Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Research Methodology
Data Exploration
The Data Mining Practitioners Page
Philosophy of the Data Mining Process
 3RD Law of Data Mining Or-Data Preparation Law
 7TH Law of Data Mining or -Prediction Law‖
The Practice of the Data Mining Process
 Record Data
 Document Data
 Transaction Data
 Data Preparation
 Format Data
 Structure of the Available Data Set
References
All Topics
Computer Science

Data mining and its applications in the education sector

Profile image of Mohamed KhaldiMohamed Khaldi

2018, International journal of applied research

Abstract

The growing volumes of data continue to outpace human capabilities to extract and discover valuable information capable of supporting sound decision-making and plausible prediction in any business or industry this can only be addressed using automated methods such as data mining. Data Mining Technology (DMT) is a robust technology that can help organizations to make optimal informed decisions-instead of guesswork decisions [1] , in addressing contemporary challenges facing higher education. Although data mining technology is progressing, its use leaves much to be desired. its adoption and implementation is expensive and any failure in implementation causes not only financial loss but also dissatisfaction among users, which makes it imperative to study the acceptance and adoption of this technology by its potential users. previous studies about data mining have always focused either on the technical aspect or the development of DMT application algorithms without considering users' perception of the technology [2]. Little effort has been used to encourage users to appreciate DMT's capabilities and let's DMT obtain the users' acknowledgement; as this could help minimizing underutilization or eventual abandonment despite the prevalent benefit. This study aims to promote and encourage the use of data mining technologies by its potential users within higher education context:  This paper aims to contribute to the conceptual and theoretical understanding of Data mining within higher education.  It introduces the notion of Data mining and outlines its relevance to higher education. From a perspective of e-learning practitioners and data mining practitioners To achieve these goals, this study aims to design a web portal that targets two types of potential users, e-learning practitioners and data mining practitioners and that can serve as a one-stop knowledge base. That allows to explore the different participants in the education system and how each of these players can benefit from the data mining system in a virtuous cycle. And allows data mining practitioners to thoroughly understand the data mining philosophy and apprehended the practice of its process by focusing on data preparation and three key areas in the modeling process; criteria to consider in order to optimize the chances of extracting useful information and to make the right choice of data mining algorithms, Model construction and model evaluation.

