Students Knowledge based Advisor System for Colleges Admission With an Applie...IJRES Journal
This paper presents a knowledge based advisor system designed to aid preparatory year students to achieve their desire of colleges they wanted to be dwelled allocated. The system predicts the 2nd term GPA, and the final term GPA by knowing the 1st semester GPA, then advise students to suitable colleges. A student who is far from achieving his desire is advised to study some aided courses. The system uses historical data extracted from the university database. Then statistical prediction algorithms are developed, to evaluate and to match students’ current desires with colleges’ qualified criteria.
Data mining in higher education university student dropout case studyIJDKP
In this paper, we apply different data mining approaches for the purpose of examining and predicting students’ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The
collected data included student study history and transcript for courses taught in the first two years of
computer science major in addition to student GPA , high school average , and class label of (yes ,No) to
indicate whether the student graduated from the chosen major or not. In order to classify and predict
dropout students, different classifiers have been trained on our data sets including Decision Tree (DT),
Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB
classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships
between student dropout status and enrolment persistence by mining a frequent cases using FP-growth
algorithm.
Bayesian Estimation of Above-Average Performance in Tertiary Institutions: A ...IOSR Journals
Bayesian approach for parameter estimation has the capacity to yield more precise estimates than methods based on sampling theory. There are several common Bayesian models; in this study we applied Empirical Bayes (EB) model called Beta-binomial model. The study is motivated by the need to beam searchlight on universities, faculties or fields of study with graduates who may not be eligible for further educational pursuits. This study provides means of assessment or a basis of evaluation of students’ performances among faculties or fields of study and overall performance of a university. This study uses Bayesian methods of inference to estimate the proportion of above-average performance of graduates from the various faculties in University of Lagos. The model adopted generated results which are of smaller variances compared with variances of sample Proportions, showing that the posterior proportions generated are more efficient estimators. This is further evidenced in narrow widths of the computed confidence intervals. The overall result shows that the proportion of above-average performance of graduates of University of Lagos, who are eligible for further educational pursuits (i.e. higher degrees), is approximately 72% of the university graduates
Data Mining Techniques in Higher Education an Empirical Study for the Univer...IJMER
Nowadays, ones of the biggest challenges that educational institutions face is the explosive
growth of educational data. and how to use these data to improve the quality of managerial decisions.
Data mining, as an analytical tools that can be used to extract meaningful knowledge from large data
sets, can be used to achieve this goal.
This paper addresses the applications of Educational Data Mining (EDM) to extract useful information
from registration information of student at university of Palestine in Gaza strip. The data include five
years period [2005-2011] by providing analytical tool to view and use this information for decision
making processes by taking real life example such as grade and GPA for the students. abstract should
summarize the content of the paper.
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.
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.
Statistical Scoring Algorithm for Learning and Study Skillsertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/statistical-scoring-algorithm-for-learning-and-study-skills/
This study examines the study skills and the learning styles of university students by using scoring method. The study investigates whether the study skills can be summarized in a single universal score that measures how hard a student works. The sample consists of 418 undergraduate students of an international university. The presented scoring was method adapted from the domain of risk management. The proposed method computes an overall score that represents the study skills, using a linear weighted summation scheme. From among 50 questions regarding to learning and study skills, the 30 highest weighted questions are suggested to be used in the future studies as a learning and study skills inventor. The proposed scoring method and study yield results and insights that can guide educators regarding how they can improve their students’ study skills. The main point drawn from this study is that the students greatly value opportunities for interaction with instructors and peers, cooperative learning and active engagement in lectures.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Students Knowledge based Advisor System for Colleges Admission With an Applie...IJRES Journal
This paper presents a knowledge based advisor system designed to aid preparatory year students to achieve their desire of colleges they wanted to be dwelled allocated. The system predicts the 2nd term GPA, and the final term GPA by knowing the 1st semester GPA, then advise students to suitable colleges. A student who is far from achieving his desire is advised to study some aided courses. The system uses historical data extracted from the university database. Then statistical prediction algorithms are developed, to evaluate and to match students’ current desires with colleges’ qualified criteria.
