Analysis Of Data Mining Model For Successful Implementation Of Data Warehouse In Tertiary Institutions (A Case Study Of Irahim Badamasi Babangida University)
This document discusses a proposed data mining model for analyzing data from tertiary institutions in order to successfully implement a data warehouse. It begins by introducing data mining and knowledge discovery in databases. Then, it describes using object oriented analysis and design methodology to analyze the current system at Ibrahim Badamasi Babangida University in Nigeria and identify problems. This includes examining the admissions process and how exam records are collected. The goals of the proposed model are then stated as finding relationships between admission results and procedures/lecturers, and allowing predictions with little user intervention.
A Model of Decision Support System for Research Topic Selection and Plagiaris...theijes
The paper proposes a model of the decision support system for deciding a research topic in academia. The biggest challenge for a student in the field of research is to identify area and topic of research. The paper explains the model which helps student to identify the most suitable area and/or topic for academic research. The model is also design to assist supervisors to explore latest areas of research as well as to get rid of non intentional plagiarism. The model facilitates the user to select either keyword bases topic search or questionnaire based topic search. The model uses local database and service of a Meta search engine in decision making activity
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
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.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
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.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
Educational Data Mining to Analyze Students Performance – Concept PlanIRJET Journal
This document discusses using data mining techniques to analyze student performance data from educational institutions. It proposes using clustering and classification algorithms like K-means and Naive Bayesian on data collected from sources like learning management systems and surveys. The goals are to classify students into performance levels, identify factors affecting performance, and make recommendations to help students improve. Clustering could group students and classification could predict performance based on attributes. Analyzing the data may provide insights to enhance guidance and outcomes. The paper presents this as a conceptual plan to apply data mining in education.
A Model of Decision Support System for Research Topic Selection and Plagiaris...theijes
The paper proposes a model of the decision support system for deciding a research topic in academia. The biggest challenge for a student in the field of research is to identify area and topic of research. The paper explains the model which helps student to identify the most suitable area and/or topic for academic research. The model is also design to assist supervisors to explore latest areas of research as well as to get rid of non intentional plagiarism. The model facilitates the user to select either keyword bases topic search or questionnaire based topic search. The model uses local database and service of a Meta search engine in decision making activity
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
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.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
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.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
Educational Data Mining to Analyze Students Performance – Concept PlanIRJET Journal
This document discusses using data mining techniques to analyze student performance data from educational institutions. It proposes using clustering and classification algorithms like K-means and Naive Bayesian on data collected from sources like learning management systems and surveys. The goals are to classify students into performance levels, identify factors affecting performance, and make recommendations to help students improve. Clustering could group students and classification could predict performance based on attributes. Analyzing the data may provide insights to enhance guidance and outcomes. The paper presents this as a conceptual plan to apply data mining in education.
A Survey And Taxonomy Of Distributed Data Mining Research Studies A Systemat...Sandra Long
This document discusses a systematic literature review of distributed data mining (DDM) research studies conducted between 2000-2015. The review aimed to map previous DDM research and identify gaps to motivate future work. It analyzed 486 studies to develop statistics on DDM research trends over time. Key findings included identifying the most influential journals, active researchers, popular research topics, commonly used datasets and methods. The review provided a taxonomy of the DDM field and conclusions to help researchers gain a comprehensive overview of the current state of DDM research.
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
This document discusses using data mining techniques to analyze faculty performance at an engineering college in India. It proposes analyzing 4 parameters - student complaints, feedback, results, and reviews - to evaluate faculty instead of just 2 parameters (feedback and results) used previously. It will use opinion mining to analyze faculty performance and calculate scores. The system will collect data, preprocess it, apply a KNN algorithm to the 4 parameters to calculate scores for each faculty, sum the scores, classify results using rule-based classification, and analyze outcomes by subject and class. It reviews related work applying educational data mining and concludes the multiple classifier approach is better, and future work could consider more parameters and expand to all college branches and departments.
