Indonesian Journal of Electrical Engineering and Computer Science
Vol 27, No 1: July 2022

Prediction of student satisfaction on mobile-learning by using fast learning network

Laman Radi Sultan (Southern Technical University)
Salwa Khalid Abdulateef (Tikrit University)
Bushra Abdullah Shtyat (Southern Technical University)



Article Info

Publish Date
01 Jul 2022

Abstract

The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.

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