scholarly journals Data Mining-based Design and Implementation of College Physical Education Performance Management and Analysis System

2019 ◽  
Vol 14 (06) ◽  
pp. 87 ◽  
Author(s):  
Yimeng Fan ◽  
Yu Liu ◽  
Haosong Chen ◽  
Jianlong Ma
Keyword(s):  
Data Mining ◽  
Physical Education ◽  
Performance Management ◽  
Student Performance ◽  
Mining Technology ◽  
Development Platform ◽  
Sql Server 2005 ◽  
Analysis System ◽  
Education Performance ◽  
Architecture And Design

The purpose of this paper was to effectively apply data mining technology to sci-entifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted in this paper was to apply ASP.NET 3-layer architecture and design and implement college PE performance management and analysis system under the premise of fully analyzing the system requirements based on Visual Studio2008 software development platform and using SQL Server 2005 database platform. Based on data mining technology, students' PE performances were analyzed, and decision tree algorithm was used to make valuable judgments on student performance. The results indicated that applying computer technology to the management and analysis of college PE per-formance can effectively reduce the teaching and managing workload of PE teachers so that the teachers concentrate more on the quality of physical educa-tion.

Research on English score analysis system based on improved decision tree algorithm and fuzzy set

2020 ◽  
Vol 39 (4) ◽  
pp. 5673-5685
Author(s):  
Weiying Zhang
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Reference Value ◽  
Mining Technology ◽  
Factors Affecting ◽  
Main Work ◽  
Score Analysis ◽  
Analysis System

At present, the data mining technology is introduced into the analysis of English scores, the data is deeply explored and analyzed reasonably, and the analysis results are used to guide the smooth development of teaching, which is conducive to improving the quality of English teaching. The main work of this thesis is based on the background of this study: taking the academic performance of college students as the application background, this paper first introduces the basic theoretical knowledge of data mining and the application status of data mining technology in education field. Secondly, this paper establishes a student performance database and uses data mining technology to carry out in-depth mining of the established performance database. Finally, the mining results are analyzed, and the factors affecting students’ academic performance are obtained. These analysis results have important reference value for the future improvement of teaching work in colleges and universities.

scholarly journals Design of Early Warning Platform for College Students’ Achievement Based on Data Mining

2021 ◽  
Vol 2066 (1) ◽  
pp. 012078
Author(s):  
Yuanfu Mao ◽  
Zhifeng Yu
Keyword(s):  
College Students ◽  
Data Mining ◽  
Performance Management ◽  
Student Performance ◽  
Early Warning ◽  
Colleges And Universities ◽  
University Management ◽  
Accuracy Rate ◽  
Mining Technology ◽  
Class B

Abstract With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

scholarly journals Predicting Student Failure in University Examination using Machine Learning Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 956-959
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Performance Management ◽  
Student Performance ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Social Characteristics ◽  
Student Failure

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

The Study of Score Analysis for Secondary School Based on Data Mining Technology

2011 ◽  
Vol 219-220 ◽  
pp. 396-399
Author(s):  
Shang Fu Hao ◽  
Zhi Qiang Zhang ◽  
Ying Hui Wei
Keyword(s):  
Data Mining ◽  
Score Distribution ◽  
Mining Technology ◽  
Factors Affecting ◽  
Architecture Model ◽  
Scientific Teaching ◽  
Score Analysis ◽  
Secondary Teaching ◽  
Using Data ◽  
Analysis System

Nowadays, the contents associated with deep score analysis is rarely involved in the existing secondary teaching management software, which is not conductive to fully develop the information implied by these data,without scientific teaching evaluation. Using data mining technology, multiple aspects of student score distribution will be shown accurately, identifying the regular factors affecting score changes. Standard score as the mathematical model is adopted in the system, choosing the standard SOA architecture model, and a scientific and efficient score analysis system based on JAVA, JSP is developed. The system provides decision support information for academic departments to promote better teaching work, and finally improve the quality of teaching.

scholarly journals College Physical Education and Training in Big Data: A Big Data Mining and Analysis System

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiqin Wang
Keyword(s):  
College Students ◽  
Data Mining ◽  
Big Data ◽  
Physical Education ◽  
Analytical Data ◽  
Data System ◽  
Education Training ◽  
Analysis System ◽  
Development And Management ◽  
And Training

Recently, big data has been broadly used as a research method in all aspects of analysis, prediction, and evaluation. The application of big data to college students’ physical education plays a significant role in encouraging the completion of physical education at various levels. The application of the Internet and the advent of smartphones impact the way college students participate in physical exercise. At present, more and more students begin to participate in sports, and students’ demand for physical training is increasing. During physical education training, a lot of data is generated every moment because of various actions and behaviors. Due to technical limitations, these data were not effectively collected and applied. In this environment, the development and management of sports data mining systems have become more and more important. This paper designs an intelligent big data system for college physical education training. The study mainly focuses on data decentralization, lack of data talents, insufficient technical support, and low utilization of venues in physical education. While designing a big data system, the data is collected based on ease of data collection, and a response framework with excellent performance in storing analytical data is selected. The design and management of this system have a certain significance for the improvement and optimization of current college physical education training.

scholarly journals Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yong-tong Ma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Education ◽  
Information Fusion ◽  
Teaching Methods ◽  
Evaluation System ◽  
Evaluation Process ◽  
Teaching Evaluation ◽  
Evaluation Index ◽  
Mining Technology

The purpose is to enrich the evaluation system of physical education (PE) teaching in colleges and universities and to improve PE teaching methods and improve teaching quality. Based on big data information fusion and data mining technology, firstly, the related theories of teaching evaluation are analyzed and expounded, as well as the characteristics and principles of the construction of college PE teaching evaluation system. Secondly, from the perspective of evaluation index system of sports teachers’ teaching and students’ sports teaching, the content and evaluation index of college sports teaching evaluation are analyzed under the background of big data information fusion and data mining by questionnaire survey. Combined with model test, the results show that traditional college sports teacher pays more attention to the design and teaching methods of PE and ignore the learning process of students. The evaluation process of PE ignores the individual differences of students, the feedback method lacks openness, and the evaluation process is isolated. Based on the big data technology and teaching evaluation theory, the evaluation index is designed for PE teaching in colleges and universities. The average value of the first layer indexes is above 4, and the coefficient of variation is less than 0.2, which can basically reflect the content of PE teaching evaluation and provide some reference for the research of PE teaching evaluation.

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