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Predictive Data Analytics using Logistic Regression for Licensure Examination Performance

Educational data mining is a valuable tool for generating new forms of educational data as it facilitates information-driven decision-making to enhance current educational practice and learning materials. Thus, this study presents the licensure examination performance prediction based on their academic grades using logistic regression. It also involves the development of a web-based application that includes the practice review for the centralized source of lectures and review notes and practice exam for the immediate feedback to students and for the strengths and weaknesses of each student and performance prediction with data mining. From the generated logistic regression model, Differential Calculus, Structural Theory 1 and Earthquake Engineering courses are significant predictors with a valid and significant accuracy of 93.33%. Lastly, the experts using the selected criteria and sub criteria of ISO 9126 metrics rated the web- based application acceptable.