DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
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.
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.
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.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
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.
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.
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.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
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.
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
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 Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
A Brief Introduction to Machine Learning techniques applied in data science. Definitions and applications of machine learning algorithms. Classification and Regression Techniques.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
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.
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.
A study model on the impact of various indicators in the performance of stude...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
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.
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
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 Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
A Brief Introduction to Machine Learning techniques applied in data science. Definitions and applications of machine learning algorithms. Classification and Regression Techniques.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
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.
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.
A study model on the impact of various indicators in the performance of stude...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
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.
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.
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.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, itstates that the best algorithm for the improvement of students performance using these algorithms.
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
ow-a-days data volumes are growing rapidly in several domains. Many factors have contributed to this growth, including inter alia proliferation of observational devices, miniaturization of various sensors ,improved logging and tracking of systems, and improvements in the quality and capacity of both disk storage and networks .Analyzing such data provides insights that can be used to guide decision making. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. As an educational management tool, predictive analytics can help and improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum Development. This paper presents an analytical study of this approach’s prospects for education planning. The goals of predictive analytics are to produce relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, value of large amounts of data and interactive exploration.
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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A Nobel Approach On Educational Data Mining
1. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 7
A Nobel Approach On Educational Data Mining
Daljit Kaur1
and Harjot Kaur2
Lecturer, Dept. Of computer science,
Sant Baba Bhag Singh Post Graduate College, Jalandhar, Punjab, India1
Lecturer, Dept. Of computer science,
Sant Baba Bhag Singh Institute of engineering and technology, Jalandhar, Punjab, India2
Abstract: Now a day in the area of information technology, information plays a very important role. We gather data
from different data sources store it and process it to generate some meaningful information which help us in decision
making. With the immense use of computer and electronic devices there is explosive growth in data collection. With
the immense growth of data we store data in data warehouse, which help us to enhance business intelligence, data
quality. So to analyze this vast amount of data which help us to draw some meaningful decisions we use a special tool
called data mining. In this paper we review data mining and its application in field of education. It is an emerging
discipline developing method and using those methods for better understanding students.
KEYWORDS: Educational Data Mining, Data Mining, Modern application of data mining
i. Introduction
Data mining is process of analyzing data and summarizing it into meaningful information. Data mining help end
user to extract useful information from large databases [1].Data mining techniques are used continuously on software
and hardware platforms to increase the value of existing information resources. Data mining tools help in predict
future trends which help organization to make knowledge driven decisions. Data mining popularly known as
knowledge discovery in database, it is the nontrivial extraction of implicit, previously unknown and potentially useful
information from data in databases [3, 5].
Data mining techniques are basically outcome of long process of research. The evolution began when business data
is first stored on the computer. As data become huge data mining techniques are used for knowledge discovery. There
are many data mining tools that are used analyze massive amount of data. Commonly used data mining tools for
creating data mining solutions are data mining wizard, model viewer, prediction query builder etc.
ii. Educational Data Mining
Educational data mining is research area which utilizes data mining techniques and research approaches for
understanding how student learns [2].EDM increase the understanding of learning by finding educational trends
which improve Student’s performance, selection regarding course, training. According to HAN and Kamber [4] Data
mining software should be developed in such a manner that it allow the user to analyze data from different
perspective, enable to categorize it and summarize the derived results.EDM can be used to evaluate student methods,
2. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 8
processes and roles helping us understand the strategies that learners develop as they participate in constructionist
learning activities [6].EDM help us in analysis and visualization of data and help to highlight useful information.
EDM uses computational approaches to analyze educational data. Educational data mining focuses on developing
new tools and algorithms for discovering data patterns .Various techniques like statistics, machine learning, neural
network etc. are applied during data mining process. Data collected from online learning systems can be aggregated
over large number of students and can contain many variables that data mining algorithm can explore for model
building. New EDM applications will focus on allowing non-technical uses use and engage in data mining tools and
activities ,making data collection and processing more accessible for all users of EDM [16]
iii. Objective of Educational data mining
A. Educational Research
It convert raw data coming from various educational systems or institutions into useful information which help us in
doing research in field of education.
B. Effective learning
EDM help us doing research in educational field, which lay down groundwork for more effective learning [7]. With
the help of data mining techniques a result evaluation system can be developed which can help teacher and students
to know weak points of the traditional classroom teaching model and it will help them to face rapidly developing real-
life environment and adapt the current teaching realities [8].
