Research on Global Higher Education Quality Based on BP Neural Network and Analytic Hierarchy Process

Mei, Yuan; Li, Chunyang

doi:10.4236/jcc.2021.96009

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…How to establish a scientific and reasonable TQE system so that it can evaluate the teaching quality objectively and fairly is a very meaningful subject. According to reference [5], the integration of information technology into higher education, the modernisation of educational material, and the general development of the quality of higher education should be a primary goal of this research. It is imperative that we tackle the challenge of evaluating the teaching quality at CAU in a fair and accurate manner in order to further the higher education reform and enhance CAU's teaching goals and quality.…”

Section: Introductionmentioning

confidence: 99%

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

Bao¹,

Wang

2022

Computational and Mathematical Methods in Medicine

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Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbacks include too much subjectivity and unpredictability, difficulty in defining index weights, sluggish convergence, and weak computer capacity. The current assessment techniques and models have these issues as well. This research uses an ANN model to assess the quality of mathematics instruction at colleges and universities (CAU) in order to address the challenging nonlinear issue of TQE and completes the following tasks. (1) The background and significance of TQE research are analyzed, and the domestic and foreign research status of TQE and neural network is systematically expounded. (2) The technical principle of DNN is introduced and the DDAE-SVR DNN model is constructed, and then, the evaluation index system of mathematics teaching quality is constructed. (3) The DDAE-SVR DNN model is put out as a potential alternative. The Adam method is used in the unsupervised training process to dynamically modify the learning step size for each training parameter. The spatial properties of the original data may be modified several times such that the reconstruction can be completed after many hidden layers have been applied. Data essentials such as precision, accuracy, and consistency are prioritized above all other considerations when generating the final product. Unsupervised prediction uses SVR and maps the complicated nonlinear connection into high-dimensional space in order to attain linearity in low-dimensional space, which is the goal of the supervised prediction process. The usefulness and benefits of the model provided in this research in the mathematics TQE may be verified by tests including the use of the TQE dataset and comparisons with other shallow models.

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Section: Introductionmentioning

confidence: 99%

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

Bao¹,

Wang

2022

Computational and Mathematical Methods in Medicine

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“…The FAHP was put forward by American strategist Sadie, aiming at solving the complex problems of multi-objective decision-making involving qualitative factors (Shen et al, 2021;Yuan and Li, 2021). It is simple and easy to understand, but at the same time, it cannot accurately measure some indicators when constructing the judgment matrix (Huang, 2021;Zheng et al, 2021a,b).…”

Section: Fuzzy Analytic Hierarchy Processmentioning

confidence: 99%

Quality improvement of undergraduate courses based on fuzzy analytic hierarchy process and entropy method

Ao-tian

2022

Front. Psychol.

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Since the curriculum is the core carrier to improve the level of talent cultivation in colleges and universities, strengthening the reform of course teaching and improving the quality of course teaching are fundamental to the survival and development of colleges and universities, and also an important part of higher education reform. In this study, a fuzzy analytic hierarchy process (AHP) and an entropy method were used to determine the weight of the core evaluation indicators of undergraduate course quality improvement, including four first-level indicators of the curriculum concept, curriculum resources, curriculum organization, and curriculum effectiveness, and 12 s-level evaluation indicators and weights. Then, based on a case study of the first-class undergraduate course “Management” of Anyang Normal University, the way to evaluate the course by the AHP and entropy method was explained. Finally, according to the evaluation results, the ideas of course construction were put forward, such as changing the course concept, enriching the course resources, paying attention to the course organization, and ensuring the course effectiveness, so as to improve the quality of undergraduate courses and also to improve the quality of undergraduate talent training with the improvement of course quality as the starting point.

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“…The application of BP learning algorithm not only is reflected in evaluation, but also can be used in decision-making model. Yuan and Li selected the principal component analysis method when studying the index system established in the higher education quality model, took 12 indexes from 50 countries as the index evaluation system, extracted a total of 4 principal component factors that play a leading role, took these indexes as the decision-making standard, and then used BP learning algorithm to establish the evaluation and decision-making model, which has a good evaluation and prediction effect [ 8 ]. Chang et al proposed a new method of undergraduate education quality evaluation based on algorithmic stress test and constructed an open index database composed of 4 dimensions and 19 variables such as teaching attitude, teaching content, teaching methods, and teachers' basic characteristics [ 9 ].…”

Section: State Of the Artmentioning

confidence: 99%

Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network

2022

Computational Intelligence and Neuroscience

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Entrepreneurship education activities in colleges and universities play an important role in improving students’ innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient. Even if quantitative analysis is adopted, it is mostly linear analysis, which is inconsistent with the actual situation. In order to improve the application level of genetic algorithm to the innovation and entrepreneurship ability of universities based on BP neural network, this paper studies the evaluation model of innovation and entrepreneurship ability of universities. Based on the simple analysis of the current situation of university innovation and entrepreneurship ability evaluation and the application progress of BP neural network, combined with the actual situation of university innovation and entrepreneurship, this paper constructs the innovation and entrepreneurship evaluation index, uses BP neural network to build the evaluation model, and uses genetic algorithm to optimize and improve the shortcomings of BP neural network. Then, the experimental analysis and application design are carried out. The results show that the improved algorithm is basically consistent with the predicted value, small error, and fast convergence. When it is used in the evaluation of innovation and entrepreneurship ability, quantitative analysis results can be obtained, which provides a certain reference for the development of enterprises.

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Research on Global Higher Education Quality Based on BP Neural Network and Analytic Hierarchy Process

Cited by 19 publications

References 6 publications

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

Quality improvement of undergraduate courses based on fuzzy analytic hierarchy process and entropy method

Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network

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