Exploring differences in self-regulated learning strategy use between high- and low-performing students in introductory programming: An analysis of eye-tracking and retrospective think-aloud data from program comprehension

Abstract

Previous studies have reported mixed results regarding the relationship between students’ use of self-regulated learning (SRL) strategies and their performance in introductory programming courses. These studies were constrained by their reliance on self-report questionnaires as a means of collecting and analysing data. To address this limitation, this study aimed to employ eye-tracking and retrospective think-aloud techniques to identify differences in SRL strategy use for program comprehension tasks between high-performing students (N = 31) and low-performing students (N = 31) in an undergraduate programming course. All participants attended individual eye-tracking sessions to comprehend two Python program codes with different constructs. Their eye-tracking data and video-recalled retrospective think-aloud data were captured and recorded for analysis. The findings reveal that higher-order cognitive skills, such as elaboration and critical thinking, were mostly adopted by high-performing students, while basic cognitive and resource management strategy, such as rehearsal and help-seeking, were mostly employed by low-performing students when comprehending the program codes. This study not only demonstrates the design of combining eye-tracking and retrospective think-aloud data to explore students’ use of SRL strategies but also provides evidence to support the notion that program comprehension is a complex process that cannot be effectively addressed by employing merely rudimentary strategies, such as repetitively reading the same code segment. In the future, researchers could explore the possibility of using a webcam to monitor and assess students’ online programming processes and provide feedback based on their eye movements. They could also examine the effects of SRL strategies training on students’ motivation, engagement, and performance in various types of programming activities.