Data-driven analysis of parental involvement and mathematics achievement via engagement and efficacy

Mary Ann L. Nim; Las Johansen B. Caluza

doi:10.61251/ijoep.v3i1.318

Authors

Mary Ann L. Nim Department of Mathematics, Cirilo Roy Montejo National High School, Philippines
Las Johansen B. Caluza College of Arts and Sciences, Leyte Normal University, Philippines

DOI:

https://doi.org/10.61251/ijoep.v3i1.318

Keywords:

Educational data mining, Family-school partnership, Motivation, Student engagement, Student performance

Abstract

This study examined the relationships among parental involvement, learning engagement, self-efficacy, and mathematics achievement to identify actionable factors for improving student performance. A data-driven quantitative design was employed using secondary data from 354 respondents, analyzed through nonparametric methods, mediation modeling, association rule mining, and exploratory predictive modeling with classification and regression trees. Reliability analysis confirmed strong internal consistency for all scales, and descriptive statistics indicated generally high levels of parental involvement, engagement, and self-efficacy among participants. Correlation and mediation analyses revealed that parental involvement significantly influenced mathematics achievement both directly and indirectly through learning engagement and self-efficacy, with combined mediators accounting for more than half of the total effect. Prescriptive analytics highlighted engagement and self-efficacy as multidimensional levers (behavioral, emotional, and cognitive) for improvement, while predictive models demonstrated good classification performance and meaningful reductions in error for continuous achievement scores. These findings underscore the importance of integrated strategies that strengthen family-school partnerships while simultaneously fostering engagement and confidence to optimize mathematics outcomes. The study concludes that parental involvement, learning engagement, and self-efficacy are critical, interrelated factors in mathematics achievement and recommends multilevel interventions while acknowledging limitations of cross-sectional secondary data. Future research should employ longitudinal and experimental designs to establish causality and validate these findings across diverse contexts.

Author Biographies

Mary Ann L. Nim, Department of Mathematics, Cirilo Roy Montejo National High School, Philippines

Mary Ann L. Nim is a Mathematics Teacher and Public Master Teacher at Cirilo Roy Montejo National High School in Tacloban City. She serves as the Grade 8 Level Coordinator, guiding colleagues in instructional practices, mentors fellow teachers, and has contributed to the development of educational resources for the division. She can be contacted at maryann.nim@deped.gov.ph

Las Johansen B. Caluza, College of Arts and Sciences, Leyte Normal University, Philippines

Dr. Las Johansen B. Caluza is a Professor VI and researcher at Leyte Normal University in Tacloban City. He specializes in information technology, artificial intelligence, machine learning, educational data mining, and image processing. His work spans ICT competence, learning analytics, cybersecurity, and AI-driven educational applications, with numerous publications in national and international journals. He also engages in university extension, innovation initiatives, and collaborative projects with institutions such as DOST and CHED.

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