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User-Adaptive Preparation of Mathematical Puzzles Using Item Response Theory and Deep Learning

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/74935

Title: User-Adaptive Preparation of Mathematical Puzzles Using Item Response Theory and Deep Learning
Authors: Sekiya, Ryota Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: Computer-Aided Test
Item Response Theory
Crowdsourcing
Deep Learning
Magic Square
Issue Date: 2019
Journal Title: Advances and Trends in Artificial Intelligence. From Theory to Practice
Volume: 11606
Start Page: 530
End Page: 537
Publisher DOI: 10.1007/978-3-030-22999-3_46
Abstract: The growing use of computer-like tablets and PCs in educational settings is enabling more students to study online courses featuring computer-aided tests. Preparing these tests imposes a large burden on teachers who have to prepare a large number of questions because they cannot reuse the same questions many times as students can easily memorize their solutions and share them with other students, which degrades test reliability. Another burden is appropriately setting the level of question difficulty to ensure test discriminability. Using magic square puzzles as examples of mathematical questions, we developed a method for automatically preparing puzzles with appropriate levels of difficulty. We used crowdsourcing to collect answers to sample questions to evaluate their difficulty.Item response theory was used to evaluate the difficulty of the questions from crowdworkers’ answers. Deep learning was then used to build a model for predicting the difficulty of new questions.
Rights: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-22999-3_46.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/74935
Appears in Collections:国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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