Learning Styles in Computer Science: A Cross-National Study of First-Year Students

This research full paper investigates the learning styles of computer science students enrolled in introductory programming courses in institutions located across four geographically distant countries or regions: Macao (Group GMo), Mozambique (Group GMz), Brazil (Group GBz), and Portugal (Group GPt). Using the Felder-Silverman learning style model and the Index of Learning Styles (ILS) instrument, we analyzed data from 302 students. Results revealed variations in learning style distributions, with all groups showing preferences for visual and sensing styles while differing notably in active/reflective and sequential/global dimensions. Students of Group GMo demonstrated a strong reflective tendency, while Group GBz and Group GPt students tended to active learning. Group GMz students generally presented more balanced profiles across dimensions. Educators should adapt their teaching methodologies to accommodate these diverse learning preferences, mainly when teaching complex subjects like programming. We propose various pedagogical strategies aligned with different learning styles to enhance student engagement and contribute to improving academic outcomes in introductory programming courses.