Study on the performance evaluation of online teaching using the quantile regression analysis and artificial neural network
This paper is totally different than the research design and research method in the related literature for investigating how information technology-based reading and learning process of network distance teaching affects the assessment result in the past, that is, innovative research architecture and...
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| Vydané v: | The Journal of supercomputing Ročník 72; číslo 3; s. 789 - 803 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Springer US
01.03.2016
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | This paper is totally different than the research design and research method in the related literature for investigating how information technology-based reading and learning process of network distance teaching affects the assessment result in the past, that is, innovative research architecture and process is adopted. Here, quantile regression analysis is applied to the investigation of how the time and frequency of log-in curriculum, browsing teaching material, and curriculum discussion in learning process record affects the final-term assessment result of multimedia design digital teaching material subject. In depth research is done under such research architecture, and it is hoped that how each independent variable affects the final-term assessment result under different quantile can be investigated. In addition, this paper has applied new artificial neural network technology to set up expert system for teacher’s assessment result in distance teaching so as to reduce teacher’s teaching pressure, moreover, the result can be used as reference by general researchers and scientific education researchers. The research result shows that the use of quantile regression to analyze the influence of different variable on the teacher’s final-term assessment result of distance teaching is a feasible way; FOAGRNN model, as compared to other five models, has better forecasting capability. |
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| AbstractList | This paper is totally different than the research design and research method in the related literature for investigating how information technology-based reading and learning process of network distance teaching affects the assessment result in the past, that is, innovative research architecture and process is adopted. Here, quantile regression analysis is applied to the investigation of how the time and frequency of log-in curriculum, browsing teaching material, and curriculum discussion in learning process record affects the final-term assessment result of multimedia design digital teaching material subject. In depth research is done under such research architecture, and it is hoped that how each independent variable affects the final-term assessment result under different quantile can be investigated. In addition, this paper has applied new artificial neural network technology to set up expert system for teacher’s assessment result in distance teaching so as to reduce teacher’s teaching pressure, moreover, the result can be used as reference by general researchers and scientific education researchers. The research result shows that the use of quantile regression to analyze the influence of different variable on the teacher’s final-term assessment result of distance teaching is a feasible way; FOAGRNN model, as compared to other five models, has better forecasting capability. |
| Author | Pan, Wen-Tsao Chiu, Chiung-Lin Huang, Chiung-En |
| Author_xml | – sequence: 1 givenname: Wen-Tsao surname: Pan fullname: Pan, Wen-Tsao organization: School of Business, Guangdong University of Foreign Studies, Department of Business Administration, Hwa Hsia University of Technology – sequence: 2 givenname: Chiung-En surname: Huang fullname: Huang, Chiung-En email: joanne.hcn@gmail.com organization: Department of Industrial Management and Enterprise Information, Aletheia University – sequence: 3 givenname: Chiung-Lin surname: Chiu fullname: Chiu, Chiung-Lin organization: Department of Business Administration, Hwa Hsia University of Technology |
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| Keywords | Quantile regression analysis Artificial neural network Distance teaching Fruit fly optimization algorithm Expert system |
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| Title | Study on the performance evaluation of online teaching using the quantile regression analysis and artificial neural network |
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