Determinants of mathematics achievement in modular distance learning: A predictive modeling study among secondary students

Abstract

The study examined the determinants of mathematics achievement in Modular Distance Learning (MDL) among secondary students and developed a predictive model for mathematics performance. A total of 353 students participated in the study using a validated questionnaire that measured demographic characteristics, technology-related factors, prior mathematics achievement, and perceived learning experiences in MDL. Descriptive statistics and multiple regression analysis were employed to analyze the data. Findings revealed that mathematics performance significantly declined during MDL compared to traditional face-to-face learning. Previous mathematics achievement, perceived learning experience, age, and grade level were identified as significant predictors of mathematics performance. The predictive model generated was: MDL Mathematics Performance = 47.95 + (0.374 × previous mathematics achievement) + (0.602 × perceived learning experience) − (0.256 × age) + (0.756 × grade level). In contrast, gender, parental educational background, household income, internet accessibility, technology use, and internet connectivity were not significant predictors. The study provides empirical evidence that may guide educators, school administrators, and policymakers in designing interventions, improving support, and enhancing mathematics learning outcomes among students engaged in alternative learning delivery modalities during disruptions.