scholarly journals A Study of the Applications of Data Mining Techniques in Higher Education

2015 ◽  
pp. 1-4
Author(s):  
SUSHIL VERMA ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Large Data ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System ◽  
Student’S Performance ◽  
Quality In Higher Education

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set. Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions and student’s performance. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of Unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance. The paper aims to purpose the use of Data mining techniques to improve the efficiency of higher educational institutions. If data mining techniques such as clustering, dicision tree and association can be applied to higher education processes, it can help improve student’s performance.

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 IN EDUCATION FOR STUDENTS ACADEMIC PERFORMANCE: A SYSTEMATIC REVIEW

2014 ◽  
Vol 13 (9) ◽  
pp. 5020-5028
Author(s):  
Anurag Jindal ◽  
Er. Williamjeet Singh
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Educational Systems ◽  
Good Prediction ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. The main objective of higher education institutes is to provide quality education to its students. Indian education sector has a lot of data that can produce valuable information which can be used to increase the quality of education. Good prediction of student’s success in higher learning institution is one way to reach the higher level of quality in higher education system. In this paper we analyzed the potential use of data mining in education section and survey the most relevant work in this area. Data Mining can be used for dropout students, student’s academic performance, teacher’s performance and student’s complaints. As we know large amount of data is stored in educational database, so in order to get required data and to find the hidden relationship, different data mining techniques are developed & used. Various algorithms and data mining techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees (CART and CHIAD), Genetic algorithms, Nearest Neighbor method etc. are used for knowledge discovery from databases and helps in prediction of students academic performance. In future work we can apply different data mining techniques on an expanded data set with more distinct attributes to get more accurate results.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 94-110
Author(s):  
Deepti Aggarwal ◽  
Sonu Mittal ◽  
Vikram Bali
Keyword(s):  
Data Mining ◽  
Uttar Pradesh ◽  
Drop Out ◽  
Primary Data ◽  
Educational Institutions ◽  
Technical Institute ◽  
Data Set ◽  
Data Mining Techniques ◽  
Preventive Actions ◽  
Using Data

The educational institutes are focusing on improving the performance of students by using several data mining techniques. Since there is an increase in the number of drop out students every year, if we are able to predict whether a student will complete the course or not, it is possible to take some preventive actions beforehand. The primary data set used for modelling has been taken from a reputed technical institute of Uttar Pradesh which consists of data of 6,807 students containing 20 academic and non-academic attributes. The most relevant attributes are extracted using CorrelationAttributeEval (in WEKA) technique using Ranker search method which ranks the attributes as per their evaluation. Synthetic minority oversampling technique (SMOTE) filter is applied to deal with the skewed data set. The models are built from eight classifiers that are analysed for predicting the most appropriate model to classify whether a student will complete the course or withdraw his/her admission.

scholarly journals Educational Data Mining To Improve The Academic Performance in Higher Education

2020 ◽  
Vol 4 (2) ◽  
pp. 13-18
Author(s):  
Alyaa A. Mahdi
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Educational Data Mining ◽  
Educational Institutions ◽  
Learning And Teaching ◽  
Global Innovation ◽  
Daily Data ◽  
E Learning ◽  
Innovative Techniques

Globalization and Innovation are mainly consider the great interest public sector and private business in the world especially in the higher education institutions. Educational Data Mining is mainly one of the business processes nowadays that attempt to bring the global innovation through improving and enhancing their processes and procedures to fulfill all the requirements and needs of the students as well as the institutions. The Educational Data Mining considered mostly concern with any research concerning the applications of the data mining and developing innovative techniques for data mining (DM) in the educational sector. This study mainly combined the use of the powerful online E-learning management system (Moodle) with data mining tools to improve the performance and effectiveness of the learning and teaching manners by using the innovative daily data that collected from the educational institutions.

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.

