ScholarOne - Employing Large Language Models to Enhance K-12 Students'
Programming Debugging Skills, Computational Thinking, and Self-Efficacy
Abstract
Programming education is gaining attention at the K-12 level. In the
digital era, computational thinking is seen as a key skill. Students in
the programming debugging process can not only fix code errors but also
exercise and cultivate computational thinking. However, learners at the
K-12 level lack confidence in debugging programming due to a lack of
foundational knowledge and difficulty in obtaining effective feedback in
a debugging environment. The emergence of large language models (LLMs)
provides a new pathway for novice programming debugging training. This
study applied the advantages of these models to programming debugging,
and explored how they can help students in debugging skills,
computational thinking, and self-efficacy. The research reveals that
through interaction with these advanced models, students can solve
programming problems more quickly and strengthen their computational
thinking and problem-solving abilities in practice. More importantly,
this type of interaction increased students’ confidence in their
self-programming abilities and enhanced persistence and motivation in
the face of challenges. This study provides educators with new
perspectives, demonstrates the great potential of large language models
in programming instruction, and provides valuable references for future
educational practices.