1) The document discusses the development of a rule-based decision support system for admission decisions in higher education institutions.
2) It reviews literature on decision support systems and decision modeling approaches. Knowledge is gathered from education administrators to develop rules for the system.
3) The system aims to help school managers make better admission decisions by building a better information system using a rule-based approach.
Ijdms050304A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDSijdms
Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying
different Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learning
process for effective education planning. In this survey work focuses on components, research trends (1998
to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlights
the Challenges EDM.
Simplifying Database Normalization within a Visual Interactive Simulation Modelijdms
Although many researchers focused on investigating the challenges of database normalization, and suggested recommendations on easing these challenges, this process remained an area of concern to database designers, developers, and learners. This paper investigated these challenges and involved Higher Education in Computer Science students learning database normalization, as they could well
represent beginning database designers/developers, who would struggle in effectively normalize their database design due to the complexity of this theoretical process, which has no similar real-life representation. The paper focused on the advantages of interactive visualization techniques to simplify database normalization, and recommended virtual world technologies, such as ‘Second Life’, as an effective platform to achieve this visualization via a simulated model of a relational database system. The simulation technique presented in this paper is novel, and is supported by extensive evidence on its
advantages to achieve an illustration of the ‘Normal Forms’ and the need for them.
Information Technology Management in Healthcare Organizations: Part 1 (Octobe...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 20, 2021
Ijdms050304A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDSijdms
Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying
different Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learning
process for effective education planning. In this survey work focuses on components, research trends (1998
to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlights
the Challenges EDM.
Simplifying Database Normalization within a Visual Interactive Simulation Modelijdms
Although many researchers focused on investigating the challenges of database normalization, and suggested recommendations on easing these challenges, this process remained an area of concern to database designers, developers, and learners. This paper investigated these challenges and involved Higher Education in Computer Science students learning database normalization, as they could well
represent beginning database designers/developers, who would struggle in effectively normalize their database design due to the complexity of this theoretical process, which has no similar real-life representation. The paper focused on the advantages of interactive visualization techniques to simplify database normalization, and recommended virtual world technologies, such as ‘Second Life’, as an effective platform to achieve this visualization via a simulated model of a relational database system. The simulation technique presented in this paper is novel, and is supported by extensive evidence on its
advantages to achieve an illustration of the ‘Normal Forms’ and the need for them.
Information Technology Management in Healthcare Organizations: Part 1 (Octobe...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 20, 2021
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
Understanding User’s Acceptance of Personal Cloud Computing: Using the Techno...Maurice Dawson
Personal Cloud Computing (PCC) is a rapidly growing technology, addressing the market demand of individual users for access to available and reliable resources. But like other new technologies, concerns and issues have surfaced with the adoption of PCC. Users deciding whether to adopt PCC may be concerned about the ease of use, usefulness, or security risks in the cloud. Negative attitudes toward using a technology have been found to negatively impact the success of that technology. The purpose of this study was to understand users’ acceptance of PCC. The population sample consisted of individual users within the United States between 18 and 80 years of age. The theoretical framework utilized in this study was based on the technology acceptance model (TAM). A web survey was conducted to assess the measurement and understanding of patterns demonstrated by participants. Our results shows that in spite of the potential benefits of PCC, security and privacy risks are deterring many users from moving towards PCC.
Developing the mathematical skills among sample of down syndrome by education...Alexander Decker
International peer-reviewed academic journals call for papers, http://www.iiste.org
International peer-reviewed academic journals call for papers, http://www.iiste.org
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
EXTENSION OF TECHNOLOGY ACCEPTANCE MODEL (TAM): A STUDY ON INDIAN INTERNET BA...IAEME Publication
Internet banking plays significant role in the development of banking business in our country. An application of electronic service brings predominant changes in the way of doing banking transactions. In simpler terms, internet banking refers to banking through bank’s website with the help of internet connection. Internet banking provides lot of benefits to the customers as well as the banks. Internet banking provides different kinds of services to the customers in the form checking balances, account statement, pay utility bills etc
An EHealth Adoption Framework for Developing Countries: A Systematic Reviewhiij
There is growing interest in the rate of eHealth uptake resulting from the increased potential to advance the quality of healthcare services in both the developed and developing countries. Although the implementation of information and communication technology to support healthcare delivery would greatly address the quality and accessibility challenges in healthcare as well as reduction in the cost of healthcare delivery, the adoption of eHealth has not been fully realized. This study aimed at conducting a systematic literature review to establish the factors associated with the adoption of eHealth and propose a context-specific framework for successful adoption of eHealth technologies in developing countries such as Uganda. The systematic literature review process was guided by the Systematic Review Protocol. The review of 29 journals from the period 2009-2021 showed that, although the most widely used frameworks in the developing countries were Technology Adoption Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) framework and Technology Organization Environment (TOE) framework, there were other salient factors reported by other researchers that contributed to the adoption of eHealth in developing countries. A novel framework for adoption of eHealth in the local context with eight (8) dimensions namely; Sociodemographic, Technology, Information, Socio-cultural, Organization, Governance, Ethical and legal and Financial dimensions is derived and presented as result of the research.