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.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
IRJET- Analysis of Student Performance using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to analyze student performance data and predict student outcomes. It begins with an abstract describing how educational data has become important for supporting student success. It then discusses prior related work applying classification algorithms like decision trees to predict student grades or performance. The document goes on to describe applying various classification algorithms like J48 decision trees, K-nearest neighbors, and others to student data and comparing their performance at predicting outcomes. It discusses preprocessing the data with k-means clustering before classification. The goal is to identify at-risk students early to better support them.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
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Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
This document describes a proposed alumni tracking system that uses a C4.5 decision tree algorithm. The system would allow colleges to maintain updated records on alumni and search for alumni by name, batch year, or other details. It aims to reduce the gap between student skills and industry requirements by validating alumni information. The system would have features for alumni registration and profile updating, as well as admin controls. It proposes an architecture with login pages, a registration process, separate home pages for admins and alumni, and sorting of alumni data. The document also discusses the C4.5 algorithm and pruning process used for decision trees, as well as the potential future scope of adding more networking and profile features.
A rule based higher institution of learning admission decision support systemAlexander Decker
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.
A rule based higher institution of learning admission decision support systemAlexander Decker
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.
The document discusses the role of computers in research. It states that computers have become indispensable research tools that are ideally suited for tasks like large-scale data analysis, storage and retrieval, and processing data using various techniques. Computers expedite research work and reduce human effort while improving quality. They allow vast amounts of data to be accurately and rapidly processed and analyzed. Computers also facilitate tasks like report generation, graphing, and formatting references.
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.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
Irjet v4 i73A Survey on Student’s Academic Experiences using Social Media Data53IRJET Journal
This document summarizes a research paper that analyzed social media posts by engineering students to understand their academic experiences. It developed a workflow that integrated qualitative analysis and data mining techniques. By applying classification algorithms to tweets with hashtags, it identified three main problems engineering students discussed: heavy study loads, lack of social engagement, and sleep deprivation. The analysis revealed students struggled with managing heavy course loads, which led to other issues affecting their health, motivation, and emotions. It also found posts discussing diversity issues and cultural stereotypes among engineering students.
Multiple educational data mining approaches to discover patterns in universit...IJICTJOURNAL
This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
A Study on Data Mining Techniques, Concepts and its Application in Higher Edu...IRJET Journal
This document discusses how data mining techniques can be applied in higher education to analyze educational data and improve various aspects of the student experience and institutional effectiveness. It provides an overview of common data mining methods like classification, clustering, association rule mining and their uses in higher education for applications such as student performance analysis, course recommendation systems, dropout prediction, and curriculum improvement. It also addresses potential issues around privacy, data security and ethics when using data mining in education.
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
Asl rof businessintelligencetechnology2019kamilHussain15
This document presents a systematic literature review on business intelligence technology, contributions, and applications for higher education. The methodology used PRISMA to identify relevant research. 12 articles were included from databases based on screening criteria. To answer the research questions, the included articles were analyzed. For technology (Q1), business intelligence techniques identified were data mining, viable system model, learning analytics, cloud computing, and behavioral analytics. Tools included Hadoop, Gephi, BigData by IBM, and web-based. Contributions (Q2) were knowledge transfer, innovation, and evaluation. Applications (Q3) included research, curriculum, assessment, behavior analysis, student enrollment, and resource management.
💐 College Argumentative Essay. 16 Easy Argumenta.pdfScott Bou
The ballet Giselle explores themes of romanticism through the story of a peasant girl named Giselle who falls in love with a nobleman disguising as a peasant. When Giselle discovers his true identity, she dies of a broken heart and is transformed into a Willi, a supernatural being. The ballet was created during the Romantic Era in 19th century Europe, a period influenced by romanticism. Elements of the ballet, like its costuming and staging innovations, reflected changes of the time and incorporated themes symbolic of the Romantic movement.
