<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Big data technology provides a detailed development of English language teaching, which is targeted through the assessment of cognitive level. In this paper, ConvSLSTM is used to describe the problem of learners’ knowledge level and build a cognitive framework. With the support of the framework, “recall rate” was introduced to quantify the English vocabulary test results, PSTM ability was adopted to reflect the acquisition of syntactic variants, and attention control was borrowed to evaluate pronunciation characteristics. Based on the initial quantification mentioned above, a hierarchical-level calculation was accomplished by combining fuzzy logic. Meanwhile, based on the estimation of different sides, a rule space model was created, and some corrections were made to achieve the cognitive diagnosis of the group level. In terms of the syntactic developmental trajectory of the girl learners, the maximum value of the range of syntactic variation of the subjects appeared in the middle and late stages. During the early stages of development, the variation range is between 10-35, and the bandwidth is 25, with very little variation. However, in the middle stage of development, it basically starts to oscillate significantly between 10-85, at which time the bandwidth increases to 75. Big data technology has created a comprehensive measurement framework for learners’ English proficiency.</p>
</abstract>

Big data technology provides a detailed development of English language teaching, which is targeted through the assessment of cognitive level. In this paper, ConvSLSTM is used to describe the problem of learners’ knowledge level and build a cognitive framework. With the support of the framework, “recall rate” was introduced to quantify the English vocabulary test results, PSTM ability was adopted to reflect the acquisition of syntactic variants, and attention control was borrowed to evaluate pronunciation characteristics. Based on the initial quantification mentioned above, a hierarchical-level calculation was accomplished by combining fuzzy logic. Meanwhile, based on the estimation of different sides, a rule space model was created, and some corrections were made to achieve the cognitive diagnosis of the group level. In terms of the syntactic developmental trajectory of the girl learners, the maximum value of the range of syntactic variation of the subjects appeared in the middle and late stages. During the early stages of development, the variation range is between 10-35, and the bandwidth is 25, with very little variation. However, in the middle stage of development, it basically starts to oscillate significantly between 10-85, at which time the bandwidth increases to 75. Big data technology has created a comprehensive measurement framework for learners’ English proficiency.

The Application of Big Data Technology in English Teaching Mode Innovation and Effect Analysis

Hunan University of Humanities, Science and Technology

Applied Mathematics and Nonlinear Sciences

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

Big data technology provides a detailed development of English language teaching, which is targeted through the assessment of cognitive level. In this paper,...

The Application of Big Data Technology in English Teaching Mode Innovation and Effect Analysis

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 25. März 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01479

Schlüsselwörter
ConvSLSTM, PSTM competence, Fuzzy logic, Group level, Rule space modeling, Cognitive diagnosis

© 2023 Yiqun Yu, published by Sciendo

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

The Application of Big Data Technology in English Teaching Mode Innovation and Effect Analysis

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 25. März 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01479

SchlüsselwörterConvSLSTM, PSTM competence, Fuzzy logic, Group level, Rule space modeling, Cognitive diagnosis

© 2023 Yiqun Yu, published by Sciendo

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

Schlüsselwörter
ConvSLSTM, PSTM competence, Fuzzy logic, Group level, Rule space modeling, Cognitive diagnosis