:22:12.335755+00:00"}