Factors Affecting Programming Skill Development among Third Year Computer Engineering Students: A Survey Based Study

Abstract

This study focuses on what shapes programming growth among third-year Computer Engineering learners at Bulacan State University. Psychological traits, classroom structure, personal study routines, alongside AI tool usage and shape skill acquisition patterns observed in these individuals. A numeric method guides this study. The surveys reach 60 selected participants via purposeful selection techniques. Information flows from responses marked on a 4-point Likert scale questionnaire, later analysed using weighted mean, standard deviation, and Pearson correlation. Results show those who believe more strongly in themselves tend to perform better in coding coursework. Despite a positive view of classroom support, learners show hesitation during difficult tasks. When mistakes occur in programming work, discomfort tends to follow. Because understanding code becomes harder, some turn to artificial intelligence systems too frequently. As time passes, personal analytical growth slows under such dependency. Without frequent engagement beyond scheduled lessons, ability to locate faults weakens. Skill advancement depends strongly on individual initiative paired with repeated exercise. Progress requires sustained effort done without guidance from instructors. The study concludes that consistent, self-directed study habits and hands-on practice are critical for skill development. Recommendations include increasing out-of-class coding practice, conducting live skill assessments for future research, and expanding study samples to include other IT-related fields.