Function Design of College Student Management Information Platform Based on Data Mining Technology

2021 ◽  
Author(s):  
Yi Liu
Keyword(s):  
Data Mining ◽  
College Student ◽  
Management Information ◽  
Mining Technology ◽  
Information Platform ◽  
Student Management ◽  
Function Design

Application of Data Mining Technology in Higher Education

2014 ◽  
Vol 574 ◽  
pp. 743-747
Author(s):  
Xiao Hong Liu
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Rapid Development ◽  
Complete Information ◽  
Focus Of Attention ◽  
Technical Support ◽  
Mining Technology ◽  
Information Platform

With the rapid development of information technology, many universities have a relatively complete information platform, and a mass of data resources. Faced a lot of data, how the data is rational used and developed, to accomplish the transformation of knowledge that provide managers with basis for decision making, has become the focus of attention in universities. Data mining technology provide technical support for achieving this goal.

scholarly journals Application of Intelligent Sensor Algorithm in Student Management Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanan Li
Keyword(s):  
College Student ◽  
Information Fusion ◽  
Mixed Data ◽  
Management Information ◽  
Simulation Research ◽  
Intelligent Sensor ◽  
Multisensor Data Fusion ◽  
Student Management ◽  
Student Information ◽  
Multisensor Data

In order to improve the effectiveness of college student management and promote the integration of college student management information, this paper applies intelligent sensor algorithms to student management. Moreover, this paper combines uncertainty theory with multisensor data fusion technology to establish a complete set of multisensor data processing tools for student information and provides a complete mathematical theoretical framework for the principles of student management information fusion. In addition, in view of the problem of comparing a large number of mixed data of information sources, it is necessary to transfer the information fragments obtained by each sensor to a common set so that the information fragments expressed in different sets can be integrated. Finally, this paper constructs an intelligent student management model and conducts research in combination with simulation experiments. Through simulation research, it can be known that the method proposed in this paper can effectively improve the effect of student management.

Design of Counsellors’ Journal Management System in CRP Digital Campus Information Platform

2014 ◽  
Vol 701-702 ◽  
pp. 1389-1392
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
Management System ◽  
Work Flow ◽  
Work Situation ◽  
Digital Campus ◽  
Information Platform ◽  
Student Management ◽  
Management Function ◽  
Function Design ◽  
Content Design ◽  
Management Work

Counsellors’ journal is an important record for everyday’s counsellors’ work situation, it plays an important role in the daily students management work, design of counsellors’ journal management system in CRP digital campus information platform (CRP) is divided into three parts: electronic form content design, the management function design and interconnection & interworking work flow design between counsellors' journal management system and other systems. Students work data collected by the counsellors' journal management system has important guiding significance to the students monitoring and management, student management work could be promoted from the analysis and use of collected data of counsellors’ journal.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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