The effect of computational thinking on learning outcomes in vocational education: a meta-analysis

The integration of Computational Thinking (CT) is a global priority for developing problem-solving skills. However, its specific effectiveness and heterogeneity within Technical and Vocational Education and Training (TVET) require rigorous empirical synthesis. This study evaluates the impact of CT interventions on student learning outcomes and identifies sources of heterogeneity through a meta-analysis. Analyzed 13 experimental studies (k = 13) using a random-effects model with a REML estimator via the metafor package in R. Parameters included Hedges’ g, heterogeneity (I2), and Egger’s regression for publication bias. CT interventions showed a statistically significant positive impact with a combined effect size of 0.82 (95% CI [0.28, 1.36], p < 0.01). High heterogeneity (I2 = 93.72%) was observed. Leave-one-out analysis identified one outlier; its removal reduced heterogeneity to 70.45% and corrected the effect size to 0.57. Publication bias tests were non-significant (p = 0.94), ensuring high validity. CT consistently enhances cognitive and practical abilities when integrated into vocational curricula. Practical implications suggest prioritizing teacher training for cross-disciplinary CT strategies. Theoretically, this study confirms CT as an essential cognitive tool for Industry 4.0 challenges in the TVET sector.