Key takeaways
sparkles

AI

  1. Data mining technology (DMT) enhances decision-making in higher education by automating information extraction.
  2. The study promotes DMT adoption among e-learning and data mining practitioners to minimize underutilization.
  3. Implementing DMT effectively requires addressing user perceptions and ensuring cost-effectiveness.
  4. The conceptual framework consists of three layers: philosophy, technical, and application, guiding knowledge discovery.
  5. DMT aids in student engagement, retention, and performance management through data-driven insights.
Figures (18)
Table 1: Description of the web portal pages 4. The detailed content of the web pages of the demonstration portal and some screen shots 4.1 The data mining system structure page
Table 1: Description of the web portal pages 4. The detailed content of the web pages of the demonstration portal and some screen shots 4.1 The data mining system structure page
Fig 2: structure of the demonstration portal of data mining system The structure of the demonstration portal is presented graphically below:
Fig 2: structure of the demonstration portal of data mining system The structure of the demonstration portal is presented graphically below:
Fig 3: the functional architecture of the data mining system ~243~ The portal shows six main parts and each web page is briefly described in Table below Figure 3 illustrates the functional architecture of the data mining system, which is composed of several layers: (appendix)
Fig 3: the functional architecture of the data mining system ~243~ The portal shows six main parts and each web page is briefly described in Table below Figure 3 illustrates the functional architecture of the data mining system, which is composed of several layers: (appendix)
Fig 4: three-layer conceptual framework These feedbacks come from the users and the customers necessitate the researchers work on the other two layers to make respond, either to complete or modify the knowledge structure, the methodology, or innovate the existing technology.
Fig 4: three-layer conceptual framework These feedbacks come from the users and the customers necessitate the researchers work on the other two layers to make respond, either to complete or modify the knowledge structure, the methodology, or innovate the existing technology.
Fig 5: Data Mining Advantages 4.2 Data mining system benefits page
Fig 5: Data Mining Advantages 4.2 Data mining system benefits page
Data mining system can help top education managers to consume more meaningful information from big data, generate actionable insights from complex education problems and make data driven decisions across pan- educational institution processes to create sustainable education impact.
Data mining system can help top education managers to consume more meaningful information from big data, generate actionable insights from complex education problems and make data driven decisions across pan- educational institution processes to create sustainable education impact.
Fig 6: Philosophy and practice of DM process This page allows data mining practitioners to thoroughly understand the data mining philosophy and apprehended the practice of its process by focusing on data preparation and three key areas in the modeling process; criteria to consider in order to optimize the chances of extracting useful information and to make the right choice of data mining algorithms, Model construction and model evaluation. Figure !!
Fig 6: Philosophy and practice of DM process This page allows data mining practitioners to thoroughly understand the data mining philosophy and apprehended the practice of its process by focusing on data preparation and three key areas in the modeling process; criteria to consider in order to optimize the chances of extracting useful information and to make the right choice of data mining algorithms, Model construction and model evaluation. Figure !!
Fig 7: Steps Involved in the Data Mining Process Data mining involves the following steps show in figure 7, which must be executed in sequence until the results are documented and reported '7!
Fig 7: Steps Involved in the Data Mining Process Data mining involves the following steps show in figure 7, which must be executed in sequence until the results are documented and reported '7!
Fig 8: What is Data Collection of data objects and their attributes
Fig 8: What is Data Collection of data objects and their attributes
Types of data sets Data that consists of a collection of records, each of which consists of a fixed set of attributes "!
Types of data sets Data that consists of a collection of records, each of which consists of a fixed set of attributes "!
‘able 2: Example Problems and the Possible Algorithms that may be adopted to solve them to find anomalies in data, one may choose to use the decision trees algorithm. The relationships between the objects/data, relationships between variables and the way that the data is stored influences the choice of an algorithm. Table 3 (below) shows examples of the data that will be required if a data miner is to implement a specified algorithm.
‘able 2: Example Problems and the Possible Algorithms that may be adopted to solve them to find anomalies in data, one may choose to use the decision trees algorithm. The relationships between the objects/data, relationships between variables and the way that the data is stored influences the choice of an algorithm. Table 3 (below) shows examples of the data that will be required if a data miner is to implement a specified algorithm.
Table 3: Examples of the Required Structure of Data Sets for Some Algorithms The table above shows the recommended structure of the data if a researcher is to adopt a specific technique. The single key column is the column that uniquely identifies each record, in other words it is the primary key in a table. The predictive column is the key column in the nested table, the foreign key. Therefore based on these examples, to implement a time series algorithm, there must be one column with time data, input columns and at least one predictable column. - Configuration Parameters
Table 3: Examples of the Required Structure of Data Sets for Some Algorithms The table above shows the recommended structure of the data if a researcher is to adopt a specific technique. The single key column is the column that uniquely identifies each record, in other words it is the primary key in a table. The predictive column is the key column in the nested table, the foreign key. Therefore based on these examples, to implement a time series algorithm, there must be one column with time data, input columns and at least one predictable column. - Configuration Parameters
Table 4: Examples of applications of data mining algorithm in the field education Model construction algorithms find patterns that are of predictive value. Next, the model is refined using the test set. The purpose of building models is to use the predictions to make more informed business decisions. The most important goal when building a model is stability, which means that the model should make predictions that will hold true when it‘s applied to yet unseen data '*!. a, The model needs to be refined to prevent it from memorizing the training set. This step ensures that the mode is more general (i.e. stable) and will perform well on unseen data. Next, the performance of the model is estimated using the evaluation set. The evaluation set is entire y separate and distinct from the training and test sets. The evaluation set (or hold out set) is used to assess the expec outsid ted accuracy of the model when it is applied to data e the model set. Here is a brief description of the most popular techniques of data mining and their real applications in the Education domain. Simply to provide the minimum information to make the final choice !!7! Regardless of the data mining technique being used, the basic steps used for building predictive models are the same. The model set first needs to be split into three components: (1) the training set, (2) the test set, and (3) the evaluation set. A fourth dataset, the score set, is not part of the model set.
Table 4: Examples of applications of data mining algorithm in the field education Model construction algorithms find patterns that are of predictive value. Next, the model is refined using the test set. The purpose of building models is to use the predictions to make more informed business decisions. The most important goal when building a model is stability, which means that the model should make predictions that will hold true when it‘s applied to yet unseen data '*!. a, The model needs to be refined to prevent it from memorizing the training set. This step ensures that the mode is more general (i.e. stable) and will perform well on unseen data. Next, the performance of the model is estimated using the evaluation set. The evaluation set is entire y separate and distinct from the training and test sets. The evaluation set (or hold out set) is used to assess the expec outsid ted accuracy of the model when it is applied to data e the model set. Here is a brief description of the most popular techniques of data mining and their real applications in the Education domain. Simply to provide the minimum information to make the final choice !!7! Regardless of the data mining technique being used, the basic steps used for building predictive models are the same. The model set first needs to be split into three components: (1) the training set, (2) the test set, and (3) the evaluation set. A fourth dataset, the score set, is not part of the model set.
Figure 11 is a view of the first page it show the structure of data mining system
Figure 11 is a view of the first page it show the structure of data mining system
Fig 10: The Process of Building a Predictive Model
Fig 10: The Process of Building a Predictive Model
Fig 11: screen shots of the demonstration portal
Fig 11: screen shots of the demonstration portal