Teagan Education Consulting Columbia College ChicagoScott Bou
The document discusses human rights violations throughout history. It provides examples such as ancient China censoring and burning books, denying people access to information. It also discusses apartheid in South Africa, where the National Party passed discriminatory laws that stripped non-whites of rights and forcibly removed 3.5 million black South Africans from their homes based solely on their race. These are clear examples of governments violating basic human rights like access to information, equality, and freedom from discrimination.
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This document discusses a systematic literature review of distributed data mining (DDM) research studies conducted between 2000-2015. The review aimed to map previous DDM research and identify gaps to motivate future work. It analyzed 486 studies to develop statistics on DDM research trends over time. Key findings included identifying the most influential journals, active researchers, popular research topics, commonly used datasets and methods. The review provided a taxonomy of the DDM field and conclusions to help researchers gain a comprehensive overview of the current state of DDM research.
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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.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
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This document discusses using machine learning techniques to analyze student performance data and predict student outcomes. It begins with an abstract describing how educational data has become important for supporting student success. It then discusses prior related work applying classification algorithms like decision trees to predict student grades or performance. The document goes on to describe applying various classification algorithms like J48 decision trees, K-nearest neighbors, and others to student data and comparing their performance at predicting outcomes. It discusses preprocessing the data with k-means clustering before classification. The goal is to identify at-risk students early to better support them.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
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This document describes a proposed alumni tracking system that uses a C4.5 decision tree algorithm. The system would allow colleges to maintain updated records on alumni and search for alumni by name, batch year, or other details. It aims to reduce the gap between student skills and industry requirements by validating alumni information. The system would have features for alumni registration and profile updating, as well as admin controls. It proposes an architecture with login pages, a registration process, separate home pages for admins and alumni, and sorting of alumni data. The document also discusses the C4.5 algorithm and pruning process used for decision trees, as well as the potential future scope of adding more networking and profile features.
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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.
A rule based higher institution of learning admission decision support systemAlexander Decker
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.
The document discusses the role of computers in research. It states that computers have become indispensable research tools that are ideally suited for tasks like large-scale data analysis, storage and retrieval, and processing data using various techniques. Computers expedite research work and reduce human effort while improving quality. They allow vast amounts of data to be accurately and rapidly processed and analyzed. Computers also facilitate tasks like report generation, graphing, and formatting references.
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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.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
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This document summarizes a research paper that analyzed social media posts by engineering students to understand their academic experiences. It developed a workflow that integrated qualitative analysis and data mining techniques. By applying classification algorithms to tweets with hashtags, it identified three main problems engineering students discussed: heavy study loads, lack of social engagement, and sleep deprivation. The analysis revealed students struggled with managing heavy course loads, which led to other issues affecting their health, motivation, and emotions. It also found posts discussing diversity issues and cultural stereotypes among engineering students.
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This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
A Study on Data Mining Techniques, Concepts and its Application in Higher Edu...IRJET Journal
This document discusses how data mining techniques can be applied in higher education to analyze educational data and improve various aspects of the student experience and institutional effectiveness. It provides an overview of common data mining methods like classification, clustering, association rule mining and their uses in higher education for applications such as student performance analysis, course recommendation systems, dropout prediction, and curriculum improvement. It also addresses potential issues around privacy, data security and ethics when using data mining in education.
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
Asl rof businessintelligencetechnology2019kamilHussain15
This document presents a systematic literature review on business intelligence technology, contributions, and applications for higher education. The methodology used PRISMA to identify relevant research. 12 articles were included from databases based on screening criteria. To answer the research questions, the included articles were analyzed. For technology (Q1), business intelligence techniques identified were data mining, viable system model, learning analytics, cloud computing, and behavioral analytics. Tools included Hadoop, Gephi, BigData by IBM, and web-based. Contributions (Q2) were knowledge transfer, innovation, and evaluation. Applications (Q3) included research, curriculum, assessment, behavior analysis, student enrollment, and resource management.
