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.
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 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.
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.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
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.
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 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.
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.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
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.
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.
Predicting student performance in higher education using multi-regression modelsTELKOMNIKA JOURNAL
Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student’ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, learning management system (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%.
MEASURING UTILIZATION OF E-LEARNING COURSE DISCRETE MATHEMATICS TOWARD MOTIVA...AM Publications
In the implementation of learning E-Learning which has been implemented by the Mercu Buana University in recent years had a positive impact on students and lecturer. However, in some course such as Discrete Mathematics perceived by lecturer and student have several problems such as difficulty from lecturer to explain some of the logic material or analysis algorithm problem. Students also do not understand clearly if only read from the text that is presented through the e learning process. Effectiveness basically is attainment of precise goal or selecting the appropriate objectives from a series of alternative or choice of ways and determine the choice of several other options. Effectiveness can also be interpreted as a measure of success in achieving the goals that have been determined. As an example if a task can finish with choice of ways which is already determine, then that way is correct or effective.
Educational Data Mining is used to find interesting patterns from the data taken from
educational settings to improve teaching and learning. Assessing student’s ability and performance with
EDM methods in e-learning environment for math education in school level in India has not been
identified in our literature review. Our method is a novel approach in providing quality math education
with assessments indicating the knowledge level of a student in each lesson. This paper illustrates how
Learning Curve – an EDM visualization method is used to compare rural and urban students’ progress
in learning mathematics in an e-learning environment. The experiment is conducted in two different
schools in Tamil Nadu, India. After practicing the problems the students attended the test and their
interaction data are collected and analyzed their performance in different aspects: Knowledge
component level, time taken to solve a problem, error rate. This work studies the student actions for
identifying learning progress. The results show that the learning curve method is much helpful to the
teachers to visualize the students’ performance in granular level which is not possible manually. Also it
helps the students in knowing about their skill level when they complete each unit.
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 approach to predict academic performance of studentsBOHRInternationalJou1
Powerful data mining techniques are available in a variety of educational fields. Educational research is
advancing rapidly due to the vast amount of student data that can be used to create insightful patterns
related to student learning. Educational data mining is a tool that helps universities assess and identify student
performance. Well-known classification techniques have been widely used to determine student success in
data mining. A decisive and growing exploration area in educational data mining (EDM) is predicting student
academic performance. This area uses data mining and automaton learning approaches to extract data from
education repositories. According to relevant research, there are several academic performance prediction
methods aimed at improving administrative and teaching staff in academic institutions. In the put-forwarded
approach, the collected data set is preprocessed to ensure data quality and labeled student education data
is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve
(ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic
models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of
OVA of 98–99.6%
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
Agricultural sector is the backbone of our country and it plays a vital role in the overall economic growth of our nation. India has about 59% of its total area for agricultural purpose. The contribution of agricultural sector to our GDP is about 17%. Advanced techniques or the betterment in the arena of agriculture will as certain to increase the competence of certain farming activities. In this paper we introduce a concept for smart farming which utilizes wireless sensor web technology with a web based application. This will play a crucial role in helping farmers. It will aim for the betterment in the facilities given to the farmers and by focussing on the measurement of production of the crops. With the help of data mining techniques and algorithms like K-nearest, decision tree we will gather each and every data related to the farming and it should be updated frequently so that farmers and the consumers will get the right knowledge of the respective crops and about the suitable equipments related to farming. Existing system are not so much efficient in displaying such data characteristics. Our main aim is to enhance the growth in the agriculture sector and make the existing system smarter so that the decision- maker can define the expansion of agriculture activities to empower the different forces in existing agriculture sector
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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.
Predicting student performance in higher education using multi-regression modelsTELKOMNIKA JOURNAL
Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student’ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, learning management system (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%.
MEASURING UTILIZATION OF E-LEARNING COURSE DISCRETE MATHEMATICS TOWARD MOTIVA...AM Publications
In the implementation of learning E-Learning which has been implemented by the Mercu Buana University in recent years had a positive impact on students and lecturer. However, in some course such as Discrete Mathematics perceived by lecturer and student have several problems such as difficulty from lecturer to explain some of the logic material or analysis algorithm problem. Students also do not understand clearly if only read from the text that is presented through the e learning process. Effectiveness basically is attainment of precise goal or selecting the appropriate objectives from a series of alternative or choice of ways and determine the choice of several other options. Effectiveness can also be interpreted as a measure of success in achieving the goals that have been determined. As an example if a task can finish with choice of ways which is already determine, then that way is correct or effective.
Educational Data Mining is used to find interesting patterns from the data taken from
educational settings to improve teaching and learning. Assessing student’s ability and performance with
EDM methods in e-learning environment for math education in school level in India has not been
identified in our literature review. Our method is a novel approach in providing quality math education
with assessments indicating the knowledge level of a student in each lesson. This paper illustrates how
Learning Curve – an EDM visualization method is used to compare rural and urban students’ progress
in learning mathematics in an e-learning environment. The experiment is conducted in two different
schools in Tamil Nadu, India. After practicing the problems the students attended the test and their
interaction data are collected and analyzed their performance in different aspects: Knowledge
component level, time taken to solve a problem, error rate. This work studies the student actions for
identifying learning progress. The results show that the learning curve method is much helpful to the
teachers to visualize the students’ performance in granular level which is not possible manually. Also it
helps the students in knowing about their skill level when they complete each unit.
