Analysis of the mathematics teachers' licensure examination results through machine learning policy implications on teacher education - Patungan, Arturo J. Jr
The Philippines consistently demonstrates poor performance in mathematics, as shown by recent international assessments such as TIMSS 2019, PISA 2018, and 2022, and the National Achievement Tests (NAT). Concerns arise over teacher education quality and declining Licensure Examination for Teachers (LET) passing rates, dropping from 31.45% to 27.28% between 2010 and 2018. Improving LEt outcomes is crucial for ensuring mathematics teachers have the necessary skills to enhance math education. This study aimed to identify a machine learning model for predicting LET results among teacher candidates and determine significant influencing factors. Using a mixed-methods approach. quantitative data from educational institutions and the Professional Regulatory Commission (PRC) were analyzed alongside qualitative insights from open-ended survey responses. The study examined 769 cases for quantitative assessment and concluded qualitative analysis with 65 key informants. Among the seven machine learning models evaluated. Gradient Boosted Tree, Naive Bayes, and Logistic Regression showed superior performance, with Logistic Regression excelling evaluation data. Key predictors of LET results included academic performance, demographic characteristics, and circumstantial factors. A framework based on the CRISP-DM methodology was proposed for deploying and implementing machine learning models to predict LET outcomes. The study highlights the potential for interventions and policy adaptations aimed at improving LET performance. Additionally, it proposes a framework for enhancing the quality of mathematics teachers' education in the Philippines.