José Carlos Sánchez Prieto, Susana Olmos Migueláñez and Francisco J. García-Peñalvo.
Research Group in InterAction and eLearning (GRIAL)
IUCE
University of Salamanca
Influence User Involvement On The Quality Of Accounting Information SystemArnol Awal
Background of this study was based on the argument that there were correlation between user involvement and quality of accounting information system. This study aims to examine: the influence of user involvement on the quality of the information system
Developing a PhD Research Topic for Your Research| PhD Assistance UKPhDAssistanceUK
According to UGC report, every year approximately 77,000 scholars are enrolling for PhD research programs in India. but only 1/3rd of them successfully complete the doctorate every year (Marg, 2015). This result clearly indicates the difficulty and standard of evaluation of this program, so it is necessary that the scholar thoroughly analyze and select a good PhD research topic before plunging into the research work. There are many methodologies and techniques introduced over a period for effective selection and decision making in various field. Data Driven Decision Making which is also referred as Data Based Decision Making is a modern method which was found to be effective and efficient for strategic and systematic development and decision making. So, this article summarizes the framework, effectiveness, benefits of DDDM in selection of Research topics for the PhD scholars.
For more Details:-
UK: +44 7424997337
Email : info@phdassistance.com
DETERMINING BUSINESS INTELLIGENCE USAGE SUCCESSijcsit
Business intelligence systems are highly complex systems that senior executives use to process vast
amounts of information when making decisions. Business intelligence systems are rarely used to their full
potential due to a poor understanding of the factors that contribute to system success. Organizations using
business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these
systems, and researchers have noted that there is limited scholarly and practical understanding of how
quality factors affect information use within these systems. This quantitative post positivist research used
the information system (IS) success model to analyze how information quality and system quality influence
information use in business intelligence systems. This study was also designed to investigate the
moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the
relationships between quality factors and information use.
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
Understanding User’s Acceptance of Personal Cloud Computing: Using the Techno...Maurice Dawson
Personal Cloud Computing (PCC) is a rapidly growing technology, addressing the market demand of individual users for access to available and reliable resources. But like other new technologies, concerns and issues have surfaced with the adoption of PCC. Users deciding whether to adopt PCC may be concerned about the ease of use, usefulness, or security risks in the cloud. Negative attitudes toward using a technology have been found to negatively impact the success of that technology. The purpose of this study was to understand users’ acceptance of PCC. The population sample consisted of individual users within the United States between 18 and 80 years of age. The theoretical framework utilized in this study was based on the technology acceptance model (TAM). A web survey was conducted to assess the measurement and understanding of patterns demonstrated by participants. Our results shows that in spite of the potential benefits of PCC, security and privacy risks are deterring many users from moving towards PCC.