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 approach to predict academic performance of studentsBOHRInternationalJou1
Powerful data mining techniques are available in a variety of educational fields. Educational research is
advancing rapidly due to the vast amount of student data that can be used to create insightful patterns
related to student learning. Educational data mining is a tool that helps universities assess and identify student
performance. Well-known classification techniques have been widely used to determine student success in
data mining. A decisive and growing exploration area in educational data mining (EDM) is predicting student
academic performance. This area uses data mining and automaton learning approaches to extract data from
education repositories. According to relevant research, there are several academic performance prediction
methods aimed at improving administrative and teaching staff in academic institutions. In the put-forwarded
approach, the collected data set is preprocessed to ensure data quality and labeled student education data
is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve
(ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic
models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of
OVA of 98–99.6%
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
Agricultural sector is the backbone of our country and it plays a vital role in the overall economic growth of our nation. India has about 59% of its total area for agricultural purpose. The contribution of agricultural sector to our GDP is about 17%. Advanced techniques or the betterment in the arena of agriculture will as certain to increase the competence of certain farming activities. In this paper we introduce a concept for smart farming which utilizes wireless sensor web technology with a web based application. This will play a crucial role in helping farmers. It will aim for the betterment in the facilities given to the farmers and by focussing on the measurement of production of the crops. With the help of data mining techniques and algorithms like K-nearest, decision tree we will gather each and every data related to the farming and it should be updated frequently so that farmers and the consumers will get the right knowledge of the respective crops and about the suitable equipments related to farming. Existing system are not so much efficient in displaying such data characteristics. Our main aim is to enhance the growth in the agriculture sector and make the existing system smarter so that the decision- maker can define the expansion of agriculture activities to empower the different forces in existing agriculture sector
A Survey on the Analysis of Dissolved Oxygen Level in Water using Data Mining...IIRindia
Data Mining (DM) is a powerful and a new field having various techniques to analyses the recent real world problems. In DM, environmental mining is one of the essential and interesting research areas. DM enables to collect fundamental insights and knowledge from massive volume of environmental data. The water quality is determining the condition of water in the environment. It represents the concentration and state (dissolved or particulate) of some or all the organic and inorganic material present in the water, together with certain physical characteristics of the water. The Dissolved Oxygen (DO) is one of the important aspects of water quality. The DO is the quantity of gaseous oxygen (O2) incorporated into the water. The DO is essential for keeping the water organisms alive. The amount of DO level in the water can be detected by various methods. The data mining techniques are properly used to find DO Level in the different types of water. A number of DM methods used to analyze the DO level such as Multi-Layer Perceptron, Multivariate Linear Regression, Factor Analysis, and Feed Forward Neural Network. This survey work discusses about such type of methods, particularly used for the analysis of DO level elaborately.
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The advancement in Information Technology (IT) has assisted in inculcating the three Nigeria major languages in text-based application such as text mining, information retrieval and natural language processing. The interest of this paper is the Igbo language, which uses compounding as a common type of word formation and as well has many vocabularies of compound words. The issues of collocation, word ordering and compounding play high role in Igbo language. The ambiguity in dealing with these compound words has made the representation of Igbo language text document very difficult because this cannot be addressed using the most common and standard approach of the Bag-Of-Words (BOW) model of text representation, which ignores the word order and relation. However, this cause for a concern and the need to develop an improved model to capture this situation. This paper presents the analysis of Igbo language text document, considering its compounding nature and describes its representation with the Word-based N-gram model to properly prepare it for any text-based application. The result shows that Bigram and Trigram n-gram text representation models provide more semantic information as well addresses the issues of compounding, word ordering and collocations which are the major language peculiarities in Igbo. They are likely to give better performance when used in any Igbo text-based system.
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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.
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The objective of this research work is focused on the right cluster creation of lung cancer data and analyzed the efficiency of k-Means and k-Medoids algorithms. This research work would help the developers to identify the characteristics and flow of algorithms. In this research work is pertinent for the department of oncology in cancer centers. This implementation helps the oncologist to make decision with lesser execution time of the algorithm.It is also enhances the medical care applications. This work is very suitable for selection of cluster development algorithm for lung cancer data analysis.Clustering is an important technique in data mining which is applied in many fields including medical diagnosis to find diseases. It is the process of grouping data, where grouping is recognized by discovering similarities between data based on their features. In this research work, the lung cancer data is used to find the performance of clustering algorithms via its computational time. Considering a limited number attributes of lung cancer data, the algorithmic steps are applied to get results and compare the performance of algorithms. The partition based clustering algorithms k-Means and k-Mediods are selected to analyze the lung cancer data.The efficiency of both the algorithms is analyzed based on the results produced by this approach. The finest outcome of the performance of the algorithm is reported for the chosen data concept.
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A comparison study between automatic and interactive methods for liver segmentation from contrast-enhanced MRI images is ocean. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to refer five error measures that highlight different aspects of segmentation accuracy. The measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods like Fuzzy Connected and Watershed Methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. In this paper only Fuzzy Connected and Watershed Methods are discussed.
