Investigating Academic Performance Patterns among Generation Z Computer Science Students using AHP

Abstract

The landscape of higher education is continuously evolving, with the emergence of new generations of students bringing diverse backgrounds, expectations, and learning styles. Among these, Generation Z (Gen Z that comprising individuals born from the mid-to-late 1990s to the early 2010s and has garnered significant attention for its unique characteristics and approaches to education. As digital natives, Gen Z students are not only adept at using technology but also exhibit distinct academic preferences and challenges, particularly in fields that demand rigorous analytical and technical skills, such as computer science. This study seeks to investigate the CGPA patterns of Gen Z Computer Science students at Al-Baha University, Saudi Arabia by employing the Analytical Hierarchy Process (AHP) methodology. Overall Performance Weights for Core Course: 0.454 (Most Important), Elective Course: 0.429 (Important) and University Course: 0.142 (Least Important). The analysis reveals that while core and elective courses significantly influence CGPA, the engagement, support mechanisms, and relevance to real-world applications are critical for the academic success of Gen Z students. Educational institutions should leverage these insights by enhancing curriculum designs that cater to the needs and preferences of Generation Z, promoting practical applications, collaborative learning, and mental well-being. This holistic approach positively impacts academic performance and overall educational experience.