A Research on Online Education Behavior and Strategy in University

Deng, Quan

doi:10.3389/fpsyg.2022.767925

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…BP neural network is a traditional feed-forward fully connected neural network, which sets the learning rate and loss function, and updates the weights and thresholds through the backward error propagation algorithm, and the activation function is usually an S-type function, which can effectively train the network, but the convergence rate is slow and there is a problem of local minima [25]. e RBF neural network only contains a three-layer network structure, and the activation function is generally Gaussian, which has the generalization ability that the BP neural network cannot, and the training accuracy is better than that of the BP neural network, but the accuracy of the test data prediction is not sufficient [26][27][28][29][30].…”

Section: Information Fusion Model Designmentioning

confidence: 99%

Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities

Chen

Wang

Zhang

2022

International Journal of Antennas and Propagation

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As a hot concept, the digital city has developed rapidly in recent years. The digital city uses information technology to realize all the contents of the past, present, and future of the city on the network, and can build a three-dimensional visual landscape. However, the traditional information fusion model has the problems of noise susceptibility and low efficiency in landscape design. In order to solve the above problems, this article proposes a wavelet transform-based 3D landscape design and optimization method for digital cities, which removes the noise influenced by wavelet change and builds an information fusion model based on neural network to complete the design optimization of the three-dimensional landscape of the digital city. Firstly, for the wavelet change denoising problem, an effective denoising algorithm for natural noise and abnormal noise is proposed by combining convolutional neural network and wavelet transform. The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. Then, aiming at the optimization of 3D landscape design, an information fusion model based on long-short time memory network and radial basis backpropagation network is proposed for fusing multiple sources of information in the digital city to evaluate the landscape. The method collects digital city feature information at the information layer and preprocesses the feature information by a rate detection algorithm. Then, LSTM and RBF-BP neural networks in deep learning are used in the feature layer for adaptive learning of multiple feature signals, and finally, fuzzy logic is used to control the system decision output to improve the efficiency of 3D landscape design. Finally, the simulation experimental results show that the proposed denoising method in this article can better retain the texture details in the images and the denoised images have better visual effects; the proposed information fusion model has higher accuracy compared with the traditional methods. Combining this method to design and optimize 3D landscapes in digital cities can improve the efficiency of landscape design.

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Section: Information Fusion Model Designmentioning

confidence: 99%

Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities

Chen

Wang

Zhang

2022

International Journal of Antennas and Propagation

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“…Although remote learning differs from traditional teaching [ 33 ], among other things, in terms of interactivity (whose deficit is caused by the absence of such important factors as social presence, social interaction and student satisfaction), it provides the opportunity for more people to continue learning [ 34 ]. What’s more, such a teaching model also shows outstanding commercial advantages [ 35 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Remote Learning in Higher Education: Evidence from Poland

Ober

Kochmańska

2022

IJERPH

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The COVID-19 pandemic has brought about a sudden transformation at universities. The previous mode of teaching has been replaced by remote education, the effectiveness of which depends, among other things, on the technological infrastructure of universities and the digital competence of lecturers and students. The main objective of this study is to evaluate remote learning in higher education from the students’ point of view. The uniqueness of the present research approach lies in the identification of four dimensions (socio-emotional, developmental, time-financial, and negative attitude) of students’ evaluation of remote learning in higher education. The survey was conducted on 999 students studying remotely, including 518 women and 481 men. Most of the students surveyed had been studying remotely for 1–2 years and were studying full-time for their first degree. The research tool consisted of 16 mixed survey questions. Six of them were related to sociodemographic factors (including those related to the respondents’ education), and eight were related to their experiences with and opinions about remote education, respectively. The remaining two questions were used to collect respondents’ evaluations of the degree of importance to them of various advantages and disadvantages of remote education. The research showed that among the advantages of remote learning for students, the most important are saving time, the possibility of studying at a university far from home (another city, another country), the possibility of combining work and study, and reduced commuting costs. On the other hand, the disadvantages of remote learning of greatest importance to students include the loss of social ties due to lack of contact with peers, feelings of fatigue resulting from excessive use of information and communication tools, and greater susceptibility to various forms of distraction. In addition, the shape of students’ education was relevant to the different dimensions of their evaluation of remote learning in higher education. The social-emotional size of remote learning is more important for students who study remotely in a blended mode (compared to uniform). The developmental dimension is essential for students who participate in remote learning activities for longer during the day. In addition, a more extended period of remote learning promotes the greater importance of the time-financial dimension when evaluating remote knowledge.

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“…“Deep learning technology” refers to the use of artificial intelligence learning ability, automatic recognition of text, image, and sound data, so that the machine learns to learn itself [ 25 ]. In recent years, with the development of multiculturalism, some young people think that traditional culture is “not up to date” and have misconceptions about traditional culture, which has led to a narrow audience for traditional culture and affected the development of innovation [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Path of Innovative Development of Traditional Culture under Big Data

Li¹

2022

Computational Intelligence and Neuroscience

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Chinese traditional culture is the treasure of our cultural field. In the new era, it is of great significance to give traditional culture a new life and vitality. The term “big data” is hotly debated all over the world, while the development of big data is gradually occupying all aspects of the society that people are compatible with society. It is an imperative initiative to build a cultural data system by making use of big data technology, and cultural big data can make Chinese traditional culture release more vitality. This paper analyzes the new characteristics of traditional culture development from big data in helping traditional culture inheritance and innovation and proposes new ideas and creates more possibilities for the development of traditional culture. Combining with big data technology, this paper proposes an improvement to the data sparsity problem and cold-start problem of collaborative filtering recommendation algorithm and also improves the recommendation algorithm based on association rules. The association rule technique is used to compensate for the cold-start and data sparsity problems of new users often encountered by collaborative filtering techniques; the aim is to obtain recommendation results with high user satisfaction. Experiments on traditional cultural resource datasets show that the method in this paper effectively solves the data sparsity and cold-start problems that exist in traditional collaborative filtering techniques, and the recommendation accuracy surpasses that of other methods.

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A Research on Online Education Behavior and Strategy in University

Cited by 8 publications

References 26 publications

Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities

Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities

Remote Learning in Higher Education: Evidence from Poland

Exploring the Path of Innovative Development of Traditional Culture under Big Data

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