A Clustering Based Collaborative and Pattern based Filtering approach for Big...IIRindia
With web services developing and aggregating in application range, benefit revelation has turned into a hot issue for benefit organization and service management. Service clustering gives a promising approach to part the entire seeking space into little areas in order to limit the disclosure time successfully. In any case, semantic data is a basic component amid the entire arranging process. Current industrialized Web Service Portrayal Language (WSPL) does not contain enough data for benefit depiction. Thusly, a service clustering technique has been proposed, which upgrades unique WSPL report with semantic data by methods for Connected Open Information (COI). Examination based genuine service information has been performed, and correlation with comparable techniques has additionally been given to exhibit the adequacy of the strategy. It is demonstrated that using semantic data from COI improves the exactness of service grouping. Furthermore, it shapes a sound base for promote thorough preparing with semantic data.
Hadoop and Hive Inspecting Maintenance of Mobile Application for Groceries Ex...IIRindia
Numerous movable applications on secure groceries expenditure and e-health have designed recently. Health aware clients respect such applications for secure groceries expenditure, particularly to avoid irritating groceries and added substances. However, there is the lack of a complete database including organized or unstructured information to help such applications. In the paper propose the Multiple Scoring Frameworks (MSF), a healthy groceries expenditure search service for movable applications using Hadoop and MapReduce (MR). The MSF works in a procedure behind a portable application to give a search service for data on groceries and groceries added substances. MSF works with similar logic from a web search engine (WSE) and it crawls over Web sources cataloguing important data for possible utilize in reacting to questions from movable applications. MSF outline and advancement are featured in the paper during its framework design, inquiry understanding, its utilization of the Hadoop/MapReduce infrastructure, and activity contents.
Performance Evaluation of Feature Selection Algorithms in Educational Data Mi...IIRindia
Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be
A Review of Edge Detection Techniques for Image SegmentationIIRindia
Edge detection is a key stride in Image investigation. Edges characterize the limits between areas in a image, which assists with division and article acknowledgment.Edge discovery is a image preparing method for finding the limits of articles inside Image. It works by distinguishing irregular in brilliance and utilized for Image division and information extraction in zones, for example, Image preparing, PC vision and Image vision. There are likely more algorithms in a writing of upgrading and distinguishing edges than whatever other single subject.In this paper, the principle is to concentrate most usually utilized edge methods for Image segmentation.
Leanness Assessment using Fuzzy Logic Approach: A Case of Indian Horn Manufac...IIRindia
Lean principles are being implemented by many industries today that focus on improving the efficiency of the operations for reducing the waste, efforts and consumption. Organizations implementing lean principles can be assessed using the some tools. This paper attempts to assess the lean implementation in a leading Horn manufacturing industry in South India. The twofold objectives are set to be achieved through this paper. First is to find the leanness level of a manufacturing organization for which a horn manufacturing company has been selected as the case company. Second is to find the critical obstacles for the lean implementation. The fuzzy logic computation method is used to extract the perceptions about the particular variables by using linguistic values and then match it with fuzzy numbers to compute the precise value of the leanness level of the organization. Based on the results obtained from this analysis, it was found that the case study company has performed in the lean to vey lean range and the weaker areas have been identified to improve the performance further.
Comparative Analysis of Weighted Emphirical Optimization Algorithm and Lazy C...IIRindia
Health care has millions of centric data to discover the essential data is more important. In data mining the discovery of hidden information can be more innovative and useful for much necessity constraint in the field of forecasting, patient’s behavior, executive information system, e-governance the data mining tools and technique play a vital role. In Parkinson health care domain the hidden concept predicts the possibility of likelihood of the disease and also ensures the important feature attribute. The explicit patterns are converted to implicit by applying various algorithms i.e., association, clustering, classification to arrive at the full potential of the medical data. In this research work Parkinson dataset have been used with different classifiers to estimate the accuracy, sensitivity, specificity, kappa and roc characteristics. The proposed weighted empirical optimization algorithm is compared with other classifiers to be efficient in terms of accuracy and other related measures. The proposed model exhibited utmost accuracy of 87.17% with a robust kappa statistics measurement and roc degree indicated the strong stability of the model when compared to other classifiers. The total penalty cost generated by the proposed model is less when compared with the penalty cost of other classifiers in addition to accuracy and other performance measures.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
An Approach for Breast Cancer Classification using Neural NetworksIIRindia
Breast Cancer,an increasing predominant death causing disease among women has become a social concern. Early detection and efficient treatment helps to reduce the breastcancerrisk.AdaptiveResonanceTheory(ART1),anunsupervised neural network has become an efficient tool in the classification of breast cancer as Benign(non dangerous tumour) or Malignant (dangerous tumour). 400 instances were pre processed to convert real data into binary data and the classification was carried out using ART1 network. The results of the classified data and the physician diagnosed data were compared and the standard performance measures accuracy, sensitivity and specificity were computed. The results show that the simulation results are analogous to the clinical results.
In the information age, data turns to be the vital. Hence it is important to understand the data in order to face the future information challenges. This paper deals with the importance of data mining while explaining the concepts and life cycle involved. It extracts the basic gist of the topic presented in a user-friendly way. Further, in developing different stages of data mining followed by its extended application usage in practical business platform.
Forklift Classes Overview by Intella PartsIntella Parts
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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• Compatible with MAFI CCR system
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Technology Enabled Learning to Improve Student Performance: A Survey
1. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
50
Technology Enabled Learning to Improve Student
Performance: A Survey
M.Pazhanivel1
, T.Velmurugan2
1
Research Scholar, Bharathiar University, Coimbatore, India.
2
Associate Professor, PG and Research Dept. of Computer Science and Applications, D. G. Vaishnav College, India.
E-Mail: mrpvel@gmail.com, velmurugan_dgvc@yahoo.co.in
Abstract–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.
Keywords: Technology Enabled Learning, Student
Performance, Clustering Algorithm, Classification Algorithm
I. INTRODUCTION
The significance of technologies cannot be overemphasized in
education. Computers have been generally accepted as modern
instruments that enable the teachers/lecturers to select the
teaching methods that will increase students’ (learners’)
interest in learning. Computer is an electromechanical device
designed to sequentially accept, process and store data on the
basis of set of instructions to produce useful information. The
students’ academic performance can be referred to as the
competencies, skills acquired and attitudes learned through the
education experience. The main objective of this paper is to
discuss the use of classification and clustering algorithms in
data mining for the improvement of students performance by
using some or most of the recent technologies in the modern
world. Only a comparative analysis is performed in this
research work using the stated DM algorithms. During this
study, it is not considered all types of classification and
clustering algorithms, which is limited to certain algorithms
alone.
DM is more familiar tool for extract the information from large
number of different kinds of data repositories. Data mining
takes major role in the part of learning data mining (LDM). It
tries to discover a system to acquire new knowledgefrom the
existing information about learners to understand the
development of learning and the social and inspiration of the
factors envelop learning. In learning field, data mining
methods are very useful for enhancing the current learning
standards and managements. The size of data is gathers from
different meadow exponentially increasing. Data mining has
been used special scheme at the intersection of Machine
Learning, Artificial Intelligence and statistics. The data mining
process is to extract information from huge datasets and
transform it into understandable structure for further use. Data
mining techniques which extract information from huge
amount of data have been becoming popular in education
domains. These methods provide a direction to a multiple level
of grade, a finding which gives a new sensitivity of how people
can becomeskillful in these learning sectors. Data mining has
different methods such as Prediction, Association rules,
Decisions Tree,Classification, Clustering, Neural Networks
and many others. In the middle of these, classification
algorithm such as Decision tree ID3 and C4.5 algorithms plays
an essential role in LDM. Decision tree induction algorithms
can be used for classification in many application areas, such
as Education, Medicine, Manufacturing, Production, Financial
analysis, Fraud Detection and Astronomy etc. There are several
data mining algorithms such as k-Means, C4.5, SVM, Apriori,
Naive Bayes, EM, PageRank, AdaBoost, kNN, and CART etc,
which are effectively utilized for classifying the students
learning category. A number of clustering algorithms are also
applied in LDM for the grouping of student’s categories in
order to partition the students based on their learning
maturities.
With some of these possible methods, one can search an
important secreteblueprint for pronouncementopportunity
direction and rally round in decision making. Data mining rally
round learners by managing effective learning units,
redesigning the prospectus and humanizing learning methods.
There are number of algorithms in Data Mining, in which
decision tree algorithms are the majorly used because of its
easy implementation. Identity-efficacy has been related to
resolution, resolve, and achievement in learning settings. A
meta-investigation of study in learning settings found that
identityvaluewas related both to academic performance and
persistence. The contribution of self-efficacy to learning
achievement is based both on the increased use of particular
cognitive activities and strategies and on the positive contact of
efficacy beliefs on the broader, more general classes of meta
cognitive skills and coping abilities.
2. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
51
Computer based experiment have been used since 1960s
to experiment information and problem solving skills.
Computer based evaluation systems have enabled learners
and trainers to author, scheduler, deliver and report on
investigation, quizzes, experiment and exams.An effective
method of student assessment technique is necessary in
assessing student knowledge. Due to an increase in student
numbers, ever-escalating work commitments for academic
staff, and the advancement of internet technology, the use of
computer assisted evaluation has been an attractive
proposition for severalsenior learning institutions.This paper
has an insight of students learning perspectives via the recent
technologies used for their knowledge growth and improving
result performance in the examination of their course
examinations.
The structure of this research paper is organized as follows.
Section II discusses about the research work carried out by
using technology oriented students’performance
improvements. The role of classification algorithms for LDM
is illustrated in section III, which also have a discussion about
the use theclassification algorithms Naïve Bayes, J48 and
Support Vector Machines. The use of clustering algorithms for
LDM is illustrated in section IV. Finally, section V concludes
with the research work.
II. TECHNOLOGY ORIENTED STUDENTS
PERFORMANCE IMPROVEMENTS
In recent days, the impact of new technologies have a powerful
influence on all aspects of our society, from various domains
of real would situations.Evidently, education is not an
exception. Many technologies have an impact on the way we
teach and learn. For example, new mobiledevices (e.g.,
smartphones and tablets) raise student engagement in both
indoor and outdoor activities with applications such as
mobileaugmented reality. Also, some of the social networks
play a very vital role in current scenarios. New motion sensors
and image-recognition technologies are giving rise to
applications with more natural interactionmethods, helping
non-technical users to interact with computer-based systems, as
in the case of new-generation video consoles. In addition,social
networks and web tools give students a more active role in
their own education, allowing them to become educational
presumes.
