Building Better Programmers: An AI System for Guided Program Decomposition
Analysing how guided program decomposition affects cognitive processes in computer science students
A. Chopra (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.M. Specht – Graduation committee member (TU Delft - Web Information Systems)
Gosia Migut – Mentor (TU Delft - Web Information Systems)
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Abstract
Generative AI has opened up new possibilities in computer science education. Large language models have made it possible for learners to get instantaneous and customised feedback on different programming concepts, as well as the ability to use natural language to implement these concepts. One such concept is program decomposition, an essential skill in software engineering. This work presents a novel method for teaching program decomposition, using a three-stage guided AI decomposition system. We analyse how this method affects a learner's program decomposition cognitive processes via a concurrent think-aloud protocol where a student decomposes three simple programming tasks. Furthermore, we measure how using the system changes a student's confidence in their decomposition skills. We find that participants do not display any significant change in confidence levels after using the system. We observe that the students display a significant improvement in performance during the course of the study. The participants also display a significant decrease in metacognitive confusion and a clear emergence of reflection based on previous errors. We conclude that the proposed method and the implemented system lead to a level of internalisation of decomposition skills in the students. We recommend that a study of change in decomposition skills is conducted over a longer time period to observe the full effects of the method.