Related papers

Educational data mining: a sample of review and study case
Science Park Research Organization & Counselling

Researchers of WOLNM a , ESIME-Z b & CIC c – b, Abstract The aim of this work is to encourage the research in a novel merged field: Educational data mining (EDM). Thereby, two subjects are outlined: The first one corresponds to a review of data mining (DM) methods and EDM applications. The second topic represents an EDM study case. As a result of the application of DM in Web-based Education Systems (WBES), stratified groups of students were found during a trial. Such groups reveal key attributes of volunteers that deserted or remained during a WBES experiment. This kind of discovered knowledge inspires the statement of correlational hypothesis to set relations between attributes and behavioral patterns of WBES users. We concluded that: When EDM findings are taken into account for designing and managing WBES, the learning objectives are improved.

View PDFchevron_right
A Proposed Data Mining Framework for Higher Education System
Prof. Ahmed Bahgat I B R A H I M A H M E D El Seddawy

Educational data mining is concerned with the development methods for exploring the unique types of data that come from the educational context. Furthermore, educational data mining is an emerging discipline that concerned with the developing methods for exploring the unique types of data that come from the educational context. This study focuses on the way of applying data mining techniques for higher education system by using the most common techniques on most common application called Moodle system in education system. There are an increasing numbers of researches that interest in using data mining in education system. The proposed system for Higher Educational Data Mining System (HEDMS) is concerned with the developing methods that discover useful knowledge from data that extracted from educational system. The data collated form historical and usage data reside in the databases of educational institutes. The proposed system helps to get sufficient results which consist of several steps in our case study starting with collected data, pre-processing, applying data mining techniques and visualization results. We collected students' data from Moodle database.

View PDFchevron_right
Using Data Mining Techniques in Higher Education
Elena Susnea

Data mining (DM) is useful for collecting and interpreting significant data from huge database. The education field offers several potential data sources for data mining applications. These applications can help both instructors and students in improving the learning process.

View PDFchevron_right
An Empirical Study of the Applications of Data Mining Techniques in Higher Education. IJACSA - International Journal of Advanced Computer Science and Applications, 2(3), 80-84. Retrieved from http://ijacsa.thesai.org. AUTHORS PROFILE Brijesh Kumar Bhardwa
Anupama Chadha

2011

— 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. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper addresses the applications of data mining in educational institution to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples. Keywords- Higher education,; Data mining; Knowledge discovery; Classification; Association rules; Prediction; Outlier ana...

View PDFchevron_right
An Empirical Study of the Applications of Data Mining Techniques in Higher Education
Varun Kumar

International Journal of Advanced Computer Science and Applications, 2011

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. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper addresses the applications of data mining in educational institution to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples.

View PDFchevron_right
Applying Data Mining in Higher Education Sector
rhea mahajan

2015

1 Archana Sharma, 2 Prof. Vibhakar Mansotra, 3 Rhea Mahajan 1 Department of Computer Application, Jodhpur National University, Jodhpur, Rajasthan, India. 2 Department of Computer Science & IT, University of Jammu, Jammu, India. 3 Department of Computer Engineering.Yogananda College Of Engineering and Technology, India. _________________________________________________________________________________________ Abstract: The new interesting subject that offered by institution to interact more student is “DATA MINING”. In this paper we will discuss about the problem that are faced by students how to choose the best institutes for learning. One of the biggest challenges that student’s faced tough time selecting the right engineering college that opens doors to exciting careers. Students would like to know, which college provides better quality education and its alumni are successful in the real world. Data Mining helps to students to take decision more accurately. Data mining is better to...

