Effects of a GenAI-based debugging approach integrating the reflective strategy on senior high school students’ learning performance and computational thinking

Abstract

Debugging constitutes a pivotal component in the learning curve of programming, serving not only to enhance coding proficiencies but also to cultivate thinking skills. However, debugging tools embedded in integrated development environments (IDEs) often provide limited error diagnosis, which may reduce students’ engagement with coding and inhibit their learning performances. This study therefore proposed a Reflective Generative Artificial Intelligence (GenAI) Debugging (RGD) approach, an innovative debugging approach based on reflective strategies and operationalized through a GenAI tool, an intelligent conversational agent. To assess the performance of the approach, we used a quasi-experimental design and recruited 80 high school students from two classes of a province in eastern China. One class with 40 students was selected to be the experimental group using the RGD approach; the other class with 40 students was the control group utilizing the conventional coding and debugging (CGD) approach. The results depicted that the RGD group better enhanced students’ learning achievement than the CGD group. Nevertheless, a significant difference was found in the two groups’ computational thinking skills. The findings could be a reference to instructors and researchers intending to use GenAI in programming classes.

Keywords

• generative ai
• computer programming
• computational thinking
• reflective strategy