Learning Analytics based on Fine-Grained Activity History in Programming Education

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K03056
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 09070:Educational technology-related
• Research Institution: Chiba Institute of Technology (2020-2021); Kyushu Institute of Technology (2019)
• Principal Investigator: MIURA MOTOKI 千葉工業大学, 工学部, 教授 (00334053)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: プログラミング学習 / Web IDE / ソースコード編集 / キャレット操作

Outline of Final Research Achievements

In order to mitigate the influence of typing in the conventional text-type programming environment, we built a programming learning environment that introduced an auto-completion function. In addition, we acquired fine-grained activity logs, including caret movements when editing source code for programming learners, and analyzed their relationship with learning outcomes.
There was no significant correlation between the number of auto-completion usages and the learner score. However, it was confirmed that the number of times of inputting　symbols that must be input using shift keys such as parentheses and curly brackets is significantly reduced depending on the setting of the completion function.　In addition, from the fine-grained activity log including caret movement, the more frequently the caret movement, the smaller the number of valid character inputs necessary to answer assignments.

Free Research Field

情報教育

Academic Significance and Societal Importance of the Research Achievements

プログラミング学習者のソースコード編集時のキャレット移動を含む、細粒度の活動ログを取得・分析することで、プログラミング学習支援の多様化および高度化を目指した研究である。キャレット移動そのものはソースコード自体には反映されない操作ではあるが、その記録をとらえることで学習者の学習状況や傾向との関連について分析することが可能となった。