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.
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.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance IJECEIAES
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
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
This document summarizes a research paper that developed a classification system for categorizing undergraduate thesis titles at a university in Indonesia using the k-nearest neighbor machine learning algorithm. The system was developed using student data from the Informatics Engineering program, including course grades and interests. The k-nearest neighbor method was used to classify thesis title categories based on student criteria. Training data from 2012 was used to create the classification model, and 2013 student data was used to test the system. The goal was to assist both students and departments in selecting appropriate thesis title categories aligned with students' fields of expertise and interest.
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.
This document discusses the development and testing of an algo-heuristic model to improve statistics learning among elementary school teacher education students. It involved three phases: 1) expert evaluation of the model, 2) testing with lecturers, and 3) pre-experimental testing with students. Test results found a significant increase in student test scores after using the model, indicating it can effectively improve statistics learning. The document also reviews various literature on algo-heuristic learning theories and algorithms.
Intelligent system for sTudent placementFemmy Johnson
This document describes a proposed intelligent system for student placement in Nigeria using fuzzy logic. It outlines the country's educational system and related works on student placement prediction. The proposed system would use fuzzy logic and linguistic variables to analyze student data like academic performance, psychomotor skills, and department choices. Membership functions would be assigned to variables and an inference engine would apply fuzzy rules to generate placement recommendations to either the science, arts, or repeat a class. The goal is to help schools accurately place students in a timely manner to improve performance and outcomes.
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.
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.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance IJECEIAES
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
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
This document summarizes a research paper that developed a classification system for categorizing undergraduate thesis titles at a university in Indonesia using the k-nearest neighbor machine learning algorithm. The system was developed using student data from the Informatics Engineering program, including course grades and interests. The k-nearest neighbor method was used to classify thesis title categories based on student criteria. Training data from 2012 was used to create the classification model, and 2013 student data was used to test the system. The goal was to assist both students and departments in selecting appropriate thesis title categories aligned with students' fields of expertise and interest.
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.
This document discusses the development and testing of an algo-heuristic model to improve statistics learning among elementary school teacher education students. It involved three phases: 1) expert evaluation of the model, 2) testing with lecturers, and 3) pre-experimental testing with students. Test results found a significant increase in student test scores after using the model, indicating it can effectively improve statistics learning. The document also reviews various literature on algo-heuristic learning theories and algorithms.
Intelligent system for sTudent placementFemmy Johnson
This document describes a proposed intelligent system for student placement in Nigeria using fuzzy logic. It outlines the country's educational system and related works on student placement prediction. The proposed system would use fuzzy logic and linguistic variables to analyze student data like academic performance, psychomotor skills, and department choices. Membership functions would be assigned to variables and an inference engine would apply fuzzy rules to generate placement recommendations to either the science, arts, or repeat a class. The goal is to help schools accurately place students in a timely manner to improve performance and outcomes.
This study tested the effectiveness of algo-heuristic models in improving elementary school teacher education students' academic achievement in statistics. It involved 3 phases: 1) small group testing by instructional design and statistical learning experts, 2) large group testing by course lecturers, and 3) testing the effectiveness on students. The evaluation consisted of expert testing the theoretical quality, testing with a small group of lecturers, and large group pre-experimental testing to determine the effectiveness of the algo-heuristic model in improving student learning. The results showed a statistically significant increase in post-test scores compared to pre-test, indicating the algo-heuristic learning was effective in improving student learning of statistics.
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.
This document provides an overview of data mining techniques designed for imbalanced datasets. It discusses how imbalanced datasets, where one class is greatly underrepresented compared to others, pose challenges for machine learning algorithms. Several approaches have been proposed to address this issue, including sampling methods like oversampling the minority class and undersampling the majority class, as well as cost-sensitive methods that assign higher misclassification costs. The document reviews common sampling strategies used for imbalanced datasets, such as SMOTE, and cost-sensitive approaches involving cost matrices and cost curves. Overall, it examines how various sampling and cost-based methods can help improve classification of imbalanced datasets in fields such as medical diagnosis and text classification.