Developing the mathematical skills among sample of down syndrome by education...Alexander Decker
International peer-reviewed academic journals call for papers, http://www.iiste.org
International peer-reviewed academic journals call for papers, http://www.iiste.org
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
EXTENSION OF TECHNOLOGY ACCEPTANCE MODEL (TAM): A STUDY ON INDIAN INTERNET BA...IAEME Publication
Internet banking plays significant role in the development of banking business in our country. An application of electronic service brings predominant changes in the way of doing banking transactions. In simpler terms, internet banking refers to banking through bank’s website with the help of internet connection. Internet banking provides lot of benefits to the customers as well as the banks. Internet banking provides different kinds of services to the customers in the form checking balances, account statement, pay utility bills etc
An EHealth Adoption Framework for Developing Countries: A Systematic Reviewhiij
There is growing interest in the rate of eHealth uptake resulting from the increased potential to advance the quality of healthcare services in both the developed and developing countries. Although the implementation of information and communication technology to support healthcare delivery would greatly address the quality and accessibility challenges in healthcare as well as reduction in the cost of healthcare delivery, the adoption of eHealth has not been fully realized. This study aimed at conducting a systematic literature review to establish the factors associated with the adoption of eHealth and propose a context-specific framework for successful adoption of eHealth technologies in developing countries such as Uganda. The systematic literature review process was guided by the Systematic Review Protocol. The review of 29 journals from the period 2009-2021 showed that, although the most widely used frameworks in the developing countries were Technology Adoption Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) framework and Technology Organization Environment (TOE) framework, there were other salient factors reported by other researchers that contributed to the adoption of eHealth in developing countries. A novel framework for adoption of eHealth in the local context with eight (8) dimensions namely; Sociodemographic, Technology, Information, Socio-cultural, Organization, Governance, Ethical and legal and Financial dimensions is derived and presented as result of the research.
José Carlos Sánchez Prieto, Susana Olmos Migueláñez and Francisco J. García-Peñalvo.
Research Group in InterAction and eLearning (GRIAL)
IUCE
University of Salamanca
Influence User Involvement On The Quality Of Accounting Information SystemArnol Awal
Background of this study was based on the argument that there were correlation between user involvement and quality of accounting information system. This study aims to examine: the influence of user involvement on the quality of the information system
Developing a PhD Research Topic for Your Research| PhD Assistance UKPhDAssistanceUK
According to UGC report, every year approximately 77,000 scholars are enrolling for PhD research programs in India. but only 1/3rd of them successfully complete the doctorate every year (Marg, 2015). This result clearly indicates the difficulty and standard of evaluation of this program, so it is necessary that the scholar thoroughly analyze and select a good PhD research topic before plunging into the research work. There are many methodologies and techniques introduced over a period for effective selection and decision making in various field. Data Driven Decision Making which is also referred as Data Based Decision Making is a modern method which was found to be effective and efficient for strategic and systematic development and decision making. So, this article summarizes the framework, effectiveness, benefits of DDDM in selection of Research topics for the PhD scholars.
For more Details:-
UK: +44 7424997337
Email : info@phdassistance.com
DETERMINING BUSINESS INTELLIGENCE USAGE SUCCESSijcsit
Business intelligence systems are highly complex systems that senior executives use to process vast
amounts of information when making decisions. Business intelligence systems are rarely used to their full
potential due to a poor understanding of the factors that contribute to system success. Organizations using
business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these
systems, and researchers have noted that there is limited scholarly and practical understanding of how
quality factors affect information use within these systems. This quantitative post positivist research used
the information system (IS) success model to analyze how information quality and system quality influence
information use in business intelligence systems. This study was also designed to investigate the
moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the
relationships between quality factors and information use.
Higher education institutions now a days are operating in an increasingly complex and
competitive environment. The application of innovation is a must for sustaining its competitive advantage.
Institution leaders are using data management and analytics to question the status quo and develop effective
solutions. Achieving these insights and information requires not a single report from a single system, but
rather the ability to access, share, and explore institution-wide data that can be transformed into meaningful
insights at every level of the institution. Consequently, institutions are facing problems in providing necessary
information technology support for fulfilling excellence in performance. More specifically, the best practices
of big data management and analytics need to be considered within higher education institutions. Therefore,
the study aimed at investigating big data and analytics, in terms of: (1) definition; (2) its most important
principles; (3) models; and (4) benefits of its use to fulfill performance excellence in higher education
institutions. This involves shedding light on big data and analytics models and the possibility of its use in
higher education institutions, and exploring the effect of using big data and analytics in achieving performance
excellence. To reach these objectives, the researcher employed a qualitative research methodology for
collecting and analyzing data. The study concluded the most important result, that there is a significant
relationship between big data and analytics and excellence of performance as big data management and
analytics mainly aims at achieving tasks quickly with the least effort and cost. These positive results support
the use of big data and analytics in institutions and improving knowledge in this field and providing a practical
guide adaptable to the institution structure. This paper also identifies the role of big data and analytics in
institutions of higher education worldwide and outlines the implementation challenges and opportunities in the
education industry.
