Academic Journal
Research on Microteaching Mode of College English Courses Based on Regression Network Models
العنوان: | Research on Microteaching Mode of College English Courses Based on Regression Network Models |
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المؤلفون: | Nie Wei |
المصدر: | Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024) |
بيانات النشر: | Sciendo |
سنة النشر: | 2024 |
المجموعة: | Directory of Open Access Journals: DOAJ Articles |
مصطلحات موضوعية: | mobile internet, english course, microlearning model, linear regression, constraint index covariates, 97c50, Mathematics, QA1-939 |
الوصف: | This paper determines the solution vector of ELT proficiency constraint covariates using the correlation fusion approach, the proficiency prediction control objective function is constructed, and the education process’ quantitative recursive properties are discovered. The K-means algorithm is employed to obtain the predicted values of the resource limitation vector for ELT competence assessment, obtain the similarity of the distribution of teaching resources, combine linear regression grouping and integrate the ranking parameters for ELT competence assessment, obtain the characteristics of the data probability density, and finish evaluating the teaching model. The results show that the maximum utilization value reaches 99.03% by assessing English teaching ability through the K-means algorithm and linear regression, which indicates that the reasonable use of mobile Internet can enhance students’ English teaching level and ensure the efficiency of teaching resources utilization. |
نوع الوثيقة: | article in journal/newspaper |
اللغة: | English |
تدمد: | 2444-8656 |
Relation: | https://doi.org/10.2478/amns.2023.2.00732; https://doaj.org/toc/2444-8656; https://doaj.org/article/f2b7623d46ff4160ac40c34f09bf27cf |
DOI: | 10.2478/amns.2023.2.00732 |
الاتاحة: | https://doi.org/10.2478/amns.2023.2.00732 https://doaj.org/article/f2b7623d46ff4160ac40c34f09bf27cf |
رقم الانضمام: | edsbas.643409AE |
قاعدة البيانات: | BASE |
تدمد: | 24448656 |
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DOI: | 10.2478/amns.2023.2.00732 |