Data mining in higher education university student dropout case studyIJDKP
In this paper, we apply different data mining approaches for the purpose of examining and predicting students’ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The
collected data included student study history and transcript for courses taught in the first two years of
computer science major in addition to student GPA , high school average , and class label of (yes ,No) to
indicate whether the student graduated from the chosen major or not. In order to classify and predict
dropout students, different classifiers have been trained on our data sets including Decision Tree (DT),
Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB
classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships
between student dropout status and enrolment persistence by mining a frequent cases using FP-growth
algorithm.
Bayesian Estimation of Above-Average Performance in Tertiary Institutions: A ...IOSR Journals
Bayesian approach for parameter estimation has the capacity to yield more precise estimates than methods based on sampling theory. There are several common Bayesian models; in this study we applied Empirical Bayes (EB) model called Beta-binomial model. The study is motivated by the need to beam searchlight on universities, faculties or fields of study with graduates who may not be eligible for further educational pursuits. This study provides means of assessment or a basis of evaluation of students’ performances among faculties or fields of study and overall performance of a university. This study uses Bayesian methods of inference to estimate the proportion of above-average performance of graduates from the various faculties in University of Lagos. The model adopted generated results which are of smaller variances compared with variances of sample Proportions, showing that the posterior proportions generated are more efficient estimators. This is further evidenced in narrow widths of the computed confidence intervals. The overall result shows that the proportion of above-average performance of graduates of University of Lagos, who are eligible for further educational pursuits (i.e. higher degrees), is approximately 72% of the university graduates
Data Mining Techniques in Higher Education an Empirical Study for the Univer...IJMER
Nowadays, ones of the biggest challenges that educational institutions face is the explosive
growth of educational data. and how to use these data to improve the quality of managerial decisions.
Data mining, as an analytical tools that can be used to extract meaningful knowledge from large data
sets, can be used to achieve this goal.
This paper addresses the applications of Educational Data Mining (EDM) to extract useful information
from registration information of student at university of Palestine in Gaza strip. The data include five
years period [2005-2011] by providing analytical tool to view and use this information for decision
making processes by taking real life example such as grade and GPA for the students. abstract should
summarize the content of the paper.
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.
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.
Statistical Scoring Algorithm for Learning and Study Skillsertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/statistical-scoring-algorithm-for-learning-and-study-skills/
This study examines the study skills and the learning styles of university students by using scoring method. The study investigates whether the study skills can be summarized in a single universal score that measures how hard a student works. The sample consists of 418 undergraduate students of an international university. The presented scoring was method adapted from the domain of risk management. The proposed method computes an overall score that represents the study skills, using a linear weighted summation scheme. From among 50 questions regarding to learning and study skills, the 30 highest weighted questions are suggested to be used in the future studies as a learning and study skills inventor. The proposed scoring method and study yield results and insights that can guide educators regarding how they can improve their students’ study skills. The main point drawn from this study is that the students greatly value opportunities for interaction with instructors and peers, cooperative learning and active engagement in lectures.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
A Mamdani Fuzzy Model to Choose Eligible Student EntryTELKOMNIKA JOURNAL
This paper presented about study that have been created a new student choosing system by
using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics
such as human perceptions on choosing of students with some specified criteria. The choosing students
who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the
process can be possible completed execute and can be reduced the time of choose. To accomplish the
process, the fuzzy variable is created by the national final exam scores, report grade, general competency
test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has
been reached 75.63% accuracy.
EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT’S ACADEMIC PERFORMANCE cscpconf
Educational data mining is used to study the data available in the educational field and bring
out the hidden knowledge from it. Classification methods like decision trees, rule mining,
Bayesian network etc can be applied on the educational data for predicting the students
behavior, performance in examination etc. This prediction will help the tutors to identify the
weak students and help them to score better marks. The C4.5 decision tree algorithm is applied
on student’s internal assessment data to predict their performance in the final exam. The
outcome of the decision tree predicted the number of students who are likely to fail or pass. The
result is given to the tutor and steps were taken to improve the performance of the students who
were predicted to fail. After the declaration of the results in the final examination the marks
obtained by the students are fed into the system and the results were analyzed. The comparative
analysis of the results states that the prediction has helped the weaker students to improve and
brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared
with ID3 algorithm and found to be more efficient in terms of the accurately predicting the
outcome of the student and time taken to derive the tree.
