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

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.

Keywords

• big data
• bp algorithm
• evaluation model
• hydraulic engineering
• accuracy rate
• 62-07