Uncovering the Hidden Layers of Thinking: Extended Computational Thinking (CT) Strategies in Problem-Based Classrooms Learning

Abstract

This study explores how broadened computational thinking (CT) models can support and enhance students’ approaches to problem solving within problem-based learning (PBL) settings. To evaluate the proposed model, the researchers introduced a structured framework that included clear metacognitive prompts, guidance for collaborative work, and support for iterative design. This framework was implemented with 70 undergraduate engineering students participating in a six-week PBL cycle. Evidence gathered over three semesters showed notable gains in students’ CT performance, more balanced group participation, reduced unnecessary task switching, and smoother workflow patterns. Regression findings indicated that equitable involvement, idea generation, and overall PBL process efficiency were strong predictors of growth in CT scores. Students’ use of plan-monitor-evaluate (PME) strategies further suggested deeper metacognitive activity. Overall, the results show that extended CT scaffolding enables PBL groups to produce stronger and more numerous outcomes, while also refining the reasoning and teamwork practices required to achieve them. These insights can help educators design PBL environments that foster more effective thinking and reinforce students’ understanding of the value of their collaborative and reasoning processes.