A Discriminant Model of Performance in Mathematics among College Students in a State University in the Philippines

This study aimed to develop a discriminant model to classify the performance of students in Mathematics in the Modern World (MMW) at Pangasinan State University – Asingan Campus based on demographic and support factors. Using a quantitative descriptive-predictive design, data were collected from 154 students enrolled in MMW during the 2024-2025 academic year. Cluster analysis categorized students into high (61%) and low (39%) achievers based on their final grades. While students reported uniformly high levels of individual, motivational, and social support, these factors did not significantly differentiate performance groups. Discriminant analysis revealed that a student’s specific academic program was the most critical predictor. Compared to the baseline course (BSBA MM 1), being an "Education student" was the strongest positive predictor of high achievement, and enrollment in "BIT AT 1" also showed a marginal positive influence. In contrast, demographic variables (age, sex, senior high school strand, parental education) and the measured support factors demonstrated negligible discriminatory power. The resulting model provides a tool for early identification of students at potential academic risk and underscores that program-specific factors outweigh broad demographic and perceived support in predicting mathematics performance. Recommendations include tailoring mathematics instruction to the contexts of non-Education programs and conducting qualitative research to explore the underlying reasons for the pronounced advantage held by Education students.

Cluster analysis, higher education, mathematics performance

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