Building a data-driven teaching platform for ESL vocabulary corpus in universities based on VR technology

Abstract

With the rapid development of information technology, the application of VR to English language teaching has gradually become an emerging trend. As the driving force of English learning, the establishment of ESL vocabulary corpus is of great significance for making great progress in teaching in colleges and universities. By analyzing the characteristics of VR technology, the study establishes a data-driven teaching platform for ESL vocabulary corpus in colleges and universities based on this technology. Then, based on the improved graph neural network, the text classification of the ESL vocabulary corpus was carried out to achieve the integration of ESL vocabulary corpus resources. The results show that the improved graph neural network vocabulary text classification model converges at 145 iterations, and the convergence speed is improved by up to 100 generations; Among different datasets, the recognition accuracy of the SADE-GNN model on the MR, R8, SST1, and SUBJ datasets is 90%, 99%, 63%, and 96%, respectively. In the recognition and classification of ESL vocabulary corpora in universities, the accuracy of this model is stable at around 95%, with a maximum value of 97%. In practical teaching applications, students' English listening, reading, writing, and speaking scores have all increased to varying degrees, with the top 50% of students achieving 95 or above in both reading and speaking. The above results indicate that the teaching platform under the research model has high accuracy in text classification, can significantly improve the effectiveness of English teaching, providing new reference ideas for the construction of ESL vocabulary corpus and teaching method reform in universities.