The document discusses using Learning Factor Analysis (LFA), an educational data mining technique, to model student knowledge based on student-tutor interaction log data. LFA uses a multiple logistic regression model with difficulty factors defined by subject experts to quantify skills. A combinatorial search method called A* search is used to select the best-fitting model. The document illustrates applying LFA to data from an online math tutor, identifying 5 skills and presenting the results of the logistic regression modeling, including fit statistics and learning rates for skills. Learning curves are used to visualize student performance over time.
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.
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.
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
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
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.
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.
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.
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
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
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.
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.
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.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
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.
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.
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
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.
Surfactant-assisted Hydrothermal Synthesis of Ceria-Zirconia Nanostructured M...IOSR Journals
CeO2–ZrO2 oxides were prepared by the surfactant-templated method using cetyl trimethyl ammonium bromide (CTAB) as template and modified with chromium nitrate. These were characterized by XRD, FT-IR, TEM, SEM, BET and TPD-CO2. The XRD data showed that as prepared CeO2-ZrO2 powder particles have single phase cubic fluorite structure. HRTEM shows mesoscopic ordering. Average particle size is 12-13 nm as calculated from particle histogram. The nitrogen adsorption/desorption isotherm were classified to be type IV isotherm, typical of mesoporous material. The presence of uni-modal mesopores are confirmed by the pore size distribution which shows pore distribution at around 60 A°. Catalytic activity was studied towards liquid-phase oxidation of benzene.
Computer aided environment for drawing (to set) fill in the blank from given ...IOSR Journals
Abstract: In this paper, we present Development of computer aided environment for drawing (to set) fill in the
blanks that can generate for given paragraph. The System finds fill in the blanks, blanking key generates from
the selected statement. Syntactic and lexical features are used in this process. NLP parser is used, part of speech
taggers are applied on each of these sentences to encode necessary information.
We present our work in designing and implementing the system which generate the blanks. The System is
developed in Java using JDBC which is open source.
Keyword: Natural Language Processing NLP, Key selection, POS tagging, Sentence selection
“Evaluation of Sewing Performance of Plain Twill and Satin Fabrics Based On S...IOSR Journals
Abstract: Seam strength is an important factor in determining the durability of a garment. Seam strength is
determined by resistance to pulling force and abrasion. Seam tenacity break the fabric or the weakest stitch of
seam. Seam abrasion resistance is the amount of rubbing action needed to wear away stitches in the seam
strength is related to stitch type, thread strength ,thread tension, seam type,seam efficiency, width ,and stitches
per inch.Loop strength of thread is more important to durability the seam need not be stronger than the fabric
being sewn.A triple stitched lapped seam would not be necessary for a pair of corduroy jeans since the fabric
itself is not strong and would wear out before the seam.It is better to have the thread is an overstressed seam
that to damage the fabric.Our project objective is to find the seam of strength by using different type of materials.
A Study on Fire Detection System using Statistic Color ModelIOSR Journals
Abstract: Normally fire detection system uses the heuristic fixed threshold values in their specific methods. However, input images may be changed, in general, so the heuristic fixed threshold values used in the fire detection systems might be modified on a case by case basis. In this paper, an automatic fire detection system without the heuristic fixed threshold values was studied. We presented an automatic method using the statistical color model and the binary background mask. We did the experiment using 600 frames from 6 typical different fire video clips. As the experimental results the proposed method showed a good performance of about average 85% detection rate without false positive, compared with the other methods with the heuristic fixed threshold values. Keywords: Emperical value,Fire detection,RGB,threshold,sensors
Periodic Table Gets Crowded In Year 2011.IOSR Journals
Abstract: Year 2011, has been specially important for teachers and students of chemistry, as after a gap of about 14 years at least five new elements were named and included in the periodic table. All these elements are synthetic and radioactive and some were actually made in 1999, but got their name and status by IUPAC, in July 2011. The total number of elements now in periodic table is 112, and scientists are trying their best to prepare elements with atomic numbers 118, 119 and 120 as well.
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.
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.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
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.
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.
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
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.
