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Corpus-Driven Resource Recommendation Algorithm for English Online Autonomous Learning.

Ling Gu1

  • 1School of Humanities and Law, Gannan University of Science and Technology, Ganzhou, Jiangxi 341000, China.

Computational and Mathematical Methods in Medicine
|June 24, 2022
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Summary
This summary is machine-generated.

This study introduces an optimized online learning resource recommendation algorithm using corpus technology and deep learning to enhance English teaching effectiveness. The new algorithm improves resource efficiency, diversity, and timeliness for autonomous English learning.

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Area of Science:

  • Educational Technology
  • Applied Linguistics
  • Computer Science

Background:

  • English language learning is crucial for comprehensive skill development but faces limitations due to environment, teacher quality, and lack of institutional support.
  • Inefficiencies in English teaching stem from low expectations, insufficient practice, and inadequate strategy training, hindering students' autonomous learning.
  • Current English teaching methods often fail to adequately address the multifaceted nature of language acquisition and skill application.

Purpose of the Study:

  • To address the inefficiencies in English language teaching by integrating corpus technology with an online autonomous learning resource recommendation algorithm.
  • To optimize the recommendation algorithm for high efficiency, diversity, and timeliness in delivering learning resources.
  • To validate the proposed algorithm's effectiveness and rationality through comparison with classical methods in an online teaching context.

Main Methods:

  • Developed an online autonomous learning resource recommendation algorithm by combining corpus technology and deep learning.
  • Optimized the model for efficiency, diversity, and timeliness in learning resource delivery.
  • Pretrained the deep learning model using a processed dataset and compared the proposed algorithm against classical algorithms for validation.

Main Results:

  • The proposed algorithm demonstrated rationality and effectiveness when compared to classical approaches.
  • The deep learning-based model achieved optimized recommendations in terms of efficiency, diversity, and timeliness.
  • The study provides a foundation for applying corpus and recommendation algorithms in online English education.

Conclusions:

  • The integration of corpus technology and a deep learning-based recommendation algorithm offers a promising approach to optimize online English teaching.
  • The developed algorithm enhances the delivery of learning resources, supporting autonomous learning and improving overall teaching effectiveness.
  • This research contributes to the evolution of English teaching methodologies by leveraging advanced computational techniques for personalized and efficient learning.