Teaching Quality Evaluation Method Research Articles

The Impact of Big Data on the Teaching Development of University Teachers in the Mobile Internet Era

With the continuous development and progress of network and information technology, Internet + has gradually penetrated into various fields. Internet + is to apply Internet technology to various traditional industries, organically combine traditional industries with information technology, and develop traditional industries. In order to objectively and accurately evaluate the teaching quality of college teachers, a teaching quality evaluation method based on sawfcm algorithm is proposed. This method takes the influencing factors related to teaching quality as samples in the feature space. Each run re classifies each state and updates the weight of each sample based on the current data, but it is highly dependent on the randomly selected initial clustering center and the randomly generated initial membership matrix. Taking our university as an example, the experimental analysis is carried out to verify the feasibility of the model.

An IoT‐based multiple teaching quality evaluation method for English translation with improved deep learning

AbstractTo enhance the teaching quality (TQ) of English translation (ET) courses, multivariate evaluation methods have garnered significant attention for their ability to identify deficiencies in the teaching process and subsequently improve instructional standards. Traditional evaluation techniques have shown limited effectiveness. Therefore, this paper proposes an ET in multiple teaching quality (MTQ) evaluation method with improved deep learning in internet of things (IoT). First, the paper establishes evaluation indicators for ET in MTQ, which mainly encompass ET teaching effectiveness, pedagogic competency in ET, teaching methods, content, as well as attitudes towards ET teaching. It then utilizes IoT technology to preliminarily collect data on these indicators, and using clustering algorithm based on weighted attributes and density (CABWAD) algorithm to mine data on English teaching MTQ evaluation indicators for ET. The extracted evaluation indicator data is then denoised using a probabilistic undirected graph model. Ultimately, the multilayer perceptron in deep learning is improved through Wolfe line search optimization, and this enhanced multilayer perceptron is employed to construct an ET in MTQ evaluation model. The denoised indicator data is inputted into the model, which then outputs precise MTQ evaluation results. The results show that the absolute value of the average Pearson correlation coefficient of this method is the highest, the Spearman correlation coefficient is the lowest, the mean average precision value is 0.965, and the positive category imbalance degree and the negative category imbalance degree are the lowest, indicating that the proposed method has outstanding performance in all aspects, and has certain application value in the field of TQ evaluation.

Physical Education Teaching Quality Evaluation Method Using Mobile Edge Computing in the Online and Offline Environment

The development of different technology has impacted the different stages of human life. Additionally, with the implication of computing technology sports and physical education can be improved. The use of mobile edge technology aids in gathering precise data related to physical education. Therefore, a specific improvement is possible. This following analysis has looked into the factor of mobile edge computing that aids in the evolution of physical education teaching. Moreover, the study has focused on developing an appropriate path for the integration of MEC into PE education. By utilising Mobile Edge Computing (MEC) technologies in both online and off-line learning contexts, this study offers a thorough way for assessing the quality of sports teaching. The suggested approach integrates multiple characteristics and performance measures to evaluate the efficacy of physical education instruction and participation, taking into consideration the changing educational landscape.

A quantitative evaluation method of online teaching quality based on Data Mining

A quantitative evaluation method of online teaching quality based on Data Mining

Evaluation Method of Ideological and Political Classroom Teaching Quality Based on Analytic Hierarchy Process

Evaluation Method of Ideological and Political Classroom Teaching Quality Based on Analytic Hierarchy Process

Study on Online English Teaching Quality Evaluation Method based on Action Orientation

Study on Online English Teaching Quality Evaluation Method based on Action Orientation

MOOC online English teaching quality evaluation method based on fuzzy algorithm

MOOC online English teaching quality evaluation method based on fuzzy algorithm

An Online multimedia teaching quality evaluation method based on Association Rule Mining

An Online multimedia teaching quality evaluation method based on Association Rule Mining

MOOC online English teaching quality evaluation method based on fuzzy algorithm

MOOC online English teaching quality evaluation method based on fuzzy algorithm

A MOOC online teaching quality evaluation method based on fuzzy algorithm

A MOOC online teaching quality evaluation method based on fuzzy algorithm

An online teaching quality evaluation method based on deep belief network

An online teaching quality evaluation method based on deep belief network

Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System

INTRODUCTION: The construction of the wisdom teaching evaluation system, as the essential part of the institution's teaching reform, is conducive to developing the institution's disciplines, making the existing teaching more standardized, and making the means of teaching diversified, intelligent, and convenient.
 OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are evaluation indexes that need to be more comprehensive, a single method, and system standard limitations.
 METHODS: Proposes an intelligent optimization algorithm for a multi-dimensional innovative teaching quality evaluation method. First of all, the multi-dimensional wisdom teaching evaluation system is constructed by analyzing the influencing factors of teaching quality evaluation; then, the parameters of the depth limit learning machine are optimized by the bird foraging search algorithm, and the multi-dimensional wisdom teaching evaluation model is constructed; finally, the validity and stability of the proposed method are verified by the analysis of simulation experiments.
 RESULTS: The results show that the proposed method improves the accuracy of the evaluation model.
 CONCLUSION: Solves the problem of low evaluation accuracy and incomplete system of teaching quality evaluation methods.

Evaluation of oral English teaching quality based on BP neural network optimized by improved crow search algorithm

Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance.