Surfactant-assisted Hydrothermal Synthesis of Ceria-Zirconia Nanostructured M...IOSR Journals
CeO2–ZrO2 oxides were prepared by the surfactant-templated method using cetyl trimethyl ammonium bromide (CTAB) as template and modified with chromium nitrate. These were characterized by XRD, FT-IR, TEM, SEM, BET and TPD-CO2. The XRD data showed that as prepared CeO2-ZrO2 powder particles have single phase cubic fluorite structure. HRTEM shows mesoscopic ordering. Average particle size is 12-13 nm as calculated from particle histogram. The nitrogen adsorption/desorption isotherm were classified to be type IV isotherm, typical of mesoporous material. The presence of uni-modal mesopores are confirmed by the pore size distribution which shows pore distribution at around 60 A°. Catalytic activity was studied towards liquid-phase oxidation of benzene.
Computer aided environment for drawing (to set) fill in the blank from given ...IOSR Journals
Abstract: In this paper, we present Development of computer aided environment for drawing (to set) fill in the
blanks that can generate for given paragraph. The System finds fill in the blanks, blanking key generates from
the selected statement. Syntactic and lexical features are used in this process. NLP parser is used, part of speech
taggers are applied on each of these sentences to encode necessary information.
We present our work in designing and implementing the system which generate the blanks. The System is
developed in Java using JDBC which is open source.
Keyword: Natural Language Processing NLP, Key selection, POS tagging, Sentence selection
“Evaluation of Sewing Performance of Plain Twill and Satin Fabrics Based On S...IOSR Journals
Abstract: Seam strength is an important factor in determining the durability of a garment. Seam strength is
determined by resistance to pulling force and abrasion. Seam tenacity break the fabric or the weakest stitch of
seam. Seam abrasion resistance is the amount of rubbing action needed to wear away stitches in the seam
strength is related to stitch type, thread strength ,thread tension, seam type,seam efficiency, width ,and stitches
per inch.Loop strength of thread is more important to durability the seam need not be stronger than the fabric
being sewn.A triple stitched lapped seam would not be necessary for a pair of corduroy jeans since the fabric
itself is not strong and would wear out before the seam.It is better to have the thread is an overstressed seam
that to damage the fabric.Our project objective is to find the seam of strength by using different type of materials.
A Study on Fire Detection System using Statistic Color ModelIOSR Journals
Abstract: Normally fire detection system uses the heuristic fixed threshold values in their specific methods. However, input images may be changed, in general, so the heuristic fixed threshold values used in the fire detection systems might be modified on a case by case basis. In this paper, an automatic fire detection system without the heuristic fixed threshold values was studied. We presented an automatic method using the statistical color model and the binary background mask. We did the experiment using 600 frames from 6 typical different fire video clips. As the experimental results the proposed method showed a good performance of about average 85% detection rate without false positive, compared with the other methods with the heuristic fixed threshold values. Keywords: Emperical value,Fire detection,RGB,threshold,sensors
Periodic Table Gets Crowded In Year 2011.IOSR Journals
Abstract: Year 2011, has been specially important for teachers and students of chemistry, as after a gap of about 14 years at least five new elements were named and included in the periodic table. All these elements are synthetic and radioactive and some were actually made in 1999, but got their name and status by IUPAC, in July 2011. The total number of elements now in periodic table is 112, and scientists are trying their best to prepare elements with atomic numbers 118, 119 and 120 as well.
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%
A comparative study of machine learning algorithms for virtual learning envir...IAESIJAI
Virtual learning environment is becoming an increasingly popular study option for students from diverse cultural and socioeconomic backgrounds around the world. Although this learning environment is quite adaptable, improving student performance is difficult due to the online-only learning method. Therefore, it is essential to investigate students' participation and performance in virtual learning in order to improve their performance. Using a publicly available Open University learning analytics dataset, this study examines a variety of machine learning-based prediction algorithms to determine the best method for predicting students' academic success, hence providing additional alternatives for enhancing their academic achievement. Support vector machine, random forest, Nave Bayes, logical regression, and decision trees are employed for the purpose of prediction using machine learning methods. It is noticed that the random forest and logistic regression approach predict student performance with the highest average accuracy values compared to the alternatives. In a number of instances, the support vector machine has been seen to outperform the other methods.
Dr. S. Saravana Kumar “A Systematic Review on the Educational Data Mining and its Implementation in the Applications ” United International Journal for Research & Technology (UIJRT), Volume 01, Issue 09, pp. 01-03, 2020. https://uijrt.com/articles/v1i9/UIJRTV1I90001.pdf
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
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, itstates that the best algorithm for the improvement of students performance using these algorithms.
