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English Research Learning and Functional Research Based on Constructivism Theory and Few-Shot Learning
1Zhengzhou ShengDa University, Zhengzhou, Henan 455000, China.
Computational Intelligence and Neuroscience
|February 10, 2022
Summary
This study introduces a college English teaching model integrating network and research-based learning. It enhances English word recommendation and pronunciation correction using novel algorithms for improved learning outcomes.
Area of Science:
- Educational Technology
- Applied Linguistics
- Computer Science
Background:
- Research-based learning (RBL) is a practical approach where students identify topics from their lives for inquiry-based knowledge acquisition.
- Constructivism theory (CT) provides a framework for understanding how learners construct knowledge through experience.
- Traditional English teaching methods often lack personalized recommendations and effective pronunciation feedback.
Purpose of the Study:
- To construct a college English teaching model integrating network resources with research-based learning.
- To explore the implementation methods of this integrated model in practical teaching scenarios.
- To enhance the effectiveness of English vocabulary acquisition and pronunciation correction.
Main Methods:
- Developed a college English teaching model combining network learning and RBL under constructivism theory.
- Utilized the UCSR-EW (user context-aware semantic-aware recommendation for English words) algorithm for personalized English word recommendations.
- Integrated a confidence coefficient (CC) into the pronunciation error correction algorithm.
- Employed a semantic model to represent English word records and matched similar users for recommendations.
Main Results:
- The proposed model facilitates knowledge acquisition through independent inquiry, mirroring scientific research processes.
- The UCSR-EW algorithm effectively generates English word recommendations based on user context and semantic understanding.
- The CC-enhanced pronunciation error correction algorithm shows improved error correction effects compared to traditional methods.
- Intelligent recommendation of English words was achieved by measuring acceptance and matching similar users.
Conclusions:
- The integrated network and research-based learning model offers a viable approach for college English education.
- The developed algorithms significantly enhance the personalization and effectiveness of English vocabulary learning and pronunciation practice.
- This model holds potential for improving overall English language proficiency through technology-enhanced, inquiry-driven learning.
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