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Classification Models for Determining Types of Academic Risk and Predicting Dropout in University Students

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第1期11卷 266-272页

作者： Bedregal-Alpaca, Norka Cornejo-Aparicio, Victor Zarate-Valderrama, Joshua Yanque-Churo, Pedro Univ Nacl San Agustin Arequipa Arequipa Peru

Academic performance is a topic studied not only to identify those students who could drop out of their studies, but also to classify them according to the type of academic risk they could find themselves. An application has been implemented that uses academic information provided by the university and generates classification models from three different algorithms: artificial neural networks, id3 and C4.5. The models created use a set of variables and criteria for their construction and can be used to classify student desertion and more specifically to predict their type of academic risk. The performance of these models was compared to define the one that provided the best results and that will serve to make the classification of students. Decision tree algorithms, C4.5 and id3, presented better measurements with respect to the artificial neural network. The tree generated using the C4.5 algorithm presented the best performance metrics with correctness, accuracy, and sensitivity equal to 0.83, 0.87, and 0.90 respectively. As a result of the classification to determine student desertion it was concluded, according to the model generated using the C4.5 algorithm, that the ratio of credits approved by a student to the credits that he should have taken is the variable more significant. The classification, depending on the type of academic risk, generated a tree model indicating that the number of abandoned subjects is the most significant variable. The admission scan modality through which the student entered the university did not turn out to be significant, as it does not appear in the generated decision tree.

关键词： Educational data mining id3 algorithm C4.5 algorithm artificial neural network classification algorithms student desertion academic risk

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Combined application of ideological and political education and big data Internet technology in the context of education reform

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Wang, Xinhua Zhejiang Univ Sci & Technol Hangzhou 310023 Peoples R China

In the course of today's socio-political and economic development, along with the advent of the era of big data and the internet, various ideas have emerged and more ideological content and political ideas have continued to emerge. In order to cope with this situation, education reform is constantly innovating, among which the ideological education is particularly important. Various ideological education applications have appeared one after another in the Internet, which allows ideological education to see a new direction of development. The article develops a rigorous computational analysis of the combination of Civic Education and Big Data Internet, applied to the development of a new system. Using decision tree classification algorithm, id3 algorithm, Bayesian classification algorithm, cohesive hierarchical clustering method for a large number of experimental data processing, the system was completed to build a framework for the combination of big data and Civic Education application. Through data analysis and comparison, it is found that the application of these big data algorithms in the field of Civic Education not only provides more detailed information on Civic Education reform, but also provides objective and comprehensive feedback on the dissemination and learning of Civic Education. In the relevant feedback information, the use of Internet applications for Civic Education reached 100%, Excel was 84%, and all the learning levels of the fixed-point test could reach over 60 points. The above data shows that people have also been able to improve their learning level in Civic Education to a great extent with the Internet technology.

关键词： Big data ideological and political education id3 algorithm Internet Feedback information

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Constructing Decision Tree By Integrating Multiple Information Metrics

Constructing Decision Tree By Integrating Multiple Informati...

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Chinese Conference on Pattern Recognition/1st CJK Joint Workshop on Pattern Recognition

作者： Chen, Guang-Hua Wang, Zheng-Qun Yu, Zhen-Zhou Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China

ISBN: (纸本)9781424441990

In this paper, a new decision tree construction algorithm(MidT) is proposed. MidT(Multiple Informative Decision Tree) uses principal component analysis to integrate information gain, samples distribution information and correlation coefficient as the basis of the selection of splitting attributes. This method can overcome the disadvantage of id3 decision tree construction method that uses information gain as the splitting attributes selection criteria as a result of its tendency to select the attribute with more values. And moreover, it can exert the complementarity between decision of entropy mean and decision of samples distribution. The results of experiments on the standard data sets provided by UCI show that the decision tree constructed by MidT has higher classification accuracy and is more stable than id3 and parametric estimation decision tree algorithm.

关键词： decision tree id3 algorithm parametric estimation correlation PCA

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Applied Research of Decision Tree Method on Football Training

Applied Research of Decision Tree Method on Football Trainin...

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The 2015 International Conference on Energy,Materials and Manufacturing Engineering(EMMS 2015)

作者： Jinhui Liu College of Physical Education Langfang Teachers University

ISBN: (纸本)9781510814585;1510814582

This paper will make an analysis of decision tree at first,and then offer a further analysis of CLS based on *** CLS contains the most substantial and most primitive decision-making idea,it can provide the basis of decision tree *** to certain limitation in details,the id3 decision tree algorithm is introduced to offer more *** applies information gain as attribute selection metrics to provide reference for seeking the optimal segmentation *** last,the id3 algorithm is applied in football *** is made on this algorithm and it has been proved effectively and reasonably.

关键词： decision tree football training id3 algorithm

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A Method for Clustering China's 3G Value-added Service Users

A Method for Clustering China's 3G Value-added Service Users

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2010 2nd International Conference on Multimedia and Computational Intelligence (ICMCI 2010)

作者： Yating Zhang School of Information Central University of Finance and Economics Beijing, China Shuhang Guo School of Information Central University of Finance and Economics Beijing, China Mo Hai School of Information Central University of Finance and Economics Beijing, China

With the arrival of 3G era, mobile value-added services are developing rapidly. To improve the core competitiveness of operators in the telecommunication industry, on the basis of China's telecommunication market, a method based on id3 algorithm and clustering analysis is proposed to screen 3G value-added service users. Firstly, from the perspective of operators and concerning about the features of 3G value- added services, a set of index system for screening 3G value- added service users is established. Secondly, id3 decision model is established, which is used to screen the users. Thirdly, to clearly determine the user clusters, a clustering analysis is used to further divide the screening results. Finally, examples show that our proposed method for screening users based on id3 algorithm and clustering analysis can effectively prospect latent users, and thus help operator make reasonable decisions.

