Formulation of physical education and training program based on multidimensional education data mining

Qiang, Sun

doi:10.1007/s10586-018-2470-y

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Big data technology can realize data mining and algorithm analysis of college students' physical and psychological health. College administrators can grasp students' social status and learning status at any time, predict their psychological status, and pay close attention to high-risk students according to the results of algorithm analysis, and timely communicate with them [ 6 ]. Multidimensional data mining can analyze the number and time of college students entering and leaving the dormitory and the library according to the campus face recognition system.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

Zou

2022

Occupational Therapy International

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In order to explore the physical exercise and psychological changes of college students, this study adopts the method of multidimensional data mining, taking 23,146 undergraduates from a university in Guangzhou, Guangdong Province, as the research object. On the basis of summarizing and analyzing the previous research literature, this study expounds the development background, current situation, and future challenges of multidimensional data mining technology. This paper introduces the methods and principles of sample selection and multidimensional assessment of the physical and mental depression of college students, analyzes the physical health status of college students, summarizes the psychological changes of college students before and after the intervention, and discusses the relationship between physical exercise and mental health of college students. In addition, this paper analyzes the influencing factors and psychological changes of college students’ physical exercise and puts forward suggestions for improving the physical and mental health of college students. The results of this study show that with increasing age, college students have a lower risk of moderate anxiety and mild depression; girls are more prone to mild depression than boys; rural college students are more prone to mild, moderate anxiety; college students with nonmedical backgrounds were more likely to experience moderate anxiety than college students with medical backgrounds. During the intervention control period, continuous connection with others may encourage college students to actively use internal resources to actively cope with obstacles and setbacks, and as a protective factor, psychological resilience can appropriately weaken the association between risk factors in life and anxiety and relieve anxiety. The results of this study can provide a reference for further research on the physical exercise and psychological changes of college students.

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Section: Introductionmentioning

confidence: 99%

Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

Zou

2022

Occupational Therapy International

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“…To solve the problem of human motion recognition, in [13], the authors proposed a recognition system for human motion tracking in physical training. In [14], the authors presented a balanced shunt point field programmable gate array-based method of physical training planning to improve the effectiveness of physical training planning. In [15], the authors proposed a design method of computer multimedia simulation action training model based on fast detection and control of attitude change space, and the computer multimedia simulation method was used to construct human action training and mathematical model of human action.…”

Section: Related Researchmentioning

confidence: 99%

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Wang

2022

Mobile Information Systems

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The rapid development of Olympic Games and the intensification of professional sports events make the competition between excellent athletes and teams become more and more fierce, so the demands on athletes’ psychology, techniques, and physical strength are becoming higher and higher, especially that the demands on physical strength are more prominent. Therefore, physical training is an important part of sports training and the core link of competitive sports, which is widely valued by coaches and athletes all over the world. At the same time, with the influence and penetration of the rapid development of modern information technology and network big data on competitive sports, the scientific and digital process of athletes’ physical training has also been accelerated, and many new ideas, scientific training methods, and advanced sports technology have emerged. During the pandemic, many aggregation activities have been disrupted, as well as physical training. Therefore, this paper improves the quality of physical training through video, so that athletes can do physical training at any time and any place. A cut vertices spanning tree algorithm based on machine learning is proposed to distribute layered multicast streaming and dynamically adjust the number of layers of sports video streaming. The cut vertices spanning tree algorithm is mainly applied to the situation when the network bandwidth resources are relatively scarce. The evaluation results indicate that the proposed method can improve the estimated quality of experience (QoE), packet loss rate, link utilization, and video delay on Mininet simulation platform. Furthermore, it can be seen from the experiments that the proposed method has a good performance on distribution of sports video stream assisting physical training.

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“…The author in this research has ignored the classification method on the optimal selected features based on important extracted rules. In the same work, Qiang ( 2019 ) proposed a prediction method to enhance the correctness and increase the performance of the physical education based on stable split argument and field programmable gate arrays (FPGA). There is no evaluation on selected features based on the FPGA method using C4.5 classifier.…”

Section: Related Workmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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Formulation of physical education and training program based on multidimensional education data mining

Cited by 4 publications

References 11 publications

Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

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