The Impact of Mobile Educational Games on Contemporary Users' Learning Behavior

Jian Huang, Gege Fang, Yang Liu, Menghan Zhang

OAI: oai:igi-global.com:313196 • DOI: 10.4018/JGIM.313196

The purpose is to cope with mobile educational game users' low continuous utilization rate. This thesis innovatively introduces the deep learning technology to study and the relationship between the mobile educational game and learners' learning behavior. It is found that learners' game experience is significantly correlated with game satisfaction. The estimation algorithm is designed based on the convolutional neural network (CNN), and the questionnaire method is adopted. The design tool of the estimation algorithm is a computer system, and the social software is used for the questionnaire. The dataset source used in the estimation algorithm design is “World Sudoku Championship.” The game satisfaction questionnaire and learning behavior questionnaire subjects are 60 primary school students and 300 college students, respectively.