View PDFchevron_right

References (15)

  1. Reference
  2. Nemati HR, Barko CD. Organizational Data Mining. In: Data Mining and knowledge Discovery Handbook, Maimon O. and L Rokach (Eds). Springer, Boston, MA, 2010, 1041-1048
  3. Romero C, Ventura S. Educational data mining: A review of the state of the art. IEEE trans. Syst. Man Cybernet Part C: Appl. Rev. 2010; 40:601-618
  4. Vercellis C. Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley & Sons Ltd, 2009.
  5. Yao Y, Zhong N. Three-layered Conceptual Framework of Data Mining, 2004
  6. Heinrichs J. Integrating web-based data mining tools with business models for knowledge management. Decision Support Systems. 2003; 35:103-112
  7. AlHammadi D, Aksoy M. Data Mining in Higher Education. Periodicals of engineering and natural sciences, 2013, 1:2
  8. Gibert K, Rodríguez-Silva G, Rodríguez-Roda I. Knowledge Discovery with Clustering based on rules by States: A water treatment application. Environmental Modelling & Software. 2010; 25:712-723
  9. Gibert K, Sànchez-Marrè M, Codina V. Choosing the Right Data Mining Technique: Classification of Methods and Intelligent Recommendation, 2010.
  10. Kumar T. Introduction to Data Mining. Lecture Notes for Chapter 2, 2004.
  11. Jackson J. data mining: a conceptual overview, 2002.
  12. Communications of the Association for Information Systems, 2002, 8:267-296
  13. Chikohora T. A Study of the Factors Considered when Choosing an Appropriate Data Mining Algorithm International Journal of Soft Computing and Engineering (IJSCE), 2014, 4(3).
  14. Bhargava N, Arya R. Selection Criteria for Data Mining Software: A Study IJCSI International Journal of Computer Science, 2013, 10(3).
  15. Walte D, Reddy H. Overview of algorithms in Educational Data Mining for Higher Education: An Application Perspective. International Journal of Engineering Research & Technology (IJERT), 2014, 3(2).

Related papers

ROLE OF DATA MINING IN EDUCATION SECTOR
IJCSMC Journal
View PDFchevron_right
DATA MINING INFLUENCE ON E-LEARNING
Dušan Perović, sura sowmya

Data analysis has improved many social aspects. Modern business is unthinkable without a proper use of data analysis tools. Also many education and security systems rely on this technique for solving their problems. With data mining there are bigger possibilities to improve on many ways, but there are still some obstacles that can slow down the whole process. Data mining has typical use in IT sector, but as the years have passed other sides have appeared and started to use data mining. The aim of this paper is show how data mining helped profit and non-profit organizations, but also to whole society to make better decisions and find a better direction for their everyday activities. Also, there will be mention some limitations and challenges about data mining use in modern world.

View PDFchevron_right
Data Mining in Education
Dr Abdulmohsen Algarni

International Journal of Advanced Computer Science and Applications, 2016

Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful information that could potentially affect an organization. The increase of technology use in educational systems has led to the storage of large amounts of student data, which makes it important to use EDM to improve teaching and learning processes. EDM is useful in many different areas including identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This paper surveys the relevant studies in the EDM field and includes the data and methodologies used in those studies.

View PDFchevron_right
Data Mining as a Torch Bearer in Education Sector
Umesh Pandey, Saurabh Pal

2012

Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data.

View PDFchevron_right
Data mining for Education Sector, a proposed concept
Huyen Trang

Journal of Applied Data Sciences

Data mining is very much needed in various fields. Accessing a large amount of data requires time and a high level of accuracy. In higher education the potential influence of data mining on the learning processes and outcomes of the students was realized. Especially in the field of education, knowing almost every educational institute, both public and private, has thousands of data from students with a variety of different programs and subjects. Understanding the benefits of data retrieval will facilitate the course of education itself. The use of Data mining in education will be useful in developing a student-focused strategy and in providing the correct tools that institutions would be able to use for quality improvement purposes. In this paper, we will find out the benefits of applying data mining in the education sector using classification, prediction, association and clustering methods.

View PDFchevron_right

Related topics

  • Computer Science
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved