Support Awareness of Anomaly in Coding Behavior using Code Revision Data
Kento Shigyo, Hidenari Kiyomitsu, Hitoshi Sato, Kazuhiro Ohtsuki · 2022
With the aim of better teaching effectiveness in programing courses, this study provides a framework to easily understand students’ coding progress using the data which is produced while they are coding. In particular, we explore the methods to find students who get stuck at coding and make little progress. In HTML courses for programing novices, we collected code revision history including the amount of code update and save times of students. In this paper, we discuss the usefulness of code revision history to understand students’ coding progress by describing coding patterns revealed by the analysis of the data, characteristics of coding progress and scalability for programing courses.