English Teaching Ability Research Articles

Research and Practice of Course Reform in English Majors of Colleges and Universities from the Ideological and Political Perspective

There is an urgent need for high-quality talents with ideological and moral character for the development of higher education and the cultivation of English majors who meet the requirements of the times. The course reform in English majors from the ideological and political perspective is an important way to improve the comprehensive quality and moral values of students, which helps to cultivate English talents with social responsibility and innovative abilities. This paper analyzes the problems in the current courses through literature review and field research, such as the lack of ideological and political perspective and low interest of students in ideological and political content. Based on these issues, taking the course “Comprehensive English” as an example, various teaching stages were practiced from the aspects of learning objectives, learning content, learning situation, implementation ideas, implementation effects, and teaching evaluation. Additionally, some suggestions are proposed, such as integrating ideological and political content into the entire process of teaching. The teaching staff needs to possess high-level English teaching ability and ideological and political perspective, standardize the evaluation system, and integrate epistemology into the evaluation process of ideological and political education, in order to promote in-depth research and exploration of ideological and political education of English majors.

Retracted: An Evaluation Method of English Teaching Ability Based on Deep Learning

Retracted: An Evaluation Method of English Teaching Ability Based on Deep Learning

The Effectiveness of a Role-Play Activity in Improving EFL learners’ Communicative Skills

English is a language subject, and language teaching is usually carried out in the form of communication. Enhancing the effectiveness of teaching communicative skills is of great significance in promoting the improvement of EFL learners’ English learning level. Role-play is a very vivid and interesting way of teaching, and its introduction into English communicative teaching can effectively enhance the enthusiasm of EFL learners’ oral communication teaching and enhance the teaching effect. In view of this, this paper mainly takes the role-playing teaching method as an example, and briefly analyses how to realize the innovation of English teaching ability teaching.

Research on Microteaching Mode of College English Courses Based on Regression Network Models

Abstract 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.

Exploring a hybrid online and offline English teaching model based on model hierarchy analysis

Abstract This article examines a hybrid online and offline teaching strategy for English that is based on the “Internet+” environment in order to create a positive learning environment for the language. First off, the essay supports the reform of the English teaching model and explains the relevance of the new online and offline hybrid teaching model for English based on the idea of “Internet+.” The secondary indicators and weights of the quality evaluation are derived to assess the online and offline hybrid English teaching modes, and the quality evaluation system for the hybrid English teaching mode is built based on AHP and fuzzy evaluation. Finally, through the evaluation of competency and final assessment, the student’s attendance rate increased by about 5%, and the overall final grade increased by 8%. Compared to the prior semester, there were 8 more students who received outstanding marks, and there were 4 fewer who received poor grades. In terms of teaching ability, the students were very satisfied with over 60%, and the overall satisfaction level was over 99%. Students’ English learning ability also improved at a high level, with more than 55% of teachers being very satisfied. It proves that blended teaching can improve English teaching ability and stimulate students’ interest in learning.

The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly, quantitative recursive analysis is used to evaluate the capabilities of big data information models and achieve entropy feature extraction of capability constrained feature information. Finally, by integrating big data information fusion and K-means clustering algorithm, the article achieves clustering and integration of indicator parameters for English teaching ability, prepares corresponding teaching resource allocation plans, and evaluates English teaching ability. The experimental results show that using this method to evaluate English teaching ability has good information fusion analysis ability and improves the accuracy of teaching ability evaluation and the efficiency of teaching resource application.

A study of multimodal education and discursive reading model in English based on multidimensional cognitive information aggregation theory

Abstract Discursive reading ability is an important component of English proficiency and an important measure of the level of language proficiency; to a large extent, the development of reading ability can contribute to the improvement of a student’s language ability, and the development of English reading ability in English teaching is one of the important requirements of English teaching under the new curriculum reform. The development of science and technology and multiculturalism in today’s society has led to the multimodality of most information transmission activities, which requires people to have corresponding multimodal reading abilities. The traditional English teaching mode is largely unable to meet the needs of the times, which makes it necessary to further explore the application of multimodal teaching mode in the English classroom. This paper focuses on the use of modality in English reading classrooms. The hypothesis of “multimodal reading classrooms can improve students’ learning interest and reading ability” is used as the theoretical support, and systemic functional linguistics, multimodal discourse analysis theory, and constructivist theory are used as the theoretical support. We used questionnaires and interviews to explore the multimodal teaching model of English reading. The experimental results show that the multimodal teaching model helps to improve students’ English performance, reading ability, and interest in English reading. To a certain extent, the multimodal teaching model can promote the development of English teaching, guarantee the quality of the course teaching, and make the English course less boring.

