The Construction Path of the University English Ecological Classroom Teaching Model Under Big Data

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書誌詳細
出版年:	International Journal of Distance Education Technologies vol. 23, no. 1 (2025), p. 1-16
第一著者:	Jin, Yaming
出版事項:	IGI Global
主題:	Teaching Colleges & universities Collaboration Higher education Student teacher relationship Classroom communication Resource allocation Distance learning Big Data Ecology Innovations Student participation Classrooms Real time Education Data collection Feedback Intelligence Transformation Students Communication (Thought Transfer) College English Student Motivation Computer Networks Database Management Systems Educational Environment Achievement Gap Learner Engagement Educational Strategies Educational Quality Educational Resources Distance Education Educational Technology Educational Change Computer Oriented Programs Student Needs Databases Data Processing College Science Educational Facilities Improvement Educational Needs
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その他の書誌記述
抄録:	With the development of big data technology, higher education is transitioning towards intelligence and ecology. University English teaching, constrained by traditional models, faces challenges such as unmet student needs, inefficient resource allocation, and insufficient teacher-student interaction. Big data, through data collection, real-time analysis, and precise feedback, offers an opportunity for teaching innovation. Based on ecological classroom theory, this paper proposes a path for constructing a university English ecological classroom, integrating the dynamic, systematic, and adaptive features of ecological classrooms to form a data-driven teaching model. The study shows that big data can optimize resource allocation, increase student engagement, and improve teaching interaction, providing new ideas for the ecological transformation of university English classrooms. Additionally, it offers important insights for teaching reforms and the application of big data in other disciplines.
ISSN:	1539-3100 1539-3119
DOI:	10.4018/IJDET.375010
ソース:	ABI/INFORM Global