Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform

TL;DR

This paper presents a machine learning approach to automatically classify error types in student programming solutions, enhancing automated feedback in online learning platforms by clustering and labeling common errors.

Contribution

It introduces a novel method that detects and labels frequent error types through clustering, improving error identification in student submissions.

Findings

Effective clustering of incorrect solutions into error types.

Improved accuracy in classifying new error submissions.

Enhanced feedback capabilities for online programming courses.

Abstract

Online programming courses are becoming more and more popular, but they still have significant drawbacks when compared to the traditional education system, e.g., the lack of feedback. In this study, we apply machine learning methods to improve the feedback of automated verification systems for programming assignments. We propose an approach that provides an insight on how to fix the code for a given incorrect submission. To achieve this, we detect frequent error types by clustering previously submitted incorrect solutions, label these clusters and use this labeled dataset to identify the type of an error in a new submission. We examine and compare several approaches to the detection of frequent error types and to the assignment of clusters to new submissions. The proposed method is evaluated on a dataset provided by a popular online learning platform.