Analysis of Improving Effect of Running APP on College Students’ Physique by Using Student Data Mining Technology

Abstract

With the continuous development of sports software, college students, as the most advanced social new ideological group, running APP gradually enter the study and life of college students. Based on students’ DM (data mining), this paper analyzes the influence of running apps on the improvement of college students’ physique, chooses DT (decision tree) algorithm to construct the structure according to the characteristics of the data used, obtains students’ DM model, and prunes it by using substitution error rate and PEP (pessimistic error pruning). The results show that the results of intra-group comparison among boys show that the scores of 1000 m in the intervention group have no obvious change compared with those before the intervention, and the difference is not statistically significant ( P = 0.516 ). The 800 m scores of girls in the intervention group were better than those in the control group, and the difference was statistically significant ( P = 0.03 ). The results of intra-group comparison showed that there was no significant difference in the scores of the intervention group before and after the intervention, and the difference was not statistically significant ( P = 0.32 ). After the experiment, the vital capacity scores of boys and girls in APP intervention group and control group were improved, with statistical significance ( P < 0.05 ). The conclusion shows that running APP can improve students’ speed level, cultivate students’ endurance level, and improve students’ physical health.

Retracted: Analysis of Improving Effect of Running APP on College Students’ Physique by Using Student Data Mining Technology

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”