Computational thinking and conceptual learning in first-year calculus: The role of teacher competence

The persistent gap between procedural fluency and conceptual understanding is one of the most challenging problems in first-year calculus education. In many developing educational systems, calculus instruction is dominated by rule-based symbolic manipulation, limited lesson preparation, and little integration of computational tools, so students often perform acceptably on routine differentiation tasks while showing weak conceptual understanding of rate of change, optimization, and graphical interpretation. This study examines the role of computational thinking and teacher competence in improving conceptual learning in introductory calculus. A quasi-experimental comparative classroom study was conducted with 144 first-year engineering students over a four-week instructional intervention on derivatives and optimization, comparing traditional procedural teaching, structured conceptual teaching, and computationally enhanced conceptual teaching. Learning was assessed through pre-tests, post-tests, delayed retention tests, and transfer tasks across five dimensions: conceptual understanding, procedural fluency, transfer ability, computational reasoning, and engagement. One-way ANOVA revealed statistically significant differences among the three groups, F(2, 141) = 19.42, p < .001, with the computationally enhanced group achieving the highest post-test performance, retention, and transfer ability, and large effect sizes for every pairwise comparison. The findings suggest that computational thinking, when embedded in pedagogically competent teaching, substantially improves calculus learning, and the study proposes a scalable instructional framework for educational systems seeking to strengthen mathematics teaching through computational pedagogy.