Examining the relationship between peer feedback classified by deep learning and online learning burnout

Huang, Changqin; Tu, Yaxin; Han, Zhongmei; Jiang, Fan; Wu, Fei; Jiang, Yunliang

doi:10.1016/j.compedu.2023.104910

Cited by 38 publications

(2 citation statements)

References 76 publications

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“…The results of this study showcased the high application value of the proposed model. Paper [33] uses a BiLSTM network to classify peer reviews in terms of cognitive content and affective states; the results show that the BiLSTM network model achieves good results in terms of classification performance and consistency with manual coding. Paper [34] employed a prediction model based on gray relational analysis and bidirectional long short-term memory neural networks (BiLSTMs) to forecast the air quality index of several cities.…”

Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

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Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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“…The BC provides a secure and immutable record of academic credentials, which addresses the issue of certification and verification in distance education. High-speed 6G network and EC can reduce the latency issues which ensure seamless, realtime interactions during online classes [163]. This enhances the quality of distance education which sometimes is much more advanced than classroom learning.…”

Section: Distance Educationmentioning

confidence: 99%

Industry 5.0 in Smart Education: Concepts, Applications, Challenges, Opportunities, and Future Directions

Supriya,

Bhulakshmi,

Bhattacharya

et al. 2024

IEEE Access

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Industry 5.0 is one of the emerging stages of industrialization in which humans collaborate with cutting-edge technologies to enhance various workplace processes. The primary objective is to emphasize meeting the needs of people and provide enhanced resilience and understanding of sustainability. Industry 5.0 enables cooperation with such advanced technologies and the stakeholders in the education sector to ensure efficiency and effectiveness in the teaching-learning process. The present study provides an exhaustive review of the role of Industry 5.0 in smart education. At the outset, a brief overview of the present scenario in the education sector and its associated challenges is presented. This sets the stage for establishing the need for Education 1.0 and its progressive transition to Education 4.0. Further, the motivation to integrate Industry 5.0 with Education 4.0 and the related enabling technologies that support are discussed. The paper extensively provides a description of the application of Industry 5.0 in various educational sectors namely in medical education, further learning, distance education, engineering education, and shop floor training. The study also presents seven case studies highlighting the successful implementation of Industry 5.0 in versatile sectors and regions. Finally, the challenges of Industry 5.0 in education are discussed pointing to potential future directions of research.

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A fuzzy clustering approach for cloud-based personalized distance music education and resource management

Jiang

2023

Soft Comput

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Examining the relationship between peer feedback classified by deep learning and online learning burnout

Cited by 38 publications

References 76 publications

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Industry 5.0 in Smart Education: Concepts, Applications, Challenges, Opportunities, and Future Directions

A fuzzy clustering approach for cloud-based personalized distance music education and resource management

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