Machine Learning Predictive Models for Faculty Selection and Promotion in Public Higher Education Institutions in the Philippines
Keywords:
Faculty Promotion, Faculty Selection, Higher Education, Logistic Regression, Machine Learning Algorithms, Predictive ModelsAbstract
This research investigates the efficacy of predictive models, specifically Logistic Regression, in the context of faculty selection and promotion within Public Higher Education Institutions (PHEIs) in the Philippines. With the ever-growing demands on academic institutions to guarantee the excellence and significance of their faculty, the study aims to develop a robust framework that leverages predictive analytics to enhance management processes. The research methodology includes the collection and study of comprehensive data sets encompassing academic qualifications, teaching experience, research contributions, and other relevant factors for faculty members. The Logistic Regression model is employed to discern patterns and relationships within these data, providing a systematic approach to evaluating faculty performance and potential. The model's predictive capabilities are then assessed through comparisons with historical promotion outcomes. Performance metrics such as accuracy, precision, and recall are employed to evaluate the predictive capabilities of the models. Results indicate that the logistic regression models exhibit promising accuracy rates and effectively identify candidates for selection and promotion with an accuracy of 80% (accurate), precision of 79% (precise), and recall or sensitivity of 89% (highly sensitive). The study underscores the significance of predictive analytics in informing strategic decision-making processes within educational institutions and highlights the potential for enhancing faculty recruitment and advancement practices.
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