C. Prediction
EDM techniques help us in predicting values or variables which help us in decision making. Now a day’s educational
organizations are getting strong competition from other academic competitors[8].To have an edge over other
organizations, needs deep and enough knowledge for better assessment, evaluation ,planning and decision making[8].
D. Providing Feedback
AS EDM include huge amount of data related to educational institute, by analyzing that data we are able to provide
feedback to teacher /administrator which help them to improve student performance.
iv. Techniques of Data Mining
A. Summarization
It is mainly generalization of data. It provides more compact representation of data set, including visualization and
report generation [9].Simple summarization method such as tabulating the mean and standard deviation are often
applied for data analysis, data visualization and automated report generation [11].
B. Cluster analysis
3. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 9
This approach is very popular in machine learning, pattern recognition, information retrieval and biometrics. The
main task of clustering is to make groups of similar objects called clusters. The clustering technique defines the
classes and put the similar objects into one class they belong. For example we make classes of students who like
music and sports after doing analysis students like music put in class music and who like sports put in class music and
make clusters. The objects are clustered based on principle of maximizing the intra class similarity and minimizing
inter class similarity [9].
C. Classification and prediction
Classification is used to classify each item in data set of data into one of predefined set of classes or groups. To make
classification it use various mathematical techniques such as decision tree, neural networks etc. For example we can
apply classification in educational institute to know students who always get marks above 80%, above 70 %, above
60% and student who get marks below 60 % are fall in class of poor of students. This classification helps us to know
students who are poor in studies and necessary steps are taken to improve their performance. The prediction of
student’s performance with high accuracy is more beneficial for identifying low academic performance of the
students at the beginning [10].
D. Decision Tree
Decision Tree is one of the most easy to learn technique of data mining. The root of decision tree is simply the
question that has multiple answers. Decision tree help us in decision making. Another use of decision tree is a
descriptive mean for calculating conditional probabilities [9].For example a decision tree is shown below to analyze
the admission criteria for institute.
+2 marks<50% Marks>50% +2 marks <60% +2 mark>60%
Entrance test qualify Management seats
No admission Admission
granted
No
admission
Admission
granted
Admission Criteria
4. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 10
FIG.1 Decision tree based on admission criteria [9].
E. Association
Association mining helps us to discover pattern based on the relationship between them. Association mining
technique is mainly used in market basket analysis to identify set of item that sell together. For example in a
supermarket on analyzing data we find out that if bread sells than eggs also sell, is association rule mining. It helps us
in discovering pair of items that sell together.
v. Data Mining Techniques in Education
We apply data mining techniques in education for knowledge discovery. Knowledge driven data help us in decision
making. It helps us to learn educational trends. It focuses on how data mining is used for improving student’s success
and processes directly related to students learning [12].Goals of educational data mining is improvement in student
models , it represent information about student behavior, their knowledge etc. EDM method have enable researcher to
model broader range of potentially relevant student attribute in real time ,including higher level constructs than were
previously possible[13].Second goal of EDM is domain knowledge structure which characterize the content to be
learned. Third goal of EDM is pedagogical approach [13]; it is a process which is used to find educational data to
help creating personalized recommendations [14].To accomplish these goals the following EDM techniques are used.
A. Statistics:
This technique is used in EDM for collection and analysis of large set of data. we use this large set of data to make
predictions and to make decisions. The module of statistical analysis is responsible for extraction and processing of
data in database, providing graphical interface to realize the real time statistical analysis of online learning behavior
[19].
B. Visualization:
Another good way of analyzing the data is through visualization. In visualization we graphically represent the data.
Data shown graphically is easy to learn and analyze. For example: we use graphical representation to show students
current performance comparison with his previous year’s performance and learn easily performance increased or
decreased. These techniques are also helpful for instructor which can manipulate the graphical representations
generated and get the understanding and interest of their learners [10].
C. Web mining
Web mining is data mining technique which is used to discover information from web pages or documents.
Information that is discovered by web mining is web graph, web activity an web content etc.web mining is mainly a
four step work i.e. collection of data from web, extracting useful data, analyzing that data and last is producing some
5. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 11
meaningful information from that data. Information collected using web mining is used to gain knowledge based on
findings and analysis.
D. Text Mining
Text mining is way to make qualitative data usable by computer. Qualitative data is descriptive data that we are
unable to measure in numbers. Information can be extracted to drive summaries of words contained in document for
the document based on the words contained in them. You could analyze documents and determine similarities
between them. Text mining used in analyzing open ended survey response, automatic processing of messages and
emails, investigating competitors etc.
