Introducing Corpus-Informed Spoken Grammar Instruction in EFL Classrooms: The Ethiopian Experience

Lakew, Anteneh Kebede; Seyoum, Teshome; Negede, Dereje

doi:10.1007/s41701-021-00106-3

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the teaching of oral English in China has always been a weak link. Oral English teaching aims to cultivate learners' oral communicative competence [3][4].…”

Section: Introductionmentioning

confidence: 99%

Oral English Course Online Recommendation System Based on Support Vector Machine

Yang,

Yuan

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Aiming at the problems of existing oral English online teaching system that the course recommendation model is not accurate enough, which leads to the low accuracy of system recommendation. This article improves the shortcomings of collaborative filtering algorithms and designs an online course recommendation system based on KNN-SVM. The course recommendation model is based on the similarity between the curriculum and student behavior recommended lesson list which is generated from the student, the history of collecting data, and the interaction of the user. Then, KNN is used to perform secondary clustering on user and topic matrices, and support vector machine cross validation is used for final classificationThe average accuracy of the oral English online education course recommendation system in this paper is 57.926%, 45.391% and 44.399%, respectively, compared with the other two kinds of course recommendation systems, indicating that the designed oral English online education course recommendation system has better effect after combining artificial intelligence technology.

show abstract

“…At present, the teaching of oral English in China has always been a weak link. Oral English teaching aims to cultivate learners' oral communicative competence [3][4].…”

Section: Introductionmentioning

confidence: 99%

Oral English Course Online Recommendation System Based on Support Vector Machine

Yang,

Yuan

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Chi

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

For the problem of the low accuracy of large-scale oral English, based on oral English from the perspective of difficulty of oral English text and phonology, a multimodal-based automatic oral English assessment method is proposed by using L2 regularized (multilayer perceptron, MLP) and 9 features affecting the automated assessment of oral English as model input. Simulation results show that the proposed method can well predictively assess oral English difficulty and performs well on RMSE and R2 metrics of 0.053 and 0.905, respectively, with certain advantages over conventional elastic network-based and random forest-based prediction models.

show abstract

Introducing Corpus-Informed Spoken Grammar Instruction in EFL Classrooms: The Ethiopian Experience

Cited by 2 publications

References 7 publications

Oral English Course Online Recommendation System Based on Support Vector Machine

Oral English Course Online Recommendation System Based on Support Vector Machine

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Contact Info

Product

Resources

About