Applying Classification Technique using DID3 Algorithm to improve Decision Su...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
USER EXPERIENCE EVALUATION OF A STUDENT INFORMATION SYSTEMijcsit
Today's academic environment, many students use technology as an integral aspect of their studies; as a
result, higher education (HE) institutions have been compelled to design student information systems (SIS)
that can facilitate students' online learning processes. However, SIS must be aligned with user needs and
should provide a pleasant user experience (UX) that enables students to attain their goals. The current
research looked at how students rated an SIS. The study was based on the responses of 307 students at
Kuwait's College of Business Studies (CBS) provided within a questionnaire. The survey's findings
revealed that students had a generally favourable impression of the SIS, with perceptions of the pragmatic
quality of the system being somewhat higher than the perceptions of hedonic quality. The findings of this
research may be valuable to authorities working to design improved SIS, particularly in terms of the
hedonic system components.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
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STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
An Empirical Study of the Applications of Classification Techniques in Studen...IJERA Editor
University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
A rule based higher institution of learning admission decision support system
1. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5782 (print) ISSN 2225-0506 (online)
Vol.4, No.1, 2014
7
A Rule-Based Higher Institution of Learning Admission Decision
Support System
1
AJAYI, Olusola Olajide
olusola.ajayi@aaua.edu.ng/ajayilastborn@yahoo.co.uk / +2347056433798 / +2348137044500
2
OJEYINKA, Taiwo O.
taiwooje@gmail.com / +2348021114227
3
ISHEYEMI, Olufemi Gabriel
femi.isheyemi@yahoo.com/+2348063069790
4
LAWAL, Muideen Adekunle
mdauthentic@yahoo.com / +2348162561823 / +2347033207233
1,2,3,4
Department of Computer Science, Adekunle Ajasin University
Akungba-Akoko, Ondo State, Nigeria
Abstract
Higher education management is key to the development of any nation. Some of the challenges that are often
managed include examination, admission, and record problem. In this work, we focused on the admission system
in higher schools of learning because it is fundamental to solving other problems. We studied the application of
Decision Support Systems in Schools and came up with a new Decision Support Tool for admission processing.
The system relied on rules generated from information gathered from admission administrators. The significant
of the work lied in the fact that uncertainty in admission process and unnecessary time wastage are eliminated.
Introduction
The task of “deciding” pervades all administrative organizations, especially education. Decision-
making is a major responsibility of all school managers. Decision-making as a very important duty of manager
can be defined as the receiving and analyzing of relevant information about managerial problem, for the purpose
of making the most suitable choice among alternative choices of actions.
An understanding of the decision- making process is crucial to all school managers, because the
education sector, like all formal organizations, is basically a decision-making structure. Although, the level and
nature of decisions may vary in a number of ways, there will always be needs to make right decisions within a
given situation. It seems imperative then that every school manager makes provision for decision making;
decisions needs to be rendered continuously.
Decision Support Systems is the area of Information Systems (IS) discipline that is focused on
supporting and improving managerial decision-making (Arnott & Pervan, 2005). DSS has moved from a radical
movement that changed the way information systems is perceived in schools, to a main stream IT movement that
all organizations engage.
A Decision Support System (DSS) is any tool used to improve the process of decision making in
complex systems, particularly where information is uncertain or incomplete (Tessella, 2005). These systems are
designed to help mid-level and senior managers make those difficult decisions about which not every relevant
parameter is known (Sodiya, 2009). There are a number of approaches to DSS systems, each of which assist the
process in a different ways. A DSS then provides decisions based on algorithms derived from an understanding
of the application domain.
The concept of a decision support system (DSS) is extremely broad and its definitions vary depending
on the author’s point of view (Druzdzel and Flynn 1999). On the one hand, Finlay (1994) and others define a
DSS broadly as "a computer-based system that aids the process of decision-making". In a more precise way,
Turban (1995) defines it as "an interactive, flexible, and adaptable computer-based information system,
especially developed for supporting the solution of a non-structured management problem for improved decision
making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker’s own insights."
Other definitions fill the gap between these two extremes. For Keen and Scott Morton (1978), DSS
couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of
decisions. "DSS are computer-based support for management decision makers who are dealing with semi-
structured problems." For Sprague and Carlson (1982), DSS are "interactive computer-based systems that help
decision makers utilize data and models to solve unstructured problems."
2. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5782 (print) ISSN 2225-0506 (online)
Vol.4, No.1, 2014
8
According to Power (1997), the term Decision Support System remains a useful and inclusive term for
many types of information systems that support decision making.
Statements of the Problem
The making of decisions, is at the very centre of the process of school management. School managers
are faced with problems in their day-to-day managerial activities; these problems come in different categories
and different solution in which the existing circumstances differ significantly from the desired situation.
These problems come from the phases inherent in admissions decision-making process: application,
examination, assessment and evaluation and selection of students, where there are no clear-cut parameters for
remedy.
At application and examination levels, school managers’ are faced with myriad of challenges while
deriving/extracting consistent patterns or trends from previous or present admission application and examination
records. This may either be for predictive or any other purposes.
At the level of assessment and evaluation, school administrators’ often face semi-structured problems
trying to gather the statistics associated with admission criteria, which may in turn; determine how selection of
students would be done.
The last phase of admissions process is selection of students. This is where the main problem lies;
because the number of qualified prospective candidates always exceeds the available capacity. Also, admissions
requirements of various faculties and their respective departments have to be referenced. During this phase, at
one point or another, parameters such as state of origin, sex, UTME score, Post-UTME score may need to be
applied, and all these can be quite challenging.
Aim and Objectives
The aim of this study is to design and implement a rule-based admission decision support system for
higher institution of learning.
To achieve the said aim, the following objectives will be accomplished:
a) Review of literature on DSS, information integration theory, decision modeling and decision theory, as
well as survey of methods used in decision making.
b) Extraction of knowledge and Information gathering from School Managers and Education
Administrators.
c) Development of a Rule-Based Admission Decision Support Tool for School Managers.
Motivation
The motivation of the proposed system is to help school managers make better decisions by building a
better Information System (IS) for them to use.
Research Methodology
In order to achieve objectives of this research, the following system development methods will be used:
Analysis, Design, Construction, and Testing
Review of Related Work
Mbilinyi et al (2005) aim was to develop a GIS-Based DSS that use remote sensing (RW), limited field
survey and GIS to identify potential sites for rainwater harvesting (RWH) technologies. Problem: the researchers
wanted to maximize the water availability and land productivity in the semi-arid areas, since the necessary
biophysical data and infrastructure are often lacking. The researchers used the following methodologies: (1) data
collection; data about soil texture, soil depth, topography etc. were collected from Bangalala and Mwembe
villages in Tanzania, (2) data processing and analysis; data obtained were processed in a GIS environment to
produce contour map and to construct digital elevation map (DEM), (3) testing and validation of the DSS; testing
aimed at checking the quality suitability and reliability of the system. Validation was done to prove the validity
of the system.
Result: the study demonstrated the capabilities of RS, GIS and field of data for identifying potential sites for
RWH technologies that may be used for development and management of RWH programmes. The application
of the developed DSS shows that it works efficiently to identify potential sites for RWH technologies in semi-
arid areas.
Limitation: despite the fact that the developed DSS is valuable tool for site selection remote areas. To increase its
usefulness, more works can be carried out to refine the model and to include other ancillary data.
The aim of Siddiqa et al (2000) work was to develop an ICT tool to assist non-specialist biologist
researcher users in performing analysis of large amount of data by applying simple simulation techniques.
Problem: the shortcomings faced researchers for their analysis at present include lack of user-friendly interface,
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and learning complicated statistical tools for not only analyzing the data but also interpreting the data. Therefore,
there is need to use tools which are more intuitive and user friendly, such as agent-based modeling simulation
techniques.
The methodologies used are: (1) data collection and conversion; data were collected from breast cancer patient
from two major onco-referral hospitals. The data was originally in paper format which was digitized based on
different citations and oncologist’s guidelines. (2) Data filtering fusion: the filtering and integration of data and
knowledge collected from disparate sources by different methods into a consistent, accurate, and useful whole
(3) System implementation: utilization of agent-based simulation models on the filtered data in order to order
non-specialist end-users (biologists).
Result: the study demonstrates the standardized conversion process of data from medical practitioners to DSS
end users. Provide tools for easy analysis of data by trickling data down to results of queries.
Limitation: the researchers developed a hybrid DSS based on a novel methodology of using agent-based
modeling and simulation tools as a black box. In the future, the researchers hope to develop more advanced DSS
based on data collected from HIV and other diseases.
