Evaluation of College English Teaching Quality Based on Grey Clustering Analysis

Liu, Haiyuan; Chen, Rui; Cao, Shuting; Haiping, Lv

doi:10.3991/ijet.v16i02.19727

Cited by 35 publications

(27 citation statements)

References 18 publications

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“…e BP algorithm solves the nonlinear classification problem 2 Scientific Programming in neural network models, making a significant advance in the difficulty and scope of its use, and people once again start to focus on artificial neural networks [7]. In the research, firstly, through the unsupervised learning method, the model was trained in multiple levels, and, on this basis, the supervised backpropagation algorithm was used for parameter tuning and model correction, which successfully solved the "gradient disappearance" problem, which marked the formal introduction of the concept of "deep learning" [8]. Once deep learning was proposed, it caused a great sensation in the academic world, and world-renowned universities, represented by Stanford University, began to spend huge amounts of money and workforce on research in deep-learning-related fields.…”

Section: Current Status Of Researchmentioning

confidence: 99%

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Meng

Ren

Huang

2021

Scientific Programming

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This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students’ employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students’ innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students’ innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented.

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Section: Current Status Of Researchmentioning

confidence: 99%

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

Meng

Ren

Huang

2021

Scientific Programming

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“…Due to many reasons such as the inaccurate pronunciation of the teacher and the limited time in the class, students have few opportunities to speak English in class, and many are even reluctant to speak because they are not confident about their pronunciation or afraid of making mistakes. Over time, they have become antagonistic against or afraid of learning spoken English [7]. Therefore, the spoken English teaching system needs to be able to teach students pronunciation, recognize students' spoken English, evaluate their pronunciation, and at the same time, correct their pronunciation and give feedbacks, and provide man-machine interaction, so that students will have the opportunity to practice spoken English.…”

Section: Analysis Of the Functional Requirements For The Systemmentioning

confidence: 99%

A Spoken English Teaching System Based on Speech Recognition and Machine Learning

Jiao

Song

Zhao

et al. 2021

Int. J. Emerg. Technol. Learn.

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The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.

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“…Summative and formative evaluations of English education are still widely used in colleges and universities, with summative evaluations taking precedence over formative evaluations. Ignoring changes in the evaluation elements during the educational process is not a good way to encourage innovative thinking and personal development of evaluation objects [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

English Teaching Evaluation Combined with End-User Computing and Neural Network

Tan

2022

Mobile Information Systems

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A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR network. The model has multiple hidden layers and performs multiple feature conversions to optimize the network. The output layer of the model uses SVR as a predictor to achieve evaluation. (3) Deploy the designed evaluation network on the server, and end users can use the API interface to directly access and test the model functions. Massive experiments have verified the effectiveness of the designed method.

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Evaluation of College English Teaching Quality Based on Grey Clustering Analysis

Cited by 35 publications

References 18 publications

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

A Quantitative Enhancement Mechanism of University Students’ Employability and Entrepreneurship Based on Deep Learning in the Context of the Digital Era

A Spoken English Teaching System Based on Speech Recognition and Machine Learning

English Teaching Evaluation Combined with End-User Computing and Neural Network

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