Relationship between the Pillars of Computational Thinking and Academic Performance, and their Link to Algorithmic Thinking

Abstract

Computational thinking represents a key competency in initial university education and is comprised of fundamental pillars such as algorithmic thinking, decomposition, pattern recognition, and abstraction. This research aimed to determine the relationship between the pillars of computational thinking and the academic performance of first-year university students, as well as the relationship between the pillars of decomposition, pattern recognition, and abstraction with algorithmic thinking. The study adopted a quantitative approach with a non-experimental, correlational design. The population consisted of 40 students enrolled in the Basic Computer Science course. The students' computational thinking was assessed using the Román-González Computational Thinking Test. Academic performance was measured by the final grade obtained in the course unit. Because one of the variables did not meet the assumption of normality, Spearman's rank correlation coefficient was used for data analysis. The results showed no statistically significant relationship between the pillars of computational thinking and academic performance. However, a positive and statistically significant relationship was identified between the pillars of decomposition, pattern recognition, and abstraction with algorithmic thinking. The research results suggest that, while the pillars of computational thinking are not directly associated with academic performance, they do maintain an internal structural relationship that supports the development of algorithmic thinking in first-year university students.