Development and demonstration experiment of LA engine aiming at early prediction of learning activities and support for LMS utilization

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K12272
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 62030:Learning support system-related
• Research Institution: Kyushu Institute of Technology
• Principal Investigator: OHNISHI Yoshimasa 九州工業大学, 情報基盤センター, 准教授 (50213806)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: 学習分析 / 学習データ / LMS / FPGA / アドバイス

Outline of Final Research Achievements

In this research, we attempted to automatically collect related learning data and construct an analysis model, aiming at the early utilization of learning analysis data for daily teaching/learning activities. We proceeded with the construction of a system that provides appropriate advice to instructors and learners, and built and verified learning data collection methods and analysis models. In addition to analyzing the structure of educational content at the affiliated institution, we defined "learning effort" and developed a tool that allows you to easily check the related "learning burden".
As for the collection and analysis of learning data on hardware, we were able to confirm the flow of transmission and reception of learning-related data, but we have not reached the point of complete implementation. Therefore, even after the subsidy period ends, we will continue to proceed with development and implementation.

Free Research Field

教育工学

Academic Significance and Societal Importance of the Research Achievements

学習活動の早期予測は、日々の教授活動を行う教授者にとって重要である。そのため、学習活動に関するデータの収集を簡単にし、分析データの早い活用を目指した本研究の成果が、他の研究に与える影響は大きい。特に、学習エフォートという概念を提案・定義し、学習負担と学習コンテンツが持つ情報を、効果的に活用する仕組みについて貢献できた。今後、構築した実験環境を用い、学習分析を継続的に行うと共に、提案した仕組みの評価も継続することで、学習効果や教授法に関する研究に寄与できると考えている。なお、試作したデータベースやハードウェア分析処理については、より完成度の高い環境に発展させる予定である。