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Analysis Of Data Mining Model For Successful Implementation Of Data Warehouse In Tertiary Institutions (A Case Study Of Irahim Badamasi Babangida University)
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then is new about data mining (DM)? The simple
answer is that, in data mining the amount of the
stored data is in the digital or electronic format and
the search is automated or improved by a computer.
In data mining, it is important to understand the
difference between a model and a pattern. Model is
an intangible representation of reality or universal
summary of the dataset and makes statement about
any point in the full measurement space. While
pattern describe an arrangement, a relationship to a
relatively small part of the data or the space in which
data would occur. In 1960’s, computer was
increasingly applied to data analysis problems and it
was renowned that if one searches long enough, one
can always find some model to fit in the dataset but
complexity and size of the model were important
considerations.
Analysis of Data Mining Model for successful
implementation of Data Warehouse in tertiary
institutions was geared towards data mining tertiary
institutions’ huge data warehouse which involves
digging through heaps of data in search of
information which the management uses for proper
running of the institution as well as to make prospect
predictions based on the current trend of events.
Object Oriented Analysis and Design (OOADM)
methodology was deployed to design this model.
Source data was collected from the department of
Admissions/academic planning and Exams and
records sections in the University.
STATEMENT OF THE PROBLEM
The topic focuses on understanding how the present
system works, the problems that are inherent in the
present system as well as analyze and design a
proposed system using Object oriented Analysis and
Design (OOADM) methodology.
OBJECTIVES OF THE STUDY
The objectives of this research work are to develop a
system that should be able to:
(i) Find relationship between
a) Results and admission procedure.
b) Results of courses and the lecturers that
taught them in different sessions.
(ii) Investigate the existing system and the
problems affecting it.
(iii) And analyzing a new system so as to enable
the System Designer, build up a computer-based
model that predict educational behaviours with little
or no user intervention.
II. METHODOLOGY
Methodology is defined as a system of wide ideology
or rules from which specific methods or procedures
may be derived to deduce or work out different
problems within the range of a particular discipline.
Methodology is not a formula but a set of practices. It
is used to refer to a specific series of steps, methods,
techniques and measures which governs the
collection, analysis and design of a particular project.
Methodology is defined as a outline that is used to
structure, plan, and control the process of developing
an information system. It is used. The researcher used
Object oriented Analysis and Design (OOADM)
methodology to analyze the present system as well as
to design the proposed system with the primary aim
of:
(i) Identifying the problems innate in the present
system.
(ii) Investigating the causes of these problems
(iii) And proffering a likely solution to these system
RESEARCH DESIGN
The researcher adopted a survey for this study. Fritz
Scheuren defines survey as a way often used to
describe a technique of assembling information from
a sample of individuals. This sample is usually just a
fraction of the population being studied. Olagbewo
(2006) also defines survey as design which documents
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the nature, scope, dimension, direction of events,
behaviours, attitude etc about things or persons.
AREA OF THE STUDY
The study was conducted in Ibrahim Badamasi
Babangida University Lapai, Niger State.
POPULATION
The research/study population consists of the
Examination Office, Faculty of Natural Science and
Academic department IBB University Lapai.
INSTRUMENT OF DATA COLLECTION
The research instrument for data collection was
interview method. The researcher conducted an
interview in order to find out the stages of admission
processing, how exams records are collated,
Association between results and admission and the
process of data storage and mining.
ANALYSIS OF THE PRESENT SYSTEM
A study to these two departments/units revealed the
different methods they engaged in carrying out their
daily activities.
ADMISSIONS PROCEDURE
The admissions department is charged with the duty
of processing: -
(i) Students’ admission application
(ii) Students’ change of Department application.
(iii) Students’ direct entry application.
