Research on Management Path and Operation Mechanism Construction of Civic Education in Colleges and Universities in the Era of Artificial Intelligence

Abstract

Abstract Under the background of the artificial intelligence era, the education management style of colleges and universities is moving towards a more intelligent direction. This paper combines the F-S learning style model and the environmental characteristics of online learning to construct an index system of online learning behavioral characteristics for Civic and Political Education in colleges and universities. On this basis, the traditional K-means clustering algorithm is improved based on three-branch decision-making and is used to cluster and divide the learning behaviors of students in the Civic and Political Education courses. At the same time, the degree of interest is introduced to optimize the Apriori algorithm, and the association rules of students’ learning behavior and performance are mined. Then, taking the student learning behavior data of the online course of X Civic and Political Education in colleges and universities as a research sample, TK-means, and Apriori algorithm are used to explore the management path and operation mechanism of Civic and Political Education in colleges and universities. The study shows that among the students in the four clusters, the students with higher task point completion, chapter completion number, and check-in number are the most, accounting for 32%, and the students with a higher course video completion progress, task completion number, and document completion are the least, accounting for 19%. The probability of receiving an ‘Excellent’ grade was 93.9% when students were classified as ‘Visual-Active-High-Commitment’. When students were of the ‘Verbal - Active - passive - High Engagement’ type, there was a 91.3% probability of getting a ‘Medium’ grade. The effectiveness of civics education can be improved by enriching curriculum resources and improving assessment methods.