Abstract
The evaluation of constructive English teaching model based on RBF algorithm is a new method to evaluate the effectiveness of different types of corrective feedback to improve learners’ English level. The purpose of this study is to determine whether there are differences between students who receive corrective feedback and students who do not receive corrective feedback, and its effectiveness in improving their language skills. In this paper, we will focus on its application in English Teaching (ELT). In ELT, RBF algorithm can be used to evaluate students’ writing quality and measure their progress in learning new vocabulary or grammatical structures. You can also use this method to evaluate content based on specific criteria, such as lexical density (the number of unique words). The results showed that students who received corrective feedback scored higher than those who did not receive any correction at all, but they also differed from each other according to the type of correction they received. This finding shows that teachers should choose the appropriate type of feedback for students,the English learning platform plays a good and positive auxiliary role for students to learn English. As a better means of English education, it has certain research value and positive significance for the research of English teaching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bb, A., Nah, B., Skc, D., et al.: YUKI algorithm and POD-RBF for Elastostatic and dynamic crack identification. J. Computat. Sci. (2021)
Rc, A., Rr, B., Rm, C., et al.: Enhanced the moving object detection and object TRACKING for traffic surveillance using RBF-FDLNN AND CBF algorithm (2021)
Li, L., Fan, X., Gong, H., et al.: Intelligent equalization based on RBF LSSVM and adaptive channel decoding in faster-than-nyquist receiver. Int. J. Pattern Recog. Artif. Intell. (2021)
Angleitner, N., et al.: \(\mathcal{H}\)-inverses for RBF interpolation (2021)
Peng, X., Yu, H., Zhu, X., et al.: Electro-hydraulic proportional position control using auto disturbance rejection based on RBF neural network. J. Beij. Inst. Technol. 30, 121–128 (2021)
Jt, A., Zheng, Y.B., Rz, B., et al.: RBF neural network modeling approach using PCA basedLM-GA optimization for coke furnace system. Appl. Soft Comput. (2021)
Zhou, X.L., Liu, M.W., Wang, L., et al.: Multi-objective function optimization for environmental control of a greenhouse based on a RBF and NSGA-II. 28(1), 15 (2021)
Hashim, S., Yusoff, N.M.: The use of reflective practice towards achieving effective English language teaching at primary schools. Int. J. Eval. Res. Edu. 10(1), 364 (2021)
Qoura, A.: New trends in English language teaching and learning new trends in ELT&L (2021)
Li, C.: Implementation and analysis of intelligent inquiry teaching model in primary school English teaching. (2021)
Zhao, W.: An empirical study of english teaching model in higher vocational colleges based on data analysis (2021)
Coniam, D., Lampropoulou, L., Cheilari, A.: Online proctoring of high-stakes english language examinations: a survey of past candidates’ attitudes and perceptions. Eng. Lang. Teach. 14(8), 58 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Du, L. (2023). Research on the Evaluation of Constructive English Teaching Model Based on RBF Algorithm. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-23947-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-23947-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23946-5
Online ISBN: 978-3-031-23947-2
eBook Packages: Computer ScienceComputer Science (R0)