Many peoples are carrying out research based on the
improvement of student’s performance using the recent
technologies. This section discusses about such articles with an
overview of how they chosen data set, what kind of data are
selected so for, and what technology utilized for their
knowledge improvements. A research paper titled as"Using
learning analytics to predict (and improve) student success: A
faculty perspective" was done by Dietz-Uhler et al. [1]. In this
paper, they define learning analytics, how it has been used in
educationalinstitutions, what learning analytics tools are
available, and how faculty can make use of data intheir courses
to monitor and predict student performance. Finally, they
discuss several issues andconcerns with the use of learning
analytics in higher education.This studies have indicated
individual faculty can make use of the data they have available
in their courses to affect changeand improve student success.
These efforts, although seemingly “small-scale”, can have a
largeimpact on student success.
Another paper titled as “New technology trends in education
seven years of forecasts and convergence” is discussed by
Martin et al. [2]. They analyzed the evolution of technology
trends from 2004 to 2014 that relate to the long-term
predictions of the most recent distancereport. The study
analyzed through bibliometric analysis which technologies
were successful and became a regular part of education
systems, which ones failed to have the predicted impact and
why, and the shape of technology flows in recent years. The
study also displays how the evolution and adulthood of some
technologies allowed the renewal of expectations for others.
They conclude that the analysis gives some of the methods
used is right and some are not in suitably handled.
Another research work titled as"Data mining application in
higher learning institutions" was done by Delavari et al.[3].
They studied capabilities of data mining in the background of
higher educational system by i) proposing an analytical
guideline for higher education institutions to improve their
current decision processes, and ii) applying data mining
techniques to find out new explicit knowledge which could be
helpful for the decision making processes.Their final results
had been analyzed and validated with real situations in a
university. The factors affecting the anomalies had been
discussed in detail. The final result from each model with
various techniques, presented that they are all performing
similarly.
Peng et al. [4] propose a survey on "A descriptive framework
for the field of data mining and knowledge discovery". This
paper analyses a vast set of data mining and knowledge
discovery(DMKD) literature to give a comprehensive image of
current DMKD research and categorize these research works
into high-level categories using grounded theory method, it
also appraises the longitudinal variations of DMKD study
behavior during the last decade. This paper agreed eight main
areas of DMKD: DM tasks, learning techniques and methods,
mining complex data, fundamentals of DM, DM software and
schemes, high performance and distributed DM, DM
applications, and DM method and project.
A paper titled "A mobile learning system for scaffolding bird
watching learning" carried out by Chen et al. [5]. They aimed
to build an outdoor mobile-learning activity by means of up-
to-date wireless technology. They future Bird-Watching
Learning (BWL) system is aimed using a wireless mobile ad-
hoc network. In the BWL method, each learner has a Personal
Digital Assistant (PDA) with a Wi-Fi-based wireless network
card. The BWL system provides a mobile learning structure
which supports the students learning through scaffolding. The
goal of a formative assessment was twofold: to shows the
expected roles and scaffolding helps that the mobile learning
method offers for bird-watching activities and to examine
whether student learning profited from the mobility,
movability, and individualization of the mobile learning
device. They concluded that The BWL system was specifically
developed to integrate the scaffolding model with the system.
Three elementary schools in Taiwan used the BWL system for
bird-watching. A formative evaluation from their experiences
3. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
52
was undertaken. Based on these findings, it was found that the
children who used the bird watching system improved their
learning, above and beyond what would normally be expected
they would learn.
Likewisea paper "A review of recent advances in learner and
skill modeling in intelligent learning environments" was
explored by Desmarais et al. [6]. They review the learner
models that have played the largest roles in the success of these
learning environments, and also the latest advances in the
modeling and assessment of learner skills. They concluded by
discussing related advancements in modeling other key
constructs such as learner motivation, emotional and attention
state, meta-cognition and selfregulated learning, group
learning, and the recent movement towards open and shared
learner models.
Aleksandraet al. [7] proposed a paper titled as "e-learning
personalization based on hybrid recommendation strategy and
learning style identification". This structure identifies different
patterns of learning stylishness and learners’ habits through
testing the learning styles for students and quarrying their
serverlogs. Firstly, it processes the clusters based on different
learning methods. It examines the habits and the interests of
the learners through mining the numerous sequences by the
Apriori algorithm. Finally,this system finishes personalized
recommendation of the learning content rendering to the scores
of these numerous sequences, provided by the Pouts system.
Some experiments were carried out with tworeal groups of
learners: the experimental and the control group. Learners of
the control group learned ina normal way and did not receive
any recommendation or guidance through the course, while
thestudents of the experimental group were required to use the
Protus system. The results show suitabilityof using this
recommendation model, in order to suggest online learning
activities to learners based ontheir learning style, knowledge
and preferences.This paper describes a personalized e-learning
system which can automatically adapt to the interests, habits
and knowledge levels oflearners. The differencesbetween the
learners are determined according to their previous knowledge
of the matter, their learning style,their learning characteristics,
preferences and goals.
