Integrating Computational Thinking into Mathematics Instruction to Enhance Higher-Order Thinking Skills (HOTS)
DOI:
https://doi.org/10.62872/t50sx702Keywords:
computational thinking, higher-order thinking skills, mathematics instruction, quasi-experimental, HOTSAbstract
Higher-order thinking skills (HOTS) remain difficult to cultivate in mathematics classrooms that emphasize procedural fluency over structured, algorithmic reasoning. This study investigates whether integrating computational thinking (CT)—decomposition, pattern recognition, abstraction, and algorithm design—into mathematics instruction can significantly improve students' HOTS. A quantitative, quasi-experimental, nonequivalent pretest-posttest control-group design was used, involving two intact classes: an experimental group taught through a CT-integrated instructional model and a control group taught conventionally. HOTS was measured with a validated essay test aligned with the analyzing, evaluating, and creating levels of Bloom's revised taxonomy. Data were examined through normality and homogeneity tests, N-gain scores, and an independent-samples t-test supplemented by ANCOVA with the pretest as covariate. Results show the experimental group achieved a significantly higher mean N-gain, in the moderate-to-high category, than the control group, with significant differences (p < .05) across all HOTS indicators, most notably analyzing and creating. These findings extend prior CT-mathematics research, which has largely been correlational, perception-based, or review-based, by providing direct experimental evidence linking structured CT integration to measurable HOTS gains and offering an empirically validated, technology-independent instructional design for mathematics teachers and curriculum developers.
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