Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment

Abstract

Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.