The processing of these applications takes the
following stages for undergraduate studies
(i) Unified Tertiary Matriculation Examination
(UTME) sends list of successful candidates to the
university.
(ii) Cut off mark is set by the school management.
(iii) Candidates who UTME score exceeds the UTME
cut off mark are short listed.
(iv) Direct entry student must:
a) Possesses Ordinary National Diploma (OND)
or Higher National Diploma (HND) or Nigerian
Certificate in Education from a reputable polytechnic
or colleges of Education.
b) HND holders must have at least lower credit.
c) OND holders must have upper credit.
d) NCE holders must have upper credit.
(v) Students pay school fees and document
themselves
The above description describes the admission
process into the various undergraduate programmes
run by the university.
Figure 1: System flowchart of Admissions Application
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Figure 2 : System flow chart of student admission and
registration
EXAMS AND RECORD PROCEDURE
The result and transcript of students are vital
documents that are derive from various departments
and finally kept at the exams and records department
of the university. This unit uses the following
technique in collating and documenting students’
results
1. Lecturers forward a copy of the result courses
they handled for a semester to exams and records in
the department.
2. These results are documented and forwarded
to the Faculty exams and record.
3. The results are then vetted by vetting
officers/senate.
4. A student request for a transcript through the
bursary, and the department is notified.
5. The Bursary raises the student transcript, and
forwards it to the academic unit for documentation
and then forwards it back to the department.
6. The transcript generated is cross checked
with the one at exams and record.
7. The transcript is recomputed at exams and
record.
8. The recomputed transcript is dispatched to
the address requesting it.
Figure 3: Flow chart of exams and transcript
computation
The faculty exams and record department also
prepare departmental lecture time table, examination
time table and final result statement.
The process of result statement is carried out thus,
a) Faculty forwards the final result of the
students at the end of their study to the university
senate.
b) The senate vets the result to make sure there
are no students who have over stayed in the school
and no form of fraud in the result computation
process.
c) Senate sends back the approved and vetted
result to the department.
d) The department forwards a copy of the
approved result to the exams and records.
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e) Exams and records section prepare a
statement of result for each student in the result
sheet.
f) Students sign the duplicate copy of the
statement of result and keep the original copy.
g) Exams and record officer documents the
duplicate copy signed by the student.
h) Exams and record officer prepare a certificate
to be awarded to the student on the next
convocation.
ANALYSIS OF THE PROPOSED SYSTEM
The following stages are used in object-oriented
analysis and design methodology to analyze and
design a system within its boundary.
1. Preliminary Investigation (initial survey) phase
This is the initial project investigation. The objective
is to identify what system is required, what should be
its broad scope, tentative cost, time and other
resources requirements. Once this broad and
tentative information is collected, the decision is
made whether to proceed developing the system or
not. The goal of system investigation phase is to
answer the following questions:
a. What is the business problem?
b. Is it a problem or an opportunity?
c. What are the main causes of the problem?
d. Can the problem be solved improving the
current information system?
e. Is a new information system needed?
f. Is this a feasible information solution to this
problem?
2. Problem Analysis Phase
This stage provides the analyst sufficient opportunity
to understand thoroughly the problem, opportunities
and/or directives that triggered the project. This stage
is primarily concerned with system owners and
system users view of the existing system.
3. Requirement (use case) Analysis Phase
This stage defines the business requirement for the
new system. Or is the process of discovering,
analyzing, defining and documenting the business
requirements. It involves measuring each functional
and non- functional requirement, technical,
operational and transitional requirements. This phase
answers the question
a. what do users need and want from the
existing system?
To answer this question, the following activities
must be embarked upon at this place:
(i) Define use cases.
A use case represent a class of functionality provided
by the system or actions that the user might carry
out in order to complete a specific task.
(ii) Analyze use cases (requirements).
4. Decision Analysis Phase
Decision making is a framework that helps project
managers solves wide range of decision- making
problems. Any decision analysis process is based on
three main policies, called the 3c’s principle.