关键词： id3 algorithm Clustering Analysis China’s Telecommunication market 3G Value-added Service Screening users

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Analysis of examination questions and scores by using decision tree algorithm

Analysis of examination questions and scores by using decisi...

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2013 International Conference on Information and Communication Technology for Education (ICTE 2013)

作者： Wei Bai ZhiGang Zhang School of Mathematics and Physics USTB

In the student achievement management of colleges and universities, a large number of student achievement data are produced annually, which lead to the traditional analytical methods not applicable for in-depth analysis needs. This paper classifies student achievement of a certain probability theory and mathematical statistics course final exam on the students in USTB using the id3 algorithm and analyses the results after the classification. In the end, the factors affecting student achievement were concluded.

关键词： data mining decision tree id3 algorithm achievement analysis

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Grade Evaluation Model Based on Fuzzy Decision Tree

Grade Evaluation Model Based on Fuzzy Decision Tree

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2018 4th International Conference on Systems,Computing,and Big Data(ICSCBD 2018)

作者： Xinyue Zhang Tinghuan Wang Statistic Institute Shanxi University of Finance and Economics

Aiming at the problem that the general decision tree classification method can't deal with the data ambiguity and uncertainty well, this paper proposes a performance evaluation model based on fuzzy decision tree to realize the student's academic level prediction based on daily behavior. In this paper, the mathematical attribute and expert suggestion method are used to determine the model attribute index, the fuzzy membership function of the design is used to fuzzy the data, the fuzzy matrix is established, and the decision tree id3 algorithm is used to make decision analysis on the achievement information related to the campus behavior of college students. The fuzzy decision tree of this model can correctly and efficiently and comprehensively analyze and predict student achievement, and provide an important basis for the information construction and teaching management decision-making work of colleges and universities.

关键词： data mining fuzzy decision tree id3 algorithm performance evaluation

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A DECISION MODEL OF idENTIFYING USER TRAVEL PATTERN BASED ON THE INTELLIGENT TERMINAL SENSOR DATA

A DECISION MODEL OF IDENTIFYING USER TRAVEL PATTERN BASED ON...

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2016 4th IEEE International Conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Shuai Wang Jiapeng Xiu Chen Liu Zhengqiu Yang Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

The identification of user travel pattern has important research value for intelligent transportation. Intelligent terminal can supply GPS, acceleration sensors, pressure sensors data which can provide data base for the identification of user travel patterns. This paper research on a user travel pattern decision model based on the sensor data. The decision algorithm adopts decision tree algorithm based on id3. Experiments data shows that this decision model is effective in identifying the pattern of user's travel.

关键词： User travel Pattern Sensor Data Travel pattern identifying Decision tree id3 algorithm

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The Analysis of Cases Based on Decsion Tree

The Analysis of Cases Based on Decsion Tree

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Yurong Zhong Research Institute of Electronic Science and Technology University of Electronic Science Technology of China

Data mining is an data intelligent analysis technology in the late 20 th century,it can automatically extract or find useful model knowledge from large amounts of data in databases,data warehouses or other *** this process,the classification of data is an important research topic in the field of data *** there are different methods for classification,the classification algorithm of decision tree is clear,easy to understand and easy to convert into certain classfication rules,so this classification algorithm is widely studied and *** on the background of "data platform for public petition",it aims to study how data mining system combined with the existing database,extracting useful information from the mass characteristics hidden in the data,and provide comprehensive analysis for system managers and decision *** paper focus on the study of basic principle of data mining and basic *** classification of the cases,this module was developed based on decision tree *** on improved TD3 decision tree algorithm,according to the case information of the library and the client information of the other library,decision tree model can be built,to give certain case an assessment of the comprehensive *** paper presents a simplified algorithm of entropy right based on the id3 *** main idea of this algorithm is to combine the principle of Taylor formula with the attribute selection of the id3 algorithm-entropy solution firstly,to simplify the entropy solution of the id3 algorithm,to change the standard of attribute selection of the id3 algorithm,to reduce the calculation complex degree of the algorithm,and to improve the algorithm running efficiency;And then give simplified entropy of every attribute a right N,this N is depend to every number of the attribute’s value,to balance uncertainty of each attribute on the data set It can make the attribute selection become more reasonable,and avoid compatibilit

关键词： component Decision Tree id3 algorithm Information Gain Entropy

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Research on Application of Data Mining Technology in CRM

Research on Application of Data Mining Technology in CRM

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2012 IEEE 5th International Conference on Management Engineering & Technology of Statistics

作者： Hong Yan School of Management,University of Jinan,Jinan 250022,Shandong Province,China Ying Gao School of Management,University of Jinan,Jinan 250022,Shandong Province,China

Customer relationship management (CRM) has been the important part of enterprise decision and management and the Data Mining technology provides a good support for the implementation of *** paper analyses the main functions and application flow in CRM and mainly discusses the id3 algorithm of decision tree classification methods.A concrete id3 algorithm instance in CRM is also *** paper will provide powerful technical support for better commanding market tendency and mining potential customers.

关键词： Customer Relation Management Data Mining Decision Tree Analysis id3 algorithm

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