An Automatic Question Paper Generation : Using Bloom's TaxonomyIRJET Journal
This document presents a system for automatically generating exam questions and categorizing them according to Bloom's Taxonomy. It uses natural language processing techniques like part-of-speech tagging to analyze questions and identify keywords and verbs. Rules are developed to match question patterns and keywords to the appropriate Bloom's Taxonomy category. A randomization algorithm is also introduced to randomly select questions from the database and avoid repetitions. The system aims to help educators automatically analyze exam questions and ensure a balance of cognitive levels according to Bloom's Taxonomy. Preliminary results found the rules could successfully categorize questions in the test set. The proposed system has applications for educational institutions, universities, and government exams.
A cluster-based analysis to diagnose students’ learning achievementsMiguel R. Artacho
The document describes a proposed methodology for diagnosing students' learning achievements using cluster-based analysis. The methodology involves using item response theory to assess students' skill levels on concepts, identifying weaknesses and misconceptions, and clustering students based on similar disabilities. The methodology aims to provide adaptive feedback to help students improve and inform teaching strategies. A software tool was developed to implement the diagnostic assessment and clustering.
The translation between mathematical representations was one of the indicators in understanding mathematical concepts. Understanding the things related to the process of student representation translation was very important in learning mathematics. One of the activities that plays a role in translation was unpacking the source. This study aimed to determine the characteristics of student activity in unpacking the source when doing the process of translational verbal representation to the graph. This research was using a qualitative research. The subject of this research were twenty mathematics education students. Selection of research subjects used purposive sampling. The data were collected by test and interview. The results showed that the characteristics of student activity in unpacking the source were two that are drawing scheme of verbal situation and interpreting verbal information with its own language. In addition, there were still many students failed in translation because of difficulties in unpacking the source. The results of this study were expected to add insight into learning to minimize student difficulties in unpacking the source.
Enhancing Performance in Medical Articles Summarization with Multi-Feature Se...IJECEIAES
This document summarizes a study that aimed to enhance the performance of medical article summarization through multi-feature selection. The study utilized 7,346 online medical articles to generate summaries using maximal marginal relevance with an n-Best value of 0.7. Feature selection techniques explored included title, word counts, noun frequency, and word category in medical content. Evaluation of the summarization system found a precision of 91.6%, recall of 92.6%, and F-measure of 92.2% when combining feature selection with word category classification. The study contributed by determining the optimal n-Best value, analyzing feature selection combinations, and providing a classification of sentence types in medical texts.
Analyzing undergraduate students’ performance in various perspectives using d...Alexander Decker
This document discusses analyzing undergraduate student performance using data mining techniques. It analyzes student performance in two perspectives: 1) supervised vs unsupervised assessment instruments and 2) performance in mathematics, English, and programming courses. The study uses association rule mining with the Apriori algorithm to discover patterns in student performance data from both analyses. The goal is to identify useful insights that can help improve assessment methods, curriculum structure, and course prerequisites.
IRJET- Predictive Analytics for Placement of Student- A Comparative StudyIRJET Journal
This document summarizes and compares 15 research papers that use predictive analytics and data mining techniques to predict student placements. Various classification, clustering, and regression algorithms are applied such as decision trees, naive Bayes, k-nearest neighbors, neural networks, fuzzy logic and more. Performance is evaluated using metrics like accuracy, error rates and time taken. Decision trees generally performed well with accuracies above 90% in most papers. The papers aim to help students and institutions understand placement probabilities based on student attributes to improve employability.
This document presents a study that uses linear regression to predict university freshmen's academic performance (GPA) based on their scores on the Joint Matriculation Examination (JME). The study finds a weak positive correlation (R=0.137) between GPA and JME scores, with the regression model only explaining 1.9% of variability in GPA. Statistical tests show no significant relationship between JME score and university GPA (p>0.05). The study concludes that JME score is not a strong predictor of freshmen academic performance.
