Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers
This paper studies the application of neural network algorithm based on sensor network in rural teacher performance evaluation simulation. Sensor networks are an emerging technology that can collect and transmit environmental data in real time, providing a new research method for the field of educat...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917424000254 |
| _version_ | 1797258662262079488 |
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| author | Zhang Ziyi |
| author_facet | Zhang Ziyi |
| author_sort | Zhang Ziyi |
| collection | DOAJ |
| description | This paper studies the application of neural network algorithm based on sensor network in rural teacher performance evaluation simulation. Sensor networks are an emerging technology that can collect and transmit environmental data in real time, providing a new research method for the field of education. This study combines sensor network technology with neural network algorithm to propose a simulation method of performance evaluation based on teacher behavior data. By deploying sensor nodes to collect environmental data in the classroom, neural network algorithm is used to process and analyze the collected data, and the correlation model between teacher behavior characteristics and performance evaluation is established. In the process of simulation evaluation, the actual teacher behavior data is input into the model, and the performance evaluation results of different teachers are predicted through simulation. Through experimental verification, the method of this study can accurately evaluate the performance level of teachers, and provides a scientific evaluation method. The application of sensor network makes the data collection more convenient and comprehensive, and the application of neural network algorithm improves the accuracy and efficiency of the evaluation model. This neural network algorithm based on sensor network has important application prospects in the performance evaluation of rural teachers, which can provide scientific basis and decision support, and promote the improvement of teacher education quality. |
| first_indexed | 2024-03-08T03:35:17Z |
| format | Article |
| id | doaj.art-720955acd7b54b8786fd8d0949125de9 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-24T22:57:06Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-720955acd7b54b8786fd8d0949125de92024-03-18T04:34:37ZengElsevierMeasurement: Sensors2665-91742024-04-0132101049Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachersZhang Ziyi0Lianyungang Normal College, School of Preschool Education, Lianyungang, 222000, ChinaThis paper studies the application of neural network algorithm based on sensor network in rural teacher performance evaluation simulation. Sensor networks are an emerging technology that can collect and transmit environmental data in real time, providing a new research method for the field of education. This study combines sensor network technology with neural network algorithm to propose a simulation method of performance evaluation based on teacher behavior data. By deploying sensor nodes to collect environmental data in the classroom, neural network algorithm is used to process and analyze the collected data, and the correlation model between teacher behavior characteristics and performance evaluation is established. In the process of simulation evaluation, the actual teacher behavior data is input into the model, and the performance evaluation results of different teachers are predicted through simulation. Through experimental verification, the method of this study can accurately evaluate the performance level of teachers, and provides a scientific evaluation method. The application of sensor network makes the data collection more convenient and comprehensive, and the application of neural network algorithm improves the accuracy and efficiency of the evaluation model. This neural network algorithm based on sensor network has important application prospects in the performance evaluation of rural teachers, which can provide scientific basis and decision support, and promote the improvement of teacher education quality.http://www.sciencedirect.com/science/article/pii/S2665917424000254Sensor networkNeural network algorithmPerformance evaluationRural teachersSimulation application |
| spellingShingle | Zhang Ziyi Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers Measurement: Sensors Sensor network Neural network algorithm Performance evaluation Rural teachers Simulation application |
| title | Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| title_full | Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| title_fullStr | Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| title_full_unstemmed | Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| title_short | Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| title_sort | application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers |
| topic | Sensor network Neural network algorithm Performance evaluation Rural teachers Simulation application |
| url | http://www.sciencedirect.com/science/article/pii/S2665917424000254 |
| work_keys_str_mv | AT zhangziyi applicationofneuralnetworkalgorithmbasedonsensornetworksinperformanceevaluationsimulationofruralteachers |