Predicting Awareness and Misconceptions on Sexual Education among Pre-Service Teachers Using Naive Bayes Classification

This study seeks to fill that gap by evaluating the awareness and misconceptions of student teachers regarding sexual education. This employed a quantitative descriptive research design integrated with a machine learning approach to examine the awareness and misconceptions of pre-service teachers regarding sexual education. A Naive Bayes classifier—specifically the BayesNet implementation in Weka with the K2 search strategy and Simple Estimator—was used to predict awareness levels based on participant responses and demographic profiles. The Naive Bayes classifier achieved a strong overall performance in predicting the awareness levels of pre-service teachers on sexual education, These findings have direct implications for teacher education institutions. By integrating predictive analytics into assessment processes, programs can accurately map awareness levels among their students, enabling more strategic allocation of training resources.