计算机科学
机器学习
人工智能
信息融合
融合
传感器融合
算法
数据挖掘
哲学
语言学
作者
Jinshuai Qu,Aijiao Liu,Renfei Liu
标识
DOI:10.1109/icccbda51879.2021.9442584
摘要
In order to solve the problem of the lack of information in the evaluation of university students' learning behavior with single data, a multi-source data fusion evaluation of university students' learning behavior is proposed to explore the application value of data fusion technology in teaching. First use Spearman to calculate the correlation between different data sources, calculate the relationship weight and optimization weight of different data sources, and then comprehensively weight for fusion calculation. The fusion calculation of students' comprehensive learning behavior evaluation through comprehensive weighted fusion can not only balance the differences in learning behavior evaluation of students under different teaching methods, but also integrate the advantages and disadvantages of different learning methods. Experiments verify that the comprehensive weighted data fusion algorithm is effective, which can provide help for a more comprehensive analysis of the evaluation of college students' learning behavior and provide a basis for adapting to their own learning.
科研通智能强力驱动
Strongly Powered by AbleSci AI