Evaluation System of Tourism Psychology Teaching Quality Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Peilin Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Evaluation System ◽  
Teaching Quality

scholarly journals Convolutional Neural Network-Assisted Strategies for Improving Teaching Quality of College English Flipped Class

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tiankun Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Student Behavior ◽  
Flipped Classroom ◽  
Classroom Behavior ◽  
Procedural Knowledge ◽  
Teaching Style ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
College English

The “flipped classroom” teaching paradigm not only follows the cognitive rules of the learners, but it also subverts and reverses the standard classroom teaching process. Problem-oriented, teacher-led, student-centered, and mixed teaching approaches are the key teaching methods in the flipped classroom teaching model, which focuses on students’ procedural knowledge acquisition and critical thinking training. There are a lot of studies on the specific practice path of the “flipped classroom” teaching style right now, but there are not many on the learning involvement of college English students in this approach. According to studies, the level of student participation in classroom learning is the most important factor limiting the efficiency of teaching. The lack of research in this subject greatly limits the “flipped classroom” teaching model’s ability to improve college English classroom teaching quality. The degree of engagement between teachers and students, the enthusiasm of students in class, and the competence of teachers to educate are all reflected in student conduct in the classroom. Understanding and evaluating the behaviors and activities of students in the classroom are helpful in determining the state of students in the classroom, as well as improving the flipped classroom teaching technique and quality. As a result, the convolutional neural network is used to recognize student behavior in the classroom. The loss function of VGG-16 has been enhanced, the distance inside the class has been lowered, the distance between classes has been increased, and the recognition accuracy has improved. Accurate recognition of classroom behavior is beneficial in developing methods to improve teaching quality.

P3-347: INDIVIDUAL EVALUATION SYSTEM FOR WHITE MATTER HYPERINTENSITY RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK

2006 ◽  
Vol 14 (7S_Part_23) ◽  
pp. P1217-P1217
Author(s):  
Jin San Lee ◽  
Kyung Mi Lee ◽  
Eui Jong Kim ◽  
Hak Young Rhee ◽  
Key-Chung Park ◽  
...  
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Convolutional Neural Network ◽  
Evaluation System ◽  
Deep Convolutional Neural Network ◽  
White Matter Hyperintensity ◽  
Individual Evaluation

scholarly journals Development Assessment of Higher Education System Based on TOPSIS-Entropy, Hopfield Neural Network, and Cobweb Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xian-Bei Liu ◽  
Yu-Jing Zhang ◽  
Wen-Kai Cui ◽  
Li-Ting Wang ◽  
Jia-Ming Zhu
Keyword(s):  
Neural Network ◽  
Higher Education ◽  
Evaluation System ◽  
Hopfield Neural Network ◽  
Teaching Quality ◽  
Entropy Method ◽  
Higher Education System ◽  
Cobweb Model ◽  
Depth Analysis ◽  
Development Assessment

This paper first extracted 11 indicators from four aspects of infrastructure, educational equity, teaching quality, and scientific research level and established a multidimensional higher education evaluation system. After that, according to TOPSIS and the entropy method, a comprehensive score of the development of higher education was obtained, and a comprehensive index of higher education was proposed. According to the level of the score, we divide the development status into 5 categories, and use discrete Hopfield neural network for verification. In addition, we applied the model to many countries and chose Vietnam to conduct an in-depth analysis of the model, including reforming policies and evaluating policy effects based on cobweb model. Finally, we found that the application of the model is very universal, but in reality the reform is very difficult.

scholarly journals Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2831 ◽  
Author(s):  
Na Wu ◽  
Chu Zhang ◽  
Xiulin Bai ◽  
Xiaoyue Du ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Evaluation System ◽  
Principal Component ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Training Set ◽  
Derivative Method ◽  
Discriminant Models

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874–1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

scholarly journals Optimization of Online Education and Teaching Evaluation System Based on GA-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Xu ◽  
Fenghu Liu
Keyword(s):  
Neural Network ◽  
Online Education ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Evaluation System ◽  
Teaching Quality ◽  
Teaching Evaluation ◽  
Classroom Teaching ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students’ online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.

scholarly journals Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Yafei Chen ◽  
Zhenbang Yu ◽  
Weihong Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
English Teaching ◽  
Algorithm Evaluation ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students’ English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.

scholarly journals College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yajun Chen
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Rbf Neural Network ◽  
Teaching Quality ◽  
Decision Algorithm ◽  
College English ◽  
English Teaching ◽  
Quality Evaluation System

In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.

scholarly journals Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Tingting Liu ◽  
Le Ning
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
Deep Convolutional Neural Network ◽  
Evaluation Index System ◽  
Assessment Model ◽  
Classification Model ◽  
Evaluation Index ◽  
Teaching Assessment

In colleges and universities, teaching quality evaluation is an integral part of the teaching management process. Many factors influence it, and the relationship between its evaluation index and instructional quality is complicated, abstract, and nonlinear. However, existing evaluation methods and models have flaws such as excessive subjectivity and randomness, difficulty determining the weight of indicators, easy over-fitting, slow convergence speed, and limited computing power, to name a few. Furthermore, the evaluation index system focuses primarily on teaching attitude, material, and methods, rarely taking into account preparation prior to teaching or the teaching situation throughout the teaching process, resulting in an incomplete evaluation. As a result, learning how to construct a model for objectively, truly, thoroughly, and accurately assessing the teaching quality of colleges and universities is beneficial not only to improving teaching quality but also to promoting scientific decision-making in education. This paper develops a teaching assessment model using a deep convolutional neural network and the weighted Naive Bayes algorithm. Based on the degree of influence of different characteristics on the assessment outcomes, a method to estimate the weight of each evaluation characteristic by employing the related probability of class attributes is proposed, and the corresponding weight is assigned for each evaluation index, resulting in a classification model ideal for teaching assessment that promotes standardization and intelligibility.

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