Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

Abstract

With the advent of the era of big data and mobile Internet, the traditional teaching model of colleges and universities has been hit unprecedentedly. In the current big data and artificial intelligence network environment, new teaching concepts, characteristics, and model applications should be analyzed, and an education model based on artificial intelligence adaptive control and negative feedback adjustment mechanisms under the big data environment should be explored. The study of this model can provide practical experience and reference value for the application, popularization, and innovation of teaching information technology in the future. Combining the characteristics of big data games, this paper proposes a college student education model based on discrete dynamic model big data game. The scores of college students represent comprehensive information such as their qualifications to provide big data university education, the credit of historical records, and the degree of reliable data transmission. Based on the general model of the adaptive learning system, we have perfected the functions of each module in the overall structure of the domain-based special subject system and designed the overall workflow of the system. Computerized evaluation is introduced into the learner module, and an adaptive evaluation system structure based on cognitive diagnosis theory is designed to achieve a personalized topic selection model for different learners and enrich learners’ experience through more detailed and specialized evaluation results. The model lays the foundation for adaptive course path recommendation. This paper proposes a discrete real-time linear dynamic logic that can express discrete real-time characteristics and all regular characteristics. It can easily describe the behavior of some complex real-time systems. Experiments show that the discrete dynamic model big data game efficiency is improved by 35.71%, which can better optimize the education model of college students. In addition, it can be concluded that the research on the effectiveness of college student education in the era of big data not only determines the safety of the ideological field of colleges and universities but also affects the thinking orientation of college students.