I.Consistency – is an important aspect of decision
analysis process for similar kinds of problems and
opportunities to enable consistency in decision
making over time.
II.Comprehensiveness –decision analysis process should
include a comprehensive assessment and analysis of
the business situation. Missing or incomplete
information can lead to incorrect decisions.
III.Continuity – the value of decision analysis will
significantly diminish if it is done only on discrete
situation during the course of the project. Decision
analysis is an unremitting process of constructing and
refining decisions during a course of a project.
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Figure 4 : the 3c’ principle of decision making
5. Design Phase
The purpose of the design phase is to devise a solution
for the problem specified by the requirement. This
stage is the first step in moving away from problem
domain to solution domain. This document is similar
to a proposal or plan for the solution, and is used later
at the implementation, testing and maintenance
stage.
The design activity is often divided into two separate
stages.
a) System design which is sometimes also called
a top-level design. This aims to identify the modules
that should be in the system, the specification and
how they interact with each other and the desired
results. At the end of the system design, all the major
data structure, file format, output format as well as
the major modules in the system and specification are
decided.
b) Detail design the internal logic of each of the
modules specified in the system design is decided.
During this stage further details of the structures and
algorithmic design of each module is specified.
6. Implementation and Testing Phase
System implementation is the development,
installation and testing of system components and
delivery of that system into production. (Bentley et.
al.,).
System implementation has several major activities.
There are five major tasks in this phase; coding,
testing, installation, documentation and training as in
Figure 5. The purpose of this phase is to convert the
physical system specifications into working and
reliable software and hardware, document the work
that have been done and provide help for the existing
and future users.
Figure 5 : Five Activities in the System
Implementation Phase
a) Coding
Coding is the process whereby the physical
specification that is created in the preceding stages is
turned into working computer codes by the
programmer team.
b) System testing
Testing is the process of running the program with a
set of data to check for the correctness of the program
(Eze, 2007.)
c) Installation
Installation is the process of moving away from an
existing system to the new, enhanced or present
system.
i. Direct installation
Direct installation is also referring to abrupt cut-over
installation. Direct installation is where an existing
system is turned off and the new system is turned on.
Figure 6 : Direct installation
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ii. Parallel Installation
Parallel installation is when both system; the existing
and new system are operating at the same time until
the management decide that the existing system can
be turned off as in Figure 7.
Figure 8 : Parallel installation
iii. Single Location Installation
Single location also refers to location or pilot
installation. This approach is where the organization
is trying out to use a new system at one location and
then use the experience in deciding how the entire
system should be deployed throughout the
organisation as in Figure 9.
Figure 9 : Single-location installation
iv. Phase Installation
Phase installation is similar with single-location but
the difference is the new system is installed in
functional components.
Figure 10 : Phased installation
Installation may also require a system acceptance test
plan. System acceptance test is a final opportunity
where end users, management and information
system operation decide either to use or reject the
system. System acceptance test is a test performed on
the final system wherein users conduct verification,
validation and audit tests (Bentley et. al., 2007).
7 Documenting the System
Every system development is unique and needs
unique documentation. There are two basic types of
documentation; system documentation and user
documentation.
i. System documentation
System documentation records detailed
information about system’s design specification,
its internal workings and its functionality
(Hoffer et. al., 2005).
ii. Internal documentation: Internal documentation
is part of the program source code or is generated
at compiled time.
iii. External documentation: external documentation
is a system that includes the outcome of
structures diagramming techniques such as data
flow entity-relationship diagrams.
b. User documentation
As stated earlier, system documentation is
intended for system maintenance programme;
user documentation is intended for the user of
the system. There are few types of user
documentations;
i. Reference guide- consists of exhaustive list of
system functions and command.
ii. Quick reference guide- provides essential
information about operating the system in a
short and concise format.
iii. User’s guide- provides information on how users
can use a computer system to perform and
accomplish some specific tasks.
