Predictive Modeling of Learning Practices among Tertiary Educators: A Multiple Regression Approach

Abstract

This study employed a quantitative predictive modeling design using multiple regression analysis to examine the extent to which content transformation and assessment integration predict learning practices among tertiary educators in teacher education programs of State Universities and Colleges in Region 12. Using total enumeration, full-time Mathematics faculty from six participating Higher Education Institutions in Region 12 served as respondents. Data were gathered through a validated 5-point Likert scale questionnaire and analyzed using mean, Pearson's r, coefficient of determination, and multiple linear regression.

Results revealed high levels of content transformation, assessment integration, and learning practices. A strong positive correlation was found between teaching practices and learning practices. Multiple regression analysis showed that both content transformation and assessment integration significantly predict learning practices. Consequently, the null hypotheses stating no significant relationship and no significant prediction were rejected.

The findings underscore that effective transformation of disciplinary content and integration of assessments are key drivers of quality learning practices. It is recommended that HEI administrators provide continuous professional development on evidence-based instructional strategies and that teacher education curricula include modules linking teaching and learning processes. Future research may explore other predictors such as motivation and learning environment.