Application Analysis of Multimedia Technology in College English Curriculum

Abstract

Over the past 30 years, neural computational models have achieved great success in the field of language learning, bringing about remarkable improvements in the study of individual differences across languages. However, most of the existing research on neural computational models is about the cognitive development of mother tongue, and there is no correlation model of phonological ability, short-term memory ability, and long-term memory ability with individual differences in the research. Therefore, the intervention suggestions in English learning disabilities cannot be targeted. Through neural network simulation and t-test, it can be seen that the factors influencing the overall English performance of learners are not phonological awareness, but rather short-term memory and long-term memory. It can be seen from the simulated individual ANN that the role of working memory is particularly obvious. This conclusion is consistent with neuroscience theories on bilingual control in the brain. Individual ANN can provide a basis for predicting learners' learning ability and risk analysis of learning disabilities by simulating the learning path of learners, and at the same time providing more accurate clues and directions for subject teachers to carry out personalized teaching.