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Realization of English Instructional Resources Clusters Reconstruction System Using the Machine Learning Model
1School of Foreign Languages, Xuchang University, Xuchang 461000, China.
Computational Intelligence and Neuroscience
|July 20, 2022
Summary
This study introduces a machine learning (ML) method for searching and organizing English instructional resources using keyword indexing and semantic query processing. The system enhances resource retrieval, improving user satisfaction and resource utilization.
Area of Science:
- Educational Technology
- Information Science
- Computer Science
Background:
- Scattered distribution of English instructional resources hinders effective utilization.
- Existing resource retrieval methods suffer from semantic deficiencies and word mismatch issues.
- Improving the precision and recall of educational resource retrieval is crucial.
Purpose of the Study:
- To develop a novel method for searching, clustering, and centrally presenting English instructional resources.
- To enhance the semantic processing of user queries for improved resource retrieval accuracy.
- To increase the utilization rate and user satisfaction with English instructional resources.
Main Methods:
- Keyword indexing technology for initial resource searching.
- Machine learning (ML) algorithms for clustering and recombining search results.
- Semantic query processing based on educational resource subject indexes to address query-document word mismatch.
- Manual selection of category features and ML-based training for category feature models in small sample environments.
Main Results:
- The system achieved high user ratings, reaching up to 93.21%.
- System stability remained excellent at 89.31% even under significant usage.
- The method effectively improved recall and precision in resource retrieval.
- The system successfully addressed the scattered distribution of resources and aligned knowledge presentation with user needs.
Conclusions:
- The proposed ML-based system offers an effective solution for organizing and retrieving scattered English instructional resources.
- Semantic query processing significantly improves the accuracy and relevance of search results.
- The system demonstrably enhances user satisfaction and the overall utilization of educational resources.
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