JUCS - Journal of Universal Computer Science 28(9): 949-966, doi: 10.3897/jucs.94161
Customized Curriculum and Learning Approach Recommendation Techniques in Application of Virtual Reality in Medical Education
expand article infoAbhishek Kumar, Abdul Khader Jilani Saudagar§, Mohammed AlKhathami|, Badr Alsamani|, Muhammad Badruddin Khan|, Mozaherul Hoque Abul Hasanat|, Ankit Kumar
‡ JAIN (Deemed to be University), Bangalore, India§ Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia| Information Systems Department,College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia¶ GLA University, Mathura, India
Open Access

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%.

Content-based Multimedia Retrieval, Hypermedia systems, Web-based services, Semantic Web, Multimedia