Physical Health Data Mining of College Students Based on DRF Algorithm

Xue, Baohong; Liu, Ting

doi:10.1007/s11277-018-5410-5

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…Carey et al applied AR (association rule) to DM them, taking individual indexes of physical fitness test as input and overall physical fitness score as output, and found that the indexes that have great influence on college girls' physical fitness are speed, flexibility, and vital capacity [ 15 ]; Xue et al used an array-based Apriori algorithm to mine and analyze the physical fitness test items of college students, found out the correlation of each test item, and judged the rationality of each test item setting [ 16 ]. Liu et al used the Apriori algorithm of AR and set the thresholds of support, confidence, and promotion to screen out the strong AR of boys' and girls' data, respectively, and got the test index that “the total score is equal to passing” [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

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

Zhuo

2022

Computational Intelligence and Neuroscience

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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.

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Section: Related Workmentioning

confidence: 99%

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

Zhuo

2022

Computational Intelligence and Neuroscience

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“…Data mining is an automatic process of acquiring knowledge from within a system with the goal of discovering undiscovered knowledge in a large amount of data [ 10 ]. Online query and analysis can be used to process information that is clearly understood by decision-makers [ 11 ]. It is beneficial to motivate teachers, improve teaching quality, and strengthen faculty construction and scientific management by establishing a good teaching quality evaluation mechanism [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Teaching Quality Analysis and Management System for PE Courses Based on Data-Mining Algorithm

Luo

2022

Computational Intelligence and Neuroscience

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Advances in network technology have led to extensive information technology construction work in all walks of life; universities, as a key component of national development, cannot be overlooked in this regard. In today’s universities, the Web-based integrated academic management information system is widely used, promoting higher education management system innovation and improving the management level of education departments and teaching management. The traditional management mode is incapable of locating “knowledge” in the mountains of student transcripts, and the original management mode must be improved. In business, finance, insurance, marketing, and other fields, digital exploration technology is widely used. This article describes the design approach for a data mining-based analysis and management system for PE course teaching quality, as well as the application of information technology and data mining technology in PE by combining actual PE teaching in schools, with the goal of realizing a data mining-based PE performance management system to serve PE teaching in schools and improve PE teaching quality. The results show that the time required to find frequent itemsets using a traditional algorithm running on a single machine, as well as the time required to scan the database several times for frequent itemset search in a distributed cluster of 20 computing nodes, is significantly longer than that required by the data mining algorithm. As a result, the proposed sports performance management system is functional, simple, and scalable, with each functional module operating independently and cooperatively, reflecting the concept of “high cohesion and low coupling.”

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“…unknown attribute values predicted or confirmed by some known data base, while findings of description function can be used to describe data and understand patterns [14]. Figure 4 shows the main functions of numerology mining.…”

Section: Data Mining Features Data Mining Generally Has Two Functions...mentioning

confidence: 99%

Quality Evaluation of Student Education Management Work Based on Wireless Network Data Mining

Chen

2022

Mathematical Problems in Engineering

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Higher education has a major role to play in social progress, technology, and economic development. However, as the number of students enrolled in higher education increases, the quality of graduates is declining and their ability to work is being questioned. Therefore, it is very important to effectively evaluate the quality of teaching and learning. With this in mind, an evaluation approach using data mining based on wireless networks is proposed in this paper. In the educational work of universities, data mining helps to assist universities to improve management efficiency and teaching effectiveness in managing students by mining and analyzing large-scale educational data. In order to improve their teaching management, this paper uses the Apriori method among correlation rule mining methods. The algorithm is improved in terms of time efficiency, and then the improved algorithm is used to mine and analyze the impact of teachers’ age, title, educational background, and student performance on teaching quality evaluation. The results of the exploration showed that, through the mining algorithm, it was found that the student’s evaluation was proportional to the student’s performance. For example, the average grade was excellent, and most evaluation grades were excellent. Its support level was 26% and its confidence level was 62.5%, indicating that more than half of the students met its characteristics. It also showed that the results were real, and the quality of teachers’ teaching could be known through the results of students.

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Physical Health Data Mining of College Students Based on DRF Algorithm

Cited by 15 publications

References 8 publications

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

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

Design of Teaching Quality Analysis and Management System for PE Courses Based on Data-Mining Algorithm

Quality Evaluation of Student Education Management Work Based on Wireless Network Data Mining

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