<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The rapid development of big data technology brings opportunities and challenges to traditional teaching methods, and online teaching mode is gradually carried out widely and deeply in colleges and universities. This paper proposes a framework for constructing students’ online learning evaluation process by using big data methods. The focus is on data analysis of the evaluation model of water resources engineering in colleges and universities through the BP algorithm. Under the accuracy index, the BP algorithm 93% performs better than the other three algorithms. The higher the accuracy rate, the more it reflects the feasibility and correctness of the model. In the recall index, the recall rate of the BP algorithm fluctuates from 80% to 93%, the fluctuation range of 13% is more stable, and the overall performance is better than the other three algorithms. The objective evaluation of learning obtained through big data will greatly reduce teachers’ teaching burden and produce more hydraulic engineering professionals for China.</p>
</abstract>

The rapid development of big data technology brings opportunities and challenges to traditional teaching methods, and online teaching mode is gradually carried out widely and deeply in colleges and universities. This paper proposes a framework for constructing students’ online learning evaluation process by using big data methods. The focus is on data analysis of the evaluation model of water resources engineering in colleges and universities through the BP algorithm. Under the accuracy index, the BP algorithm 93% performs better than the other three algorithms. The higher the accuracy rate, the more it reflects the feasibility and correctness of the model. In the recall index, the recall rate of the BP algorithm fluctuates from 80% to 93%, the fluctuation range of 13% is more stable, and the overall performance is better than the other three algorithms. The objective evaluation of learning obtained through big data will greatly reduce teachers’ teaching burden and produce more hydraulic engineering professionals for China.

Research on the blended teaching path of water conservancy engineering in universities based on big data analysis

School of Hydraulic and Environmental Engineering

Zhejiang University of Water Resources and Electric Power

Changsha University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The rapid development of big data technology brings opportunities and challenges to traditional teaching methods, and online teaching mode is gradually carried...

Research on the blended teaching path of water conservancy engineering in universities based on big data analysis

Online veröffentlicht: 07. Aug. 2023

Seitenbereich: -

Eingereicht: 23. Sept. 2022

Akzeptiert: 15. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00136

Schlüsselwörter
Big data, BP algorithm, Evaluation model, Hydraulic engineering, Accuracy rate

© 2023 Huawei Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the blended teaching path of water conservancy engineering in universities based on big data analysis

Online veröffentlicht: 07. Aug. 2023

Seitenbereich: -

Eingereicht: 23. Sept. 2022

Akzeptiert: 15. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00136

SchlüsselwörterBig data, BP algorithm, Evaluation model, Hydraulic engineering, Accuracy rate

© 2023 Huawei Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data, BP algorithm, Evaluation model, Hydraulic engineering, Accuracy rate