Volume 17, No 2, 2020

Towards Intelligent E-Learning Systems: A Hybrid Model for Predicatingthe Learning Continuity in Iraqi Higher Education


Mohammed K. Kadhim and Alia K. Hassan

Abstract

E-Learning system gains a great attention in the past years; with advance of the internet and the information exchange techniques the importance to merge the traditional learning means with the internet-based learning methods became a must especially in Iraq, the Iraqi higher education is now coping with the new information and communication technologies and adopting a modern methods for upgrading their education and learning ways. There are great efforts to blend E-Learning systems with the educational process, in order to fulfill this purposes the proposed research is advancing E-Learning systems by suggesting a hybrid method that combines two Artificial Intelligence Techniques (AI) inside the design and the development of an intelligent E-Learning system for computer science department at university of technology. The utilization of Artificial Neural Networks algorithm (ANNs) especially Recurrent Neural Networks (RNN) is a way of implementing deep learning technique to predict the students' final out comes in virtual class room based on their grades and their learning behaviors. RNN is optimized by utilizing ADAM optimizer to lift the accuracy of the proposed algorithm, the dataset are gathered and processed to suite the education purposes and was divided into80% for training the model and 20% for testing the model, the results of the hybrid model are compared with other machine learning methods like Multi-Layer Perceptron (MLP), decision tree, naïve Bayesian, and random forest using WEKA environment, the results of the proposed model showed a promising accuracy when compared with the mentioned machine learning algorithms.


Pages: 172-188

DOI: 10.14704/WEB/V17I2/WEB17023

Keywords: E-Learning, Learning Continuity, Artificial Intelligence, Artificial Neural Networks, Recurrent Neural Network, ADAM, and WEKA.

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