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%.
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.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection
for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school
students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and
science were studied and compared. The purpose of this research is to predict the academic major of high
school students using Bayesian networks. The effective factors have been used in academic major selection
for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on
each other, discretization data and processing them was performed by GeNIe. The proper course would be
advised for students to continue their education.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and
science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be
advised for students to continue their education.
Applying adaptive learning by integrating semantic and machine learning in p...IJECEIAES
Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
K0176495101
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov – Dec. 2015), PP 95-101
www.iosrjournals.org
DOI: 10.9790/0661-176495101 www.iosrjournals.org 95 | Page
A Study on Learning Factor Analysis – An Educational Data
Mining Technique for Student Knowledge Modeling
S. Lakshmi Prabha1
, Dr.A.R.Mohamed Shanavas2
1
Ph.D Research Scholar, Bharathidasan University & Associate professor, Department of Computer Science,
Seethalakshmi Ramaswami College, Tiruchirappalli, Tamilnadu, India,
2
Associate professor,Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, Tamilnadu,
India,
Abstract: The increase in dissemination of interactive e-learning environments has allowed the collection of
large repositories of data. The new emerging field, Educational Data Mining (EDM) concerns with developing
methods to discover knowledge from data collected from e-learning and educational environments. EDM can be
applied in modeling user knowledge, user behavior and user experience in e-learning platforms. This paper
explains how Learning Factor Analysis (LFA), a data mining method is used for evaluating cognitive model and
analyzing student-tutor log data for knowledge modeling. Also illustrates how learning curves can be used for
visualizing the performance of the students.
Keywords: e-learning, Educational Data Mining (EDM), Learning Factor Analysis (LFA)
I. Introduction
Educational Data Mining is an inter-disciplinary field utilizes methods from machine learning,
cognitive science, data mining, statistics, and psychometrics. The main aim of EDM is to construct
computational models and tools to discover knowledge by mining data taken from educational settings. The
increase of e-learning resources such as interactive learning environments, learning management systems
(LMS), intelligent tutoring systems (ITS), and hypermedia systems, as well as the establishment of school
databases of student test scores, has created large repositories of data that can be explored by EDM researchers
to understand how students learn and find out models to improve their performance.
Baker [1] has classified the methods in EDM as: prediction, clustering, relationship mining, distillation
of data for human judgment and discovery with models. These methods are used by the researchers [1][2] to
find solutions for the following goals:
1. Predicting students‟ future learning behavior by creating student models that incorporate detailed information
about students‟ knowledge, meta-cognition, motivation, and attitudes.
2. Discovering or improving domain models that characterize the content to be learned and optimal instructional
sequences.
3. Studying the effects of different kinds of pedagogical support that can be provided by learning software, and
4. Advancing scientific knowledge about learning and learners through building computational models that
incorporate models of the student, the software‟s pedagogy and the domain.
The application areas [3] of EDM are: 1) User modeling 2) User grouping or Profiling 3) Domain
modeling and 4) trend analysis. These application areas utilize EDM methods to find solutions. User modeling
[3] encompasses what a learner knows, what the user experience is like, what a learner‟s behavior and
motivation are, and how satisfied users are with online learning. User models are used to customize and adapt
the system behaviors‟ to users specific needs so that the systems „say‟ the „right‟ thing at the „right‟ time in the
„right „way [4]. This paper concerns with applying EDM method Learning factor Analysis (LFA) for User
knowledge Modeling. This paper is organized as follows: section 2 lists the related works done in this research
area; section 3 explains LFA method used in this research; section 4 describes methodology used, section 5
discusses the results and section 6 concludes the work.
II. Literature Review
A number of studies have been conducted in EDM to find the effect of using the discovered methods
on student modeling. This section provides an overview of related works done by other EDM researchers.
Newell and Rosenbloom[5] found a power relationship between the error rate of performance and the
amount of practice .Corbett and Anderson [6] discovered a popular method for estimating students‟ knowledge
is knowledge tracing model, an approach that uses a Bayesian-network-based model for estimating the
probability that a student knows a skill based on observations of him or her attempting to perform the skill.
