Keywords
self-regulated learning
metacognition
Abstract
This study investigates how novice programming students perceive and utilize automated feedback to support their self-regulated learning and metacognitive processes. Using a qualitative case study approach, we collected data from 20 students in an introductory programming course through interviews, think-aloud sessions, and reflective journals. Our findings reveal diverse perceptions of automated feedback, ranging from helpful tool to frustrating obstacle. We identified five main utilization strategies: immediate error correction, systematic debugging, concept reinforcement, progress monitoring, and ignore and continue. The study also found that automated feedback enhanced self-monitoring, promoted strategic planning, and triggered reflective thinking for many students, though its impact varied. These results contribute to our understanding of how automated feedback systems can be designed and implemented to better support students' self-regulated learning in programming education.
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