Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

Improving Classification Accuracy of Decision Trees for Different Abstraction Levels of Data

2008 ◽  
pp. 1103-1115
Author(s):  
Mina Jeong ◽  
Doheon Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Error Rates ◽  
Fuzzy Relation ◽  
Classification Error ◽  
Abstraction Level ◽  
Decision Tree Classification ◽  
Level Problem ◽  
Abstraction Levels

Classification is an important problem in data mining. Given a database of records, each tagged with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. Since the data is collected from disparate sources in many actual data mining environments, it is common to have data values in different abstraction levels. This article introduces the multiple abstraction level problem in decision tree classification, and proposes a method to deal with it. The proposed method adopts the notion of fuzzy relation for solving the multiple abstraction level problem. The experimental results show that the proposed method reduces classification error rates significantly when multiple abstraction levels of data are involved.

scholarly journals Composition and Optimization of Higher Education Management System Based on Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingnan Hu ◽  
Hui Li
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Management System ◽  
Large Scale ◽  
Higher Education Management ◽  
Mining Technology ◽  
Unequal Distribution ◽  
Education Management ◽  
Management Platform ◽  
Education Management System

In order to improve the low efficiency of higher education management and the unequal distribution of curriculum resources, facing the actual situation of higher education management, a higher education management system based on data mining technology is constructed and optimized. Using the correlation between MVC components of higher education management platform and the support of data mining technology, this paper constructs the MVC model 2 framework of higher education management platform which is a powerful framework used for developing large-scale projects with ease. We collect the data structure in the database, extract the evaluation indicators of higher education management system, separate the display results and business logic, and improve code reusability. We adopt data input, curriculum management, curriculum scheduling, teacher scheduling, and system maintenance and help to form and optimize the higher education management system. The experimental results show that the constructed and optimized higher education management system can maintain good scheduling performance and provide better teaching services for teachers and students.

DATA MINING APPROACHES IN BUSINESS INTELLIGENCE: POSTGRADUATE DATA ANALYTIC

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Mohd Shahizan Othman ◽  
Shamini Raja Kumaran ◽  
Lizawati Mi Yusuf
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Support Vector ◽  
Svm Classifier ◽  
Higher Education Management ◽  
Education Management ◽  
Layer 4 ◽  
Using Data

Over recent years, there has been tremendous growth of interest in business intelligence (BI) for higher education. BI analysis solutions are operated to extract useful information from a multi-dimensional datasets. However, higher education-based business intelligence is complex to build, maintain and it faces the knowledge constraints. Therefore, data mining techniques provide an effective computational methods for higher education-based business intelligence. The main purpose of using data mining approaches in business intelligence is to provide decision making solution to higher education management. This paper presents the implementation of data mining approaches in business intelligence using a total of 13508 postgraduates (PG) data. These PG data are to allow the research to identify the postgraduates who Graduate On Time (GOT) via business intelligence process integrating data mining approaches. There are four layers will be discussed in this paper: data source layer (Layer 1), data integration layer (Layer 2), logic layer (Layer 3), and reporting layer (Layer 4). The main scope of this paper is to identify suitable data mining which is to allow decision making on GOT so as to an appropriate analysis to education management on GOT. The results show that Support Vector Machine (SVM) classifier is with better accuracy of 99%. Hence, the contribution of data mining in business intelligence allows an accurate decision making in higher education.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

EFFECTIVENESS OF MANAGEMENT IN THE HIGHER EDUCATION

2021 ◽  
Vol 4 (48) ◽  
pp. 5
Author(s):  
O. Vorobyova
Keyword(s):  
Higher Education ◽  
Empirical Research ◽  
Performance Indicators ◽  
Point Of View ◽  
Educational Services ◽  
Management Effectiveness ◽  
Higher Education Management ◽  
Education Management ◽  
Management Efficiency ◽  
Theoretical Approaches

The article presents the results of empirical research of the main theoretical approaches to the concept of efficiency of management services in the field of higher education. It is determined that the concept of efficiency management is a complex multifaceted indicator that includes certain performance indicators related to efficiency, profitability, effectiveness, etc., in economics and management there is no single general approach to determining the effectiveness of management. It is proved that the effectiveness of management is a complex indicator that determines the use of resource opportunities to achieve a certain goal, and these concepts are complementary and interrelated, this approach from the point of view of economics is integrated with all management functions.Ключові слова: higher education; management efficiency; management effectiveness; management efficiency in higher education; educational services.

scholarly journals Higher Education Management

Education ◽  
2019 ◽  
Author(s):  
Paul Temple
Keyword(s):  
Higher Education ◽  
United States ◽  
Academic Staff ◽  
The United States ◽  
Individual Autonomy ◽  
Usual Sense ◽  
Institutional Autonomy ◽  
Higher Education Management ◽  
Distinctive Character ◽  
Education Management

Universities are distinctive as organizational forms in having emerged in early medieval Europe and then spread around the world while remaining recognizably similar. The management of early universities appears to have been based on what has become known as the “collegial” model: shared decision making by the more senior academic staff, with rotating functional responsibilities fitted into normal teaching duties. In England, Oxford and Cambridge colleges (and to some extent the universities they constitute) continue to exhibit this pattern. Early universities were clearly able to take decisions that could be described as “strategic”: commitments to major building projects, for example. As universities began in the 19th century to expand in size and numbers in Europe, the United States, and elsewhere, more senior permanent managerial posts were created to support the underlying collegial arrangements: the University of London’s first paid administrator, the registrar, was appointed in 1838. But it was the expansion of higher education, particularly in western Europe and the United States in the second half of the 20th century, which created the modern profession of higher education management. In most countries in the 21st century, higher education is either state directed or steered at a distance by state agencies (even when actual ownership is private). Yet typically institutional autonomy is publicly prized, often by the same governmental agencies that seek to limit it. This tension, between governmental control (both finance and politics are involved) and institutional autonomy (which is associated with high academic achievements internationally), mean that the skills demanded of university managers are of a distinctive character, and have some broad similarities across countries and cultures. These include the ability to recognize that university academics do not generally see themselves as employees of an enterprise in the usual sense and demand (with varying degrees of success) substantial autonomy in how they carry out their duties. The tension between what academics may see as reasonable demands for individual autonomy and the requirements of operating an organization employing thousands of staff, along with tens of thousands of students, has become more acute in recent years. It should be noted that while the categories chosen for the listing of works in this article would be generally understood by most scholars of higher education, there are considerable conceptual overlaps, as the various category topics inevitably influence one another: strategy cannot be wholly divorced from finance or governance, and so on.

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