Teaching Quality Evaluation Model Research Articles

English Teaching Quality Evaluation Model for Higher Education Based on Educational Data Mining Technology

Constructing teaching quality evaluation systems for higher vocational education is beneficial for schools, serving as the cornerstone for developing abilities and enabling precise education management. In this publication, a Dual Relational Graph Attention Network (ETQE-DRGAN)-Based Educational Data Mining Technology English Teaching Quality Evaluation Model for Higher Education is proposed. In three ways, this study assesses and defends the system. An overview of the system's primary components, such as the user and data administration modules, data mining, online assessment, and inquiry modules, is given in the first section. Frequent item sets are divided into two stages and the development of invalid candidate sets is limited to avoid the formation of negative association rules. The Tid-list Vertical Partitioning Query-Apriori Algorithm (TPQ-Apriori algorithm) is improved by these two approaches. The system analysis is covered in section three. In order to assess the success of instruction, this work incorporates a Dual Relational Graph Attention Networks method that takes into account several factors such as age, gender, education, teaching attitude, teaching topic, teaching technique, and teaching effectiveness. The proposed ETQE-DRGAN is implemented in MATLAB, using the School Database for calculation. Numerous performance metrics like response time, precision, accuracy, sensitivity, and F1-score are utilized to calculate the effectiveness of ETQE-DRGAN technique. The obtained outcomes indicate that the ETQE-DRGAN approach achieves the highest precision of 98.5%, Accuracy of 98% and fastest response time of 6.95swhen compared to existing methods like ETQE-AA, ETQE-DTA and ETQE-ML.

Preschool Teachers Teaching Quality Evaluation Based on Neural Network Algorithms

Based on the current teaching situation of preschool teachers, in order to comprehensively evaluate the effectiveness of early childhood teaching, research have constructed a teaching quality evaluation model using fuzzy synthesis method and expert method. This model can handle the fuzzy relationship between evaluation indicators and achieve the evaluation of teaching quality. Considering that the teaching evaluation is influenced by many factors, the Genetic Algorithm back propagation (GA-BP) neural network algorithm is chosen for the solution model construction of the preschool teacher teaching quality. The entropy method chosen for the data calculation is to complete the preschool teaching quality evaluation. In the mean square error test for solving the model, the improved GA-BP model converged after 40 iterations with the model convergence speed increased by 34.65%. In the evaluation and prediction of preschool teacher indicators, the improved GA-BP model accurately evaluated the teacher classroom teaching indicators. In sample 3, sample 6, and sample 9, the improved GA-BP model scored 91, 89, and 88 points, respectively, close to the true scoring results. The improved model’s accuracy was high as 90% in teacher skills, personality charm, and academic research evaluation. The improved model also effected better in the application of the preschool teaching quality evaluation. The application effect of this model in teaching effectiveness evaluation and teacher quality evaluation is good, providing valuable reference for the establishment of early childhood education evaluation system.

A construction of online teaching quality evaluation model based on big data mining

A construction of online teaching quality evaluation model based on big data mining

A Construction of online teaching quality evaluation model based on big data mining

A Construction of online teaching quality evaluation model based on big data mining

Implementation Mechanism of Industry-Education-Creation Mixed Ownership Industrial College Based on AHP Fuzzy Evaluation Model

Abstract The management of modern industrial colleges is the deepening and developing the fusion of industry-teaching-creation and school-enterprise cooperation talent cultivation mode, which has strategic height and practical significance. This paper establishes the evaluation index system of the implementation mechanism of industrial colleges based on BSC theory. It creates the implementation mechanism model of mixed teaching and learning in industrial colleges. Combining qualitative and quantitative analysis, the hierarchical and fuzzy hierarchical analysis methods are used to construct the hybrid teaching quality evaluation model of industry-teaching-creation. Then the weights of the evaluation indexes of the implementation mechanism of industrial colleges are calculated. The industrial colleges of college A and college B are selected as the research objects to comprehensively evaluate the implementation effect of their mixed industry-teaching-creation teaching. The optimal model is determined by calculating the comprehensive scores of the five industry-education-creation hybrid teaching implementation mechanisms. The study shows that the highest weight of finance is 0.333. The total scores of college A and B are 80.56 and 83.33, and college B has a higher level of industry-teaching-creation hybrid teaching implementation mechanism. The composite score of the co-constructed science and technology park model is 0.233, the optimal mixed teaching implementation mechanism of Industry, education and creativity.