Vinnik et al (2005) aim was to contribute to the next generation of academic DSS based on the Data
Warehousing Technology with incorporated Data Mining and Knowledge Discovering functionalities. Problem:
the researchers saw academic resource planning as a highly complex administrative procedure based on
extensive analysis of the entire data related to educational framework. Unavailability of the data in the
appropriate form and lack of tools and approaches for it evaluation prompted the move towards more systematic
and efficient management of universities asset.
The following methodologies were used: (1) Educational Capacity Analysis; it measures the available teaching
capacities and describes the consumption of school’s academic services. (2) Model development; the researchers
performed University Structure Modelling, the hierarchical structure consists of faculties, degrees and courses.
(3) Model Implementation; the researchers chose a solution which is a database-enabled web-application since it
best fulfils the requirements of a DSS with high availability and a differentiated multi-user access.
Results: the system integrates data from heterogeneous University’s systems. Decision Support functionality is
realized by offering reporting tools for solving particularly capacity-related tasks as well as by allowing users to
navigate through the data and query it, generate interactive visualizations and explore those for retrieving
interesting details.
Limitation: the future work of the researchers will be directed towards improving the data integration routines
and enhancing the user interface to enable intuitive and interactive visual exploration of accumulated data with
incorporated data mining techniques for expert trend analysis.
The aim of Nazari (2005) study was to proposed DSS framework particularly for construction
consultants that involves in the preparing the feasibility study in order to improve the effectiveness of the
decision-making process. Problem: A feasibility study is an important tool that investors can rely on to help them
make management and development decisions. Where there is uncertainty as to which events might occur, the
logic of the decision process should include necessary information. Feasibility study needs a lot of information
and the process to analyze the information is very tedious and time-consuming. The DSS is an attempt to
simplify the analyzing process and the time needed in preparing the study.
The researcher methodologies were divided into three stages. Stage 1: introduction of the research field; Stage 2:
data collection –this stage is the guideline to acquire the data needed to achieve the objective of his research.
Stage 3: data analysis and interpretation that have been obtained from the Stage 2 was conducted.
Result: the data collected were verified, edited and then analyzed to the aim of the research. The following
results were identified and developed: (a) decision making process of feasibility study (b) cost estimation of DSS
and (c) DSS Framework for feasibility study.
Limitation: this research covers only the feasibility stage, further research on the implementation of DSS on
other stage in the development of construction project can be explored.
Nakakawa (2006) aim was develop a Spatial Decision Support Tool for landfill site selection for
municipal solid waste for Kampala and neighbouring districts in order to improve waste disposal crisis in these
districts. Problem: effective management of solid waste was a major problem of Kampala. The situation may be
due to several factors, including a poorly managed and uncoordinated approach to waste management practice
and landfill. There exist mostly dump sites, which are poorly sited and lack of management to ensure proper
operation.
The following are the methods the researcher used: (1) identification of parameters (geographical and social
parameters) that decision makers considers when locating landfill sites and collection of dataset (2) architecture
design for the Spatial Decision Support Tool (3) development of a computer-based prototype Spatial Decision
Support Tool for identifying and selecting a suitable landfill sites (4) tool evaluation –adjustment of weights
assigned to parameters was done.
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Results indicated the system provides the functionality of selecting features from GIS database, which was use to
select only potential landfill sites from the entire overlay map.
The limitation of the tool is as follows: (a) ground water level was not included in the tool (b) it does not contain
digital map of the entire country (c) further research can be done to incorporate the opinions of other
stakeholders.
Transforming Education Decision-Making
The day-to-day administration of schools involves considering large amount of records and
management data, which have to be collected, stored, and selectively retrieved, updated and statistically
analyzed.
Utilizing a decision support system is a proactive way to use data to manage, operate, and evaluate education
institutions. Depending on the availability and quality of the underlying data, such a system could address a wide
range of questions by distilling data from any combination of school records systems. School decision-making
can be transformed by Extraction, Transform and Loading (ETL) functions.
Extract, Transform and Loading (ETL) Process
The extract, transform, and load (ETL) process is necessary when source data in a decision support
system reside in separate, non-interoperable databases. As the name implies, ETL is a three-stage process
designed to move data from legacy source systems into an interoperable format in the decision support system.
