A Study on Innovative Strategies for Teaching English to College Students Based on Knowledge Mapping

This paper proposes a personalized teaching method based on knowledge mapping and the commonly used personalized learning methods. A web crawler is used to crawl English exercises data on the Internet and classify them according to the knowledge categories, establish the connection between topics according to their cosine similarity, and build an English knowledge graph using NEO4J. After creating the knowledge map, a select few nodes are categorized with labels that reflect the characteristics of various pupils, and the node categories are divided into two categories: mastered and unmastered, after which the whole knowledge map is labeled using a graph convolutional neural network to generate a personalized knowledge map. After testing, the model works best when the threshold value is increased to 0.8 when generating the English knowledge map. And the overall English score of the experimental class taught with this paper’s method improved by 5.27 points, among which the average scores of completion, writing, and reading increased by 2.58, 1.32, and 1.17 points, while the overall English level of the control class did not change significantly. As a result, the approach used in this research to create tailored learning routes based on personalized knowledge mapping has excellent applicability.