Deep learning and multimodal target recognition of complex and ambiguous words in automated English learning system

College English Multimodal Teaching Based on Digital Information Technology

In the era of in-depth application of knowledge technology in the field of education, the teaching mode combining school English teaching and network data technology is widely used in school English education. Teaching resources have become electronic resources under the influence of digital information technology. However, English teaching often ignores all aspects of English ability, so there are still shortcomings in English teaching. The role of information technology makes a single English teaching text model unable to meet the needs of scholars. In recent years, multimodal events have enriched the expression of language input and output content, which is in line with the learning needs of current students. Therefore, under the premise of digital information technology, this paper constructs a multimodal English teaching model from the aspects of multimodal English teaching resources, multimodal English teaching activities, and multimodal English teaching analysis. It aims to mobilize the enthusiasm of scholars to learn English from various aspects and build a multimodal teaching mode of English for teachers supported by digital data technology. Therefore, the experiment shows that the multimodal teaching mode of college English based on digital information technology proposed in this paper will mobilize the enthusiasm of students to learn in a comprehensive way. It greatly improves students' English performance, promotes the development of students' autonomous learning ability and comprehensive language ability, and improves the quality of English teaching by 20%.

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.

Dual Coding or Cognitive Load? Exploring the Effect of Multimodal Input on English as a Foreign Language Learners’ Vocabulary Learning

In the era of eLearning 4.0, many researchers have suggested that multimodal input helps to enhance second language (L2) vocabulary learning. However, previous studies on the effects of multimodal teaching have failed to yield definitive conclusions. Furthermore, only few studies on the multimodal input of vocabulary learning have aimed at junior high school students and have focused on explicit vocabulary instruction in class. To explore the effects of multimodal input on English as a foreign language (EFL) learners’ vocabulary learning and summarize effective methods, this study adopts a mixed-method approach. Based on dual coding theory and cognitive load theory, the teaching materials in this study were designed using the resources provided by the multimodal corpus iWeb and other websites. A total of 60 junior high school students who learned EFL and had a similar English proficiency level were divided into an experimental group (EG) and a control group (CG). Target words were selected through questionnaire I. During the experiment, the CG learned from monomodal materials while the EG received multimodal input, and an immediate post-test was delivered to the two groups. Questionnaire II was distributed in the EG, and five students of the EG were randomly selected for an interview. One week later, a delayed post-test was conducted on the EG and CG. The results showed that the EG performed better in the post-test but did worse than the CG in the delayed post-test. The results of the questionnaire and the interview suggest that students held both positive and negative attitudes toward the multimodal input approach in vocabulary learning. The study concludes with some implications for choosing a multimodal input approach in vocabulary learning, along with a number of suggestions on how to optimize its positive influence and minimize its negative effects.