Effects evaluation of English listening comprehension in new energy majors with multimedia assistance
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Xi Wang
Abstract
Energy Engineering is a well-liked branch of engineering, wherein the graduates in this discipline are estimated for their English skills under several cases. The capability of listening broadly enhances the effectiveness of English listening teaching; thus, this makes college English listening lessons dynamic, engaging, information-rich, and facilitates an immersive learning experience for students. This is to cultivate students’ attention in learning, form learning inspiration, and finally, attain the objective of enhancing their English skills. This work explores the findings from experimental work associated with establishing an educational setting specifically designed for the foreign language instruction of upcoming energy engineering students. The aim of this work is to a) design a series of short interactive videos or reusable learning objects (RLOs) covering an extensive area of psychosocial and practical problems appropriate to the listening capability of Energy Engineering students; b) establish the ease of access, suitability, take-up, and adherence of the RLOs; and c) evaluate the advantages and cost-effectiveness of the RLOs. Ultimately, the experimentation analysis is performed for the student’s average score for basic level, middle level, and higher level students from five universities. The analysis states that regarding university 5, the basic level females attained the highest score of 92.9, while the middle-level female attained the average score of 86.27. Meanwhile, for higher level, both attained average score of 68.89. From the analysis, it is clear that the basic level student’s average score is better than the middle level and higher level.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author Contributions: Xi Wang, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflicts of interests: Authors do not have any conflicts.
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Research funding: Authors did not receive any funding.
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Data availability: No datasets were generated or analyzed during the current study.
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Code availability: Not applicable.
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