Optimization of gamified Learning Education Model based on XR + 5G technology

Abstract

The new intelligent technology represented by 5G and XR has become the core driving force of the fourth industrial revolution, and the integrated application of the two in the field of education is also promoting the continuous transformation of teaching to gamification. Based on XR + 5G fusion technology, this paper proposes the optimization design of a gamified education model. Firstly, the problem of how to improve the network performance through D2D communication in a full-load 5G network is studied, and a D2D communication resource allocation framework based on cross-layer optimization design is proposed. In D2D user access control, a D2D user access criterion based on the distance between the CU user and D2D user receiver is designed according to the QoS requirement, maximum transmit power limit, and user channel information. XR simulation platform was designed based on a 5G network with optimized communication resource allocation. UMa and InH simulation scenarios were set according to XR business model working scenarios, and Wrap Around algorithm was used to model two circles of interference for users, ensuring the reliability of results while improving simulation efficiency. Finally, the platform channel was modeled strictly according to the channel modeling method in technical document 38.901, and the channel was calibrated with the channel provided by 3GPP. Based on the simulation platform, gamified education design is optimized. Including class platform and class platform gamification design, system demand analysis, and database design. In the gamification design, a relatively novel classroom interaction mode is introduced to increase students’ enthusiasm in class. In the demand analysis of the system, the main problems in class are listed, based on which the business and functional requirements of the gamification platform are determined. Experiments show that in the IID scenario, the proposed XR + 5G fusion technology can achieve 1.09 times the training speed and obtain 0.83 % learning accuracy gain compared with the individual learning algorithm. In the Non-IID scenario, the training speed of the proposed fusion technique is 1.03 times that of the individual learning algorithm, and there is a learning accuracy gain of 2.27 %.