DM scheme have to do with the discovery of secreted
association to facilitateindividual in learning databases. As we
identifyextensiveassortment of information is stored in learning
databases, so in order to get required data and to find the
secreted relationship, for that different data mining scheme are
developed and used. Some of the articles discussed here
provide a better understanding in learning resources.
III. ROLE OF CLASSIFICATION ALGORITHMS FOR
LDM
Learning data mining is explain part of scientific doubt come
to mind mainly and around the progress of methods for making
breakthrough within the singular kinds of data that come from
learning settings, and using those methods to better understand
students and the settings which they learn. This section
discusses about the difference between consequences of
students performance learning evaluation system that they use
the classification algorithms. Many investigators explores
about Students Performance Learning System (SPLS) in their
research work.Classification is aneasymethod of noticea
prototype that recognizes the relevant features of information
classes or impression, for the purpose of being able to use the
model to guess the class of stuff whose class marker is
nameless. It calculateseparate and unordered marker in huge
information sets.Institutions across the sphere are migrating
toward the use of Technology Based Learning (TBL) to
improve students’ knowledge. The advantages of using
computer knowledge for learning measurement in a global
sense have been recognized and these include lower
administrative cost, time saving and less demand upon
teachers among others.
Saeed et al. [8] proposed a case study on "Emerging Web
technologies in higher education: A Case of incorporating
blogs, podcasts and social bookmarks in a web programming
course based on Students' Learning Styles and technology
preferences". This research work has been suggested to
incorporate evolving web technologies into higher education
based on student’s learning methods and technology
preferences and a case study has been approved to authorize
the proposed structure. An achievement research methodology
has been assumed to carry out the study, which covers a study
about student’s learning methods and technology preferences
and incorporating a combination of developing web
technologies based on the review findings; and examining key
achievements and deficiencies of the study to redefine research
purposes. The study delivers support for the suggested
framework by highlighting the significant relationships among
student’s learning methods and technology preferences and
their impact on academic presentation. Their findings propose
that today’s learners are flexible in stretching their learning
methods and are able to accommodate different instructional
policies including the use of evolving web technologies. They
further proposed that learning methods of today’s learners are
flexible enough to understanding different technologies and
their technology preferences are not restricted to a particular
technology.
Another article titled as "Microblogs in Higher Education–A
chance to facilitate informal and process-oriented learning?"
was done by Ebner et al. [9]. This paper reports on a research
study that was carried outon the use of a microblogging
platform for process-oriented learning in Higher Education.
Students ofthe University of Applied Sciencesin Upper Austria
used the tool throughout their course. All postingswere
carefully tracked, examined and analyzed in order to explore
the possibilities offered by microbloggingin education. It can
be concluded that microblogging should be seen as a
completely new form ofcommunication that can support
informal learning beyond classrooms.
Yadav et al.[10] proposed prediction in a paper titled "Data
mining: A prediction for performance improvement of
engineering students using classification". In this paper,
Classification methods like decision trees, Bayesian network
etc can be applied on the learner data for calculating the
student’s performance in examination. This prediction will
help to find the weak students and help them to score better
marks. The C4.5, ID3 and CART decision tree algorithms are
applied on engineering student’s data to predict their
performance in the last semester examinations. The result of
4. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
53
the decision tree expected the number of students who are
likely to pass, fail or promoted to succeeding year. The results
offer steps to increase the performance of the students who
were expected to fail or promoted. After the announcement of
the results in the final examination the marks acquired by the
students are fed into the system and the results were examined
for the next session. The comparative study of the results states
that the prediction has helped the weaker students to improve
and got out improvement in the result.They suggested that the
Machine learning algorithms such as the C4.5 decision tree
algorithm can learn effective predictive models from the
student data accumulated from the previous years. The
empirical results show that we can produce short but accurate
prediction list for the student by applying the predictive models
to the records of incoming new students. This study will also
work to identify those students which needed special.
Wu et al. [11] have conducted a survey on “Top 10 algorithms
in data mining”. This paper offerings the top 10 data mining
algorithms approved by the IEEE International Conference on
Data Mining (ICDM) in Dec. 2006. C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and
CART are considered for this analysis. These top 10
algorithmsare among the most influential data mining
algorithms in the research community in all fields of DM and
its applications. They analyzed with a formal tie with the
ICDM conference, Knowledge and Information
Systemshasbeen publishing the best papers from ICDM every
year, and several of this papers citedfor classification,
clustering, statistical learning, and association analysis were
selected bythe previous year ICDM program committees for
journal publication in Knowledge and Information
Systemsafter their revisions and expansions. In the same
conference many of the articles discussed about the use DM
algorithms for the prediction of student’s performance by using
the various DM algorithms.
A paper titled as "Constructing a personalized e-learning
system based on genetic algorithm and case-based reasoning
approach" was done by Huang et al. [12]. Their suggested
approach is built on the evolvement method through
computerized adaptive testing (CAT). Then thegenetic
algorithm (GA) and case-based reasoning (CBR) are engaged
to build an optimal learning path for each pupil. This paper
makes three critical contributions: (1) it presents a genetic-
based curriculum sequencing approach that will generate a
personalized curriculumsequencing; (2) it illustrates the case-
based reasoning to develop a summative examination or
assessment analysis; and (3) it usesempirical research to
indicate that the proposed approach can generate the
appropriate course materials for learners, based on
individuallearner requirements, to help them to learn more
effectively in a Web-based environment.
