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2009
Using classification algorithms can lead to discovering relevant knowledge contained in educational databases. These findings can be used for providing feedback to learners in the educational process.
The aim of this paper is to provide an overview of application of data mining methods in e-learning process. E-learning is concerned with web-based learning which is totally depending upon internet. Use of data mining algorithms can help to discover the relevant information from database obtained from web based education system. This paper focused on e-learning problems to which data mining techniques have been applied, including: student’s classification based on their learning performance, detection of irregular learning behavior of students. This paper shows types of various modeling techniques used which includes: neural network, fuzzy logic, graph and trees, association rules and multi agent systems.
2018
The Electronic (E)-Learning attracts the attention of researchers in the recent years. This for different reasons, such as easing the board studying and guarantee the education for busy people. Different methods and algorithm have been adopted in e-learning systems to offer more flexible services for students. In addition, the recent smart systems consider the prediction strategies for expecting the logical results of different categories in e-learning. The researcher goes further with decision making for students, presented as a recommendation for each type of classification. Moreover, the e-learning systems use the classification and clustering methods for classifying the investigated dataset. In this paper, a comprehensive study of the recent e-learning decision making and prediction is presented. It offers a wide information regarding the subject of decision making and prediction in e-learning that can improve them efficiently. Discussion and recommendations have been included in this paper.
International Journal of Computer Applications, 2010
Data Mining is a powerful tool for academic intervention. The educational institutions can use classification for comprehensive analysis of students" characteristics. In our work, we collected student"s data from engineering course. And then apply four different classification methods for classifying students based on their Final Grade obtained in their Courses. We compare these algorithms of classification and check which algorithm is optimal for classifying students" based on their final grade.
IJCSMC, 2019
The prediction analysis is the approach which can predict future possibilities based on the current information. The prediction analysis can be done using the technique of classification and neural networks. Every educational institute aims at delivering quality education to their students, to meet this institute must able to evaluate teachers' as well as students' performance so that they can provide appropriate guideline to student and can able to arrange proper training for teachers also. Many researchers have developed systems which able to evaluate students' performance but improving students' performance is not the sufficient to provide quality education as teacher plays an important role in educating student.
Encyclopedia of Data Warehousing and Mining, Second Edition
Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, ...
Data Mining is a dominant tool for academic and educational field. Mining data in education atmosphere is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational/academic database and can be used for decision making in educational/academic systems. This work demonstrates an effective mining of students performance data in accordance with placement/recruitment process. The mining result predicts weather a student will be recruited or not based on academic and other performance during the entire course. To mine the students’ performance data, the data mining classification techniques such as – Decision tree- Random Tree and J48 classification models were built with 10 cross validation fold using WEKA. The constructed classification models are tested for predicting class label for new instances. The performance of the classification models used are tested and compared. Also the misclassification rates for the classification experiment are analyzed
www.ijarst.com, 2013
Classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "nonspam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.).In the terminology of machine learning, [1] classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm that maps input data to a category.
Informatics in education, 2015
The growing amount of information in the world has increased the need for computerized classification of different objects. This situation is present in higher education as well where the possibility of effortless detection of similarity between different study courses would give the opportunity to organize student exchange programmes effectively and facilitate curriculum management and development. This area which currently relies on manual timeconsuming expert activities could benefit from application of smartly adapted machine learning technologies. Data in this problem domain is complex leading to inability for automatic classification approaches to always reach the desired result in terms of classification accuracy. Therefore, our approach suggests an automated/semi-automated classification solution, which incorporates both machine learning facilities and interactive involvement of a domain expert for improving classification results. The system's prototype has been implemented and experiments are carried out. This interactive classification system allows to classify educational data, which often comes in unstructured or semi-structured, incomplete and/or insufficient form, thus reducing the number of misclassified instances significantly in comparison with the automatic machine learning approach.
This paper aims to present a model for the students performance. The predictive model was developed based on students performance in the second semester. Classification techniques from Data mining were applied to develop the models like Naïve Bayes, Support Vector Machine (SMO) and K-nearest neighbors (IBK). Comparative analysis is conducted on the three selected algorithms to find the best classification model. Moreover, this research also aims to find out the most influential subjects' grades on their study duration. Courses, gender, and grades that serve as the independent parameters to predict the dependent parameter. The resulting models of the three algorithms show no significant difference between Naïve Bayes and SVM performances, while K-NN has the highest performance. Basic subject's grades found to be the most influence parameter to the students' study duration, followed by general subjects, grades, gender, and major subjects grades parameters.
Advances in Intelligent Systems and Computing, 2021
It is a well-known fact that the supreme intention of any educational institute or an organization is to deliver better quality of education and hence improvising the overall performance of that particular organization by looking at the individual performances. Educational data mining (EDM) area is mostly used for the prediction when compared to generation of exact results. This paper focuses on identifying the students who are not likely to go for the higher studies and recognizing the factors responsible for it. This is done by using predictive classification-based algorithms. A data set of 395 students is used and the process is carried out using WEKA tool. Five classification algorithms used are Naive Bayes (NB), logistic, multilayer perceptron (MLP), J48, and random tree. Finally, a comparison of these classifiers is done to identify the best classifier among them. This paper concentrates on importance of DM techniques in education sector.