Analysis of Improving Students' Language Ability in English Teaching in the Information Age

Analysis of Improving Students' Language Ability in English Teaching in the Information Age

The Evaluation of College English Writing Teaching Ability in English Teaching Courses Based on Big Data

Under the background of big data information fusion, the use of stepwise regression analysis method to evaluate the English writing teaching ability of college teachers usually results in inaccurate classification of data flow and great errors of constraint index parameters in information fusion. In this paper, k-means fuzzy clustering algorithm is proposed. By constructing parameter constraint index system and big data analysis model, various nonlinear English teaching parameters are clustered and integrated to achieve the extraction of entropy characteristics of English constraint information. On this basis, the recursive quantitative analysis method is used to intuitively analyze various parameters of English teaching ability and the law of data change in time series. Finally, the correlation of constraint parameters and disturbance data of various English teaching parameters is analyzed by Matlab simulation algorithm. Research shows that the use of k-means algorithm to carry out information fusion of English writing teaching ability data can further improve the efficiency and accuracy of English teaching ability assessment of teachers.

Research on the Promoting Effect of English Speech Course on the Development of English Teaching Ability

Research on the Promoting Effect of English Speech Course on the Development of English Teaching Ability

Analysis and countermeasures of cultivating independent learning ability in colleges teaching English based on OBE theory

Abstract In recent years, influenced by the deepening reform of colleges teaching English in China, teaching English for college students has gradually been favoured and promoted. At present, many universities still use traditional English teaching methods. The traditional English teaching method only allows college students to learn basic grammar and vocabulary, without the ability of independent learning. Given these problems, this paper first proposes a framework for cultivating college students’ autonomous learning ability in English teaching based on output-based education (OBE) theory. Then, the OBE theory is introduced in detail. At the same time, the framework is applied in a university. After a semester of study, the results are combined with teachers’ and college students’ questionnaire surveys. Finally, after the questionnaire results and analysis, the framework proposed in this paper is better than other traditional English teaching methods. The experiment also shows that it not only improves the adaptability of college students’ English teaching but also improves their independent learning ability while learning English.

Retracted: An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

Retracted: An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

English Teaching Ability Evaluation Algorithm Based on Random Matrix Model and Fuzzy K-Means Clustering

This paper proposes a nonelliptic extended English teaching ability evaluation algorithm based on an adaptive random matrix model. The algorithm models a nonelliptical expansion target as multiple elliptical sub-objectives, and the expansion state of each sub-target is described by an inverse Wishart distribution. The new method is combined to improve the robustness problem caused by the initialization after the algorithm expansion. This research uses the smart teacher education platform to conduct research and analysis on the evaluation of teaching practice ability of intern normal students. From the perspective of data evaluation, we explore the influence of normal students’ curriculum training on the teaching practice ability of normal students. By analyzing the correlation between normal students’ course grades and practice grades, this paper explores the influence of normal students’ prepractice training on normal students’ teaching practice ability. This paper explores the influence of the training of normal students’ course learning on the teaching practice ability of normal students. The results show that the learning level of normal students’ professional courses has a significant impact on the development of normal students’ practice performance and teaching practice ability; the impact of normal students’ pedagogical course level on normal students’ practice performance and teaching practice ability is relatively low.

Evaluation and Analysis of English Teaching Ability Based on Nonlinear Random Matrix Model

With the sustained, rapid, and steady development of China’s economy, the development of higher education is also faster and faster. With economic development and social progress, the demand for talents is also growing. Colleges and universities are the cradle of cultivating excellent talents and undertake the important task of cultivating reserve forces for the country. As an engineer of human soul, English teachers shoulder the responsibility of cultivating excellent talents and the hope of countless parents. For a long time, there have been many problems in the evaluation of College English teachers in China. For example, the assessment objective is too simple, only related to salary promotion and professional title evaluation. In terms of the content of evaluation, they often only pay attention to the quantity and level of scientific research results, ignoring the evaluation of potential, attitude, and other aspects, especially the social responsibility of teachers. Based on the theory of nonlinear random matrix and the theory of competence, this study establishes the quality evaluation model of university teachers in China by combining qualitative and quantitative methods. On this basis, it plays an important role in the selection, training, promotion, and further development of university teachers in China. First of all, according to a large number of domestic and foreign literature on competency, this study summarizes the competency theory and competency model. Using the hypothetical model, a questionnaire was designed and distributed to build a competency model of university teachers from four aspects: professional knowledge, related skills, work attitude, and personality motivation. Finally, the weight of each index in the model is calculated. This study not only constructs the competency evaluation model but also uses the principal component analysis method to calculate it in detail, which provides a good quantitative basis for the selection and performance evaluation of college teachers, so as to prevent the situation beyond its control. It paves an endless bright road for further stable and healthy development. Therefore, the overall research results of this study have greater practical application value and rich connotation.

An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality.

Application of Clustering Algorithm in English Proficiency Evaluation under the Framework of Big Data

With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data.

