Abhishek Kumar‡, Abdul Khader Jilani Saudagar§, Mohammed AlKhathami|, Badr Alsamani|, Muhammad Badruddin Khan|, Mozaherul Hoque Abul Hasanat|, Ankit Kumar¶Corresponding author: Abdul Khader Jilani Saudagar ( aksaudagar@imamu.edu.sa ) © Abhishek Kumar, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, Badr Alsamani, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Ankit Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Kumar A, Saudagar AKJ, AlKhathami M, Alsamani B, Khan MB, Hasanat MHA, Kumar A (2022) Customized Curriculum and Learning Approach Recommendation Techniques in Application of Virtual Reality in Medical Education. JUCS - Journal of Universal Computer Science 28(9): 949-966. https://doi.org/10.3897/jucs.94161 |  |
Virtual Reality (VR) has made considerable gains in the consumer and professional markets. As VR has progressed as a technology, its overall usefulness for educational purposes has grown. On the other hand, the educational field struggles to keep up with the latest innovations, changing affordances, and pedagogical applications due to the rapid evolution of technology. Therefore, many have elaborated on the potential of virtual reality (VR) in learning. This research proposes a novel techniques customized curriculum for medical students and recommendations for their learning process based on deep learning techniques. Here the data has been collected based on the pre-historic performance of the student and their current requirement and these data have been created as a dataset. Then this has been processed for analysis based on CAD system integrated with deep learning techniques for creating a customized curriculum. Initially this data has been processed and analysed to remove missing and invalid data. Then these data were classified for creation of the curriculum using a gradient decision tree integrated with naïve Bayes. From this, the customized curriculum has been generated. Based on this customized curriculum, the learning approach recommendation has been carried out using the fuzzy rules integrated knowledge-based recommendation system. The experimental results of the proposed technique have been carried out with an accuracy of 98%, specificity of 82%, F-1 score of 79%, information overload of 75%, and precision of 81%.