E. Educational system
EDM techniques are also used for e-learning. E-learning means online learning on computer using web services. In
this there are basically to groups of peoples one is learner and another is provider. In e-learning data bases are used to
store information of service provider Applying data mining would enable us to help the learner who are interested in
certain areas by suggesting relevant and complimentary courses of which they are might be not aware in an efficient
way[15].
vi. Modern Applications of Data mining
A. Online learning
In online learning lectures are delivered using internet instead of classroom. Using data mining techniques are apply
to make online learning more effective. it is convenient way of learning and different methods of learning can be used
on online learning.
B. Educational data research
Educational data research is a wide area of research. In educational data research data is related to teachers, students,
placement and admissions of students etc. Various data mining techniques are applied in this area to generate some
useful information, which help us in making decisions.
C. Automatic Grading
Automatic grading is also a new area of educational data mining. In this automatic grades are generated on the basis
of previous and current data of student related to their performance in exam. Various artificial intelligence methods
could be used with data mining methods. In this method there are less chance of errors and is more fairer.
D. Social network analysis
6. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 12
Social network analysis helps us to find relation b/w persons and groups. Social network analysis is can be tool of
considerable utility in the educational context for addressing difficult problems like uncovering student’s level of
cohesion, their degree of participation in forums, or identification of most influential ones.
vii. Conclusion
In the present paper, we review data mining and its application in field of education. In this paper we learn some
basic goals of educational data mining and its techniques that are used for knowledge discovery and help us in
making useful decisions. There are number of data mining tools are developed that are used in educational data
mining. In this paper we discuss some new applications of EDM and future research work done in this field help us
in making useful analysis on students performance, their behavior which help us in improving students learning
.Hopefully some new techniques of Educational data mining with new applications are discussed in our next paper.
REFRENCES
1. Alex berson,Stephen j.smith “data warehousing,Data minig and OlAP”, Tata McGraw-Hill
Education, ISBN no0070587418.
2. S.Lakshmi Prabha,Dr. A.r Mohaamed Shanavas, ”Educational Data Mining Applications”, Operation
Research and Applications : An International Journal Vol. 1,No. 1,August 2014.
3. Dunham, M. H., Sridhar S., “Data Mining: Introductory and Advanced Topics”, Pearson Education,
4. New Delhi, ISBN: 81-7758-785-4, 1st Edition, 2006.
5. Han Jiawei, Michelin Kamber,” Data Mining: Concepts and Techniques”. Morgan Kaufmann
Publisher, 2000.
6. Fayyad, U., Piatetsky-Shapiro, G., and Smyth P., “From Data Mining to Knowledge Discovery in
Databases,” AI Magazine, American Association for Artificial Intelligence, 1996.
7. Mattew Berland,Ryan s. baker,Paulo blikstein, “Educational data mining and learning
analytics:Application to Constructionist Research”
8. Cristobal Romero,Sebastian ventura , “ Educational data mining ”.
9. Dr. varun Kumar,Anupama Chadha, “ AN Empirical Study of the Applications of Data Mining
Techniques inHhigher Education”, International Journal of Advanced computer science and
Application Vol.2,No. 3,March 2011.
10. Monika Goyal,Rajan Vohra,” Application od data mining in higher education”, International journal
of computer science Vol.9,Issue 2,No 1,March 2012.
11. Pratiyush Guleria,Manu Sood ,” Data Mining In Education: A review on The Knowledge Discovery
Perspective”, International Journal Of Data Mining & Knowledge Management Process
Vol.4,No.5,September 2014.
12. Varun Chandola,Vipin kumar “summarization –Compressing data into an informative
representation”,
13. Richard A. Huebner,”A Survey On Educational Data Mining Research”.
14. Ryan J.D.Bkaer,Kalina Yacef,”The State Of Educational data Mining in 2009:A Review and Future
Vision”,Vol.1,No.1,2009.
7. ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 13
15. Ranilson Oscar Araujo Paiva at all “A Systematic Approach for providing personalized pedagogical
recommendation based on educational data mining”, Vol.8474, ISBN: 978-3-319-07220-3,2014.
16. Margo hanna,”Data mining in the e-learning domain”, vol.21, No.1, 2004.
17. Huebner,Richard,”A survey of educational data mining research”, Research in higher educational
journal,Retrived 30 march,2014.
18. Diego gracia,Marta Zorrilla,” social network analysis and data mining: An approach to e-learning
context”,5th
international conference on computational collective intelligence technologies and
applications,at Craiova,Rumania.