Baker et.al [7] have proposed a new way to contextually estimate the probability that a student obtained a
correct answer by guessing, or an incorrect answer by slipping, within Bayesian Knowledge Tracing. Koedinger
2. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 96 | Page
et. al [8]demonstrated that a tutor unit, redesigned based on data-driven cognitive model improvements, helped
students reach mastery more efficiently. It produced better learning on the problem-decomposition planning
skills that were the focus of the cognitive model improvements. Stamper and Koedinger [9], presented a data-
driven method for researchers to use data from educational technologies to identify and validate improvements
in a cognitive model which used Knowledge or skill components equivalent to latent variables in a logistic
regression model called the Additive Factors Model (AFM). Brent et. al [10] used learning curves to analyze a
large volume of user data to explore the feasibility of using them as a reliable method for fine tuning adaptive
educational system. Feng et. al[11], addressed the assessment challenge in the ASSISTment system, which is a
web-based tutoring system that serves as an e-learning and e-assessment environment. They presented that the
on line assessment system did a better job of predicting student knowledge by considering how much tutoring
assistance was needed, how fast a student solves a problem and how many attempts were needed to finish a
problem. Saranya et. al [12] proposed system regards the student‟s holistic performance by mining student data
and Institutional data. Naive Bayes classification algorithm is used for classifying students into three classes –
Elite, Average and Poor. Koedinger, K.R.,[13] Professor, Human Computer Interaction Institute, Carnegie
Mellon University, Pittsburgh has done lot to this EDM research. He developed cognitive models and used
students interaction log taken from the Cognitive Tutors, analyzed for the betterment of student learning process
Better assessment models always result with quality education.
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.
III. Learning Factor Analysis
User modeling or student modeling identifies what a learner knows, what the learner experience is like,
what a learner‟s behavior and motivation are, and how satisfied users are with e-learning. Item Response
Theory and Rash model [20] is Psychometric Methods to measure students‟ ability. They lack in providing
results that are easy to interpret by the users. This paper deals with identifying learners‟ knowledge level
(knowledge modeling) using LFA in an e-learning environment.
LFA is an EDM method for evaluating cognitive models and analysing student-tutor log data. LFA
uses three components: 1) Statistical model – multiple logistic regression model is used to quantify the skills.
2) Human expertise- difficulty factors (concepts or KCs) defined by the subject experts (teachers): a
set of factors that make a problem-solving step more difficult for a student and
3) A* search – a combinatorial search for model selection.
A good cognitive model for a tutor uses a set of production rules or skills which specify how students
solve problems. The tutor should estimate the skills learnt by each student when they practice with the tutor. The
power law [5] defines the relationship between the error rate of performance and the amount of practice,
depicted by equation (1).This shows that the error rate decreases according to a power function as the amount of
practice increase.
Y= aXb .....
(1)
Where
Y = the error rate
X = the number of opportunities to practice a skill
a = the error rate on the first trial, reflecting the intrinsic difficulty of a skill
b = the learning rate, reflecting how easy a skill is to learn
While the power law model applies to individual skills, it does not include student effects. In order to
accommodate student effects for a cognitive model that has multiple rules, and that contains multiple students,
the power law model is extended to a multiple logistic regression model (equation 2)[24].
ln[Pijt/(1-Pijt)]= Σ αi Xi + Σ βjYj + Σ γjYjTjt …….(2)
Where Pijt is the probability of getting a step in a tutoring question right by the ith student‟s t th
opportunity to practice the jth KC; X = the covariates for students; Y = the covariates for skills(knowledge
components); T = the number of practice opportunities student i has had on knowledge component j; α = the
coefficient for each student, that is, the student intercept; β = the coefficient for each knowledge component, that
is, the knowledge component intercept; γ = the coefficient for the interaction between a knowledge component
and its opportunities, that is, the learning curve slope. The model says that the log odds of Pijt is proportional to
the overall “smarts” of that student (αi) plus the “easiness” of that KC (βj) plus the amount gained (γj) for each
practice opportunity. This model can show the learning growth of students at any current or past moment.
A difficulty factor refers specifically to a property of the problem that causes student difficulties. The
tutor considered for this research has metric measures as lesson 1 which requires 5 skills (conversion, division,
3. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 97 | Page
multiplication, addition, and result). These are the factors (KCs) in this tutor (Table 1) to be learnt by the
students in solving the steps. Each step has a KC assigned to it for this study.
