Publication Type : Journal Article
Publisher : PeerJ
Source : PeerJ Computer Science
Url : https://doi.org/10.7717/peerj-cs.1670
Campus : Chennai
School : School of Computing
Department : Computer Science and Engineering
Year : 2023
Abstract : Deep learning, a subset of artificial intelligence, gives easy way for the analytical and physical tasks to be done automatically. There is a less necessity for human intervention while performing these tasks. Deep hybrid learning is a blended approach to combine machine learning with deep learning. A hybrid deep learning (HDL) model using convolutional neural network (CNN), residual network (ResNet) and long short term memory (LSTM) is proposed for better course selection of the enrolled candidates in an online learning platform. In this work, a hybrid framework that facilitates the analysis and design of a recommendation system for course selection is developed. A student’s schedule for the next course should consist of classes in which the student has shown interest. For universities to schedule classes optimally, they need to know what courses each student wants to take before each course begins. The proposed recommendation system selects the most appropriate course that can encourage students to base their selection on informed decision making. This system will enable learners to obtain the correct choices of courses to be studied.
Cite this Research Publication : Subha S, Baghavathi Priya Sankaralingam, Anitha Gurusamy, Sountharrajan Sehar, Durga Prasad Bavirisetti, Personalization-based deep hybrid E-learning model for online course recommendation system, PeerJ Computer Science, PeerJ, 2023, https://doi.org/10.7717/peerj-cs.1670