Date of Publication :30th April 2023

Abstract: Academic assessment is based on providing instant and specific feedback after each learning step to avoid unnecessary delays in correcting students’ errors. For such type of evaluation to realize its maximum benefit, it is suggested that assessment should not be a once-and-done activity. Rather, it must be a continuous act, which guides the teaching-learning process of provision for timely feedback. The Machine Learning Algorithm using linear regression was developed to Predict Student Performance probability rate to complete the course taken. It also provides students with information such as ID number, course, year and grades. The demographic data were used for personal, financial and psychological information, family background and health record. The respondents of the study are Student Affairs personnel, IT Experts, and students coming from different departments. This study used the Prototyping Model as a guide in developing the application. To evaluate the application, McCall’s Quality Model was applied. The mean was used to analyze the data. In terms of system operation, system revision and system transition criteria all mean equivalents for each characteristic were described as “very effective”. Based on the result of the study, the researchers concluded that the Machine Learning Algorithm to Predict Student Academic Performance is a big help to the institution, especially to the Office of Student Affairs to predict the probability rate of the student finish their studies and increase student’s awareness.

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