Table 1. Factors for the Metric measures and their values
Factor Names Factor Values
Converion Correct formula, Incorrect
Addition Correct, Wrong
Multiplication Correct, Wrong
Division Correct, Wrong
Result Correct, Wrong
The combinatorial search will select a model within the logistic regression model space. Difficulty
factors are incorporated into an existing cognitive model through a model operator called Binary Split, which
splits a skill a skill with a factor value, and a skill without the factor value. For example, splitting production
Measurement by factor conversion leads to two productions: Measurement with the factor value Correct formula
and Measurement with the factor value Incorrect. A* search is the combinatorial search algorithm [25] in LFA.
It starts from an initial node, iteratively creates new adjoining nodes, explores them to reach a goal node. To
limit the search space, it employs a heuristic to rank each node and visits the nodes in order of this heuristic
estimate. In this study, the initial node is the existing cognitive model. Its adjoining nodes are the new models
created by splitting the model on the difficulty factors. We do not specify a model to be the goal state because
the structure of the best model is unknown. For this paper 25 node expansions per search is defined as the
stopping criterion. AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are two
estimators used as heuristics in the search.
AIC = -2*log-likelihood + 2*number of parameters. .... (3)
BIC = -2*log-likelihood + number of parameters * number of observations. ..... (4)
Where log-likelihood measures the fit, and the number of parameters, which is the number of
covariates in equation 2, measures the complexity. Lower AIC & BIC scores, mean a better balance between
model fit and complexity.
IV. Methodology
In this paper the LFA methodology is illustrated using data obtained from the Metric measures lesson
of Mensuration Tutor MathsTutor[18] . Our dataset consist of 2,247 transactions involving 60 students, 32
unique steps and 5 Skills (KCs) in students exercise log. All the students were solving 9 problems 5 in mental
problem category, 3 in simple and one in big. Total steps involved are 32. While solving exercise problem a
student can ask for a hint in solving a step. Each data point is a correct or incorrect student action corresponding
to a single skill execution. Student actions are coded as correct or incorrect and categorized in terms of
“knowledge components” (KCs) needed to perform that action. Each step the student performs is related to a
KC and is recorded as an “opportunity” for the student to show mastery of that KC. This lesson has 5 skills
(conversion, division, multiplication, addition, and result) correspond to the skill needed in a step. Each step has
a KC assigned to it for this study. The table 2 shows a sample data with columns: Student- name of the student;
Step – problem 1 Step1; Success – Whether the student did that step correctly or not in the first attempt. 1-
success and 0-failure; Skill – Knowledge component used in that step; Opportunities – Number of times the skill
is used by the same student computed from the first and fourth column.
Table 2. The sample data
Student Step Success Skill Opportunities
X P1s1 1 conversion 1
X P1s2 1 result 1
X P2s1 0 conversion 2
To find fitness of the model logistic regression values are calculated with Additive Factor Model
(AFM)[26]. The values are present in Table 3.Number of parameters and number of observations in equation 3
and 4 is 60 (students) and 1920 (32unique steps x 60 students) respectively. Lower values of AIC, BIC and Root
Mean Squared Error (RMSE) indicate a better fit between the model's predictions and the observed data. Two
types of cross validation are run for each KC model in the dataset. These types are a 3-fold cross validation of
the Additive Factor Model's (AFM)[25] error rate predictions. In student stratified, data points are grouped by
student, the full set of students is divided into 3 groups. 3-fold cross validation is then performed across these 3
groups. In Item stratified, data points are grouped by step, the full set of steps is divided into 3 groups. 3-fold
cross validation is then performed across these 3 groups. The Slope parameter represents how quickly students
will learn the knowledge component. The larger the KC slope, the faster students learn the knowledge
4. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 98 | Page
component. The conversion KC has 0 slope representing no learning takes place to be attended by the teacher.
The addition KC has higher value indicating that students find it easier to solve. This table shows that this model
best fitted the current tutor dataset with lower AIC, BIC, and RMSE values for the KC models used.
