Construction of Evaluation System Platform of University Work in Data Mining Technology

2021 ◽  
pp. 759-762
Author(s):  
Junfeng Huang
Keyword(s):  
Data Mining ◽  
Evaluation System ◽  
Mining Technology

Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Hongli Lou
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Process Evaluation ◽  
Evaluation System ◽  
Current Situation ◽  
The Internet ◽  
Mining Technology ◽  
College English ◽  
Procedural Evaluation ◽  
The Internet Of Things

This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

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.

The Application of Data Mining Technology in the Teaching Evaluation in Colleges and Universities

2017 ◽  
Vol 14 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Xiaoqi Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Evaluation System ◽  
Information Gain ◽  
Original Data ◽  
Teaching Evaluation ◽  
Personal Factors ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
College Work

As the teaching management informationization level is higher and higher, Network based teaching evaluation system has been widely used, and a lot of evaluation of the original data has been accumulated. This research, taking recent five years teaching evaluation data of the college work for as basis, analyzes teachers’ personal factors and teaching operation factors respectively with the data mining technology of decision tree ID3 algorithm. By calculating the factors of information entropy and information gain value, the corresponding decision tree is gained. The teaching evaluation results are made use of really rather than become a mere formality, and thus provide powerful basis for the effectiveness and scientificalness of teaching evaluation.

Research on Assessment Method for Credit Risk in Commercial Banks of China Based on Data Mining

2013 ◽  
Vol 303-306 ◽  
pp. 1361-1364
Author(s):  
Wen Juan Li ◽  
Shi Min Wang
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Commercial Banks ◽  
Evaluation System ◽  
Debt Crisis ◽  
Measurement Model ◽  
Risk Measurement ◽  
Risk Level ◽  
Mining Technology ◽  
Measurement Models

Due to the huge loss to the bank caused by credit risk in the financial debt crisis of the international Banking industry in 1980s, the research on Credit Assessment Methods is becoming the central issue of the study of financial theory in China and abroad. This paper builded the assets financial evaluation system of credit risk level based on the association rules-Apriori algorithm of data mining technology, which aimed the problems and the serious shortage of risk quantification study in domestic banks credit risk management. At the same time, taking into account the actual situation of our country, this paper analyzed that there are certain difficulties to use modern credit risk measurement models to evaluation the credit risk of commercial banks. And it suggests building a credit portfolio risk measurement model suitable for China's commercial banks with using logistic regression model of data mining technology.

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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