Paper Info
Title
Deep neural network based m-learning model for predicting mobile learners' performance
Abstract
The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.
Year
DOI
Venue
2020
10.3906/elk-1907-8
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
Keywords
DocType
Volume
M-learning model,artificial neural network,deep learning algorithm,features importance,machine learning,mobile learning
Journal
28
Issue
ISSN
Citations 
3.0
1300-0632
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Muhammad Adnan100.34
Asad Habib200.34
Jawad Ashraf300.34
Shafaq Mussadiq400.34
Arsalan Ali Raza500.34