Grade prediction for improved efficiency in programming education using e-Learning activities within a cloud-based development environment

• Published in: Journal of Learning Analytics Vol. 3; pp. 1 - 6
• Main Author: Amano, Naoki
• Format: Journal Article
• Language: Japanese
• Published: Japanese Society for Learning Analytics 2019; 特定非営利活動法人 学習分析学会
• Subjects: Grade Prediction; Programming Education; Results Forecasting, Anomaly Detection; プログラミング教育; 成績予測; 異常検知
• ISSN: 2436-6862
• DOI: 10.51034/jasla.3.0_1

Japanese educators often face major challenges when providing programming education, including severe budget constraints and a high student-teacher ratio. This paper reports our experience of introducing e-Learning content within a cloud-based development environment, directly linked to student profile information, including past performance history. To increase educational efficiency, we attempted to predict students’ grades from information obtained through the lessons and the cloud service use history. By applying an anomaly detection algorithm to the objects, we confirmed that it is possible to forecast results that can help us anticipate ways to maximize efficiency.