Evaluation Method of Practice Teaching Quality of Tourism Management Major Based on STEAM Distance Education Concept and Cloud Computing

INTRODUCTION: Tourism Management based on cloud computing is critical in developing quality human resources. Establishing a scientific and practical system to assess the quality of Tourism Management based on cloud computing and identify and improve problems in Tourism Management based on cloud computing primarily based on cloud computing is essential to ensure the quality of Tourism Management based on cloud computing and realize human development goals.OBJECTIVES: To solve the current problems of China's Tourism Management based on cloud computing, such as weak teachers, STEAM being too "soft," and leadership being too "hard," the concept of STEAM distance learning, literature analysis and other research methods are proposed, and the quality assessment system of Tourism Management based on cloud computing for the bachelor's degree in Tourism Management based on cloud computing is researched in the standard documents of education and management and related literature.METHODS: Established through interviews with qualified experts, tourism teachers, tourism professionals, and others. Factor analysis was used to adjust the system of indicators for tourism students and define an evaluation system consisting of level 1 and corresponding level 2 hands to determine the weights of the hands.RESULTS: The results show that students are satisfied with "average" internships. The lowest level of satisfaction was with the "evaluation of the training process," but this indicator was the highest. Further analysis of the results suggests insufficient investment in professional practice in Tourism Management based on cloud computing.CONCLUSION: Argues that the empirical research and analysis of the current Tourism Management based on cloud computing students can be used to understand the accurate evaluation of Tourism Management based on cloud computing activities based on the solution of the existing problems to improve students' innovative and practical abilities.

Monitoring and Quality Evaluation Method of English Teaching in Machine Manufacturing Based on Machine Learning and Internet of Things

INTRODUCTION: With the continuous development of society, education is getting more and more attention, and all stages of education, such as elementary school, middle school, high school, Etc., are the fundamental objects of attention, while English in the university, as a critical foreign language, is also given considerable attention. Among the various majors in the university, different majors pay different attention to English, among which in the mechanical engineering major, the importance of English is still relatively front and centre, which is helpful for the improvement of students' mechanical engineering level. However, in the current teaching, there are still a series of problems in the English teaching of mechanical majors, which have certain obstacles to improving students' English level. OBJECTIVES: To give university English teachers more feedback and opinions through a reasonable and perfect teaching monitoring and quality evaluation method; combining their English reading teaching with relevant rules and regulations can better promote the educational quality level of university English and help students improve their English more reasonably. METHODS: To study and analyze the monitoring and quality evaluation of English teaching in mechanical majors to get the relevant optimization suggestions of teaching mode, and to solve the correlation problem based on multiple linear regression through the calculation of SPSS software, to analyze the final impact mechanism of each item on the quality of teaching. RESULTS: The results of the sample survey indicate that, firstly, five factors, namely, research direction, disciplinary ladder, scientific research, talent cultivation and management, will have a significant impact on the monitoring and quality evaluation of English teaching in the mechanical program. Secondly, further research on the influence mechanism suggests that the monitoring and quality evaluation of English language teaching in mechanical majors needs some improvement, affecting the English language proficiency of students in this field. CONCLUSION: After summarizing several factors that impact the evaluation of the quality of English teaching, it is clear that such English classroom teaching has a positive effect on the training of mechanical students.

Retracted: Evaluation Method of Vocal Music Teaching Quality for Music Majors Based on the Theory of Multiple Intelligences

Retracted: Evaluation Method of Vocal Music Teaching Quality for Music Majors Based on the Theory of Multiple Intelligences

Deep Learning Network-Based Evaluation method of Online teaching quality of International Chinese Education

The development of vocational education in the information age requires us to think about the path and strategy of active change. Course teaching quality evaluation should also shift from passive evaluation of online teaching development to active construction of a mixed teaching quality evaluation system. In the information age, the development of teaching resources is dizzying. From paper to digital, from single to diverse, from offline to online, from scarcity to mass—various changes impact the traditional teaching model. Aiming at the online teaching quality evaluation of international Chinese education on the Internet, this paper proposes a method based on deep learning. Firstly, this paper proposes an index system construction and evaluation index weighting for online teaching of international Chinese education, and collects online data as a corpus at the same time. Then construct the CNN_BiLSTM_Att model, which is composed of the CNN module, the BiLSTM module and the Att module. Finally, compare with other model experiments. The experimental results show that CNN_BiLSTM_Att has achieved the best results in the evaluation index results, with P and F1 reaching 97.89% and 97.85%. Compared with other models, the overall effect is improved by 2%~5%. From this, the superiority of the model in the online teaching quality evaluation standard task of this paper can be obtained.

Application of Machine Learning Technology in Classical Music Education

The goal is to promote the healthy and stable development of music education in China. The time-frequency sequence topology in frequency domain can improve the effect of convolution operation. Therefore, this paper applies the above algorithms to classical music education, including the recognition of classical instruments, the feature extraction and recognition of classical music, and the quality evaluation of classical music education. The quality of the music quality evaluation system can be judged according to the correlation between the output results and the subjective evaluation. The higher the correlation, the better the music quality evaluation method. Through relevant experiments, it is proved that DTW score alignment and end-to-end are more successful in extracting the features of classical music, and more accurate in identifying classical instruments. The objective evaluation method of pronunciation teaching quality is more objective and accurate than P.563 music teaching quality evaluation.

Research on online evaluation method of MOOC teaching quality based on decision tree-based big data classification

Research on online evaluation method of MOOC teaching quality based on decision tree-based big data classification

An evaluation method of English teaching quality based on Markov chain

An evaluation method of English teaching quality based on Markov chain