The Use of Schoology as Learning Management System in the College of Computing Education: A Response Assessment using Data Mining Techniques

Abstract

This paper evaluates the accuracy of the students’ responses in the actual evaluation conducted in assessing student satisfaction in the use of the Learning Management System (LMS) in the College of Computing Education of the University of Mindanao in Davao City, Philippines. The use of data mining algorithms, namely the C4.5, K-Nearest Neighbor (KNN), and Naïve Bayes algorithms in the prediction of LMS assessment dataset consisting of 257 instances and 24 variables, performed using the 10-folds cross-validation scheme in WEKA software, were undertaken. Simulation results revealed that the optimal model used for prediction is the Naïve Bayes algorithm with 100% prediction accuracy. The high percentage accuracy denotes that the knowledge generated from the actual study is worthy of implementation.