Pensamiento computacional unplugged en EGB: diseño y evidencia de aprendizaje
Why this work is in the frame
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it