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.

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

2014 ◽  
Vol 623 ◽  
pp. 229-233 ◽  
Author(s):  
De Jiang Qi ◽  
Hai Yan Hu
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Warning System ◽  
Colleges And Universities ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
Financial Management System ◽  
Analysis System

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

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.

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

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 Predicting Student Performance by Using Data Mining Methods for Classification

2013 ◽  
Vol 13 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Dorina Kabakchieva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Student Performance ◽  
High Potential ◽  
University Management ◽  
Mining Methods ◽  
Predicting Student Performance ◽  
Using Data ◽  
Initial Results ◽  
Support Decision Making

Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

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