Psychometric validation of Computer Programming Self-Efficacy Scale (CPSES) among tertiary engineering students

Self-efficacy beliefs reflect confidence in executing actions necessary to manage prospective situations and respond dynamically to changes in learning contexts. Although computer programming enhances problem-solving through problem decomposition, students frequently perceive it as difficult due to compiler and coding challenges. Low programming self-efficacy undermines persistence and steers learners away from technical fields, yet validated assessment measures remain scarce. This study psychometrically validated the Computer Programming Self-Efficacy Scale (CPSES) for tertiary engineering students, adapting Kukul et al.’s (2017) 31-item instrument. Using a descriptive quantitative design, data were gathered via Google Forms from 351 graduating engineering students at Holy Angel University during AY 2023–2024. Principal Component Analysis extracted three reliable factors explaining 70.65% of the total variance: Programming Problem Solving Proficiency (Alpha = .956), Programming Competency and Code Management (.958), and Programming Problem Solving and Communication Aptitude (.930). The overall instrument exhibited exceptional internal consistency (.979). The findings confirm that the adapted CPSES is a structurally sound, highly reliable diagnostic tool for tertiary engineering education. It equips educators and curriculum planners to proactively identify struggling students, scaffold difficult programming concepts, and enhance academic outcomes in engineering computing coursework.