Identification of College Students’ Online Behavior Patterns and Optimization of Online Civic Education Strategies

Focusing on the learning behavior patterns of students with network behavior, this study mainly adopts sequence cluster analysis and lag sequence analysis to convert learning behaviors into sequences, and constructs a learning behavior pattern recognition model based on network behavior sequences.Aiming at different types of classroom learning behaviors in civic education under the network behavior sequence, a targeted teaching intervention mechanism is designed to help students convert their learning behavior patterns and thus improve their learning effects.In this paper, the online behaviors are clustered into four categories of "integrated, autonomous, compliant, and deviant" according to six level 1 codes, and the correlation coefficients of the online behaviors in the four learning categories range from 0.8539 to 0.9944, which is a very strong correlation.Finally, a survey of the results of the intervention in the classroom of Civic Education found that 75.22% of the students believed that the intervention had improved the learning effect of Civic Education.67.7% and 77.54% of the students believed that the intervention had improved the enthusiasm and motivation of Civic Education learning.79.04% of the students were willing to continue to learn independently according to the learning behavior pattern after the intervention.