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

Authors

  • Cheene Rose C. Pilarca College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Defaje Marie O. Opiña College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Jhon Simon B. Leuterio College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Fretzl Ann S. Toledo College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Marjorie M. Bellones College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Rexes S. Gerona College of Computer Studies, University of Antique, Sibalom, Antique, Philippines
  • Jason P. Sermeno College of Computer Studies, University of Antique, Sibalom, Antique, Philippines

DOI:

https://doi.org/10.69478/JITC2023v5n1a05

Keywords:

Academic Performance, Least Squares Regression, Forecasting Method, Prediction, Percentage Forecast Error

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.

Downloads

Published

2023-06-30

How to Cite

Development of an Academic Performance Monitoring System Using Least Squares Regression for Predicting Student Academic Performance Status on Professional Courses. (2023). Journal of Innovative Technology Convergence, 5(1). https://doi.org/10.69478/JITC2023v5n1a05

Similar Articles

21-30 of 40

You may also start an advanced similarity search for this article.