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OBJECT-ORIENTED ANALYSIS AND DESIGN
TERMINOLOGY
I. Actor: this represents anything (such as users or
roles) that needs to interact or interface with to
exchange information.
Use case: a use case describes a series of activities or
actions perform by a system to yield visible results.
Use case Modelling: a use case model is a view of a
system that emphasizes the behaviour as appears to
the outside users.
Polymorphism: is the attribute of being able to as
assign more than one meaning to a thing in different
contexts specially to allow an entity such as a
variable, a function or an object to have more one
form.
Message Passing: it is done when one object hooks to
one or more other object methods to request
information or action. Messages are intrinsic
elements of unified modelling language interaction
diagrams.
Inheritance: It explain methods or attributes defined
in one object class and can be reused in another
object class. Inheritance is the method that permits
new classes to be created out of the existing classes by
extending and refining its capabilities.
Class: It is a set of an object that shares the same
features and methods behaviour.
Behaviour: are things that an object can perform
which correspond to functions that act on their
features (data).
Attribute: are data that represents features or
characteristics of interest about an object. The
characteristics of an instance of a class are called
attributes that are implemented as variable inside a
class when defining software entity.
Object: is anything that is capable of being seen,
touched or sensed and about which users store data
(features) and combine behaviour.
Unified modelling language: It is a set of modelling
tools or equipment used to specify a software system
in terms of objects.
Encapsulation: It is a conceptual independence where
everything in the product that relates to the portion
of the world is modelled by that object is found in the
object itself. (Stephen, 1996) defines encapsulation as
the packing of several items together into one unit.
USE CASE DIAGRAM
The use case diagram represents the interaction
among actors and use cases and equally specifies the
communication between a user and the system. It
defines the environment and its interaction with the
use cases of the proposed systems.
Figure 11: A use case diagram of Admissions
subsystem
Figure 12 : a use case diagram of transcript subsystem
System
Student
Portal
Admission board
Admission officer
request for admission offer
registers new student
document unsuccessful students
publishes successful candidates names
conduct UTME exams
submit files
post students profile on net
Admission subsystem
System
Request for transcript
Produce course result
Regenerate transcript
Vet regnerated transcript
Post transcript
Generate transcript
Student
Department
Exams and Records Office
Lecturer
Senate
Transcript Subsystem
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Table 1 : use case analysis table for generating information admission use case
USE CASE NAME REGISTER NEW STUDENT
USE CASE DESCRIPTION This use case describes the procedure for student registration. On
Completion the student is given a matriculation number.
Typical courses of events Actor action
STEP 1: this use case is initiated when a
student prints his/her letter of admission
from the university portal.
STEP 2: student submits the admission
offer to the admission office.
System response
STEP 4: the admission officer
verifies the student particulars with
the list of admitted student from the
admission board.
STEP 5: student pays schools fees.
STEP 6: accepts student form and
issues student matriculation
number.
ALTERNATE COURSE STEP 3: if the student records are not found in the original list, the student
arrested and investigated.
PRE-CONDITION Registration can only be carried out only by the students who have met the
requirements and criteria for admission.
Figure 13: analysis sequence diagram for student registration
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Table 2: Use case analysis for generating student transcript
USE CASE NAME GENERATE TRANSCRIPT
ACTORS STUDENTS, EAXAMS AND RECORD OFFICER AND DEPARTMENT
DESCRIPTION This use case describes the process of generating students transcript
Typical course of
Action
Actor action
STEP 1: this use case is
initiated when a student
apply for his/her
transcript.
STEP 6: the use case
terminates when the
generated transcript is
received by those
demanding it.
System response
STEP 2: student personal academic records are
validated
STEP3: student file is retrieved at exams and record
and transcript request is forwarded to the department
STEP 4: transcript is generated for the student.
