学習者回答判別に対する信頼度分類を用いた事例研修のための助言ルール選択方式

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/06/01

タイトル(英語): A Tutoring Rule Selection Method for Case-based e-Learning by Confidence Classification on Learner's Answer Discrimination

著者名: 久兼　大知（大阪大学大学院 情報科学研究科），大月　みなみ（大阪大学大学院 情報科学研究科），鮫島　正樹（大阪大学大学院 情報科学研究科）

著者名(英語): Daichi Hisakane (Graduate School of Information Science and Technology Osaka University), Minami Otsuki (Graduate School of Information Science and Technology Osaka University), Masaki Samejima (Graduate School of Information Science and Technology Osaka University)

キーワード: 助言提示システム，多クラスSVM (Support Vector Machine)，k-means法　　Intelligent Tutoring System，Multi-class SVM (Support Vector Machine)，k-means clustering

要約(英語): We develop an intelligent tutoring system on learners' answers to problems in case-based e-learning. A facilitator instantiates answers and tutoring advice as a tutoring rule preliminary, and the system automatically identifies an appropriate instantiated answer which corresponds to the input sentence of an answer from the learner. Although various kinds of tutoring rules are given on a certain problem, the instantiated answers are very similar to each other among tutoring rules, even if tutoring rules are different. So the input sentence is similar to the wrong instantiated answer of the tutoring rule, which makes it difficult to select the tutoring rule correctly. The proposed method selects the tutoring rule for the input sentence by machine learning of selecting the tutoring rules with the multi-class SVM (Support Vector Machine). The experimental result shows that the proposed method can improve the accuracy of selecting tutoring rules by 21% compared to the method that selects the most similar instantiated answer.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.6 （2015） 特集：データからの知識発見とその応用

本誌掲載ページ: 594-601 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/6/135_594/_article/-char/ja/