Kotsiantis et al. [13] predicting students performance in the
article "A combinational incremental ensemble of classifiers as
a technique for predicting students’ performance in distance
education". This paper predicts a student’s performance could
be useful in a great number of different ways associatedwith
university-level distance learning. Students’ marks in a few
written assignments can constitutethe training set for a
supervised machine learning algorithm. Along with the
explosive increase ofdata and information, incremental
learning ability has become more and more important for
machinelearning approaches. The online algorithms try to
forget irrelevant information instead of synthesizingall
available information. Nowadays, combining classifiersis
proposed as a new direction for the improvement of the
classification accuracy. Among other significant conclusions it
was found that the proposed algorithm isthe most appropriate
to be used for the construction of a software support tool.
Recent Methodologies for Improving and Evaluating
Academic Performance is carried out by Ajay Varmaand Y. S.
Chouhan in [14]. The aim of this study is to provide the review
of different data mining techniques that have been used in
educational field with regard to evaluation of students’
academic performance. Academic Data Mining used many
techniques including classification algorithms and many
others. Using these techniques many kinds of knowledge can
be discovered such as association rules, classifications and
clustering. The discovered knowledge can be used for
prediction and analysis purposes of student patterns.The
literature indicates that among the three methods of
classification with respect to academic performance, the most
widely used classification techniques in educational domain is
decision tree.
Zafra et al. [15] analyzed a paper titled "Multiple instance
learning for classifying students in learning management
systems". In this paper, a new attitude built on multiple
instance learning is suggested to predict student’s performance
and to increase the gained results using typical single instance
learning. Multiple instance learning provides a more suitable
and optimized representation that is adapted to available
information of each student and course eliminating the missing
values that make difficult to find efficient solutions when
traditional supervised learning is used. To check the efficiency
of the new proposed representation, the most popular
techniques of traditional supervised learning based on single
instances are matched to those based on numerous instance
learning. They concluded that computational experiments show
that when the problem is regarded as a traditional supervised
learning problem, the results obtained are lower than when the
problem is regarded as a multi-instance problem. Concretely,
the Wilcoxon rank sum test determines with a confidence of
95% that algorithms using multiple instance learning
representation achieve significantly better solutions.
A Comparative Analysis on the Evaluation of Classification
Algorithms in the Prediction of Students Performance is
discussed by Anuratha and Velmurugan in a research work
[16]. The goal of this study provides an analysis of final year
marks of UG degree students using data mining methods,
which carried out in three of the private colleges in Tamil
Nadu state of India. The major objective of this research work
is to apply the classification techniques to the prediction of the
performance of students in end semester university
examinations. Particularly, the decision tree algorithm C4.5
(J48), Bayesian classifiers, k Nearest Neighbor algorithm and
two rule learner’s algorithms namely OneR and JRip are used
for classifying the performance of students as well as to
develop a model of student performance predictors. The result
of this study reveals that overall accuracy of the tested
5. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
54
classifiers is above 60%. In addition classification accuracy for
the different classes reveals that the predictions are worst for
distinction class and fairly good for the first class. The JRip
produces highest classification accuracy for the Distinction.
Classification of the students based on the attributes reveals
that prediction rates are not uniform among the classification
algorithms. Also shows that selected data attributes have found
to be influenced the classification process. The results showed
to be satisfactory.
Thus, this section discussed about the methods used for
learning data mining and some other appropriate theme which
are used for the principle of improving both learner
performance as well as the syllabus of a particular institution.
In the largest part of learningsetup, a considerable quantity of
instructor and learner time is committed to activities which
involve assessment by the instructor of learner products or
activities.Consideration is given to outcomes involving
learning approach, inspiration, and reaching. Where promising,
mechanisms are elective that could version for the reported
effects. The end resulting from the charactermeadows are then
merged to manufacture an incorporated sketch with clear
inference for activelearning practice. The mainend is that
classroom evaluation has powerful direct and circumlocutory
Condit, which may be optimistic or pessimistic, and thus merit
very thoughtful arrangement and achievement.
V. CLUSTERING ALGORITHM FOR LDM
Clustering as the name suggests is the process of grouping data
into classes, so that objects within a cluster have high
similarity in comparison to one another, but are very dissimilar
to objects in other cluster. Dissimilarities have been observed
on the basis of attribute value describing the objects often
distance used. It is the process of identification of similar
classes of object. It has been used as a grouping a set of
physical or abstract objects into classes of similar objects. A
cluster is a collection of data objects which are similar to
another within the same cluster and dissimilar to the object in
other cluster. It is an unsupervised learning technique that
shows the natural groupings in data.
Application of k-Means Clusteringalgorithm forprediction of
Students’ Academic Performance was discussed by Oyelade et
al. in [17]. In this paper, they implemented k-means clustering
algorithm for analyzing students’result data in a good manner.
The model was combined with the deterministicmodel to
analyze the students’ results of a private Institution inNigeria
which is a good standard to monitor the evolution ofacademic
performance of students in higher Institution for thepurpose of
making an effective decision by the academicorganizers.It also
enhances the decisionmaking by academic developers to
monitor the candidates’performance semester by semester by
improving on the upcomingacademic results in the subsequent
academic meeting.
