Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness - Joel Weijia Lai; Lei Zhang; Chun Chau Sze; Fun Siong Lim (Education Sciences)
As the demands of the modern workforce evolve, universities are increasingly challenged to provide academic knowledge and the practical and transferable skills necessary for students’ career success. This study investigates the alignment between undergraduate students’ career aspirations, their perceived skill development, and the role of higher education institutions in bridging the skills gap. To address this issue, a comprehensive survey was conducted among undergraduate students to gather data on their career aspirations, their awareness of the skills required for their chosen careers, and their perceptions of how well their university supports their skill development. Using machine learning methods such as hierarchical clustering and k-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney U and Chi-squared (<semantics>χ2</semantics>) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals.