An Analysis of Foundation Students' Satisfaction with the Service Quality at Centre for Pre-University Studies

Abstract

This paper investigates satisfaction levels among foundation-year students regarding the quality of services at the Centre for Pre-University Studies (CPUS), University Malaysia Sarawak (UNIMAS). Beginning with a review of the literature on service quality and student satisfaction in higher education, the study addresses its research question through four dimensions: teaching and learning, academic support, campus facilities, and campus life. A quantitative research design was employed, with data collected through a structured questionnaire administered to 1 001 foundation students. Exploratory factor analysis supported a three-factor solution — student experience, teaching quality, and campus facilities — with excellent sampling adequacy (Kaiser-Meyer-Olkin value = 0.983) and a significant Bartlett's test of sphericity (χ2=30111.73, p<0.001). The estimated reliability of all factors was high (Cronbach's α between 0.802 and 0.966). Regression analysis indicated that student experience and teaching quality together explained 92.2\% of the variance in overall satisfaction (R2=0.922). Nonetheless, student experience was the strongest predictor. Another regression model demonstrated that these factors were also significantly associated with retention intention (R2=0.743). Random forest classifiers were then employed as a complementary machine learning approach. The items related to campus life, aggregated under the student experience factor and the composite scores, emerged as the most significant predictors of retention and the degree of satisfaction. The machine learning models predicted satisfaction levels with 76.2\% accuracy and retention with 88.1% accuracy. Overall, the findings underscore the significance of a holistic student experience such as a sense of belonging, well-being, and willingness to remain at the institution-in predicting satisfaction and retention metrics at the CPUS stage, alongside instructional quality. The study therefore has practical implications for the CPUS, which seeks to enhance student experience through the intelligent application of aggregate statistical and machine learning findings.