Keywords:
computational thinking
digital learning
vector concepts
instructional model
higher education

  • Abstract

    Computational thinking (CT) is increasingly positioned as a transferable competence required for complex problem solving in mathematics and other STEM domains, yet CT instruction is often implemented through fragmented activities rather than coherent, theory-driven learning models. This study developed and evaluated ARICT, an innovative learning model that integrates APOS-informed cognitive construction, Realistic Mathematics Education (RME) contextualization, and digital learning supports to strengthen students’ CT while maintaining disciplinary understanding. The research adopted a research and development approach aligned with the Borg and Gall model, implemented within a mixed methods paradigm. After needs analysis and evidence mapping, the ARICT package was produced, validated by experts, refined through a pilot study, and tested through a nonequivalent control group pretest–posttest field trial. The main study involved four intact Linear Algebra classes focusing on vectors (N = 80), comprising an experimental group (N = 41) receiving ARICT and a control group (N = 39) receiving conventional instruction. Baseline equivalence was supported by nonsignificant pretest differences (p > .05). Effectiveness was evaluated using ANCOVA with pretest scores as covariates. The results showed that ARICT significantly improved CT compared with conventional instruction, F(1, 77) = 98.67, p < .001, with higher adjusted posttest means for the experimental group (Madj = 78.52) than the control group (Madj = 61.34) and a large effect (partial η² = .56). ARICT also significantly improved vector conceptual understanding, F(1, 77) = 45.21, p < .001, with adjusted means of 82.15 (experimental) and 70.98 (control) and a large effect (partial η² = .37). Fidelity observations indicated high adherence to intended instructional conditions in both groups, supporting internal validity. These findings suggest that ARICT provides a coherent, scalable instructional model for embedding CT within mathematics learning, yielding strong CT gains without sacrificing conceptual mastery.

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Trisnawati, Sutiarso, S., Firdaus, R., & Yunarti, T. (2026). ARICT: An innovative learning model to enhance students’ computational thinking using APOS–RME and digital learning integration. Multidisciplinary Science Journal, 8(11), 2026740. https://doi.org/10.31893/multiscience.2026740
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