Extracting Error Resolution Patterns for Novice Programming Students using Apriori Algorithm

Abstract

Introduction: Novice programmers often struggle with error resolution, affecting their learning and performance. This study analyzes error resolution patterns among first-year programming students using the Apriori algorithm.

Objectives: The study aims to identify common programming errors, analyze resolution difficulty and time, and uncover patterns using association rules. It also seeks to provide data-driven recommendations to enhance programming education.

Methods: A dataset of 150 first-year students was analyzed, focusing on error frequency, severity, and resolution time. The Apriori algorithm was applied to identify associations between error type, resolution attempts, and time required.

Results: Syntax errors (319 occurrences) were the most frequent and resolved quickly, while logical (193) and runtime errors (164) were more challenging. Association rules showed that highly difficult errors took over 30 minutes to resolve (80% confidence), whereas low-severity syntax errors were fixed within 30 minutes (75% confidence).

Conclusions: The study revealed the relationship between error type, resolution attempts, and correction time. Findings suggest tiered instructional strategies, such as automated feedback for syntax errors and structured debugging workshops, to improve student proficiency and reduce dropout rates.