Design and Innovative Methods of English Teaching Tutorials in the Context of the Information Age

Abstract In this work, the problem of teaching English in colleges and universities throughout the information age is addressed by using augmented reality (AR) technology to finish designing the teaching activities and objectives. It establishes the English teaching evaluation index system, uses the Hummingbird optimization algorithm to optimize the convolutional neural network’s parameters, and builds the English teaching quality evaluation model based on this optimization. We assess students’ happiness and their capacity for autonomous learning in English instruction in the information era by statistical analysis, and we investigate novel approaches for English teaching tutorials in the information age. The results show that, in terms of overall satisfaction with English teaching, freshmen students have the highest level of satisfaction with the three phases of English teaching, with values of 216.71, 215.36, and 218.13, and sophomores have the lowest level of satisfaction, with values of 137.41, 133.01, and 138.76, indicating that there is a difference in student satisfaction by grade level. Establishing goals and monitoring the use of strategies were highly correlated with teacher support (P=0.534, 0.554 >0.5), which indicates a significant correlation between teacher support and students’ ability to learn English independently. This study can have a positive impact on the creative impact of teaching English in colleges and universities and successfully promote innovative transformation of the teaching mode.

Retracted: Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Retracted: Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Teaching quality evaluation of college civic and political science courses based on BP neural algorithm

Constructing the evaluation system of ideological and political education of new media in colleges is both beneficial to evaluate the established ideological and political education work and an important guide to improve the corresponding work. At present, promoting ideological and political education work with high integration of information technology has become an important way of ideological and political education work in colleges. However, the theoretical circles are still not focused enough on how to evaluate the ideological and political education work in colleges. Based on the characteristics of the information age, this paper establishes the teaching quality evaluation system of ideological and political education courses in colleges, introduces BP neural network evaluation method, and obtains strong empirical support through simulation experiments, so as to build a feasible teaching quality evaluation model of ideological and political courses in colleges. At the same time, the corresponding optimization suggestions are put forward, including improving the relevance of ideological and political education work, dynamically grasping students’ ideological and political information and doing a good job of data processing, and improving the professional information literacy of the ideological and political work team, in order to provide some reference for the efficient development of ideological and political education work in colleges.

Application of Weighted Comprehensive Evaluation Model in Teaching Quality Evaluation System of Computer Class Vocational Education

Abstract This paper firstly constructs the entropy weight-TOPSIS weighted comprehensive evaluation model, uses the entropy weight method to determine the indicator weights, and establishes a standardized evaluation matrix to evaluate the evaluation object through the TOPSIS model. Then, the evaluation model of teaching quality of vocational education, including teaching environment, curriculum construction, teaching content, implementation effect and teaching support level, was established, and the weight teaching evaluation of each index was made by using the entropy weight-TOPSIS model. Finally, the quality of teaching in the school was measured and analyzed, and the expected and perceived mean of different indicators were compared and calculated. The results show that the gap between the perceived teaching quality of each dimension of the indicators is basically controlled between [-1.0,-0.7], and the overall teaching quality evaluation result is -0.86, and the teaching quality needs to be improved urgently. This paper examines the teaching quality of vocational education, finds its problems, puts forward reform opinions, and promotes the development of the vocational education talent force in the direction of higher quality.

The Path of Integrating Curriculum Civics into Advertising Design Teaching in Higher Vocational Colleges and Universities Based on Big Data Analysis

Abstract In order to cultivate advertising design applied talents, this paper firstly combs through the status quo of the current teaching system of advertising design and designs the model of advertising design teaching system under the concept of curriculum ideology and political education. Secondly, on the basis of the classical division clustering algorithm, by adding a fuzzy set, fuzzy clustering and fuzzy center of mass and explaining the importance of distance, a weighted fuzzy clustering teaching quality evaluation model based on big data analysis is designed. In the end, the teaching model was employed to evaluate the clustering of teaching quality by accumulating educational data. The percentage of excellent teaching quality in the clustering results is about 0.35, the smallest average percentage is failing and average, which are 0.12 and 0.11, respectively, and the highest average weight is the teacher’s professional knowledge and the student’s learning achievement, which are 0.12 and 0.125, respectively.