Table 3. Logistic Regression Model values
KC Model AIC BIC Log
likelihood
RMSE
(student
stratified)
RMSE
(item
stratified)
Slope
Addition 1,189.43 1,545.18 -530.72 0.302511 0.288114 0.732
Conversion 1,155.22 1,511.02 -513.61 0.298859 0.284691 0.000
Division 1,190.19 1546.03 -513.09 0.301930 0.289071 0.623
Multiplication 1,193.94 1,549.76 -532.97 0.301943 0.287855 0.112
Result 1,197.65 1,553.49 -534.82 0.301916 0.287417 0.075
Learning curves [10] have become a standard tool for measurement of students‟ learning in intelligent
tutoring systems. Here in our study we used learning curve to visualize the student performance over
opportunities. Slope and fit of learning curves show the rate at which a student learns over time, and reveal how
well the system model fits what the student is learning. We used learning curves to measure the performance of
tutoring system domain or student models. Measures of student performance are described below in table 3.
Regardless of metric, each point on the graph is an average across all selected knowledge components and
students.
Table 3. Measures of student performance
Measure Description
Assistance
Score
The number of incorrect attempts plus hint requests for a given opportunity
Error Rate The percentage of students that asked for a hint or were incorrect on their first attempt. For example, an
error rate of 45% means that 45% of students asked for a hint or performed an incorrect action on their
first attempt. Error rate differs from assistance score in that it provides data based only on the first attempt.
As such, an error rate provides no distinction between a student that made multiple incorrect attempts and
a student that made only one.
Number of
Incorrect
The number of incorrect attempts for each opportunity
Number of
Hints
The number of hints requested for each opportunity
Step Duration The elapsed time of a step in seconds, calculated by adding all of the durations for transactions that
were attributed to the step.
Correct Step
Duration
The step duration if the first attempt for the step was correct. The duration of time for which students
are "silent", with respect to their interaction with the tutor, before they complete the step correctly. This is
often called "reaction time" (on correct trials) in the psychology literature. If the first attempt is an error
(incorrect attempt or hint request), the observation is dropped.
Error Step
Duration
The step duration if the first attempt for the step was an error (hint request or incorrect attempt). If the
first attempt is a correct attempt, the observation is dropped.
Learning curve is categorised as follows:
low and flat:. The low error rate shows that students mastered the KCs but continued to receive
tasks for them
no learning: the slope of the predicted learning curve shows no apparent learning for these KCs.
still high: students continued to have difficulty with these KCs. Consider increasing opportunities
for practice.
too little data: students didn't practice these KCs enough for the data to be interpretable.
good: these KCs did not fall into any of the above "bad" or "at risk" categories. Thus, these are
"good" learning curves in the sense that they appear to indicate substantial student learning.
The above categorisations assist the teacher in knowing about the students‟ knowledge level in specific
concepts to be mastered by the students
V. Results And Discussions
To analyse the performance of student(s), we used Datashop[13] analysis and visualization tool for
generating learning curves by uploading our dataset. The fig. 1 shows the problem steps involved in the first
problem and number of correct/incorrect attempts done by 60 students.
5. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 99 | Page
Fig. 1, Problem steps and Attempts made in problem1
The following chart (Fig. 2) shows that the KC-conversion had maximum error rate compared with
other KCs. This explains that the students struggled in conversion step (converting from one unit to other unit in
metric measures lesson).
Fig. 2. Error rate Vs KCs Fig. 3. Average number of hints Vs KCs
From Fig. 3 it is identified that average number of hints requested by the students for conversion KC is
greater than other KCs. The difficulty level of Conversion KC is greater than other KCs. It indicates that
conversion KC has to be explained by the teacher in the class or more practice has to be given to the students.
The Fig. 4 shows the assistance score made the students in all the 9 problems they solved. Though the
fourth problem is defined in mental problem category requires 2 or 3 steps to find the solution, the students
made maximum number of incorrect attempts and requested for hints. This indicates that the problem is tough
for the learners and they did not understand the concept. Students took more time for solving the conversion KC
than other KCs (Fig. 5). This indicates the difficulty level of that skill.
Fig. 4. Assistance Score Vs Problems Fig. 5. Step Duration Vs KCs
The empirical learning curve give a visual clue as to how well a student may do over a set of learning
opportunities, the predicted curves allow for a more precise prediction of a success rate at any learning
opportunity. The predicted learning curve is much smoother. It is computed using the Additive Factor Model
(AFM)[25], which uses a set of customized Item-Response models to predict how a student will perform for
each skill on each learning opportunity. The predicted learning curves are the average predicted error of a skill
over each of the learning opportunities. The blue line in learning curves shows the predicted value and
category is defined using the predicted value. The learning curve has some blips depending on error rate but the
predicted line is very smooth.
6. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 100 | Page
Fig. 6. Learning Curve for Conversion KC Fig. 7. Learning Curve for Multiplication KC
Fig. 8. Learning Curve for Division Fig. 9. Learning Curve for Result KC
Fig. 10. Learning Curve for Addition KC Fig. 11. Learning Curve for Single-KC
From the predicted learning curve for conversion KC (Fig. 6) we can infer that „no learning‟ took place
while practicing. There were 11 opportunities for conversion and 4th
conversion has maximum error rate 33.3%.
We understood that no conversion was at 0% error rate. The teacher can better guide the students in that area.
He can do changes in domain modeling by adding new problems in examples and providing more exercises.
Learning curves shown in Fig. 7 and 9 are in the category „Low and Flat‟ explains that students likely received
too much practice for these KCs. This shows that the students were mastered in these skills and do not require
any more practice. Fig.8 and 11 are in the category „good‟ indicate that the students got sufficient learning in
that. Single-KC model in Fig. 11 shows the overall performance of the students in all the 32 unique steps are
good. In 32 steps only 2 steps used addition so fig. 10 shows „too little data‟. We can add problems for this KC
or it can be merged with other KCs.
VI. Conclusion
Student knowledge models can be improved by mining students‟ interaction data. This paper analyzed
the use of LFA in student knowledge modeling in maths education with learning curves by mining the students
log data. This method assists the teacher in: 1) measuring the difficulty and learning rates of Knowledge
Components (KCs). 2) predict student performance in practicing each KC. 3) identify over-practiced or under-
practiced KCs. The learners can understand what they know and do not know. The students with poor
performance can be given with more problems for practicing. This method provides more insight into the
performance of skills in every step for each student. The next step of this research is to provide a personalized
tutoring environment for the students by incorporating the results into the tutor and providing automated
suggestion to improve their performance. Clustering algorithms can be used to suggest the teacher in grouping
the students according to their performance
References
[1] Baker, R. S. J. d., ( 2011), “Data Mining for Education.” In International Encyclopedia of Education, 3rd
ed., Edited by B. McGaw,
P. Peterson, and E. Baker. Oxford, UK: Elsevier.
[2] Baker, R. S. J. D., and K. Yacef, ( 2009), “The State of Educational Data Mining in 2009: A Review and Future Visions.” Journal
of Educational Data Mining 1 (1): 3–17.
7. A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student…
DOI: 10.9790/0661-176495101 www.iosrjournals.org 101 | Page
[3] S. Lakshmi Prabha, Dr.A.R.Mohamed Shanavas, (2014), EDUCATIONAL DATA MINING APPLICATIONS, Operations
Research and Applications: An International Journal (ORAJ), Vol. 1, No. 1, August 2014, 23-29.
[4] Feng, M., N. T. Heffernan, and K. R. Koedinger, (2009), “User Modeling and User-Adapted Interaction: Addressing the
Assessment Challenge in an Online System That Tutors as It Assesses.” The Journal of Personalization Research (UMUAI journal)
19 (3): 243–266.
[5] Newell, A., Rosenbloom, P.,(1981), Mechanisms of Skill Acquisition and the Law of Practice. In Anderson J. (ed.): Cognitive
Skills and Their Acquisition, Erlbaum Hillsdale NJ (1981)
[6] Corbett, A. T., and J. R. Anderson, (1994), “Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge.” User
Modeling and User-Adapted Interaction 4 (4): 253–278. doi: 10.1007/BFO1099821
[7] Baker, R.S.J.d., Corbett, A.T., Aleven, V., (2008), More Accurate Student Modeling Through Contextual Estimation of Slip and
Guess Probabilities in Bayesian Knowledge Tracing. Proceedings of the 9th International Conference on Intelligent Tutoring
Systems, 406-415.
[8] Koedinger, K.R., Stamper, J.C., McLaughlin, E.A., & Nixon, T., (2013), Using data-driven discovery of better student models to
improve student learning. In Yacef, K., Lane, H., Mostow, J., & Pavlik, P. (Eds.) In Proceedings of the 16th International
Conference on Artificial Intelligence in Education, pp. 421-430.
