This document discusses research on using machine learning techniques to predict student performance and career outcomes. It provides an overview of various studies that have used methods like decision trees, naive Bayes classification, neural networks, and clustering algorithms. The studies aimed to identify factors influencing student performance and predict outcomes like course grades, dropout risk, and placement success. The document also compares the different techniques, finding that deep neural networks and ensemble methods can achieve relatively high prediction accuracy, above 80% in some cases. Overall, the research aims to help educational institutions identify at-risk students and improve student performance.
Correlation based feature selection (cfs) technique to predict student perfro...IJCNCJournal
Education data mining is an emerging stream which h
elps in mining academic data for solving various
types of problems. One of the problems is the selec
tion of a proper academic track. The admission of a
student in engineering college depends on many fact
ors. In this paper we have tried to implement a
classification technique to assist students in pred
icting their success in admission in an engineering
stream.We have analyzed the data set containing inf
ormation about student’s academic as well as socio-
demographic variables, with attributes such as fami
ly pressure, interest, gender, XII marks and CET ra
nk
in entrance examinations and historical data of pre
vious batch of students. Feature selection is a pro
cess
for removing irrelevant and redundant features whic
h will help improve the predictive accuracy of
classifiers. In this paper first we have used featu
re selection attribute algorithms Chi-square.InfoGa
in, and
GainRatio to predict the relevant features. Then we
have applied fast correlation base filter on given
features. Later classification is done using NBTree
, MultilayerPerceptron, NaiveBayes and Instance bas
ed
–K- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Correlation based feature selection (cfs) technique to predict student perfro...IJCNCJournal
Education data mining is an emerging stream which h
elps in mining academic data for solving various
types of problems. One of the problems is the selec
tion of a proper academic track. The admission of a
student in engineering college depends on many fact
ors. In this paper we have tried to implement a
classification technique to assist students in pred
icting their success in admission in an engineering
stream.We have analyzed the data set containing inf
ormation about student’s academic as well as socio-
demographic variables, with attributes such as fami
ly pressure, interest, gender, XII marks and CET ra
nk
in entrance examinations and historical data of pre
vious batch of students. Feature selection is a pro
cess
for removing irrelevant and redundant features whic
h will help improve the predictive accuracy of
classifiers. In this paper first we have used featu
re selection attribute algorithms Chi-square.InfoGa
in, and
GainRatio to predict the relevant features. Then we
have applied fast correlation base filter on given
features. Later classification is done using NBTree
, MultilayerPerceptron, NaiveBayes and Instance bas
ed
–K- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Association rule discovery for student performance prediction using metaheuri...csandit
According to the increase of using data mining tech
niques in improving educational systems
operations, Educational Data Mining has been introd
uced as a new and fast growing research
area. Educational Data Mining aims to analyze data
in educational environments in order to
solve educational research problems. In this paper
a new associative classification technique
has been proposed to predict students final perform
ance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative cla
ssifiers maintain interpretability along
with high accuracy. In this research work, we have
employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract associat
ion rule for student performance prediction
as a multi-objective classification problem. Result
s indicate that the proposed swarm based
algorithm outperforms well-known classification tec
hniques on student performance prediction
classification problem.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
A New Active Learning Technique Using Furthest Nearest Neighbour Criterion fo...ijcsa
Active learning is a supervised learning method that is based on the idea that a machine learning algorithm can achieve greater accuracy with fewer labelled training images if it is allowed to choose the image from which it learns. Facial age classification is a technique to classify face images into one of the several predefined age groups. The proposed study applies an active learning approach to facial age classification which allows a classifier to select the data from which it learns. The classifier is initially trained using a small pool of labeled training images. This is achieved by using the bilateral two dimension linear discriminant analysis. Then the most informative unlabeled image is found out from the unlabeled pool using the furthest nearest neighbor criterion, labeled by the user and added to the
appropriate class in the training set. The incremental learning is performed using an incremental version of bilateral two dimension linear discriminant analysis. This active learning paradigm is proposed to be applied to the k nearest neighbor classifier and the support vector machine classifier and to compare the performance of these two classifiers.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
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.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various
types of problems. One of the problems is the selection of a proper academic track. The admission of a
student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank
in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various types of problems. One of the problems is the selection of a proper academic track. The admission of a student in engineering college depends on many factors. In this paper we have tried to implement a classification technique to assist students in predicting their success in admission in an engineering stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank in entrance examinations and historical data of previous batch of students. Feature selection is a process for removing irrelevant and redundant features which will help improve the predictive accuracy of classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based –K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model
