Development of Customized Strategies for Emergency Language Teaching Content Based on Big Data

Abstract

This paper studies the application and effect of customized content strategy for emergency language teaching driven by big data. Using quantitative research methods, 300 college students and teachers were surveyed and standardized. Descriptive statistics, correlation analysis and regression analysis were used to evaluate the practical impact of big data technology on emergency language education. With the support of big data technology, customized teaching content can improve students’ learning performance, learning attitude, course satisfaction and teaching feedback evaluation, and improve teachers’ teaching strategies. This study proposes four strategies: real-time data collection and analysis, personalized learning path design, dynamic adjustment of teaching content, and intelligent learning feedback and evaluation. Use big data analytics and instructional design to dynamically adjust content based on students’ learning behaviors and needs to provide a highly personalized learning experience. The study highlights the unique role of big data technology in improving classroom interaction, fostering student self-directed learning, and optimizing instructional feedback. This paper verifies the significant impact of big data technology on emergency language teaching and provides theoretical and empirical support for its wide application in the field of education. The research results provide specific guidance for the integration of big data technology in education practice in the future to improve education quality, promote personalized learning, and enhance emergency education response ability.