19. Qiuxlang shi,” Application of data mining in network instructional platform of “modern educational
technology “for different teaching, international journal of database theory andapplication, Vol.7,
No.3, 2014.
![ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 7
A Nobel Approach On Educational Data Mining
Daljit Kaur1
and Harjot Kaur2
Lecturer, Dept. Of computer science,
Sant Baba Bhag Singh Post Graduate College, Jalandhar, Punjab, India1
Lecturer, Dept. Of computer science,
Sant Baba Bhag Singh Institute of engineering and technology, Jalandhar, Punjab, India2
Abstract: Now a day in the area of information technology, information plays a very important role. We gather data
from different data sources store it and process it to generate some meaningful information which help us in decision
making. With the immense use of computer and electronic devices there is explosive growth in data collection. With
the immense growth of data we store data in data warehouse, which help us to enhance business intelligence, data
quality. So to analyze this vast amount of data which help us to draw some meaningful decisions we use a special tool
called data mining. In this paper we review data mining and its application in field of education. It is an emerging
discipline developing method and using those methods for better understanding students.
KEYWORDS: Educational Data Mining, Data Mining, Modern application of data mining
i. Introduction
Data mining is process of analyzing data and summarizing it into meaningful information. Data mining help end
user to extract useful information from large databases [1].Data mining techniques are used continuously on software
and hardware platforms to increase the value of existing information resources. Data mining tools help in predict
future trends which help organization to make knowledge driven decisions. Data mining popularly known as
knowledge discovery in database, it is the nontrivial extraction of implicit, previously unknown and potentially useful
information from data in databases [3, 5].
Data mining techniques are basically outcome of long process of research. The evolution began when business data
is first stored on the computer. As data become huge data mining techniques are used for knowledge discovery. There
are many data mining tools that are used analyze massive amount of data. Commonly used data mining tools for
creating data mining solutions are data mining wizard, model viewer, prediction query builder etc.
ii. Educational Data Mining
Educational data mining is research area which utilizes data mining techniques and research approaches for
understanding how student learns [2].EDM increase the understanding of learning by finding educational trends
which improve Student’s performance, selection regarding course, training. According to HAN and Kamber [4] Data
mining software should be developed in such a manner that it allow the user to analyze data from different
perspective, enable to categorize it and summarize the derived results.EDM can be used to evaluate student methods,](https://image.slidesharecdn.com/nobel-apr201504p005-150506003308-conversion-gate01/85/A-Nobel-Approach-On-Educational-Data-Mining-1-320.jpg)
![ISSN(Online): 2395-xxxx
International Journal of Innovative Research in Computer
and Electronics Engineering
Vol. 1, Issue 4, April 2015
Copyright to IJIRCEE www.ijircee.com 8
processes and roles helping us understand the strategies that learners develop as they participate in constructionist
learning activities [6].EDM help us in analysis and visualization of data and help to highlight useful information.
EDM uses computational approaches to analyze educational data. Educational data mining focuses on developing
new tools and algorithms for discovering data patterns .Various techniques like statistics, machine learning, neural
network etc. are applied during data mining process. Data collected from online learning systems can be aggregated
over large number of students and can contain many variables that data mining algorithm can explore for model
building. New EDM applications will focus on allowing non-technical uses use and engage in data mining tools and
activities ,making data collection and processing more accessible for all users of EDM [16]
iii. Objective of Educational data mining
A. Educational Research
It convert raw data coming from various educational systems or institutions into useful information which help us in
doing research in field of education.
B. Effective learning
EDM help us doing research in educational field, which lay down groundwork for more effective learning [7]. With
the help of data mining techniques a result evaluation system can be developed which can help teacher and students
to know weak points of the traditional classroom teaching model and it will help them to face rapidly developing real-
life environment and adapt the current teaching realities [8].
C. Prediction
EDM techniques help us in predicting values or variables which help us in decision making. Now a day’s educational
organizations are getting strong competition from other academic competitors[8].To have an edge over other
organizations, needs deep and enough knowledge for better assessment, evaluation ,planning and decision making[8].
D. Providing Feedback
AS EDM include huge amount of data related to educational institute, by analyzing that data we are able to provide
feedback to teacher /administrator which help them to improve student performance.
iv. Techniques of Data Mining
A. Summarization
It is mainly generalization of data. It provides more compact representation of data set, including visualization and
report generation [9].Simple summarization method such as tabulating the mean and standard deviation are often
applied for data analysis, data visualization and automated report generation [11].
B. Cluster analysis](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)