[Retracted] Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

Liu, Tingting; Ning, Le

doi:10.1155/2021/1859065

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…(1) Teaching attitude (2) Teaching content (3) Teaching ability (4) Teaching method (5) Teaching and educating people (6) Teaching effect e emergence of artificial intelligence has changed the single and closed limitations of traditional teaching, and the system of evaluating teaching has been developed accordingly. In order to make the evaluation indicators more accurate, reference [11] first used factor analysis and then used cluster analysis, effectively combining three common methods to calculate the weight of each indicator, using two methods to verify the reliability of the empirical data.…”

Section: Related Workmentioning

confidence: 99%

“…Second, during the operation process, some students fill in some data and submit it, while others fill in the incorrect data, resulting in data loss or incompleteness. Finally, in the evaluation process, students' personal characteristics, including aesthetics, learning goals, personal interests, etc., are due to different personal starting points, resulting in uneven evaluation results when grading teachers [6][7][8][9][10]. Addressing the present state and existing challenges with the teaching quality evaluation system, this topic suggests a more effective technique for resolving such issues via the application of data network name (DNN) technology.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Japanese Teaching Quality Based on Deep Neural Network

Liu

2022

Security and Communication Networks

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The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors’ teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor’s weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers’ teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers’ teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation’s dependability and makes the outcomes more effective and objective.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

Liu

2022

Security and Communication Networks

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“…Secondly, the evaluation always puts the improvement and improvement of the quality of education in the first place, and according to the special needs of the society, the goal is to train experts in the field of specialization [5,6]. Therefore, professional evaluation of vocational undergraduate pilot colleges can not only improve the quality of vocational undergraduate pilot colleges and shape the characteristics of vocational undergraduate pilot colleges but also promote the professional evaluation system of vocational undergraduate pilot colleges to a certain extent [7,8].…”

Section: Introductionmentioning

confidence: 99%

The Teaching Evaluation Index System and Intelligent Evaluation Methods of Vocational Undergraduate Pilot Colleges

Wu¹

2022

Wireless Communications and Mobile Computing

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In recent years, with the popularization of higher education, quality problems have become increasingly prominent, and government documents often propose that the main task of education reform and development is to improve the quality of higher education. As a result, teaching evaluations have appeared one after another. Although various evaluation activities are carried out dynamically, expert evaluation has proven to be one of the most effective methods to ensure professional quality. This will help improve the quality of human resource development, promote regional economic development, improve the quality assurance system of higher education, and promote the formation of professional skills development mechanisms. This article studies the teaching evaluation index system and intelligent evaluation methods of vocational undergraduate pilot colleges, understands the relevant knowledge of the teaching evaluation index system on the basis of literature data, and then, constructs the teaching evaluation system of vocational undergraduate pilot colleges. The constructed system is tested, and the test results show that the error of the results of teacher self-evaluation and student evaluation is controlled within the two, which also verifies that the construction of the teaching ability evaluation index system this time is reasonable and scientific.

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“…is kind of teaching method does not simply increase the form of online teaching but needs to better integrate resources and apply resources to innovative curriculum teaching mode so as to provide students with a wider way of knowledge acquisition, create a benign competitive environment, and better implement personalized learning requirements without the limitation of time and space [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Application of College English Mixed Flipping Classroom Teaching Quality Based on the Fuzzy Judgment Model

Gao¹

2022

Security and Communication Networks

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With the development of big data technology, there are more and more evaluation models for college English teaching quality, which can better promote the improvement of college English teaching quality. Based on the fuzzy decision model, this paper describes the algorithm establishment process of the model in detail. Then, the fuzzy evaluation model between teachers and students according to different grades is established. Finally, aiming at the problems in college English teaching, this paper obtained the corresponding countermeasures for the college English teaching platform based on fuzzy judgment model. The results indicate the model improves the quality of college English teaching in different grades and promotes the development of college English. In addition, the model platform can also well predict the measures to improve the quality. In short, this paper provides some theoretical and experimental support for the quality of college English hybrid flipping classroom.

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[Retracted] Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

Cited by 15 publications

References 31 publications

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

The Teaching Evaluation Index System and Intelligent Evaluation Methods of Vocational Undergraduate Pilot Colleges

Evaluation and Application of College English Mixed Flipping Classroom Teaching Quality Based on the Fuzzy Judgment Model

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