Predicting Success : An Application of Data Mining Techniques to Student Outc...IJDKP
This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
or discontinued their education. Operating on over 30,000 student observations, random forests are used
to determine the relative importance of the student characteristics with genetic algorithms to perform
feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter
tuning was also implemented. Overall predictive strength is relatively high as measured by the
Matthews Correlation Coefficient, and both intuitive and novel features which provide support for the
learning model are explored.
An Evaluation of Feature Selection Methods for Positive - Unlabeled Learning ...Editor IJCATR
Feature Selection is important in the processing of data in domains such
as text because such data can be of very high
dimension. Because in positive
-
unlabeled (PU) learning problems, there are no labeled negative data for training, we need
unsupervised feature selection methods that do not use the class information in the trai
ning documents when selecting features for the
classifier. There are few feature selection methods that are available for use in document classification with PU learning. I
n this paper
we evaluate four unsupervised methods including, collection frequency (
CF), document frequency (DF), collection frequency
-
inverse
document frequency (CF
-
IDF) and term frequency
-
document frequency (TF
-
DF). We found DF most effective in our experiments.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
Managers Perceptions towards the Success of E-performance Reporting SystemTELKOMNIKA JOURNAL
Managers are the key informants in the information system (IS) success measurements. In fact, besides the determinant agents are rarely involved in the assessments, most of the measurements are also often performed by the technical stakeholders of the systems. Therefore, the results may questionable. This study was carried to explain the factors that influence the success of an e-performance reporting system in an Indonesian university by involving ± 70% of the managers (n=66) in the sampled institution. The DeLone and McLean model was adopted and adapted here following the suggestions of the previous meta-analysis studies. The collected data was analyzed using the partial least squares-structural equation modelling (PLS-SEM) for examining the four hypotheses. Despite the findings revealed acceptances of the overall hypotheses, the weak explanation of the user satisfaction variable towards the net benefit one had been the highlighted point. Besides the study limitations, the point may also be the practical and theoretical considerations for the next studies, especially for the IS success studies in Indonesia
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
A Longitudinal Study of Undergraduate Performance in Mathematics, an Applicat...iosrjce
Students’ performance in mathematics has been an issue of great concern to most countries,
especially the developing nations. So many programmes have been put in place to improve performances and to
also encourage student to study the course in tertiary institution. In this study we investigate the relationship of
semester, department of a student, age and load unit on marginal mathematics performance o f undergraduate
students. A marginal model was formulated using four working correlation structure where the exchangeable
working correlation structure was selected as the best that models the dataset using quasi information criteria.
