Detecting Struggling Student Programmers using Proficiency Taxonomies

TL;DR

This paper introduces the Proficiency Taxonomy Model (PTM), an AI approach that explicitly incorporates educators' structured skill categories to improve early detection of struggling student programmers in introductory courses.

Contribution

The study develops a novel proficiency taxonomy integrated into a predictive model, enhancing early identification of students who need help in learning to code.

Findings

PTM outperforms existing models in predicting struggling students.

The model is effective across Java and Python beginner courses.

Structured skill taxonomies improve detection accuracy.

Abstract

Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students' programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model (PTM), simultaneously learns the student's coding skills based on their coding history and predicts whether they will struggle on a new task. We extensively evaluated the effectiveness of the PTM model on two separate datasets from introductory Java and Python courses for beginner programmers. Experimental results demonstrate that PTM outperforms state-of-the-art models in predicting struggling students. The paper showcases the potential of combining structured insights from teachers for early identification of those needing assistance in learning to code.