[9] Stamper, J.C., Koedinger, K.R.,(2011), Human-machine student model discovery and improvement using DataShop. In: Biswas, G.,
Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 353–360. Springer, Heidelberg (2011).
[10] Brent Martin , Antonija Mitrovic , Kenneth R Koedinger , Santosh Mathan, (2011), Evaluating and Improving Adaptive
Educational Systems with Learning Curves, User Modeling and User-Adapted Interaction , 2011; 21(3):249-283.
DOI: 10.1007/s11257-010-9084-2.
[11] Feng, M., Heffernan, N.T., & Koedinger, K.R., (2009), Addressing the assessment challenge in an Online System that tutors as it
assesses. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI journal). 19(3), 243-266,
August, 2009.
[12] S.Saranya, R.Ayyappan , N.Kumar, (2014), Student Progress Analysis and Educational Institutional Growth Prognosis Using Data
Mining, International Journal Of Engineering Sciences & Research Technology, 3(4): April, 2014, 1982-1987.
[13] Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J., (2010), A Data Repository for the EDM
community: The PSLC DataShop. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (Eds.) Handbook of Educational
Data Mining. Boca Raton, FL: CRC Press.
[14] Surjeet Kumar Yadav, Saurabh pal, (2012), Data Mining Application in Enrollment Management: A Case Study, International
Journal of Computer Applications (0975 – 8887) Volume 41– No.5, March 2012, pg:1-6.
[15] Wilson, M., de Boeck, P.,(2004), Descriptive and explanatory item response models. In: de Boeck, P., Wilson, M. (eds.)
Explanatory Item Response Models, pp. 43–74. Springer (2004)
[16] Pooja Gulati, Dr. Archana Sharma, (2012), Educational Data Mining for Improving Educational Quality, IRACST - International
Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 2, No.3, June 2012,
pg.648-650.
[17] Pooja Thakar, Anil Mehta, Manisha, (2015), Performance Analysis and Prediction in Educational Data Mining: A Research
Travelogue, International Journal of Computer Applications (0975 – 8887) Volume 110 – No. 15, January 2015, pg:60-68.
[18] Prabha, S.Lakshmi; Shanavas, A.R.Mohamed, (2014), "Implementation of E-Learning Package for Mensuration-A Branch of
Mathematics," Computing and Communication Technologies (WCCCT), 2014 World Congress on , vol., no., pp.219,221, Feb. 27
2014-March 1 2014,doi:10.1109/WCCCT.2014.37
[19] Brett Van De Sande, (2013), Properties of the Bayesian Knowledge Tracing Model, Journal of Educational Data Mining, Volume 5,
No 2, August, 2013,1-10.
[20] Wu, M. & Adams, R., (2007), Applying the Rasch model to psycho-social measurement: A practical approach. Educational
Measurement Solutions, Melbourne.
[21] Romero, C.,&Ventura,S.,(2010), Educational data mining: A review of the state of the art,IEEE Transactions on systems man and
Cybernetics Part C.Applications and review, 40(6),601-618.
[22] Wasserman L.,(2004), All of Statistics, 1st edition, Springer-Verlag New York, LLC
[23] Cen, H., Koedinger, K. & Junker, B., (2005), Automating Cognitive Model Improvement by A* Search and Logistic Regression. In
Proceedings of AAAI 2005 Educational Data Mining Workshop.
[24] Russell S., Norvig P.,(2003), Artificial Intelligence, 2nd edn. Prentice Hall (2003).
[25] Cen, H., Koedinger, K., Junker, B., (2007), Is Over Practice Necessary? Improving Learning Efficiency with the Cognitive Tutor
through Education. The 13th International Conference on Artificial Intelligence in Education (AIED 2007). 2007.
[26] S. Lakshmi Prabha et al, (2015), Performance of Classification Algorithms on Students‟ Data – A Comparative Study, International
Journal of Computer Science and Mobile Applications, Vol.3 Issue. 9, pg. 1-8.
[27] S. Lakshmi Prabha, A.R. Mohamed Shanavas,(2015), Analysing Students Performance Using Educational Data Mining Methods,
International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.82, pg. 667-671.