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<p>Emerging from the advancements of the Internet+ era, the blended teaching mode synergistically combines online and offline methodologies, thereby fostering innovative pathways for the enhancement of English grammar instruction. The architecture of a hybrid model for English grammar teaching is presented in this paper that seamlessly integrates digital and traditional educational practices. We structured the implementation of this model through a three-part framework: initial pedagogical analysis, development of teaching resources, and design of teaching practices. Our approach involves extracting English grammar data from the Baidu Encyclopedia and authoritative grammar texts, followed by the construction of an English grammar knowledge graph utilizing a BiLSTM-CRF method for grammar entity extraction and a BERT-BiLSTM-Attention model for delineating grammar relationships. We assessed the accuracy of extracting grammar concepts and validated the effectiveness of our blended teaching model through a combination of student self-assessment, peer reviews, and instructor evaluations. Statistical analysis of self and peer assessments reveals that 50% of the evaluated questions scored above 4.0, with the average peer assessment exceeding 3.90. These findings underscore that the blended teaching approach is not only well-Received by students but also significantly enhances their overall language competencies.</p>
</abstract>

Emerging from the advancements of the Internet+ era, the blended teaching mode synergistically combines online and offline methodologies, thereby fostering innovative pathways for the enhancement of English grammar instruction. The architecture of a hybrid model for English grammar teaching is presented in this paper that seamlessly integrates digital and traditional educational practices. We structured the implementation of this model through a three-part framework: initial pedagogical analysis, development of teaching resources, and design of teaching practices. Our approach involves extracting English grammar data from the Baidu Encyclopedia and authoritative grammar texts, followed by the construction of an English grammar knowledge graph utilizing a BiLSTM-CRF method for grammar entity extraction and a BERT-BiLSTM-Attention model for delineating grammar relationships. We assessed the accuracy of extracting grammar concepts and validated the effectiveness of our blended teaching model through a combination of student self-assessment, peer reviews, and instructor evaluations. Statistical analysis of self and peer assessments reveals that 50% of the evaluated questions scored above 4.0, with the average peer assessment exceeding 3.90. These findings underscore that the blended teaching approach is not only well-Received by students but also significantly enhances their overall language competencies.

Research on the Application of Internet+-based Blended Teaching Mode in English Grammar Teaching

Foreign Language School, Hunan University of Humanities, Science and Technology

Jiayuan Branch of Beijing No. 80 Middle School

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Emerging from the advancements of the Internet+ era, the blended teaching mode synergistically combines online and offline methodologies, thereby fostering...

Research on the Application of Internet+-based Blended Teaching Mode in English Grammar Teaching

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 02. Apr. 2024

Akzeptiert: 24. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2021

Schlüsselwörter
BiLSTM-CRF, BERT, Knowledge graph, English grammar teaching, Blended learning

© 2024 Menglin Deng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Application of Internet+-based Blended Teaching Mode in English Grammar Teaching

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 02. Apr. 2024

Akzeptiert: 24. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2021

SchlüsselwörterBiLSTM-CRF, BERT, Knowledge graph, English grammar teaching, Blended learning

© 2024 Menglin Deng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
BiLSTM-CRF, BERT, Knowledge graph, English grammar teaching, Blended learning