Relevance Index for Inferred Knowledge in Higher Education Domain Using Data Mining

2017 ◽  
pp. 279-287
Author(s):  
Preeti Gupta ◽  
Deepti Mehrotra ◽  
Tarun Kumar Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Using Data ◽  
Relevance Index

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.

A Case Study on Student Attrition Prediction in Higher Education Using Data Mining Techniques

2019 ◽  
pp. 181-192
Author(s):  
Syaidatus Syahira Ahmad Tarmizi ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul Hamid ◽  
Shuzlina Abdul-Rahman ◽  
Ariff Md Ab Malik
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Attrition ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Implementation of C4.5 Algorithm and Forward Chaining Method for Higher Education Performance Analysis

2019 ◽  
Vol 125 ◽  
pp. 21002
Author(s):  
Mochamad Idris ◽  
Mustafid ◽  
Jatmiko Endro Suseno
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Individual Factors ◽  
Economic Growth And Development ◽  
Data Mining Techniques ◽  
Forward Chaining ◽  
C4.5 Algorithm ◽  
Using Data ◽  
Education Performance ◽  
Analyze Data

Higher education has an important role to develop human resources in the economic growth and development of the country. One of specific way of evaluating and analyze data in education is to use data mining techniques. C4.5 algorithm as one of the data mining techniques that have good performance is very relevant used for data analysis tools. In this research using data on the performance of lecturers in college, there are 100 records with a 6 variable that affects individual factors in the productivity of lecturers including age, employment, attendance, certification, position, Education, and additional duties. In the end of the mining result, the forward chaining method is used to extract the rules that are generated by C4.5 algorithm. The input premises are examined by forwarding chaining to generate the prediction result.

A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques

2021 ◽  
pp. 48-67
Author(s):  
Madhavi Arun Vaidya ◽  
Meghana Sanjeeva
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Large Volume ◽  
Research Data ◽  
Research Data Management ◽  
Visualization Tools ◽  
Using Data ◽  
Analytical Frameworks

Research, which is an integral part of higher education, is undergoing a metamorphosis. Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. This “data deluge” creates a need to develop policies, infrastructures, and services in organisations, with the objective of assisting researchers in creating, collecting, manipulating, analysing, transporting, storing, and preserving datasets. Research is now conducted in the digital realm, with researchers generating and exchanging data among themselves. Research data management in context with library data could also be treated as big data without doubt due its properties of large volume, high velocity, and obvious variety. To sum up, it can be said that big datasets need to be more useful, visible, and accessible. With new and powerful analytics of big data, such as information visualization tools, researchers can look at data in new ways and mine it for information they intend to have.

EMPIRICAL ANALYSIS OF KNOWLEDGE DEVELOPMENT IN HIGHER EDUCATION USING DATA MINING METHODOLOGIES

2017 ◽  
Vol 73 (8) ◽  
Author(s):  
V. V. Jaya Rama Krishnaiah ◽  
Penubothu Ajith ◽  
Kurra Rajasekhara Rao
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Empirical Analysis ◽  
Knowledge Development ◽  
Using Data ◽  
Analysis Of Knowledge
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