In the first step, an “extract” function reads from a specified database and pulls out the desired data. In step two,
a “transform” function uses predetermined business rules to convert the extracted data into a format that is
interoperable with other system data. Finally, in step three, a “load” function moves the edited and cleaned data
to a database repository within a decision support system.
Reason for Support Systems in Schools
Many education stakeholders want access to more data to help them decide how best to operate,
manage, and evaluate our schools. But they do not want just any data—they want better data. They want real-
time data they can use to run their schools more efficiently today; up-to-date information that permits them to
compare school inputs, processes, and outcomes during the current grading period; and longitudinal information
that enables them to anticipate their schools’ needs in the future. In other words, they want data to be useful,
accurate, well organized, and readily accessible to those who need it to make decisions about the operation and
management of the education enterprise.
DSS: The Functional Components
A DSS typically contains the following: user interface, knowledge acquisition interface, knowledge
base and inference engine. The functional architecture is depicted in the figure below.
Figure 1: Functional components of DSS
Source: Haettenschwiler (1999).
DSS: The Framework
The DSS consists of three major subsystems, namely, (a) the dialogue subsystem, (b) the input
management subsystem and (c) the knowledge management subsystem which is consistent with the general
architecture of DSS.
The dialogue subsystem serves to integrate various other subsystems as well as to be responsible for
user-friendly communications between the DSS and the decision maker; the subsystem coordinates all functions
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or commands selected by the decision maker. The input management subsystem organizes and manages all the
inputs for solving the IS project problem; the type and the quantity of data inputs for solving the problem vary
typically from one problem to another. The knowledge management subsystem manages all the multi-criteria
analysis methods available in the DSS.
Figure 2: The DSS framework
Source: Deng and Wibowo (2008).
Rule-Based System
A rule-based system is a ‘knowledge based system’, which works as a production system in which rules
encode expert knowledge. Most decision support systems are rule-based. It is a DSS based on a set of rules that a
human expert would follow in solving a problem. A classic example of a rule-based system is the domain-
specific expert system that uses rules to make deductions or choices.
A rule-based system consists of if-then rules, a bunch of facts, and an interpreter controlling the
application of the rules, given the facts. If-then rules are one of the most common forms of knowledge
representation used in decision support systems. Systems employing such rules as the major representation
paradigm are called rule based systems. Some people refer to them as production systems.
Design Methodology
In any software development project, it is very important to select and utilize the correct combination of
design methodologies. For the purpose of this study, we use a blend of four development process design
methodologies, namely Analysis, Design, Construction and Testing.
System Study and Design
System Study - Information Gathering and Analysis
The first phase of any software engineering process is analysis and specification of requirements.
This phase involved acquiring a general understanding of the problem and formalizing the expert knowledge.
Questionnaire, interviews and discussions with the domain expert (Admissions Officer) were used to gather
information and formulate rules based on the current practice. The researcher found out the kind of admissions
requirements that were being used as parameters in the admissions process.
Systems Design
A system design is a conceptual representation of the components/modules that are required by a
system. The system design includes the DSS database design, user interface design, the associations among the
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modules of the system, and the rules which govern operations on the system modules. A model of the decision
support tool was designed using Unified Modelling Language (UML) use case diagram.
End-User
Figure 3: Admission Decision Support Tool use case diagram.
Source: Author
Admission Decision Support Tool
View Order of
Performance
Selection of
Candidate
s
View & Upload
Application
Data
View Performance
Ratings
Login
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System Flow
Figure 4: Schematic diagram of Admission Decision Support System
Source: Author
System Implementation
This section describes the implementation of the design solution presented earlier to meet the requirements of
users in admission management. The technologies used here were selected based on their relevance to the
proposed system. MySQL DBMS was used to design the back end of the ADSS while HTML and PHP were
used to design the user interface and the inference mechanism respectively.
Program Logic
The logic of the program is based on the concepts of rule-based systems. When the user logs on, the system will
first and foremost, validate the user’s authenticity. However, if the information provided is found to be valid, the
system simply takes the user to the menu page. The menu provides the user with four core admission-related
functions, most importantly, candidates’ selection.
To select candidates, the user just need to select faculty, department, and input admission quota. After this has
been done, the system will determine the cut-off mark, and select candidates that satisfies admission
requirements and within cut-off mark range.
