Computational Thinking and Academic Performance Across Different Instructional Modalities in Pre-University Courses: A Data-Driven Study

Abstract

In preuniversity education, educators and decision-makers need to understand how teaching methods affect student learning in computational thinking (CT). This helps identify factors influencing student outcomes and inform the development of personalized learning programs. In this article, we conduct a comparative analysis of the academic performance of students enrolled in CT courses delivered through online (OL), blended (BL), and face-to-face (FTF) modalities in one public university in Ecuador. Our analysis focuses on preuniversity students during the academic year 2023. First, we collect data on student demographics and academic performance in each modality. Then, we applied statistical analysis to determine significant relationships between modalities. Next, we examine patterns and relationships between sociodemographic factors and academic results. Our results reveal that instructional modality has a significant impact on CT performance, with FTF students achieving better outcomes. Across all formats, admission scores and gender emerged as key predictors. While sociodemographic factors had greater influence in the FTF modality, academic factors played a more prominent role in BL and OL escenarios.