Building a Neural Network Model to Analyze Teachers’ Satisfaction with Online Teaching during the COVID-19 Ravages

Wen, Gangxin; Guan, Quanlong; Wu, Xiaofeng; Luo, Weiqi

doi:10.4236/jcc.2022.101005

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Most of the thought and politics teaching in schools adopts the method 5of statistical communication at all levels to control the thought and politics risks of students. Establishing a whole set of early warning system suitable for the balanced development of compulsory education in China is [24]. Cao used LSTM to predict the performance of students in Beijing Labor and Social Security Vocational University as an example, and based on this, he provided reasonable suggestions for teachers and schools to improve the quality of education [25].…”

Section: Related Workmentioning

confidence: 99%

Dynamical Alert of Thought and Politics Teaching Based on the Long- and Short-Term Memory Neural Network

Sturdivant²

2022

Wireless Communications and Mobile Computing

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To strengthen and develop the thought and politics work in state-owned schools, schools must explore the theoretical system of thought and politics construction in the practice of scientific development ideas guiding school development, actively innovate the practice model and implement early warning management for thought and politics projects. This is because only by accurately analyzing the problems and causes can we seek more reasonable measures for the laws of thought and politics practice education for modern students. At present, promoting the deep integration of thought and politics teaching projects with information technology has become an important means of thought and politics teaching projects in schools. However, with the explosive growth of network data, the structure becomes more and more complex, and learners face the problem of information overload as more and more information overflows in the network environment. Precise support for students with learning disabilities is a research direction for precision thinking education, and most existing support strategies in schools include manual statistics of failed subjects, written warnings, or corrective measures through simple correlation algorithms. In this paper, we propose a dynamical alert for thought and politics teaching based on the Long-Short-Term Memory Neural Network (LSTM), which uses a powerful global optimization function to optimize the parameters of the deep LSTM neural network. The experimental results show that the average execution time of LSTM is 19.46 seconds and 8.24 seconds lower than that of SCB-DBSCAN and CFSFDP, respectively, which shows that the execution time of the LSTM algorithm is faster and more accurate. Therefore, the LSTM algorithm is feasible and effective. The LSTM-based dynamic warning of thought and politics teaching can predict students’ subject performance more accurately and has certain validity and feasibility.

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Section: Related Workmentioning

confidence: 99%

Dynamical Alert of Thought and Politics Teaching Based on the Long- and Short-Term Memory Neural Network

Sturdivant²

2022

Wireless Communications and Mobile Computing

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“…In this modern era technology e.g., internet of things (IoT) [18], data mining [19], [20], neural network [21] and machine learning [22]- [25] and so on plays a vital role where in our study we worked on data mining to predict our desire result to contribute the research area. In this part, we are analysis result data which check into cross-validation then convert to binary formation and the learning algorithms in terms of accuracy of evaluating the performance.…”

Section: Data and Results Analysismentioning

confidence: 99%

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

Poushy

Bhuiyan²,

Parvin³

et al. 2022

IJECE

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<span>This research focuses on the education-based online learning platform. Due to the <a name="_Hlk109304176"></a>coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".</span>

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“…Indian scholars like Orchek (2022) combined other physiological signals (i.e., heart rate, blood pressure, skin conductance) to obtain learners' emotional changes. They explored ways to make use of learners' emotional feedback to improve the learning experience (Wen et al, 2022). Rojas (2022) proposed the FILTWAM learning framework, combining facial expression recognition with speech expression.…”

Section: Research Status Of Students' Learning Emotions In Informatio...mentioning

confidence: 99%

The Attitudes of Students' Parents Towards Their Children's Information-Based Learning Under the Background of the Combination of Large-Scale Online Learning and Multimedia Technology

Liu

2023

International Journal of Web-Based Learning and Teaching Technologies

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As a stakeholder group in the promotion of basic education informatization, parents' attitudes towards children's informatization learning is an important factor affecting the smooth development of school informatization teaching. Based on the classic convolutional neural network and CK+ dataset, this paper proposes a convolutional neural network model to evaluate the attitude of parents to children's information-based learning in the context of large-scale online learning and multimedia technology. It aims to provide an important reference for promoting the informatization teaching reform in the basic education stage in the post-pandemic era. The experiment shows that the convolution neural network model proposed in this paper can accurately identify the facial information of learners in the live classroom. Based on learners' emotional changes, teachers can adjust teaching strategies in time to improve the teaching process.

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Building a Neural Network Model to Analyze Teachers’ Satisfaction with Online Teaching during the COVID-19 Ravages

Cited by 6 publications

References 30 publications

Dynamical Alert of Thought and Politics Teaching Based on the Long- and Short-Term Memory Neural Network

Dynamical Alert of Thought and Politics Teaching Based on the Long- and Short-Term Memory Neural Network

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

The Attitudes of Students' Parents Towards Their Children's Information-Based Learning Under the Background of the Combination of Large-Scale Online Learning and Multimedia Technology

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