The semester, age and load unit were found to be related to the marginal performance in mathematics
A COMPARATIVE ANALYSIS OF SELECTED STUDIES IN STUDENT PERFORMANCE PREDICTIONIJDKP
Predicting student success has long been an interest of institutions of higher education as well as
organisations responsible for preparing high-stake, standardised tests administered at national and
international levels. This study discusses how performance prediction studies have evolved from those that
use demographic data and high school grades to predict success in college to those that utilise
sophisticated data collected in non-traditional educational platforms to predict end-of-course performance
and to those that show how student progress can be tracked in a continuous manner. A total of 56 studies
published since the nineties are discussed. Views on strengths and weaknesses as well as observed
opportunities for improvement are presented. The consistently high results reported in many of the studies
shall convince the reader that automated solutions to the problem of predicting student progress and
performance can either be tailored for specific settings or can be adopted from similar settings in which
they have been utilized successfully. A recommendation on how to build upon recent success is provided
Influence of Table of Specification on the Construction of Ordinary Level Phy...ijtsrd
A table of specification is fundamental in a test construction. The use of table of specifications when construction a teacher made achievement test and standardized test is very essential, because it will make the test valid and reliable. Unfortunately, because of lack of inadequate training on its use, it is usually not used by many teachers when constructing a test. The results from these types of assessments are likely not to be valid and reliable. In this situation, some topics that the teacher spent little time in teaching may carry more weighting leading to students’ poor performance in the subject Physics . Most teachers and administrators are still relatively blank as far as skills in test construction and interpretation are concerned. Classroom test provides teachers with essential information that they can use to make decisions about instructions, students learning and student grades. This paper is centred on the following meaning of weighting, table of specification, the purpose of the table of specification, the benefits of a table of specification in test construction, what should be taken into account when building a TOS, a practical example of TOS, Bloom’s taxonomy of educational objectives and item analysis. The importance of table of specifications and the inherent dangers of not using it are highlighted and recommendations to ameliorate the situation are proffered. Awandia Joseph Tazitabong | Dikande Alain Moise | Ndifon Isaiah Ngek "Influence of Table of Specification on the Construction of Ordinary Level Physics Examination in Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53979.pdf Paper URL: https://www.ijtsrd.com.com/physics/other/53979/influence-of-table-of-specification-on-the-construction-of-ordinary-level-physics-examination-in-cameroon/awandia-joseph-tazitabong
A Mamdani Fuzzy Model to Choose Eligible Student EntryTELKOMNIKA JOURNAL
This paper presented about study that have been created a new student choosing system by
using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics
such as human perceptions on choosing of students with some specified criteria. The choosing students
who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the
process can be possible completed execute and can be reduced the time of choose. To accomplish the
process, the fuzzy variable is created by the national final exam scores, report grade, general competency
test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has
been reached 75.63% accuracy.
EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT’S ACADEMIC PERFORMANCE cscpconf
Educational data mining is used to study the data available in the educational field and bring
out the hidden knowledge from it. Classification methods like decision trees, rule mining,
Bayesian network etc can be applied on the educational data for predicting the students
behavior, performance in examination etc. This prediction will help the tutors to identify the
weak students and help them to score better marks. The C4.5 decision tree algorithm is applied
on student’s internal assessment data to predict their performance in the final exam. The
outcome of the decision tree predicted the number of students who are likely to fail or pass. The
result is given to the tutor and steps were taken to improve the performance of the students who
were predicted to fail. After the declaration of the results in the final examination the marks
obtained by the students are fed into the system and the results were analyzed. The comparative
analysis of the results states that the prediction has helped the weaker students to improve and
brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared
with ID3 algorithm and found to be more efficient in terms of the accurately predicting the
outcome of the student and time taken to derive the tree.
Predicting Success : An Application of Data Mining Techniques to Student Outc...IJDKP
This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
or discontinued their education. Operating on over 30,000 student observations, random forests are used
to determine the relative importance of the student characteristics with genetic algorithms to perform
feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter
tuning was also implemented. Overall predictive strength is relatively high as measured by the
Matthews Correlation Coefficient, and both intuitive and novel features which provide support for the
learning model are explored.