Assessment of nursing-related teaching quality based on fuzzy comprehensive evaluation

Abstract This paper designs a scheme for a nursing professional teaching quality evaluation system, including constructing an evaluation index system and a nursing teaching quality evaluation model based on a fuzzy comprehensive evaluation. Among them, the establishment of a fuzzy comprehensive evaluation model involves the determination of fuzzy operators and the processing of evaluation indexes. Finally, the constructed method was used to conduct a study on evaluating nursing teaching quality in College H for students of different academic years. The results showed that freshman, sophomore, and junior students were inconsistent in all aspects of faculty teaching effectiveness and overall evaluations. The total rating of freshmen was 18.34±0.17. The total rating of sophomores was 20.50±0.15. The total rating of juniors was 23.46±0.12. And 66.7% of the faculty members thought that the quality of nursing teaching at the university was not up to standard. Therefore, this paper provides a valid assessment method for nursing education.

Application of an intelligent evaluation model of online teaching based on improved BPNN

Online teaching quality evaluation is essential for university management, but the current evaluation model of online teaching quality has problems such as subjectivity, high randomness and slow convergence speed. Therefore, the study uses adaptive learning rate with momentum term for optimizing and improving the traditional back propagation neural network, and the improved adaptive back propagation neural network rating model has good application in small-scale low-dimensional datasets, and its computational power is weak in large-scale datasets. Therefore, the study uses deep noise reduction autoencoder to improve the model structure, and then obtains an improved backpropagation neural network. The model shows better performance in a self-made university online teaching evaluation dataset. When the quantity of hidden layers of the improved model is 2, the mean square error value of the model is 0.0026; when the quantity of hidden layer neurons is 25, the mean square error of the model is 0.0012; when the model is made comparison with the shallow model, the mean absolute percentage error, the mean square error, and the root mean square error perform the best with the values of 0.0502, 24.53, and 4.95, respectively. the experimental results verify that the experiment verified that the improved model possesses powerful computational ability, and also proved that it also possesses excellent forecasting convergence, which can better help universities to complete the evaluation of online teaching quality.

A study on the application of digital technology in improving the quality and effectiveness of dance teaching

Abstract In this paper, the dance teaching digitization process and virtual character dance movement dataset are designed, combined with the Vicon motion capture system, neural network fusion shape technology and dance movement recognition algorithm to realize dance teaching digitization. The traditional K-modes algorithm is improved from three aspects: determining the number of clustering families, the determination of the initial clustering center, and the clustering distance metric to construct a digital dance teaching quality evaluation model. After the application analysis, it was concluded that the difference between the students of the experimental group and the students of the conventional group of dance movements was P<0.05, and the learning situation of the experimental group showed an upward trend, the learning enthusiasm increased by 0.21, the learning efficiency increased by 0.23, and the clustering index were higher than 0.9. Dance teaching based on digital technology can improve the quality and effect of the teaching.

Current situation and reform trends of physical education teaching evaluation in the context of deep learning

Abstract Based on the conventional genetic algorithm, this work suggests an adaptive variation genetic algorithm (AGA), which increases population diversity and speeds up convergence by increasing variation probability. The entropy approach is used to first assess the caliber of physical education and to produce a priori assessment samples. It is combined with the AGA-BP model based on adaptive variation probability. The BP neural network is then utilized for assessment learning in the field of evaluating the quality of physical education. It is optimized by an adaptive variation-based genetic algorithm. Finally, student physical activity levels were assessed both before and after the physical education reform using a more thorough and scientifically based EM-AGA-BP teaching quality evaluation model. The findings revealed that, at 41.2% and 53.4%, respectively, the percentage of students’ static activity time after the reform was much lower than that before the reform. By using independent samples t-tests, each revealed significant differences (P 0.05).