A paper titled as "Clustering and sequential pattern mining of
online collaborative learning data" was done by Perera et al.
[18]. They explore how useful reflect information can be
mined via a theory-driven approach and a range of clustering
and sequential pattern mining. The context is a senior software
development project where students use the collaboration tool
track. They mined patterns distinguishing the better from the
weaker groupsand get insights in the success factors. The
results point to the importance of leadership and group
interaction, and givepromising indications if they are
occurring. Patterns indicating good individual practices were
also identified. They found thatsome key measures can be
mined from early data. The results are promising for advising
groups at the start and earlyidentification of effective and poor
practices, in time for remediation. They suggested that this
work will enable the learner to provide regular feedback to
students throughout the semester if their present work
performance is more expected to be associated with positive
ornegative outcomes and where the problems are.A
comparative analysis of various technologies used by different
researchers is given in the table 1, which gives an insight about
the each and every paper taken for analysis in this survey work.
Table 1: Comparative Analysis
Reference Authors Methods
used
Results
1 Dietz-
Uhler et
al.
Learning
analytics
This studies have
indicated individual
faculty can make
use of the data they
have available in
their courses to
affect changeand
improve student
success.
2 Martin et
al.
Bibliometri
c analysis
The study analyzed
by bibliometric
analysis which
technologies were
successful and
became a regular
part of learning
systems
3 Delavari
et al.
HAL The final result from
each model with
various techniques,
presented that they
are all performing
similarly.
4 Peng et al. DMKD This paper
acknowledged eight
main areas of
DMKD
5 Chen et al. BWL The BWL system
was specifically
developed to
integrate the
scaffolding model
with the system.
6 Desmarais
et al.
Cognitive
Modeling
Modeling other key
constructs such as
learner motivation,
emotional and
attention state,
meta-cognition and
self regulated
learning, group
learning, and the
6. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
55
recent movement
towards open and
shared learner
models.
7 Aleksandr
aet al.
Tutoring
system
Describing learning
style,learning
characteristics,
preferences and
goals.
8 Saeed et
al.
Learning
styles
Suggested that
learning styles of
today’s learners are
flexible enough to
experience varying
technologies and
their technology
preferences are not
limited to a
particular tool.
9 Ebner et
al.
Microblog
ging
Microblogging
should be seen as a
completely new
form
ofcommunication
that can support
informal learning
beyond classrooms.
10 Yadav et
al.
Decision
tree
The Machine
learning algorithms
such as the C4.5
decision tree
algorithm can learn
effective predictive
models from the
student data
accumulated from
the previous years.
11 Wu et al. Classificati
on
Algorithms
Citedfor
classification,
clustering, statistical
learning, and
association analysis.
12 Huang et
al.
Genetic
algorithm
Suggested three
critical
contributions.
13 Kotsiantis
et al.
Naive
Bayes
Among other
significant
conclusions it was
found that the
proposed algorithm
isthe most
appropriate to be
used for the
construction of a
software support
tool.
14 Ajay
Varma
and Y. S.
Chouhan
Bayesian
Network
The most widely
used classification
techniques in
educational domain
is decision tree.
15 Zafra et Multiple Single instances are
al. instance
learning
compared to those
based on multiple
instance learning.
16 Anuratha.
C and
Velmurug
an. T
C4.5,
Bayesian
classifiers,
kNNalgorit
hm , OneR
and JRip
JRip produces
highest
classification
accuracy
17 Oyelade et
al.
k – mean
clustering
Enhances the
decisionmaking by
academic
developers to
monitor the
candidates’performa
nce semester by
semester by
improving on the
upcomingacademic
results in the
subsequent
academic meeting.
18 Perera et
al.
Collaborati
ve
Learning
Enable the learner to
provide
regularfeedback to
students during the
semester if their
current
workbehavior is
more likely to be
associated with
positive ornegative
outcomes.
VI. CONCLUSION
The recent technologies like educational websites, smart
phones, internets, mobile phones, PDA devises, iPods and
software oriented methods like power point presentations,
social networks and information repositories are create high
impact in the learning as well as teaching process. These
technologies are the main keys of modern world nowadays.
This survey work addresses the significance of LDM in order
to make use of such technology oriented facilities. A number
of articles written by using DM algorithms are reviewed in this
work, which is utilized for the improvement of student’s
performance. Based on the observation, many DM algorithms
are suitable for LDM. From the various researchers’
perspectives, it is enlisted that many advanced technologies are
effectively used for learning process. Also, most of the recent
teaching methodologies are effectively created high impact in
improving the personally as well as the performance of
students. Most of the research work has new methods and
patterns to maximize the student’s performance through LDM
techniques. Some of the clustering algorithms and
classification algorithms like ID3 and C4.5 are used to identify
the various categories of students’ performances. For the
chosen data concept by the various researchers, the behavior
and performance of both the categories of algorithms differ.
Many research works finds that C4.5 classification algorithm
stamps its superiority in classifying student categories. Thus,
7. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.50-56
ISSN: 2278-2419
56
this work concludes that the recent technologies are more
useful and a gift for quick learning with less cost.
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