Study on evaluation of individual learner's exercise situation in programming exercise

Research Project

Project/Area Number	18K02903
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	Prefectural University of Hiroshima
Principal Investigator	UNO TAKESHI 県立広島大学, 地域創生学部, 准教授 (20305783)
Project Period (FY)	2018-04-01 – 2022-03-31
Project Status	Completed (Fiscal Year 2021)
Budget Amount *help	¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000) Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000) Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000) Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000) Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Keywords	プログラミング演習 / リアルタイム演習状況把握 / 成績不振兆候者抽出 / リアルタイム学習状況把握 / プログラミング演習システム / オンライン演習 / 学習成果のフィードバック / Webアプリケーション / プログラミング教育
Outline of Final Research Achievements	The purpose of this study was to quantitatively grasp the learning situation of each learner in programming learning and to provide accurate guidance. First, we developed a C language programming learning environment that can be used on the Web and stored the learning data on the server. Next, using the accumulated learning data, we developed and operated a feedback system that evaluates the exercise process. By applying this, we have developed and operated a system that makes it possible to grasp the progress of individual learners' exercises in class in real time and improve the efficiency of teaching. Utilizing these data, we devised a procedure for early detection of learner with poor grades, and made it possible to detect learner with poor grades during class.
Academic Significance and Societal Importance of the Research Achievements	近年，小中学校で必修化されるなど，プログラミング教育の重要性が認識されている．プログラミング教育には実習が不可欠となるが，実習環境の構築や，指導側の進捗把握が難しいという問題があった．そこで本研究では，これらの問題を解決するため，Web上で利用可能なC言語プログラミング学習環境の開発と運用を行い，サーバに蓄積した学習データを用い，演習過程の評価を行うフィードバックシステムの開発と運用を行った．さらに，これらの学習データを活用し，定量的な手法で成績不振兆候者の早期発見を行う手順を考案し，授業進行中における成績不振兆候者の検出を可能とした．