Development of an Academic Performance Monitoring System Using Least Squares Regression for Predicting Student Academic Performance Status on Professional Courses

Abstract

This study deals with the development of an academic performance monitoring system using least squares regression to predict a student’s academic performance status on professional courses. The proposed system monitors the student’s academic performance to promote education and achieve learning outcomes. The study aims to design and create a Graphical User Interface (GUI) and its functions, to utilize MATLAB for forecasting and a least squares regression model to show and predict the relationship between two factors, and to evaluate the system’s predicting accuracy using Percentage Forecast Error (PFE) and Mean Absolute Percent Error (MAPE) to compare actual and forecasted data for every semester. The sample size includes 182 students of BS Information Technology, of whom thirteen (13) were successfully chosen using simple random sampling with their GPAs for the purpose of predicting future academic performance.