An immersive learning model using evolutionary learning. (January 2018)

• Record Type: Journal Article
• Authors: Bhattacharjee, Deblina; Paul, Anand; Kim, Jeong Hong; Karthigaikumar, P.
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 236
• Page End: 249
• Publication Date: 2018-01
• Subjects: m-learning -- Immersive virtual reality -- Immersive learning -- Education -- Personalized learning -- Evolutionary learning -- Reinforcement learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.023
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An evolutionary virtual reality model for m-learning is proposed. An evolutionary learning algorithm with reinforcement is proposed. The algorithm personalizes the learning path of every user based on his actions. The virtual environment evolves using the reinforcement signal and user action. Results show increased retention by 83.75% across 3 case study groups. Abstract: In this article, we have proposed an educational model using virtual reality on a mobile platform by personalizing the simulated environments as per user actions. We have also proposed an evolutionary learning algorithm based on which the user learning path is designed and the corresponding simulated learning environment is modified. The main objective of this study is to create a personalized learning path for each student as per their calibre and make the learning immersive and retainable using virtual reality. Our proposed model emulates the innate natural learning process in humans and uses that to customize the virtual simulations of the lessons by applying the evolutionary learning technique. A quasi-experimental study is conducted by taking different case studies to establish the effectiveness of our learning model. The results show that our learning model is immersive and gives long term retention while enhancing creativity through reinforced customization of the simulations. Graphical abstract: The immersive virtual reality framework for an m-learning educational model consisting of an evolutionary learning network giving a personalized learning path for every student.