IMPROVING THE SELECTION PROCESS OF STUDENTS IN HIGHER EDUCATION BASED ON DATA WAREHOUSE AND DATA MINING TECHNIQUES

2016 ◽  
Author(s):  
Mohammed Qbadou ◽  
Morad Hajji ◽  
Abderrazzak Samadi ◽  
Khalifa Mansouri
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Data Warehouse ◽  
Selection Process ◽  
Data Mining Techniques

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.

scholarly journals DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

scholarly journals Data Mining Techniques and its Uses in different fields: A Review Paper

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Gaurav Dhawan
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Data Warehouse ◽  
Review Paper ◽  
High Standard ◽  
Raw Data ◽  
Data Mining Techniques ◽  
Useful Knowledge

Abstract: The paper introduced the Data Mining and issues related to it. Data mining is a technique by which we can extract useful knowledge from urge set of data. Data mining tasks used to perform various operations and used to solve various problems related to data mining. Data warehouse is the collection of different method and techniques used to extract useful information from raw data. Genetic Algorithm is based upon the Darwin’s Theory in which low standard chromosomes are removed from the population because of their inability to survive the process of selection. The high standard chromosomes survive and are mixed by recombination to form more appropriate individuals. In this urge amount of data is used to predict future result by following several steps.

Applying Fuzzy Data Mining to Tourism Area

2011 ◽  
pp. 563-584 ◽  
Author(s):  
R. A. Carrasco ◽  
F. Araque ◽  
A. Salguero ◽  
M. A. Vila
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Selection Process ◽  
Fuzzy Data ◽  
Competitive Sport ◽  
Location Selection ◽  
Number Of Factors ◽  
Complex Decision Making ◽  
Complex Decision

Soaring is a recreational activity and a competitive sport where individuals fly un-powered aircrafts known as gliders. The soaring location selection process depends on a number of factors, resulting in a complex decision-making task. In this chapter, we propose the use of an extension of the FSQL language for fuzzy queries as one of the techniques of data mining that can be used to solve the problem of offering a better place for soaring given the environmental conditions and customer characteristics. The FSQL language is an extension of the SQL language that permits us to write flexible conditions in our queries to a fuzzy or traditional database. After doing a process of clustering and characterization of a large customer database in a data warehouse, we are able of classify the next clients in a cluster and offer an answer according to it.

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

TAM and stages of adoption of blended learning in higher education by application of data mining techniques

2019 ◽  
Vol 50 (5) ◽  
pp. 2484-2500 ◽  
Author(s):  
Antonio Víctor Martín‐García ◽  
Fernando Martínez‐Abad ◽  
David Reyes‐González
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Blended Learning ◽  
Data Mining Techniques

scholarly journals THE ENHANCEMENT OF ODL STUDENT RECRUITING CAMPAIGN WITH DATA WAREHOUSE DEVELOPMENT AND DATA MINING TECHNIQUES

2010 ◽  
Vol 5 (1) ◽  
pp. 41-47
Author(s):  
Waranya Poonnawat ◽  
Sumruay Komlayut ◽  
Nuttaporn Henchareonlert
Keyword(s):  
Data Mining ◽  
Marital Status ◽  
Data Warehouse ◽  
Low Income ◽  
Historical Data ◽  
Demographic Data ◽  
Annual Reports ◽  
User Requirements ◽  
Data Mining Techniques ◽  
Student Data

The purpose of this research was to develop an OLAP cube data warehouse, and, using data mining techniques, to support the university's public relations, admissions, and planning divisions in the efficient recruiting of students by surveying, through interviews; the opinions of management and operational personnel, and through documents; the attributes in application forms and annual reports. User requirements, source data and systems were all examined. The data warehouse and front-end applications developed are described below. 1. Student Data Warehouse—this repository was designed to store students' historical data and to facilitate analysis and reporting following the user requirements. Students' historical data including demographic data from 2001-2005 were extracted, loaded and transformed from source systems, then they were cleaned before uploading to the data warehouse using star schema. 2. OLAP Cub—this 122 multidimensional structure enables users to analyze the students' demographic data in many dimensions such as “Number of Registered Students in each year by Semester, Major, School, Gender, Occupation, Region, etc.” Predefined reports were created and published to an intranet and users were able to create ad-hoc reports through web browsers as well as XLAddin. 3. Data Mining—this technique finds hidden knowledge and patterns in ODL student data supporting decision making, using three algorithms: Naïve Bayes, Clustering and Association Rules. Occupation of students is the strongest factor influencing students' choices of Schools. Students' demographic data can be clustered into groups with similar or dissimilar characteristics such as “Single, Unemployed, Low Income (<3,000 Baht)” or “Married, Male, Studying Law, High Income”, and can generate rules from frequently occurring cases such as “Occupation=Teacher-Lecturer (private sector), Marital Status=Single > School=School of Educational Studies” or “Occupation=Police, Marital Status=Single -> School=School of Law”. The results from the study indicated that users were satisfied using information and applications from the data warehouse, OLAP cube and data mining techniques which enable the university to reduce costs and to reach the desired enrolment target effectively.

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