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.
Hybrid Classifier for Sentiment Analysis using Effective PipeliningIRJET Journal
The document describes a hybrid approach for sentiment analysis of tweets that uses a pipeline of rules-based classification, lexicon-based classification, and machine learning classification. Tweets are first classified using rules and a lexicon, and only tweets that do not meet a confidence threshold are passed to a machine learning classifier. The hybrid approach aims to optimize performance, speed, accuracy, and processing requirements compared to using individual classification methods alone. The document provides background on sentiment analysis methods and evaluates the performance of the hybrid approach versus individual classifiers.
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...iosrjce
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.
IRJET - A Study on Student Career PredictionIRJET Journal
This document discusses research on using machine learning techniques to predict student performance and career outcomes. It provides an overview of various studies that have used methods like decision trees, naive Bayes classification, neural networks, and clustering algorithms. The studies aimed to identify factors influencing student performance and predict outcomes like course grades, dropout risk, and placement success. The document also compares the different techniques, finding that deep neural networks and ensemble methods can achieve relatively high prediction accuracy, above 80% in some cases. Overall, the research aims to help educational institutions identify at-risk students and improve student performance.
The factors importance to economization produced cheese mozzarella from cow's...inventy
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.
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.
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.
Design and Analysis of Auger in KAMCO Power Tillerinventy
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.
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.
Gravimetric, mechanical and chemical characterization of different materials used in sewers systems: Polyvinyl chloride (PVC), polypropylene (PP) and high density polyethylene (HDPE), aged in sulfuric acid at 60°C
Mangrove Woods Damage Based On Time of Water Absorption Rate of Woodsinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Common Fixed Theorems Using Random Implicit Iterative Schemesinventy
This document summarizes research on common fixed point theorems using random implicit iterative schemes. It defines random Mann, Ishikawa, and SP iterative schemes. It also defines modified implicit random iterative schemes associated with families of random asymptotically nonexpansive operators. The paper proves the convergence of two random implicit iterative schemes to a random common fixed point. This generalizes previous results and provides new convergence theorems for random operators in Banach spaces.
This study tested the effectiveness of algo-heuristic models in improving elementary school teacher education students' academic achievement in statistics. It involved 3 phases: 1) small group testing by instructional design and statistical learning experts, 2) large group testing by course lecturers, and 3) testing the effectiveness on students. The evaluation consisted of expert testing the theoretical quality, testing with a small group of lecturers, and large group pre-experimental testing to determine the effectiveness of the algo-heuristic model in improving student learning. The results showed a statistically significant increase in post-test scores compared to pre-test, indicating the algo-heuristic learning was effective in improving student learning of statistics.
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.
This document provides an overview of data mining techniques designed for imbalanced datasets. It discusses how imbalanced datasets, where one class is greatly underrepresented compared to others, pose challenges for machine learning algorithms. Several approaches have been proposed to address this issue, including sampling methods like oversampling the minority class and undersampling the majority class, as well as cost-sensitive methods that assign higher misclassification costs. The document reviews common sampling strategies used for imbalanced datasets, such as SMOTE, and cost-sensitive approaches involving cost matrices and cost curves. Overall, it examines how various sampling and cost-based methods can help improve classification of imbalanced datasets in fields such as medical diagnosis and text classification.
An Automatic Question Paper Generation : Using Bloom's TaxonomyIRJET Journal
This document presents a system for automatically generating exam questions and categorizing them according to Bloom's Taxonomy. It uses natural language processing techniques like part-of-speech tagging to analyze questions and identify keywords and verbs. Rules are developed to match question patterns and keywords to the appropriate Bloom's Taxonomy category. A randomization algorithm is also introduced to randomly select questions from the database and avoid repetitions. The system aims to help educators automatically analyze exam questions and ensure a balance of cognitive levels according to Bloom's Taxonomy. Preliminary results found the rules could successfully categorize questions in the test set. The proposed system has applications for educational institutions, universities, and government exams.
