Rule-Based Error Classification for Analyzing Differences in Frequent Errors

TL;DR

This paper introduces a rule-based tool for classifying programming errors in code pairs, revealing distinct error patterns between novice and expert programmers to aid targeted instruction and educational research.

Contribution

The paper presents a novel rule-based error classification method applied to a large dataset, highlighting differences in error tendencies between novices and experts.

Findings

Novices' errors stem from lack of programming knowledge.

Experts' errors are due to carelessness or different problem-solving approaches.

The tool classifies an average of 3.47 errors per code pair.

Abstract

Finding and fixing errors is a time-consuming task not only for novice programmers but also for expert programmers. Prior work has identified frequent error patterns among various levels of programmers. However, the differences in the tendencies between novices and experts have yet to be revealed. From the knowledge of the frequent errors in each level of programmers, instructors will be able to provide helpful advice for each level of learners. In this paper, we propose a rule-based error classification tool to classify errors in code pairs consisting of wrong and correct programs. We classify errors for 95,631 code pairs and identify 3.47 errors on average, which are submitted by various levels of programmers on an online judge system. The classified errors are used to analyze the differences in frequent errors between novice and expert programmers. The analyzed results show that, as for the same introductory problems, errors made by novices are due to the lack of knowledge in programming, and the mistakes are considered an essential part of the learning process. On the other hand, errors made by experts are due to misunderstandings caused by the carelessness of reading problems or the challenges of solving problems differently than usual. The proposed tool can be used to create error-labeled datasets and for further code-related educational research.