Int. J. Next Gener. Comput., 2020
Educational Data Mining (EDM) is a process in which data mining is applied on students’ data obtained from any educational institution. The importance of data mining is increasing in this field as it can help both in the improvement of education system and in the growth of students by making predictions. Many techniques are used for doing classification and predictions regarding different aspects of education. In this paper, the data mining techniques that are used in education have been discussed with their applications.
Educational organizations are one of the important parts of our society and playing a vital role for growth and development of any nation. Data Mining is an emerging technique with the help of this one can efficiently learn with historical data and use that knowledge for predicting future behavior of concern areas. Growth of current education system is surely enhanced if data mining has been adopted as a futuristic strategic management tool. The Data Mining tool is able to facilitate better resource utilization in terms of student performance, course development and finally the development of nation's education related standards. In this paper a student data from a community college database has been taken and various classification approaches have been performed and a comparative analysis has been done. In this research work Support Vector Machines (SVM) are established as a best classifier with maximum accuracy and minimum root mean square error (RMSE). The study also includes a comparative analysis of all Support Vector Machine Kernel types and in this the Radial Basis Kernel is identified as a best choice for Support Vector Machine. A Decision tree approach is proposed which may be taken as an important basis of selection of student during any course program. The paper is aimed to develop a faith on Data Mining techniques so that present education and business system may adopt this as a strategic management tool.
Data mining techniques (DMT) are extensively used in educational field to find new hidden patterns from student's data. In recent years, the greatest issues that educational institutions are facing the unstable expansion of educational data and to utilize this information data to progress the quality of managerial decisions. Educational institutions are playing a prominent role in the public and also playing an essential role for enlargement and progress of nation. The idea is predicting the paths of students, thus identifying the student achievement. The data mining methods are very useful in predicting the educational database. Educational data mining is concerns with improving techniques for determining knowledge from data which comes from the educational database. However it has issue with accuracy of classification algorithms. To overcome this problem the higher accuracy of the classification J48 algorithm is used. This work takes consideration with the locality and the performance of the student in education in order to analyse the student achievement is high over schooling or in graduation. Data mining (DM) is called as knowledge discovery in database (KDD), is known for its powerful role in discovering hidden information from large volumes of data. Generally, data mining is the search for hidden patterns that could be present in huge databases. Data mining is becoming gradually more important tool to make over this data into information. Educational Data Mining (EDM) develops methods and applies techniques from machine learning, statistics and data mining to analyse data collected during teaching and learning [1]. Educational Data Mining (EDM) is a growing field, concerned with developing methods for recognising the unique characters of data that come from educational surroundings, and applying those methods to better understand students, and helps in decision making. Educational data mining is an interesting research area which extracts useful, previously unknown patterns from educational database for better understanding, improved educational performance and assessment of the student learning process.. The general attributes are student roll number, name, and gender, date of birth, graduation year, address, phone number, location and city. The specific attributes are school name, school location, student's mark in school, college name, department, college location and student's mark in college. The algorithms are suggested to evaluate the performance of student in school academic and college academic. The location details are such as urban school, urban home, rural school, urban college and rural home for students. The specified dataset which provides more accurate analysis as well as prediction results based on the clustering and classification algorithms. Secondarily the main parameters are considered.
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.
2008
Abstract. In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. We have used real data from seven Moodle courses with Cordoba University students.
Educational data mining (EDM) is a branch of study that focuses on the application of data mining, machine learning, and statistics of data, generated in educational contexts. This research area has been popular and some related terms like academic analytics, institutional analytics, and teaching analytics.Data mining is crucial in the subject of education, especially when assessing behaviour in an online learning environment. Cluster analysis and decision trees were applied as data mining techniques, in this study. The limits of the current study are discussed, as well as suggestions for further research.
International Journal Of Engineering And Computer Science, 2016
Data mining is the process of finding of hidden information from a huge amount of data. Data mining analyzing the data from different source and convert it into meaningful information. In the world of internet there are several online open source resources that are utilized by various academicians. Unfortunately, all the resources that are available in a scattered manner. Because of this factor it required a proper arrangement of data in meaningful information for this we are going to use data mining process which is popularly know as EDM. educational data mining is the emerging topic for research community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden knowledge from the educational data. In this paper, we focus on the educational data mining and classification techniques for a specific sector of Education system.
Classification is a data mining technique based on machine learning which is used to classify each item in a set of data into a set of predefined classes or groups. Classification methods make use of mathematical and statistical techniques such as decision trees, linear programming, neural network and other methods like Genetic Approach, Fuzzy set Approach, Ruled based etc. This paper is an survey of different classification methods and there advantages. In this paper Classification Method is considered, it focuses on a survey on various classification techniques that are most commonly used in data-mining.
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