Knowledge Discovery for Large Databases in Education Institutes

2017 ◽  
pp. 27-110
Author(s):  
Robab Saadatdoost ◽  
Alex Tze Hiang Sim ◽  
Hosein Jafarkarimi ◽  
Jee Mei Hee
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Learning Style ◽  
Science Research ◽  
Data Set ◽  
Use Of Data ◽  
Education Data ◽  
Decision Making System

This project presents the patterns and relations between attributes of Iran Higher Education data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns, we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology's universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more.

Knowledge Discovery for Large Databases in Education Institutes

2018 ◽  
pp. 158-245
Author(s):  
Robab Saadatdoost ◽  
Alex Tze Hiang Sim ◽  
Hosein Jafarkarimi ◽  
Jee Mei Hee
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Learning Style ◽  
Science Research ◽  
Data Set ◽  
Use Of Data ◽  
Education Data ◽  
Decision Making System

This project presents the patterns and relations between attributes of Iran Higher Education data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns, we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology's universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more.

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.

Data Mining Approach to Decision Support in Social Welfare

2018 ◽  
pp. 1783-1802
Author(s):  
Ricardo Anderson ◽  
Gunjan Mansingh
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Social Welfare ◽  
Knowledge Discovery ◽  
Support System ◽  
Process Model ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Data Mining Approach

Knowledge discovery and data-mining techniques have the potential to provide insights into data that can improve decision making. This paper explores the use of data mining to extract patterns from data in the domain of social welfare. It discusses the application of the Integrated Knowledge Discovery and Data Mining process model (IKDDM) a social welfare programme in Jamaica. Further, it demonstrates how the knowledge acquired from the data is used to develop a knowledge driven decision support system (DSS) in the PATH CCT programme. This system was successfully tested in the domain showing over 94% accuracy in the comparative decisions produced.

Data Mining Approach to Decision Support in Social Welfare

2014 ◽  
Vol 5 (2) ◽  
pp. 39-61 ◽  
Author(s):  
Ricardo Anderson ◽  
Gunjan Mansingh
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Social Welfare ◽  
Knowledge Discovery ◽  
Support System ◽  
Process Model ◽  
Use Of Data ◽  
Data Mining Approach

Knowledge discovery and data-mining techniques have the potential to provide insights into data that can improve decision making. This paper explores the use of data mining to extract patterns from data in the domain of social welfare. It discusses the application of the Integrated Knowledge Discovery and Data Mining process model (IKDDM) a social welfare programme in Jamaica. Further, it demonstrates how the knowledge acquired from the data is used to develop a knowledge driven decision support system (DSS) in the PATH CCT programme. This system was successfully tested in the domain showing over 94% accuracy in the comparative decisions produced.

scholarly journals Data Mining and Knowledge Discovery in Big Data for Decision Making in Higher Education

2021 ◽  
Vol 14 (4) ◽  
pp. 2013-2019
Author(s):  
Hanna Mohammad Said
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Real World ◽  
Rapid Development ◽  
Teaching Quality ◽  
World Knowledge

Artificial intelligence and data mining plays a fundamental role in improving the intelligence of education through special standards for improving teaching quality, better learning experience, predictive teaching, assessment method, effective decision-making, and improved data analysis. BD (Big Data) are also used to assess, detect, and anticipate decision-making, failure risk, and consequences to improve decision-making and maintain high-quality standards. According to the findings of this study, certain universities and governments have adopted BD to help students transition from traditional to smart digital education. Many obstacles remain in the way of complete adoption, including security, privacy, ethics, a scarcity of qualified specialists, data processing, storage, and interoperability. Learning today is getting smarter, thanks to the rapid development of the use of data and knowledge for big data analysis. Besides delivering real-world knowledge discovery applications, specialized data mining methodologies, and obstacles have real-world applications. Therefore, this article aims to explain the current concept of an intelligent learning environment in higher education. It explores the main criteria, and presents evaluation methods through the use of the proposed model.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

Using Data Mining for Forecasting Data Management Needs

2008 ◽  
pp. 2088-2104
Author(s):  
Qingyu Zhang ◽  
Richard S. Segall
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Forest Cover ◽  
Human Lung Cancer ◽  
Production Environment ◽  
Data Set ◽  
Biotechnology Research ◽  
Use Of Data ◽  
Using Data ◽  
Tools And Techniques

This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of data mining with multidimensional databases for determining data management needs for the selected biotechnology data of forest cover data (63,377 rows and 54 attributes) and human lung cancer data set (12,600 rows of transcript sequences and 156 columns of gene types). The data mining is performed using four selected software of SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and Bio- Discovery GeneSight®. The analysis and results will be used to enhance the intelligence capabilities of biotechnology research by improving data visualization and forecasting for organizations. The tools and techniques discussed here can be representative of those applicable in a typical manufacturing and production environment. Screen shots of each of the four selected software are presented, as are conclusions and future directions.

Semantic Integration and Knowledge Discovery for Environmental Research

2008 ◽  
pp. 213-235
Author(s):  
Zhiyuan Chen ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Michael McGuire ◽  
Claire Welty
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Semantic Network ◽  
Semantic Integration ◽  
Environmental Data ◽  
Data Sources ◽  
Environmental Research ◽  
Related Data ◽  
Use Of Data ◽  
Data Semantics

Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental 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.

On meeting students where they are: Teacher judgment and the use of data in higher education

2017 ◽  
Vol 15 (3) ◽  
pp. 321-338 ◽  
Author(s):  
Gina Schouten
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Data Use ◽  
Statistical Information ◽  
Educational Context ◽  
Ongoing Debate ◽  
Moral Hazards ◽  
Use Of Data ◽  
Legal Context

It is treated as a truism that teaching well requires ‘meeting students where they are’. Data enable us to know better where that is. Data can improve instructional practice by informing predictions about which pedagogies will be most successful for which students, and it can improve advising practice by informing predictions about which students are likely to thrive on which pathways moving forward. But moral hazards lurk, and these have been highlighted especially in response to the burgeoning use of new data mining technologies to produce ‘big data’. This article explores the ethics of data use in higher education. I consider the ethics of aggregate data as a tool for meeting students where they are, comparing it to an ongoing debate about the use of statistics in the legal context. The comparison generates two important insights: First, even the most viable moral concerns about using statistical information in the educational context are not deal-breakers: Those concerns should lead us to exercise careful judgment in the use of statistical information but do not justify eschewing that information altogether. Second, surprisingly, those viable moral concerns show big data to have a moral advantage over traditional little data, suggesting that some of the resistance to the use of big data in education is either unfounded or at least needs to be balanced against the moral advantages big data offer.

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