Construction and Analysis of a Resource Optimization and Allocation Model for Large-Scale Online Classrooms

Abstract

Online classrooms are characterized by a large number of resources and a complex composition of student users, so how to mine the association between students’ learning habits, grades and other characteristics to achieve more effective resource provisioning and optimization. To achieve this objective, this paper proposes an Apriori classroom resource provisioning model that is optimized using the MPM algorithm. The model regards the data points as prime points, uses the MPM algorithm to track the trajectory of the prime points to avoid redundant polling, and then projects the data points onto the background grid, derives the derivation, and then projects it onto the data points to discover the correlation between the data and realize the effective allocation of classroom resources. According to the experimental findings, MPM-Apriori’s average BRPD and average RPD values are 0.32 and 3.33 when dealing with large-scale online classroom resource data, indicating improved optimality and stability. The average post-test mean score of each dimension of “Introduction to International Politics” is 0.378 points higher than the pre-test, and 66.6% of the indicators in the satisfaction survey on the effectiveness of classroom resource allocation have a satisfaction rate of more than 50%. It shows that the proposed online classroom resource allocation model has good application effects.