Computational Thinking and Self-Regulated Learning as Drivers of Programming Achievement: Evidence from a Systematic Review

The increasing demand for stronger programming skills has created additional challenges for educators who must also foster students’ technical understanding and metacognitive development. This systematic review examines how integrating Computational Thinking (CT) with Self Regulated Learning (SRL) operates as a pedagogical strategy within programming instruction. Based on findings from fifteen research studies published from 2020 to 2025, the review compiles evidence on instructional frameworks, technological supports, and learning outcomes associated with CT and SRL integration. Findings across the studies indicate that programming instruction that integrates SRL practices such as goal setting, progress monitoring, and reflective thinking can promote greater learner autonomy, deeper conceptual understanding, and stronger problem solving abilities. Technology supported learning environments, including adaptive tutoring systems and coding dashboards, contribute to this process by offering timely feedback that encourages sustained metacognitive engagement throughout programming activities. Overall, the review provides several recommendations that can assist educators in creating meaningful and engaging learning environments. Effective integration of CT and SRL has the potential to strengthen both cognitive and metacognitive development among students learning to program.

Computational Thinking, Instructional Frameworks, Self-regulated Learning, and Programming Education

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