Association between mathematical ability and computational thinking dimensions in vocational students using Bebras tasks

Source abstract

Computational thinking (CT) has emerged as an essential twenty-first-century competency for Industry 4.0; however, its relationship with mathematical ability in vocational education remains insufficiently examined. This study investigated the association between mathematical ability and five dimensions of CT among 55 tenth-grade manufacturing and engineering students enrolled in Center of Excellence Vocational High Schools in Bali, Indonesia. Employing an explanatory sequential mixed-methods design, CT was assessed using Bebras Challenge tasks, whereas mathematical ability was measured through students’ mathematics achievement scores. Quantitative findings revealed a very strong positive correlation between mathematical ability and overall CT performance (r = .839, p < .001), with mathematical ability statistically explaining 70.4% of the variance in CT scores; nevertheless, this cross-sectional estimate should be interpreted as correlational rather than causal. Strong correlations were identified for pattern recognition, algorithmic thinking, abstraction, and debugging, whereas the association with decomposition was not statistically significant. Students with high mathematical ability demonstrated proficiency across all CT dimensions, while students with lower mathematical ability encountered substantial difficulties particularly in abstraction and debugging. Qualitative analysis further indicated that high-ability students applied systematic decomposition and iterative debugging strategies, whereas low-ability students were unable to generalize patterns or construct algorithms despite demonstrating adequate decomposition skills. These findings underscore the importance of strengthening mathematical foundations to foster Industry 4.0 competencies in vocational education and workforce readiness.