Comparing Students' Learning Preferences through Cluster Analysis: Implications for Higher Education

In response to the disruptive changes within society and technology, higher education institutions need to transform their content-centric curricula into learning pathways that effectively equip students for the workforce. Adapting to the challenges posed by evolving learner dynamics is a crucial approach for institutions to enhance their responsiveness to such changes. This research aims to investigate the categorization of potential students based on their learning preferences, study self-efficacy, and learning motivation. Furthermore, the study seeks to compare the attributes of students across these different clusters. The participants were secondary high school students from various school types in Thailand, using a multi-stage random sampling method for an online survey. Analyzing responses from 1137 students, a two-step cluster analysis identified three distinct clusters. The comparison of student characteristics among clusters showed significant differences according to the student's study self-efficacy, motivation, and learning preferences. Students in a cluster where the majority perceived their academic accomplishments to be at or above an average level exhibited significantly stronger preferences for non-traditional and traditional study approaches than the other clusters. The study also discussed how students' learning preferences and interests in academic disciplines are associated with their psychological attributes and perceived academic achievements. The distribution of cluster memberships holds significance for institutions, particularly in communicating innovative learning approaches to potential students.