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
Association rule discovery for student performance prediction using metaheuri...csandit
According to the increase of using data mining tech
niques in improving educational systems
operations, Educational Data Mining has been introd
uced as a new and fast growing research
area. Educational Data Mining aims to analyze data
in educational environments in order to
solve educational research problems. In this paper
a new associative classification technique
has been proposed to predict students final perform
ance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative cla
ssifiers maintain interpretability along
with high accuracy. In this research work, we have
employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract associat
ion rule for student performance prediction
as a multi-objective classification problem. Result
s indicate that the proposed swarm based
algorithm outperforms well-known classification tec
hniques on student performance prediction
classification problem.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
A New Active Learning Technique Using Furthest Nearest Neighbour Criterion fo...ijcsa
Active learning is a supervised learning method that is based on the idea that a machine learning algorithm can achieve greater accuracy with fewer labelled training images if it is allowed to choose the image from which it learns. Facial age classification is a technique to classify face images into one of the several predefined age groups. The proposed study applies an active learning approach to facial age classification which allows a classifier to select the data from which it learns. The classifier is initially trained using a small pool of labeled training images. This is achieved by using the bilateral two dimension linear discriminant analysis. Then the most informative unlabeled image is found out from the unlabeled pool using the furthest nearest neighbor criterion, labeled by the user and added to the
appropriate class in the training set. The incremental learning is performed using an incremental version of bilateral two dimension linear discriminant analysis. This active learning paradigm is proposed to be applied to the k nearest neighbor classifier and the support vector machine classifier and to compare the performance of these two classifiers.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
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.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various
types of problems. One of the problems is the selection of a proper academic track. The admission of a
student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank
in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various types of problems. One of the problems is the selection of a proper academic track. The admission of a student in engineering college depends on many factors. In this paper we have tried to implement a classification technique to assist students in predicting their success in admission in an engineering stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank in entrance examinations and historical data of previous batch of students. Feature selection is a process for removing irrelevant and redundant features which will help improve the predictive accuracy of classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based –K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model
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 Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...indexPub
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
ASSOCIATION RULE DISCOVERY FOR STUDENT PERFORMANCE PREDICTION USING METAHEURI...cscpconf
According to the increase of using data mining techniques in improving educational systems
operations, Educational Data Mining has been introduced as a new and fast growing research
area. Educational Data Mining aims to analyze data in educational environments in order to
solve educational research problems. In this paper a new associative classification technique
has been proposed to predict students final performance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative classifiers maintain interpretability along
with high accuracy. In this research work, we have employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract association rule for student performance prediction
as a multi-objective classification problem. Results indicate that the proposed swarm based
algorithm outperforms well-known classification techniques on student performance prediction
classification problem.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published Quarterly offers fast publication schedule whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites the process All submitted research articles are subjected to the immediate rapid screening by editors consultation with Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
ISSN 2454-535X
International Journal of Mechanical Engineering Research and Technology aims to provide the best possible service to authors of original research articles, and the fairest system of peer review.
The International Journal of Mechanical Engineering Research and Technology is an international online journal in English published Quarterly. This offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
The journal publishes original works with practical significance and academic value. Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management.
Similar to IRJET - A Study on Student Career Prediction (20)
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
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About
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.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
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.
Key Features
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.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.