An Evaluation of Feature Selection Methods for Positive - Unlabeled Learning ...Editor IJCATR
Feature Selection is important in the processing of data in domains such
as text because such data can be of very high
dimension. Because in positive
-
unlabeled (PU) learning problems, there are no labeled negative data for training, we need
unsupervised feature selection methods that do not use the class information in the trai
ning documents when selecting features for the
classifier. There are few feature selection methods that are available for use in document classification with PU learning. I
n this paper
we evaluate four unsupervised methods including, collection frequency (
CF), document frequency (DF), collection frequency
-
inverse
document frequency (CF
-
IDF) and term frequency
-
document frequency (TF
-
DF). We found DF most effective in our experiments.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
Managers Perceptions towards the Success of E-performance Reporting SystemTELKOMNIKA JOURNAL
Managers are the key informants in the information system (IS) success measurements. In fact, besides the determinant agents are rarely involved in the assessments, most of the measurements are also often performed by the technical stakeholders of the systems. Therefore, the results may questionable. This study was carried to explain the factors that influence the success of an e-performance reporting system in an Indonesian university by involving ± 70% of the managers (n=66) in the sampled institution. The DeLone and McLean model was adopted and adapted here following the suggestions of the previous meta-analysis studies. The collected data was analyzed using the partial least squares-structural equation modelling (PLS-SEM) for examining the four hypotheses. Despite the findings revealed acceptances of the overall hypotheses, the weak explanation of the user satisfaction variable towards the net benefit one had been the highlighted point. Besides the study limitations, the point may also be the practical and theoretical considerations for the next studies, especially for the IS success studies in Indonesia
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
A Longitudinal Study of Undergraduate Performance in Mathematics, an Applicat...iosrjce
Students’ performance in mathematics has been an issue of great concern to most countries,
especially the developing nations. So many programmes have been put in place to improve performances and to
also encourage student to study the course in tertiary institution. In this study we investigate the relationship of
semester, department of a student, age and load unit on marginal mathematics performance o f undergraduate
students. A marginal model was formulated using four working correlation structure where the exchangeable
working correlation structure was selected as the best that models the dataset using quasi information criteria.
The semester, age and load unit were found to be related to the marginal performance in mathematics
A COMPARATIVE ANALYSIS OF SELECTED STUDIES IN STUDENT PERFORMANCE PREDICTIONIJDKP
Predicting student success has long been an interest of institutions of higher education as well as
organisations responsible for preparing high-stake, standardised tests administered at national and
international levels. This study discusses how performance prediction studies have evolved from those that
use demographic data and high school grades to predict success in college to those that utilise
sophisticated data collected in non-traditional educational platforms to predict end-of-course performance
and to those that show how student progress can be tracked in a continuous manner. A total of 56 studies
published since the nineties are discussed. Views on strengths and weaknesses as well as observed
opportunities for improvement are presented. The consistently high results reported in many of the studies
shall convince the reader that automated solutions to the problem of predicting student progress and
performance can either be tailored for specific settings or can be adopted from similar settings in which
they have been utilized successfully. A recommendation on how to build upon recent success is provided
Influence of Table of Specification on the Construction of Ordinary Level Phy...ijtsrd
A table of specification is fundamental in a test construction. The use of table of specifications when construction a teacher made achievement test and standardized test is very essential, because it will make the test valid and reliable. Unfortunately, because of lack of inadequate training on its use, it is usually not used by many teachers when constructing a test. The results from these types of assessments are likely not to be valid and reliable. In this situation, some topics that the teacher spent little time in teaching may carry more weighting leading to students’ poor performance in the subject Physics . Most teachers and administrators are still relatively blank as far as skills in test construction and interpretation are concerned. Classroom test provides teachers with essential information that they can use to make decisions about instructions, students learning and student grades. This paper is centred on the following meaning of weighting, table of specification, the purpose of the table of specification, the benefits of a table of specification in test construction, what should be taken into account when building a TOS, a practical example of TOS, Bloom’s taxonomy of educational objectives and item analysis. The importance of table of specifications and the inherent dangers of not using it are highlighted and recommendations to ameliorate the situation are proffered. Awandia Joseph Tazitabong | Dikande Alain Moise | Ndifon Isaiah Ngek "Influence of Table of Specification on the Construction of Ordinary Level Physics Examination in Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53979.pdf Paper URL: https://www.ijtsrd.com.com/physics/other/53979/influence-of-table-of-specification-on-the-construction-of-ordinary-level-physics-examination-in-cameroon/awandia-joseph-tazitabong
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.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published Quarterly offers fast publication schedule whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites the process All submitted research articles are subjected to the immediate rapid screening by editors consultation with Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
ISSN 2454-535X
International Journal of Mechanical Engineering Research and Technology aims to provide the best possible service to authors of original research articles, and the fairest system of peer review.
The International Journal of Mechanical Engineering Research and Technology is an international online journal in English published Quarterly. This offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/