Trap-States Found in Problem-Posing Activity Sequences Based on Triplet Structure Model

• Ahmad Supianto

  Information Engineering, Hiroshima University, JAPAN Department of Informatics, Brawijaya University, INDONESIA

• Yusuke Hayashi

  Information Engineering, Hiroshima University, JAPAN

• Tsukasa Hirashima

  Information Engineering, Hiroshima University, JAPAN

抄録

Problem-posing activities can provide a significant insights into learners' understanding about structure of problem. Finding an interesting pattern in a problem-posing learning environment is crucial to identify an important situation that learner may have difficulty to complete an assignment. This paper expects visualizations of the activity sequences to finding turning points where learners lose a way to reach the goal of an assignment. The activity sequences are considered to represent thinking process of learners and reflect their understanding and misunderstanding about the structure of problems. This paper proposes detection of ``trap-states'' that is an intermediate state of thinking in which learners have difficulty in achieving to the correct answer. As the results from an exercise detection of trap-states from real data, trap-states have found.

収録刊行物

• 人工知能学会研究会資料 先進的学習科学と工学研究会

  人工知能学会研究会資料 先進的学習科学と工学研究会 75 (0), 03-, 2015-11-10

  一般社団法人 人工知能学会

詳細情報

• CRID: 1390570000439583744
• NII論文ID: 40020646511
• NII論文ID: 130008057950
• NII書誌ID: AA11970412
• DOI: 10.11517/jsaialst.75.0_03
• ISSN: 24364606
• ISSN: 13494104
• NDL書誌ID: 026859416
• Web Site: http://id.ndl.go.jp/bib/026859416
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I026859416
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • NDL
  • CiNii Articles
• 抄録ライセンスフラグ: 使用可