The application of effective tracking model in the design of student knowledge education games

As the boost of the economy and the improvement of residents’ living standards, education issues are receiving increasing attention; In response to the issue of rational design of educational games, this study investigates the application of effective tracking models in the design of student knowledge education games. Firstly, this study constructs a personalized knowledge tracking model in view of multi feature fusion; Then, it builds a contextualized narrative education game model integrated with the Adaptive learning framework. The results show that when 40% training set is selected, the accuracy of the personalized knowledge tracking model in view of multi feature fusion is the highest, 0.98, the lowest root mean square error is 0.25, and the lowest Mean absolute error is 0.21; The accuracy of Bayesian knowledge tracking is the lowest, 0.57 in the best case, and the Mean absolute error and Root-mean-square deviation are the highest; When the hidden unit is 16, the area index under the working characteristic curve of the model’s subjects is the highest, around 0.964; The average attention level of students in contextual narrative games reached 4.2245, with a SD of 0.7511; The average of knowledge transfer reached 4.1082, with a SD of 0.7881; The average score of students for the feedback dimension of the research design game is 4.736, and the average score for the learning content dimension is 4.529. In summary, the model possesses excellent application effects in the design of educational games and possesses a promoting influence on the advancement of the education industry.