Predictive Modeling of PowerSchool Usage: Comparative Analysis of Linear Regression and Data Mining Techniques using Student Attributes

Edelyn Rose R. Dawat
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Edelyn Rose R. Dawat: Graduate School Department, University of the Immaculate Conception, Davao City, Philippines

International Journal of Research and Innovation in Social Science, 2023, vol. 07, issue 11, 75-85

Abstract: This research investigates Linear Regression, Artificial Neural Network (ANN), and Decision Tree Analysis in predicting PowerSchool usage based on student attributes (GPA, attendance, behavior). Linear Regression highlights GPA as the strongest predictor, achieving 62% predictive adequacy. The ANN model displays high accuracy but with increased incorrect predictions during testing, emphasizing the importance of GPA. Decision Tree reveals a 0.298 uncertainty despite high recall. The ANN model outperforms, demonstrating superior accuracy and recall, while the Linear Model shows good accuracy and precision. Although the Decision Tree Model presents high recall, it slightly lags in accuracy and precision. The F1-Measure peaks at 0.9231 for the ANN Model, offering directions for future model enhancements.

Date: 2023
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