A Study on the Relationship between Non-English Majors' Perception of Learning Environment Affordances and English Learning Engagement

Yifan Cheng

doi:10.54097/wmwyy565

Authors

Yifan Cheng

DOI:

https://doi.org/10.54097/wmwyy565

Keywords:

English Learning Affordances, Perception of English Learning Affordances, English Learning Engagement, Non-English Major College Students

Abstract

This study aims to explore how non-English major college students' perception of learning affordances affects their English learning engagement through a questionnaire survey, providing a theoretical basis and practical guidance for improving the learning environment and promoting personal development. A stratified sampling method was used to select 173 non-English major students from a university in Jiangsu Province as the research subjects. The survey results showed that there is no significant difference in the perception of learning environment affordances among different grades, nor different majors. Learners who spent longer time on the online English learning platforms showed a higher level of perception of all learning affordances. The perception of the supportive role of the learning environment can effectively promote learning engagement. Specifically, the physical affordances perception and interpersonal perception have a significant promoting effect on learners' cognitive engagement, behavioral engagement, and emotional engagement. However, resource affordances perception has a positive impact on cognitive engagement and emotional engagement, but no influence on behavioral engagement. What's more, the functional affordances perception has no effect on any dimension of the learning engagement. This study not only reveals the differential mechanisms of affordance perception across digital ecosystems but also empowers teachers to accurately identify students with "low affordance perception" and develop tiered intervention strategies. Furthermore, it fosters students' metacognitive abilities, encouraging them to actively transform environmental affordances into deep learning behaviors.

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