Educational Entrepreneurs in Tamil Nadu - A Profile

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Siluvai Raja
Keyword(s):  
Higher Education ◽  
United States ◽  
World Bank ◽  
Education System ◽  
Tamil Nadu ◽  
The United States ◽  
Educational Institutions ◽  
Higher Education System ◽  
The Third ◽  
The World

Education has been considered as an indispensable asset of every individual, community and nation today. Indias higher education system is the third largest in the world, after China and the United States (World Bank). Tamil Nadu occupies the first place in terms of possession of higher educational institutions in the private sector in the country with over 46 percent(27) universities, 94 percent(464) professional colleges and 65 percent(383) arts and science colleges(2011). Studies to understand the profile of the entrepreneurs providing higher education either in India or Tamil Nadu were hardly available. This paper attempts to map the demographic profile of the entrepreneurs providing higher education in Arts and Science colleges in Tamil Nadu through an empirical analysis, carried out among 25 entrepreneurs spread across the state. This paper presents a summary of major inferences of the analysis.

Analysis of educational migration processes: regional aspect

2020 ◽  
pp. 65-71
Author(s):  
Mariya V. Bachynska ◽  
Lyubov K. Semiv ◽  
Serhiy R. Semiv
Keyword(s):  
Higher Education ◽  
National Policy ◽  
Migration Policy ◽  
Social Progress ◽  
Educational Institutions ◽  
Educational Systems ◽  
Interstate Migration ◽  
Higher Education System ◽  
Migration System ◽  
Educational Migration

Analysis and consideration of current migration trends in our country, in particular mass interstate migration movements in the context of Ukraine's participation in the European and world migration space, are considered among the important factors in shaping its national policy. Educational migration is a special threat to the socio-economic development of the country and sustainable development of society, as the formation of highly qualified personnel, accumulation of intellectual capital, and increasing scientific and technological potential of the country are among the important factors of economic and social progress. Due to the intensification of interstate migration flows and the development of the quality of the higher education system in foreign educational institutions, migration for education takes a larger share, which is mostly accompanied by constant migration and departure of Ukrainian citizens for permanent residence abroad. The analysis of publications on this issue proves the urgency of educational migration research, and today it remains important to analyze trends in educational migration, find mechanisms to counter the transformation of Ukraine into a country of origin of educational migrants, as well as their repatriation. The article analyzes the educational migration processes in Ukraine in the regional context. The main regional centers of higher education are identified. The focus is on the migration intentions of the population to study abroad and a comparative description of the educational systems of different countries. The order and stages of the admission campaign in foreign educational institutions are described. The essence and features of educational migration are studied and the main reasons that help Ukrainian students to choose foreign higher education institutions are highlighted. The main causes and consequences of educational migration, as well as potential opportunities for educational migration, are identified. Certain benefits and costs associated with educational migration processes are justified. The directions of development of educational migration and effective migration system in Ukraine and its regions, which should take a worthy place in the migration policy of the state, are offered.

scholarly journals A Comprehensive Study on Private Universities in Bangladesh: Service Rules and Job Satisfaction

2021 ◽  
Vol 5 (10) ◽  
pp. 43-48
Author(s):  
Mohammad Golam Moula ◽  
Md. Obaidullah ◽  
Kamrunnahar .
Keyword(s):  
Higher Education ◽  
Job Satisfaction ◽  
Private Universities ◽  
Daily Basis ◽  
Quality Education ◽  
Educational Institutions ◽  
Labor Laws ◽  
Higher Education System ◽  
Job Activities ◽  
Comprehensive Study

Service rules make it easier for an institution to pursue higher education. In order to ensure quality education, it is essential that educational institutions have service rules. The service rules of university employment are considered to be conducive to the higher education system. On this article discusses the importance of service rules. Each step of the service rules is shown with the help of diagrams and each of the steps is discussed separately. To certify higher education has played a noteworthy part in Bangladesh. The rules clearly specify the rights and duties of workers and employers where every institution must have its own service rules regulating any employment but cannot be contradictory to any establishment of the labor laws. Job satisfaction is considered as a vital determinant of job activities. Some of the universities are providing quality education but rests of them are not quality concerned, most of them are depending on Daily basis teachers, poor infrastructures, without service rules etc. The trial was taken on a random basis from four private universities in Bangladesh. The sample size is forty-five. The result of this study shows that about job satisfaction level of employees in the selected universities. In the end, based on results, researchers have offered some suggestions that can be taken into thought in strategy level.

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