SELF-REGULATED LEARNING AND COMPUTATIONAL THINKING: ANALYZING THE PERFORMANCE OF JUNIOR HIGH SCHOOL STUDENTS ON PISA-LIKE PROBLEMS

Abstract

This study investigates the relationship between self-regulated learning (SRL) and computational thinking (CT) in solving PISA-like mathematics problems among junior high school students. Using a qualitative descriptive design, data were collected from 33 ninth-grade students through SRL questionnaires, PISA-like problem-solving tasks, think-aloud protocols, and semi-structured interviews. Students were categorized into three SRL levels: high (12.12%), medium (72.73%), and low (15.15%), with two students from each level selected for in-depth analysis. The analysis focused on four CT indicators: decomposition, pattern recognition, abstraction, and algorithmic thinking. Results showed that students with high SRL demonstrated strong CT performance across all indicators, especially in abstraction and algorithmic thinking. Medium-SRL students showed moderate CT ability, though with inconsistencies in strategy and reasoning. Students with low SRL exhibited weak CT skills and lacked systematic problem-solving approaches. These findings highlight the significant role of SRL in supporting students' ability to apply CT in complex mathematical contexts. Future studies may explore digital SRL scaffolding tools to enhance CT development across diverse mathematical domains.