Algorithm/Rules for Selecting Candidates
--from end-user--
Select faculty and department
Input admission quota
--system---
Generate admission requirements of the selected departments
merit1=0.3*quota; merit2=0.1*quota
catchment1=0.4*quota; catchment2=0.2*quota
counter=0
UME, NUC, ACADEMIC AFFAIRS
Knowledge-Based
System
Admission
Decision
Inference Engine
End-User
(Admission Officer)
Database
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-merit list-
Repeat Until (counter<=merit1) {
Select candidates that satisfies admission requirements, chose
AAUA as 1
st
choice institution and are within merit score range
Update counter
}
Repeat Until (counter<=merit2) {
Select candidates that satisfies admission requirements, chose AAUA as 2
nd
choice institution and are within
merit score range
Update counter
}
-catchment list-
Repeat Until (counter<=catchment1) {
Select candidates that satisfies admission requirements and state-of-origin condition, chose AAUA as 1
st
choice
institution and are within catchment score range
Update counter
}
Repeat Until (counter<=catchment2) {
Select candidates that satisfies admission requirements and state-of-origin condition, chose AAUA as 2
nd
choice
institution and are within catchment score range
Update counter
}
Database
Database tables
The following database tables were created using MySQL database management system.
Table 4.1: Personal_data table
This table has attributes of applicants in the system.
S/N NAME TYPE DESCRIPTION
1 Id Tinyint(4) Primary key for identification
2 Reg_no Varchar(10) Holding candidates’ JAMB registration ID
3 Surname Varchar(20) Holding candidates’ surname
4 Last_name Varchar(20) Holding candidates’ last name
5 Mid_name Varchar(20) Holding candidates’ middle name
6 Sex Varchar(6) Holding candidates’ gender
7 Dob Varchar(10) Holding candidates’ date-of-birth
8 state_of_origin Varchar(15) Holding candidates’ state-of-origin
9 Lga Varchar(20) Holding candidates’ state’s local govt. area
Table 4.2: Login_detail table
This table has the attributes of the admission officer login data
S/N NAME TYPE DESCRIPTION
1 Username Varchar(12) Holding user’s secret username
2 Password Varchar(12) Holding user’s password
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Table 4.3: Utme_de_info table
This table shows all the required attributes of UME information of applicants.
S/N NAME TYPE DESCRIPTION
1 Id Tinyint(4) Primary key for identification
2 Reg_no Varchar(10) Holding candidates’ JAMB registration ID
3 Entry_mode Varchar(3) Holding candidates mode of entry
4 Subject1 Varchar(12) Holding first subject
5 Subject1_score Varchar(2) Holding first subject’s score
6 Subject2 Varchar(12) Holding second subject
7 Subject2_score Varchar(2) Holding second subject’s score
8 Subject3 Varchar(12) Holding third subject
9 Subject3_score Varchar(2) Holding third subject’s score
10 Subject4 Varchar(12) Holding fourth subject
11 Subject4_score Varchar(2) Holding fourth subject’s score
12 Total_score Varchar(3) Holding aggregate scores
13 Putme_score Varchar(2) Holding post-UME score
14 First_choice_sch Varchar(6) Holding 1st
choice institution
15 Faculty1 Varchar(12) Holding 1st
choice faculty
16 Course1 Varchar(15) Holding 1st
choice course
17 second_choice_sch Varchar(6) Holding 2nd
choice institution
18 Faculty2 Varchar(12) Holding 2nd
choice faculty
19 Course2 Varchar(15) Holding 2nd
choice course
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Design Outputs
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Conclusion and Recommendation
Conclusion
In many educational institutions, the management of admissions related information has been poorly handled.
The admissions officer has therefore been confronted with the problem of generation of accurate and up-to-date
information during each of the stages inherent in admission process.
The development of a knowledge-driven ADSS seems to be the needed shift away from the awkward manual
way of admitting students into higher institution of learning; a system which clearly permits for corruption and
man-know-man in admission process. The ADSS is a standard way of admission management. It allows easy
processing of admission related data into meaningful information. If well administered, the ADSS will reduce
admissions rigours to a manageable range.
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Recommendation
This ADST was developed with the intention of using it in the admissions process management in higher
education institutes. The researcher recommends that this ADST be put to its intended use and provide an avenue
for proper, efficient and effective admissions process management. This ADST is also recommended for use by
admissions officers to enable them to carry out proper admission management. In future, the use of Fuzzy
Control System shall be considered.
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