Design and Effectiveness Evaluation of Data Mining-Based Artificial Intelligence-Driven Intelligent Teaching and Assisting System for Civics Classes

With the continuous integration and development of data mining technology and education, intelligent teaching and support technology has been more and more widely used in the field of education. In this paper, we take the field of Civics education as the research area, and carry out the design and development of the intelligent teaching aid system for Civics class, which mainly covers deep knowledge tracking and exercise recommendation. Taking the DKVMN model as the framework, a forgettable knowledge tracking model based on IRT is composed of deep forgetting and deep IRT modeling. Introducing the knowledge map in the field of Civics and Politics, mining the implicit relationship between knowledge points, and constructing DRSS, an exercise recommendation model based on deep reinforcement learning and strategy selection, we carry out experiments on the effectiveness of the application of the intelligent teaching and support system for Civics and Politics classes in this text with the students of the first-year class 1 of the Chinese language and literature major in H university culture as the research object. In the dimension of Civics cultural literacy, the students in the experimental class showed significant differences in the pre and post-test scores of the rule of law awareness, public participation, and moral cultivation dimensions (P<0.05), with the scores increasing by 3.38, 2.54, and 1.53 compared to the pre-test, respectively, while in the Civics values and concepts dimensions, the difference in the scores between the pre-test and post-test in the social responsibility dimension and the idealism dimension amounted to 1.77, to 1.45, demonstrating a difference that reaches a significant level (p<0.05).