Deep Learning: The Impact on Future eLearning

Deep Learning: The Impact on Future eLearning

Table of Contents


eLearning as technology becomes more affordable in higher education but having a big barrier in the cost of developing its resources. Deep learning using artificial intelligence continues to become more and more popular and having impacts on many areas of eLearning. It offers online learners of the future with intuitive algorithms and automated delivery of eLearning content through modern LMS platforms. This paper aims to survey various applications of deep learning approaches for developing the resources of the eLearning platform, in which predictions, algorithms, and analytics come together to create more personalized future eLearning experiences. In addition, deep learning models for developing the contents of the eLearning platform, deep learning framework that enable deep learning systems into eLearning and its development, benefits & future trends of deep learning in eLearning, the relevant deep learning based artificial intelligence tools and a platform enabling the developer and learners to quickly reuse resources are clearly summarized. Thus, deep learning has evolved into developing ways to repurpose existing resources can mitigate the expense of content development of future eLearning.


eLearning, Deep Learning (DL), Learning Management System (LMS), Artificial Intelligence, Machine Learning


Due to the advent of information and communication technology (ICT) in the current era, there has emerged a multiplicity of applications in Higher Education (HE). In that context, eLearning was launched as a way of responding to the new set of educational demands. eLearning has been defined as learning management software systems that synthesize the functions of computer-mediated communications software and online methods of delivering course materials [7]. One of the most important reasons given for the large-scale investment in web-based technology is their potential to enhance teaching and learning [22], as well as to encourage the development of student-centered, independent learning [30] and to foster a deeper approach to learning [12]. According to a survey conducted by [9], eLearning content development for one hour varied from 49 to 125 hours. Although the advent of eLearning environment promised more flexible and independent learning due to its scalability, it can still pose a barrier to institutions that can’t afford the initial investment. So, developing ways to repurpose existing eLearning resources can mitigate the expense of content development.

As we progress in the field of AI, new techniques such as deep learning and artificial neural network are being developed to improve the effectiveness of machine learning and making the applications of AI far-reaching and meaningful. Machine learning tries to model the world, but deep learning attempts to model the human brain to create and maintain its own representations of the world. Deep learning involves algorithms that predict possible outcomes based on user data, which allow a computer to display behaviors learned from experiences, rather than human interactions. It enables automation using algorithms to learn from data and make determinations and predictions. Every new information that the deep learning model receives makes it more intuitive.

Due to massive information overload on the web, it’s hard to index and reuse existing contents. Classifying contents according to domain-specific concept hierarchies could address the problem of indexing and reusability. As a result, automatic classifiers are in high demand due to the difficulties in manual classification. Deep learning can aid in eLearning developing by improving the classification of elements of content, as digital learners increasingly expect content to be offered in multiple formats and on a variety of platforms. In the eLearning domain, the deep learning process takes place autonomously, from extracting and evaluating the data sets from the LMS to predicting what online learners need to be based on their past performance. This paper summarizes the impacts of deep learning for resource management in eLearning, as well as discuss how it will shape eLearning in the future. We briefly review four relevant aspects from learner and developer perspectives:

  • Motivations of applying deep learning in eLearning
  • Deep Learning Framework for developing the contents of the eLearning platform
  • Tools and platforms that enable deep learning systems into eLearning and its development
  • Benefits & future trends of DL in eLearning

The rest of the paper is planned as follows: Section 2 focuses on a survey related to deep learning in eLearning. Section 3 describes deep learning techniques in the development of eLearning platform. Section 4 covers the benefits of DL in eLearning. Tools, platforms and Future trends of DL in eLearning is discussed in section 5 and section 6 concludes the summary.


The use of technology to deliver learning has become a trend in the industry and has been termed, eLearning. It is necessary today for being an active learning perspective and engaging the students with the learning resources, deeply participating in the class and collaborating with each other and the teacher, rather than listening and memorizing. The students need to be motivated to demonstrate a process by simulation, analyzing an argument, or applying a concept to a real-world situation. Therefore, the care in organizing materials in the eLearning platform is essential. To improve existing e-learning applications, smart deep learning environments should, however, to provide personal services to help a learner use, manage, and interact with the learning system. Several studies have investigated the use of virtual and intelligent tutoring techniques, such as personalized learning interfaces and adaptive learning. These efforts have generally highlighted technology development but had little concern for effective instruction or pedagogy to enhance learning performance. Deep learning can apply user-centered design principles, to create new content, to know the target learners and design accordingly based on their needs and personalization of learning will exponentially improve as the learners acquire knowledge and process information and deploy learning best suited to learners.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:

Deep Learning: The Impact on Future eLearning




Anandhavalli Muniasamy , Areej Alasiry




Deep Learning: The Impact on Future eLearning

Publish in

International Journal of Emerging Technologies in Learning (iJET


PDF reference and original file: Click here

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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