MORE | Fall 2024

Educational Data Mining to Assess Code Quality in Programming Courses

This research aims to enhance automated assessment (AA) capabilities in programming courses by integrating educational data mining techniques with the autograder in a Data Structures & Algorithms course. The study will analyze performance metrics and code quality indicators using Python libraries and static analysis tools to evaluate non-functional requirements. By identifying trends, correlating measures with grades, and tracking code quality evolution, the project seeks to provide instructors with valuable insights. These findings will enable more effective assessment and teaching of code quality, better preparing students for software engineering careers. Future work will explore applying these techniques to other courses.