Big Data Analysis Methods for Higher Education Physical Education Teaching Interactive Platform and Its Teaching Effectiveness

Abstract

The use of big data comprehensively serves physical education, so that students can clarify their learning situation and make reasonable changes in physical education teaching. In this paper, firstly, we do an analysis and investigation on the application of big data in physical education teaching in colleges and universities, apply the tree hierarchy algorithm to collect the key data indexes of physical education teaching classrooms, and use the SMDA aggregation algorithm to aggregate and analyze the collected key data. The weight of each index system of teaching evaluation is calculated using the hierarchical analysis evaluation method. The role and application of analytical methods in physical education teaching are discussed in the context of big data. This paper constructs a system for evaluating teaching effects, which includes 5 primary indicators and 20 secondary indicators. The evaluation results of the 10-factor index system in the teaching process are divided into five grades to obtain the distribution intervals of excellent, good, medium, qualified, and poor students’ learning performance. The results of data analysis of the attributes of this paper show that the percentages of Cluster l, Cluster 2, and Cluster 3 are 22%, 66%, and 12% respectively. The weights of teaching ability, method, content, attitude, and effect in the actual teaching process were 20.97, 24.29, 24.08, 3.89, and 6.31, respectively. This paper elaborates on the effects and roles of four aspects of the interactive platform Big Data application in physical education teaching and learning in the context of Big Data analytics methods. Ideas for the use of big data analysis methods in sports teaching in colleges and universities are presented.

Keywords

• smda aggregation algorithm
• hierarchical analysis
• aggregation hierarchy algorithm
• physical education teaching
• 62-07