Unplugged Computational Thinking in Basic Education: Design and Learning Evidence (Mathematics subject focus)
DOI:
https://doi.org/10.64747/166bex17Keywords:
unplugged computational thinking, Mathematics, rural basic education, implementation fidelity, transferAbstract
This study assessed the effectiveness of an unplugged computational thinking (CT) program embedded in Mathematics for lower secondary Basic Education, implemented in rural public schools in Tenguel (Guayas, Ecuador). We conducted a clustered quasi-experimental design with pretest–posttest measures and a qualitative sub-study. The 10-week intervention (two 40–50-minute sessions per week) mapped CT practices—decomposition, pattern recognition, abstraction, algorithm design, and verification—onto grade-level Mathematics topics (proportionality, graphs and shortest paths, combinatorics/probability, modular arithmetic). Parallel A/B tests were used for Mathematics (30 items) and unplugged CT (24 items), along with an implementation fidelity (IF) rubric and a brief attitudes scale. A total of 430 students participated in the Treatment group and 420 in Control. Pre–post gains were larger for Treatment in both Mathematics (+9.5 points) and CT (+9.2), compared with Control (+3.4 in both). Posttest effect sizes were moderate (Hedges g≈0.53 for Mathematics; g≈0.54 for CT). A moderate correlation between posttest Mathematics and CT was observed in Treatment (r≈0.46), supporting near transfer from algorithmic practices to mathematical problem solving. IF ≥ 80% was associated with greater improvements. Findings indicate that the unplugged approach improved Mathematics performance and CT competencies under digital divide constraints by minimizing logistical friction and focusing cognitive activity on structures and procedures. The package is scalable, low-cost, and aligned with national priorities on Mathematics and digital competencies; it offers a bridge strategy while school connectivity improves. Future work should include stepped-wedge rollouts, longitudinal follow-up, and item response models to enhance cross-cohort comparability and cost-effectiveness estimates.
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