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Factors Influencing Students' Integration Into English Classrooms in Ecologically Fragile Environments: An Analysis

Factors Influencing Students' Integration Into English Classrooms in Ecologically Fragile Environments: An Analysis

Yali Zhang
Copyright: © 2024 |Volume: 19 |Issue: 1 |Pages: 15
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9798369324585|DOI: 10.4018/IJWLTT.336854
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MLA

Zhang, Yali. "Factors Influencing Students' Integration Into English Classrooms in Ecologically Fragile Environments: An Analysis." IJWLTT vol.19, no.1 2024: pp.1-15. http://doi.org/10.4018/IJWLTT.336854

APA

Zhang, Y. (2024). Factors Influencing Students' Integration Into English Classrooms in Ecologically Fragile Environments: An Analysis. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 19(1), 1-15. http://doi.org/10.4018/IJWLTT.336854

Chicago

Zhang, Yali. "Factors Influencing Students' Integration Into English Classrooms in Ecologically Fragile Environments: An Analysis," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 19, no.1: 1-15. http://doi.org/10.4018/IJWLTT.336854

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Abstract

The internet and informatization have brought a great impact on English classrooms in ecologically fragile areas, bringing convenience to the teaching of some colleges and universities, but also negatively affecting some colleges and universities in ecologically fragile areas that still use traditional lecture-style classroom teaching methods. The entire classroom is an ecological environment, and it is necessary to control the appropriate density (the number of students) and the teaching methods of teachers. How to accurately evaluate whether students in ecologically fragile areas integrate into English classrooms? The authors use the linear regression method in the teaching evaluation model and the density-based outlier detection method to clean the abnormal data. Such independence attributes, a correlation analysis method proposed, which judges dependencies and attributes according to the confidence of the rules, and then combines the correlation coefficients between attributes to determine the feature items with strong correlation.