Research on Intelligent Educational Technology in Business English Education in Building Multi-model Learning Environments and Personalized Learning Paths

Authors

  • Xiaoying Lu School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Norazrena Samah School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/humentech.v3n2.84

Keywords:

Business English, Multi-model learning environments, Personalized learning paths, Intelligent education

Abstract

This study investigates the efficacy of intelligent educational technology business English education. It tries to solve the problem of traditional teaching strategies which often fail to meet the diverse needs of students by providing engaging, practical course materials. With an explanatory sequential mixed methods research design, it utilizes thematic analysis for qualitative data from instructors and students, followed by a quantitative survey of 215 business English students. The findings from statistical study indicate that there is a strong positive association between the usage of intelligent educational technology and the improvement of students’ business language competency, communication effectiveness, and their overall satisfaction. Moreover, the correlation between students' idea of personalized learning and their motivation, engagement, and performance were significantly positive. In a word, the application of intelligent educational technology can support the development of multi-modal and personalized business English teaching, as well as technology-driven language learning practice, curriculum development, and intelligent educational technology design.

Published

09-08-2024 — Updated on 18-08-2024

Versions

How to Cite

Lu, X., & Samah, N. (2024). Research on Intelligent Educational Technology in Business English Education in Building Multi-model Learning Environments and Personalized Learning Paths. Journal of Human Centered Technology, 3(2), 84–92. https://doi.org/10.11113/humentech.v3n2.84 (Original work published August 9, 2024)

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