Innovation of interdisciplinary emergency language talent training model based on big data

Abstract

Background: With the acceleration of globalization and the complex and changing international environment, emergency language communication skills have become more important, and traditional education models have failed to adequately meet these needs. Objective: The purpose of this study is to design an innovative education model that is more in line with the needs of modern education through big data technology. It is mainly to design a personalized learning path based on big data to improve the personalization and precision of teaching. Method: Use real-time feedback and interactive teaching methods to enhance students’ dynamic participation in learning. Enhance students’ emergency language application ability and interdisciplinary collaboration skills through interdisciplinary teamwork projects. Result: The research results show that compared with the traditional teaching mode, the teaching mode based on big data has significant advantages in improving students’ emergency language ability and interdisciplinary cooperation ability. Analysis of Variance (ANOVA) and multiple linear regression model proved the effectiveness of the teaching model, and the results showed that there was a significant positive correlation between teaching methods, individual factors and learning outcomes. Conclusion: This study provides an innovative educational model design and verifies its effectiveness in practical teaching through empirical research. These findings provide strong theoretical and practical support for the innovation of future education models.