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タイトル: Applying Key Concepts Extraction for Evaluating the Quality of Students’ Highlights on e-Book
著者: YANG, Albert
CHEN, Y.L Irene
FLANAGAN, Brendan
OGATA, Hiroaki  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-5216-1576 (unconfirmed)
著者名の別形: 緒方, 広明
キーワード: E-learning
text summarization
learning analytics
発行日: 23-Nov-2020
出版者: Asia-Pacific Society for Computers in Education (APSCE)
誌名: 28th International Conference on Computers in Education Conference Proceedings
巻: 1
開始ページ: 284
終了ページ: 288
抄録: The quality of students’ highlights can be an indicator of their learning performance. While the most common approach to grade their highlights is by humans, human grading can be inconsistent, especially when the number of highlights are large or when graders have different background knowledge. In this research, we propose a model to automatically extract important concepts from class materials, analyze students’ highlights and find the correlation between highlight quality and students’ learning performance. We first compared different text summarization algorithms with different evaluations to see which of them generates the summarization that is closest to the reference answer generated by humans. Then we used the selected algorithm to summarize the text from learning materials as important concepts, and compared the summaries with students’ highlights to calculate their highlight scores. Finally, we considered the highlight score from the best method as the highlight quality and observed whether it has a correlation to students’ learning performance.
記述: 28th International Conference on Computers in Education, 23-27 November 2020, Web conference.
著作権等: Copyright 2020 Asia-Pacific Society for Computers in Education.
許諾条件に基づいて掲載しています。
URI: http://hdl.handle.net/2433/259796
出現コレクション:学術雑誌掲載論文等

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