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11 December 2024 Enhanced design of an English teaching resource management system utilizing collaborative filtering algorithm for optimal resource allocation and user experience
Xingchun Chu
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Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134452V (2024) https://doi.org/10.1117/12.3054967
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China
Abstract
Corpus linguistics studies the actual use of language in the real world, paying attention to the actual language use context and following the rules and laws of language use. Corpus linguistics serves as a rich repository of resources for foreign language education. Initially, printed texts like word dictionaries, grammar guides, and diverse teaching aids constituted vital components of the corpus. However, users cannot classify according to their actual needs, resulting in stereotyped and personalized resource databases that naturally reduce the due functions of the resource database. To address the limitations of collaborative filtering algorithms in English education applications, this paper emphasizes the importance of establishing an English instructional resource management system. This system can help improve the effectiveness of English education by providing teachers with a wealth of resources to choose from based on their specific needs and teaching goals. The management system should be able to categorize and label resources to ensure easy retrieval and utilization, as well as provide support for online education courses to improve their quality and accessibility.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xingchun Chu "Enhanced design of an English teaching resource management system utilizing collaborative filtering algorithm for optimal resource allocation and user experience", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134452V (11 December 2024); https://doi.org/10.1117/12.3054967
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KEYWORDS
Tunable filters
Databases
Design
Associative arrays
Optimal filtering
Statistical analysis
Computing systems
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