STEP 5: the transcript is vetted and then sent to
where it is being requested.
ALTERNATE COURSE STEP 7:if student file is not found, the admission record is checked to see if the
student was properly admitted into the university and if so, new file is generated.
PRE-CONDITION Transcript can only be raised for bonafide student of the university who have
Complete their undergraduate studies.
POST-CONDITION transcript is sealed and sent to its destination
Figure 14 : analysis sequence diagram for transcript generation
Student Transcript homepage Transcript page Transcript page controller Transcript table
1 : click transcript button()
2 : displays()
3 : click apply for transcript()
4 : send details()
5 : verifies details()
Exp 1: if invalid details
6 : save details()
7 : return confirmation()
8 : display transcript()
9 : prints transcript()
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FINDING AND IDENTIFYING OBJECTS
The next stage of this analysis is fining and identifying business objects. The business objects that are
identified at the course of this research are mentioned below:
- Lecturers
- Students
- Admission officer
- Admission list
- Destination
- Transcript
- Exams and record officer
- Faculty
- Department
- Session
- Admission record
- Result
- Student file
- Portal
- University senate
- School fees
- Academic program
Figure 15 : Diagram that shows the weaknesses
identified in the present system
PROBLEMS IDENTIFIED WITH THE PRESENT
SYSTEM
The problems that are identified in the existing
system are enumerated below:
I.Poor performance evaluation
II.Lack of data mining algorithm
III.Unstable admission procedures and statistics
IV.Records still stored in files and excel format
ADVANTAGES OF THE PROPOSED SYSTEM
OVER THE PRESENT SYSTEM
The proposed system has better and great
advantage than the existing system because it
provides the following facilities:
I.Provide algorithm for mining and extracting data
II.Provide a stable admission procedure
III.Provide data warehouse infrastructure
IV.Load, clean and extract data in warehouse
LIMITATIONS OF THE PROPOSED SYSTEM
i. The proposed system requires costly and expensive
computing equipment.
ii.It requires too much work load in building it.
iii. Requires extensive research laboratory ready with
internet connections to source information.
iv. The proposed system requires a lot of fund to begin.
v.It requires a lot of brain storming ideas.
JUSTIFICATION OF THE NEW SYSTEM
i.The proposed system will help the institution to
find the relationship between results of courses and
the lecturers that taught in different sessions.
ii.The proposed system will help in finding the
relationship between results and admission
procedure.
iii.The proposed system will provide a better
performance evaluation
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iv.The new system will help in providing stable and
normal admission procedures and statistics.
Figure 16: High level model of the proposed system
Summary
The research investigated on analysis of data mining
model for successful implementation of data warehouse
in tertiary institution in Ibrahim Badamasi Babangida
University Lapai, Niger State. The literature review
focused on several works done on data mining
techniques/models by other researchers. The method
used in data collection was interview which was
carried out by the researcher and it was validated by
experts before it was administered and the
methodology used was object-oriented analysis and
design to propose the new system.
III. CONCLUSION
Using a particular methodology to analyze a given
system in a dynamic world is the major activity in
software development. The researcher applied all the
required methods, techniques and procedures to
investigate and analyze the present system in other to
proffer solutions to the identified problems. Finally,
the proposed system tends to provide inter-relationship
amongst these two departments in the institution for
appropriate decision making and proper management.
IV. REFERENCES
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Cite this article as :
Amit Mishra, "Analysis of Data Mining Model for
Successful Implementation of Data Warehouse in
Tertiary Institutions (A Case Study of Irahim
Badamasi Babangida University) ", International
Journal of Scientific Research in Computer Science,
Engineering and Information Technology
(IJSRCSEIT), ISSN : 2456-3307, Volume 8 Issue 5, pp.
305-316, September-October 2022. Available at doi :
https://doi.org/10.32628/CSEIT228531
Journal URL : https://ijsrcseit.com/CSEIT228531