An Analysis of the Efficacy of L2-MT Mixed Modeling in Second Language Acquisition - A Cognitive Ability Perspective

Abstract

Abstract Cognitive ability provides a new perspective for second language acquisition research, but there are still many problems to be solved. Therefore, after fully exploring the concept of cognitive synergy, this paper proposes two research methods: qualitative multimodal interaction and hybridization of cognitive synergy. The deep neural network algorithm is interpreted layer by layer, and the model is applied to the construction of bilingual language models. Based on this, the L2-MT hybrid model is introduced, and the generalization performance of the bilingual language model is improved using the joint approach. The model’s speech recognition function for monolingual multi-task learning is used to aid learners in learning a second language. The effectiveness of the L2-MT hybrid model in second language acquisition was tested through statistical correlation analysis. Under this model, the attention of the second language learners fluctuated twice in the first half of the learning process, with the fluctuation range between 80% and 100%, and finally stabilized at about 70%. The subjects were more concentrated in the second language acquisition process. In the second language achievement comparison experiment, class A performed better than class B in terms of speaking, writing, translation, and total achievement. The difference between the two classes is 7.83, 5.002, and 3.6, respectively. Meanwhile, the p-value is 0, which is less than 0.01, and there is a significant difference between the two classes. The L2-MT model has a higher value of application in second language acquisition, which provides a better method for learners.