Level of Engagement in the READY Program as a Predictor of Performance in the Licensure Examination for Professional Teachers: A Mathematical Modeling Approach
DOI:
https://doi.org/10.69569/jip.2026.309Keywords:
Engagement, LEPT performance, Mathematical modeling, Predictive relationship, READY programAbstract
This study examined whether the level of engagement in the READY Program predicts the performance of graduates in the Licensure Examination for Professional Teachers (LEPT). Despite the implementation of institutional review interventions, limited studies have investigated learner engagement as a predictor of licensure examination performance using predictive modeling approaches. Employing a descriptive-correlational quantitative research design and total population sampling, the study involved 43 graduates (12 BEED and 31 BSED) from a private deregulated university in Bacoor City who participated in the READY Program and took the LEPT from March 2023 to March 2026. Data were obtained from official LEPT records of the Professional Regulation Commission (PRC) and the validated researcher-developed Level of Engagement in the READY Program Questionnaire (LERPQ), which demonstrated an overall Cronbach's alpha coefficient of 0.88. Descriptive statistics, Pearson Product-Moment Correlation, simple linear regression, and multiple linear regression were employed for data analysis. Findings revealed that participants generally exhibited high engagement in the READY Program, particularly among LEPT passers, who achieved an overall institutional passing rate of 79.1%. Significant high positive relationships were likewise observed between all engagement dimensions and LEPT performance (r = 0.833 to 0.881, p < .001). Simple linear regression confirmed that overall engagement significantly predicted LEPT performance, yielding the predictive model ŷ = 7.25x + 53.90. Multiple regression analysis further revealed that attendance (B = 4.26, p = .029) and completion of review activities (B = 5.85, p= .003) were the strongest significant predictors, collectively explaining 80.9% of the variance in LEPT performance (R²= .809). The findings underscore the value of structured, evidence-based review programs and provide empirical support for strengthening attendance monitoring, completion of review activities, and targeted institutional interventions to enhance graduates' examination readiness and licensure performance.
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