Research on Optimized Allocation of University English Hybrid Teaching Resources under Cloud Computing Environment

Abstract

Driven by the informationization of education, a large number of educational resources have been developed, which usually exist in the form of data, and the situation of “data explosion” has challenged the storage and retrieval capabilities of the hybrid teaching resource platform in universities. In this paper, we construct a hybrid teaching resource platform for university English in the cloud computing environment and introduce the improved bat algorithm using dynamic inertia weights and Gaussian perturbation terms into the teaching resource platform to optimize the process of English teaching resource allocation. The experimental results of the benchmark performance test show that the teaching resource platform has no abnormalities, such as program execution failure in the process of processing files, indicating that the stability of the teaching resource platform is good. The analysis of its application effect shows that the indicators of resource allocation are optimized after the teaching resource allocation experiment, and the variability among college English classes decreases. The English learning effectiveness of students in the experimental classes assisted by the teaching resource platform is significantly better than that of students in the control classes (P=0.001<0.05). This paper lays a foundation for improving the informatization of university English teaching and provides a reference basis for improving students’ English learning effectiveness.

Keywords

• cloud computing
• bat algorithm
• inertia weights
• resource allocation optimization
• teaching resources
• 97a40