Novel evaluation model of teaching quality based on the improved IVIF-BWM and IVIFWMSM

The evaluation of teaching quality plays a crucial role in promoting the improvement of education quality and ensuring the healthy development of education. This study presents a novel teaching quality evaluation model based on improved interval-valued intuitionistic fuzzy Best-Worst method (IVIF-BWM) and interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operators (IVIFWMSM). The study is divided into three parts. Firstly, to derive the optimal interval-valued intuitionistic fuzzy weights of criteria, we develop an improved IVIF-BWM by establishing a goal programming model based on the multiplicative consistent interval-valued intuitionistic fuzzy preference relation(IVIFPR), and then we propose the new consistency index (CI) and the consistency ratio (CR) under interval-valued intuitionistic fuzzy environment to verify the reliability of the derived results. Secondly, with regard to the importance and interaction relationships among criteria, IVIFWMSM is used to aggregate evaluation values of alternatives on each evaluation criteria in multi-criteria decision making process. Finally, the proposed teaching quality evaluation model is applied to a case of teaching quality evaluation in higher education and a comparison study with other existing methods are performed. The results demonstrate that the proposed teaching quality evaluation model not only overcomes the shortcomings of previous methods, but also is more accuracy, effective and reasonable for dealing with the teaching quality evaluation under the intuitionistic fuzzy environments.

Retracted: Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network

Retracted: Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network

Retracted: Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Retracted: Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

The assessment model of online vocal music teaching quality under the optimized DL model

In recent years, art disciplines have flourished in response to the requirements of the times, and online vocal music courses have been offered by major colleges and universities. The quality of education in colleges and universities is a key indicator of the quality of a school's talents, and improving its quality is the most important part of modernizing education. In this study, a new education index system is constructed to address the problems of traditional online vocal teaching quality evaluation methods, and a vocal teaching quality evaluation model based on an adaptive variant Genetic Algorithm improved Back Propagation neural network is proposed. The model is combined with the established new index system and used in the evaluation of vocal music teaching quality. Comparing the performance between the model and the model constructed by the traditional algorithm, the experimental results showed that the convergence speed of the optimized model was improved by 79.32%. The model adaptation reached convergence in the 19th time, and the adaptation value was stabilized at 1.34, which can be seen that the improved model has a stronger adaptive ability and a faster convergence speed. In summary, the results predicted by the model basically coincide with the trend of the actual evaluation results, and the existence of the error is small, indicating that the model can achieve a more comprehensive and scientific evaluation of education quality.

Online Teaching Quality Evaluation: Entropy TOPSIS and Grouped Regression Model

With the continuous progress in Chinese higher education, the quality of online teaching has become the key to influencing that of the operation and reputation of universities and colleges. Nevertheless, the results of traditional teaching quality evaluation methods are considerably influenced by objectivity due to limitations in single-index and outdated methods. Hence, the construction of a reasonable online teaching quality evaluation model for universities and colleges presents important research significance to optimize the existing evaluation process. An online teaching quality evaluation index system for teachers at 26 observation points was set up from the perspectives of teaching objectives, process, and effect. The Technique for Order Preference by Similarity to Solution (TOPSIS) scores of 215 teachers from six universities in Henan Province, China, were evaluated using the entropy TOPSIS method. In addition, the significance of influencing factors in the ranking results of online teaching quality by teachers was analyzed using a hierarchical regression model. Results demonstrate that the weights of teaching attitude, teaching contents, and cognitive objectives were the highest and occupied the top three positions with weights of 14.94%, 12.99%, and 12.96%. By using three level-1 indexes of teaching objectives, process, and effect as the explanatory variables, students’ scores for teachers are all significant under the 1% level. According to the Chow test, the results are F (4, 207) = 2.725 and p = 0.031 < 0.05, indicating that using the online teaching duration of teachers as a grouping variable brings structural changes. Results can optimize online teaching quality evaluation and provide scientific references to evaluate the teaching quality of teachers.

Retracted: Research on Teaching Quality Evaluation Model of Physical Education Based on Simulated Annealing Algorithm

Retracted: Research on Teaching Quality Evaluation Model of Physical Education Based on Simulated Annealing Algorithm