College English Teaching Evaluation with Neural Network

The notion of abilities in colleges and universities is undergoing a substantial transition, and the accompanying curricular view is also evolving in response to the demands of social and economic development. Students who are not English majors in college or university play a critical role in growing their knowledge of foreign languages, improving the quality of foreign languages, and fostering their capacity to use the language in real-world situations. As a result, one of the most important methods to assess the quality of a college's curriculum is to look at how well it teaches English. Consequently, how to evaluate collegiate English instruction has become a major concern. This research offers a neural network (NNs) for evaluating collegiate English education based on the BP network’s application principle. The main work is as follows: (1) based on the peculiarities of college English teaching assessment, the weights and thresholds of the BP network are tuned using the global optimization ability of the ant colony algorithm. (2) To improve optimization ability of ACO, an update of pheromone is realized by combining global as well as local methods. In formula of the global update pheromone, a function is added to adjust the information residual coefficient according to the distribution of the solution. The residual coefficient of the local pheromone is adjusted according to the way of the minimum error judgment. (3) Optimize the BP algorithm with the improved ant colony optimization (IACO), build the IACO-BP network, and comprehend the optimal selection of weights and thresholds. Optimized BP algorithm is applied to the English teaching evaluation.