• 研究課題をさがす
  • 研究者をさがす
  • KAKENの使い方
  1. 課題ページに戻る

2021 年度 実施状況報告書

Learning Support by Novel Modality Process Analysis of Educational Big Data

研究課題

研究課題/領域番号 21K19824
研究機関京都大学

研究代表者

Flanagan Brendan  京都大学, 学術情報メディアセンター, 特定講師 (00807612)

研究分担者 久富 望  京都大学, 教育学研究科, 助教 (70825992)
研究期間 (年度) 2021-07-09 – 2023-03-31
キーワードModality analysis / Learning process / Learning Analytics / Recommendation
研究実績の概要

Preliminary analysis of pen strokes as one of the learner modalities indicated that learners could be clustered into different groups, however it was unclear if pedagogical meaning could be interpreted from the results. This presented a problem for when feedback was given to teachers, so a method of students annotating the answering process with self-explanation was combined to identify stuck points. Using this analysis, a feedback interface to inform students was developed, implemented and the results were disseminated as an international conference paper. The overall cycle of the system was also conceptualized in an international conference paper in the context of a feedback process where students were recommended additional tasks based on identified stuck points.

現在までの達成度 (区分)
現在までの達成度 (区分)

2: おおむね順調に進展している

理由

The project is proceeding as planned with several results already published, however the continuing restrictions from the Covid pandemic can present challenges when conducting experiments in schools and planning to disseminate results at international conferences.

今後の研究の推進方策

The project will continue to focus analysis on two main sub-themes: the behavioral data from students inputting answers (pen stroke and keyboard/text input, etc) and annotated metadata of the input (self-explanations of the answering process, etc). While the results of the current research have been used to inform recommendation of learning tasks, further analysis results features should be tightly integration to produce more target recommendations. Development of the feedback interface will continue to further improve the usability for learners and teachers.

次年度使用額が生じた理由

While the project progressed smoothly, the continuing restrictions from the Covid pandemic have caused delays in purchasing of goods/services that would incur article and travel costs. We anticipate as the restrictions and delays ease that the project will be able to continue as planned in the next fiscal year.

  • 研究成果

    (6件)

すべて 2022 2021 その他

すべて 雑誌論文 (3件) (うち査読あり 3件、 オープンアクセス 3件) 学会発表 (2件) (うち国際学会 2件) 備考 (1件)

  • [雑誌論文] Fine Grain Synthetic Educational Data: Challenges and Limitations of Collaborative Learning Analytics2022

    • 著者名/発表者名
      Flanagan Brendan、Majumdar Rwitajit、Ogata Hiroaki
    • 雑誌名

      IEEE Access

      巻: 10 ページ: 26230~26241

    • DOI

      10.1109/ACCESS.2022.3156073

    • 査読あり / オープンアクセス
  • [雑誌論文] Identifying Students’ Stuck Points Using Self-Explanations and Pen Stroke Data in a Mathematics Quiz2021

    • 著者名/発表者名
      Nakamoto Ryosuke、Flanagan Brendan、Takami Kyosuke、Dai Yiling、Ogata Hiroaki
    • 雑誌名

      29th International Conference on Computers in Education Conference Proceedings

      巻: 1 ページ: 522-531

    • 査読あり / オープンアクセス
  • [雑誌論文] EXAIT: A Symbiotic Explanation Learning System2021

    • 著者名/発表者名
      Flanagan Brendan、Takami Kyosuke、Takii Kensuke、Dai Yiling、Majumdar Rwitajit、Ogata Hiroaki
    • 雑誌名

      29th International Conference on Computers in Education Conference Proceedings

      巻: 1 ページ: 404-409

    • 査読あり / オープンアクセス
  • [学会発表] Identifying Students’ Stuck Points Using Self-Explanations and Pen Stroke Data in a Mathematics Quiz2022

    • 著者名/発表者名
      Ryosuke Nakamoto
    • 学会等名
      29th International Conference on Computers in Education Conference
    • 国際学会
  • [学会発表] EXAIT: A Symbiotic Explanation Learning System2022

    • 著者名/発表者名
      Brendan Flanagan
    • 学会等名
      29th International Conference on Computers in Education Conference
    • 国際学会
  • [備考] Project Homepage

    • URL

      https://flanaganacademic.wordpress.com

URL: 

公開日: 2022-12-28  

サービス概要 検索マニュアル よくある質問 お知らせ 利用規程 科研費による研究の帰属

Powered by NII kakenhi