Abstract
The kinetic theory is utilized to study the impact of student interaction on the grade point average (GPA) in social networks. The interactions between students can not only be through offline means but also social networks. To analyze the evolution of the student's GPA on social networks, we set up a joint GPA-connectivity density function and two Boltzmann models, which consider the general connectivity distribution of individuals and describe the structure of social networks statistically. In the model, we use the standard Heaviside function to divide the GPA into two parts, corresponding to the students’ different learning attitudes. In addition, the number of contacts for each student is assumed to determine the probability of their learning attitudes being transmitted and successfully influencing another student. We investigate the evolution of the overall students’ average GPA through the kinetic models and find the conditions for the reversal of GPA. Finally, numerical simulations reveal how social networks affect the students’ GPA.
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