An Improved Computational Solution for Cloud-Enabled E-Learning Platforms Using a Deep Learning Technique

An Improved Computational Solution for Cloud-Enabled E-Learning Platforms Using a Deep Learning Technique

Wenyi Xu
Copyright: © 2023 |Pages: 19
DOI: 10.4018/IJeC.316664
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Abstract

The sharable e-learning platform can be presented as a useful learning environment for students on the cloud computing infrastructure. Virtual classrooms are momentarily taking the place of conventional ones, which means that e-learning is becoming more popular. There are currently no strategies for estimating how much cloud resources will be used. Because of this, students can access learning objects without deciding to follow a different learning management system (LMS). The proposed deep learning-based e-learning platform (DL-E-LP) can enable separate LMS embedded in multiple e-learning standards to share the learning objects. Using a smart learning system, teachers can keep track of their students' progress more easily. The convolutional neural network has been used to develop face recognition and monitor students' knowledge learning level in deep learning. The use of modern technologies and smart classrooms makes learning easier for all students. The proposed paradigm is both efficient and productive through experimentation.
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Introduction Of E-Learning Platform

In online learning, teachers and students can connect using a variety of mediums, including email, online chat, and video conferencing (Semerci et al., 2021). Students will only have one means of communicating (Díaz Redondo et al., 2021). Because many students have a visual memory, they find online learning approaches more appealing (Muzaffar et al., 2021). The classroom is the only place where an effective teacher-student relationship can be formed (Al Rawashdeh et al., 2021). Engagement and questions from a student in a class cannot be duplicated online (Ali et al., 2022). E-learning can be an effective substitute for classroom learning (Ray et al., 2021). It is still the most popular option since it is easier to teach discipline and is a better method of passing on information and knowledge (Gurcan et al., 2021). Due to the advent of online learning, even students with busy schedules and limited wiggle room can now acquire an excellent education (Tokarieva et al., 2021). Web-based education has made it possible to provide courses worldwide using a single Internet connection (Chen et al., 2021). Standard, digital, and cloud-based education have all come and gone in the education system's history, with the arrival of smart education in the cloud. Traditional learning has been replaced with smart learning, which provides a comprehensive awareness of how to use today's technology to prepare students for a quickly changing world in which adaptability is critical. Students are equipped with a wide range of resources and methods for gaining information at any time and from any location.

E-learning technology, such as mobile devices, digital material, and online learning sources and possibilities, is becoming more popular (Ly et al., 2021). These numbers will expand faster than anticipated under global circumstances (Shetu et al., 2021). There is a growing interest in e-learning research in nations that are very important, such as India, concentrating on computer science and engineering, health, and social science themes (Gherheș et al., 2021). End-user technologies and server-based technologies are now employed in e-learning (Giray et al., 2021). Students employ end-user technology, such as mobile and desktop applications and virtual and augmented reality, to access platforms and material they can interact with remotely (Alshahrani et al., 2021). Server-side technology holds the application and data for cloud-based services (Darejeh et al., 2021). Up to Massive Open Online Courses, there are a variety of courses available (Mangaroska et al., 2021). Due to the growing popularity of e-learning and the vast variety of technologies utilized on both sides, a suitable design of server-side resources has become important (El-Ariss et al., 2021). Cloud resource provisioning has been a research focus, emphasizing forecasting virtual machine resource use (Tawafak et al., 2021). Current research focuses on resource allocation and consolidation, elasticity, workload analysis, and prediction (Mahrlamova et al., 2021). Workload prediction is a major focus of current research, and our study likewise focuses on this kind of analysis (Siew et al., 2021).

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