English Teaching Ability Evaluation Algorithm based on management, leadership and entrepreneurship including education, Data Fusion and Notch Filtering

All universities and colleges has been offering English as one of the important courses. The effectiveness of English teaching is inextricably related to the quality of talent acquisition and student growth. The term evaluation of teaching quality describes the type of content knowledge in which the focus is to analyze quality teaching. One of the significant aims of every school and university is to improve the English teaching quality and promote the development of English teaching and entrepreneurship. As the existing evaluation algorithms have been facing various problems of fuzzy core dimension characteristics, which result in poor data integrity. Therefore, we proposed an English teaching ability evaluation algorithm based on data fusion, entrepreneurship, and notch filtering. Moreover, for the purpose, to achieve the goal of education entrepreneurship and quality teaching, strengthen of the English teaching quality algorithm is necessary, as well as the leadership and management qualities of the teachers also have a lot of impact on the performance of teaching quality and students working performance and it is a topic worthy of in-depth and significant investigation. We select the explicit indicators that are easy to measure, establish the hierarchical structure system of evaluation indicators, construct the evaluation model by data fusion and notch filtering, obtain multi-source heterogeneous data, extract the core dimension characteristics of English teaching ability, make rational use of teaching resources, convert variables into standard units, and set up the evaluation mechanism by using random walk algorithm. Experimental results: the average data integrity of the designed English teaching ability evaluation algorithm is 4.33, and the average data integrity of the other two evaluation algorithms is 1.921 and 1.885, which proves that the English teaching ability evaluation algorithm integrating data fusion and notch filtering technology has a broader application prospect.

An Evaluation Method of English Teaching Ability Based on Deep Learning

The traditional English teaching ability assessment algorithm has some problems, such as low accuracy and poor effect. Therefore, this paper proposes an English teaching ability assessment algorithm based on deep learning. According to the proposed method, it analyzes the characteristics of the input layer, convolution layer, pooling layer, and fully connected layer in the deep learning technology. And then, it also help to determine the dimensions of the convolution kernel matrix and derive the weight parameters through back propagation. Finally, it introduces the open-minded computing for deep learning assessment of English teaching ability, calculate the best standard data and best vector data ratio, and establish a deep learning model to realize English teaching ability assessment. The experimental results show that this method can improve the accuracy of English teaching ability assessment.

An English Teaching Ability Assessment Method Based on Fuzzy Mean-Shift Clustering

This paper presents an in-depth study and analysis of the assessment of English teaching ability using the algorithm of fuzzy mean-shift clustering. The paper proposes an automatic scoring model for Chinese-English sentence-level interpretation based on semantic scoring. The automatic scoring model calculates candidates’ Chinese-English sentence interpretation scores by fusing the feature parameters at both the phonological and content levels. Adaptive weights are introduced to fuse the current pixel and the neighborhood mean with adaptive weighting, and the weighted entropy constraint term is embedded in the clustering objective function to solve the selection problem of the weighting parameters. Finally, the graphical fuzzy division information of the neighboring pixels is used to construct the local spatial information constraint term of the current pixel, and the graphical fuzzy division term of the current pixel is adjusted to correct the clustering center obtained from the iteration. Fluency is selected as the feature scoring parameter at the speech level, and the automatic scoring model directly scores the fluency features of the candidates’ recordings; two feature scoring parameters, keywords, and sentence semantics are selected at the content level, and the content features are scored after converting the candidates’ recordings to text by manual conversion. The zero-energy product method is used to extract speech features to calculate fluency feature scores; the semantic scoring model introduced in this paper is used to calculate keyword and sentence semantic feature scores; finally, the random forest algorithm is used to fuse the above three feature scoring parameters to obtain the total quality score of Chinese-English interpretation. Considering the correlation of neighborhood pixel affiliation, the KL scatter and affiliation space information is used to supervise the current pixel affiliation, to further improve the segmentation accuracy of the algorithm; finally, segmentation tests are conducted on synthetic images, medical images, and remote sensing images. The results show that the proposed algorithm has a stronger noise suppression ability and can obtain more satisfactory segmentation results than other robust fuzzy clustering algorithms.

The Evaluation Method of English Teaching Ability in View of Internet of Things

This paper firstly investigates the current situation of English teaching evaluation research at home and abroad and the evaluation of basic English teaching through literature review. Most of the research involves personal experience summary, lack of academic, and empirical research proof. Research starting points are often very similar, with repetitive content. Most of these studies are limited to teaching evaluation or learning specific English knowledge, and the evaluation of teaching methods is limited. The article explains the importance and scope of English teaching evaluation research through the Internet of Things technology and explains that the guiding theories of this English teaching evaluation research are system theory, structure theory, and multiscientific theory. Finally, this paper conducts relevant experimental research and analyzes all aspects of English teaching. Through experiments, this paper finds that students’ input and level in collective or group English activities are both above 4.2 points. The self-assessment method obtained a score of 4.8 or more in the teacher’s evaluation. It is necessary and realistic to establish an independent teacher-student-centered teacher education evaluation system, to optimize the teacher’s beliefs of English teachers, to enrich and update the knowledge system of English teachers, to develop the practical wisdom of English teachers, to focus on students as the center of evaluation and decision-making, and to develop good teaching evaluation and decision-making habits.