ENHANCING COMPUTER SCIENCE LEARNING THROUGH REVERSE ENGINEERING: AN EMPIRICAL STUDY OF UNDERGRADUATE STUDENTS

Abstract

Reverse engineering (RE), traditionally linked to engineering and software analysis, has gained increasing attention as a pedagogical strategy in higher education. This study empirically investigates the effectiveness of reverse engineering–based instruction in undergraduate computer science education. Using a pre-test/post-test experimental design with 60 undergraduate students, the study assesses the impact of reverse engineering on conceptual understanding, problem-solving skills, and student perceptions. Quantitative results reveal statistically significant improvements in learning outcomes for students exposed to reverse engineering activities, with medium to large effect sizes across key dimensions. Survey findings further indicate high levels of engagement, perceived learning effectiveness, and confidence in analyzing unfamiliar software systems. The results provide strong empirical support for constructivist, experiential learning, and cognitive apprenticeship theories, confirming that reverse engineering is an effective learner-centered approach for enhancing deep learning and professional readiness in computer science education.