A cluster-based analysis to diagnose students’ learning achievementsMiguel R. Artacho
The document describes a proposed methodology for diagnosing students' learning achievements using cluster-based analysis. The methodology involves using item response theory to assess students' skill levels on concepts, identifying weaknesses and misconceptions, and clustering students based on similar disabilities. The methodology aims to provide adaptive feedback to help students improve and inform teaching strategies. A software tool was developed to implement the diagnostic assessment and clustering.
The translation between mathematical representations was one of the indicators in understanding mathematical concepts. Understanding the things related to the process of student representation translation was very important in learning mathematics. One of the activities that plays a role in translation was unpacking the source. This study aimed to determine the characteristics of student activity in unpacking the source when doing the process of translational verbal representation to the graph. This research was using a qualitative research. The subject of this research were twenty mathematics education students. Selection of research subjects used purposive sampling. The data were collected by test and interview. The results showed that the characteristics of student activity in unpacking the source were two that are drawing scheme of verbal situation and interpreting verbal information with its own language. In addition, there were still many students failed in translation because of difficulties in unpacking the source. The results of this study were expected to add insight into learning to minimize student difficulties in unpacking the source.
Enhancing Performance in Medical Articles Summarization with Multi-Feature Se...IJECEIAES
This document summarizes a study that aimed to enhance the performance of medical article summarization through multi-feature selection. The study utilized 7,346 online medical articles to generate summaries using maximal marginal relevance with an n-Best value of 0.7. Feature selection techniques explored included title, word counts, noun frequency, and word category in medical content. Evaluation of the summarization system found a precision of 91.6%, recall of 92.6%, and F-measure of 92.2% when combining feature selection with word category classification. The study contributed by determining the optimal n-Best value, analyzing feature selection combinations, and providing a classification of sentence types in medical texts.
Analyzing undergraduate students’ performance in various perspectives using d...Alexander Decker
This document discusses analyzing undergraduate student performance using data mining techniques. It analyzes student performance in two perspectives: 1) supervised vs unsupervised assessment instruments and 2) performance in mathematics, English, and programming courses. The study uses association rule mining with the Apriori algorithm to discover patterns in student performance data from both analyses. The goal is to identify useful insights that can help improve assessment methods, curriculum structure, and course prerequisites.
IRJET- Predictive Analytics for Placement of Student- A Comparative StudyIRJET Journal
This document summarizes and compares 15 research papers that use predictive analytics and data mining techniques to predict student placements. Various classification, clustering, and regression algorithms are applied such as decision trees, naive Bayes, k-nearest neighbors, neural networks, fuzzy logic and more. Performance is evaluated using metrics like accuracy, error rates and time taken. Decision trees generally performed well with accuracies above 90% in most papers. The papers aim to help students and institutions understand placement probabilities based on student attributes to improve employability.
This document presents a study that uses linear regression to predict university freshmen's academic performance (GPA) based on their scores on the Joint Matriculation Examination (JME). The study finds a weak positive correlation (R=0.137) between GPA and JME scores, with the regression model only explaining 1.9% of variability in GPA. Statistical tests show no significant relationship between JME score and university GPA (p>0.05). The study concludes that JME score is not a strong predictor of freshmen academic performance.
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.
Hybrid Classifier for Sentiment Analysis using Effective PipeliningIRJET Journal
The document describes a hybrid approach for sentiment analysis of tweets that uses a pipeline of rules-based classification, lexicon-based classification, and machine learning classification. Tweets are first classified using rules and a lexicon, and only tweets that do not meet a confidence threshold are passed to a machine learning classifier. The hybrid approach aims to optimize performance, speed, accuracy, and processing requirements compared to using individual classification methods alone. The document provides background on sentiment analysis methods and evaluates the performance of the hybrid approach versus individual classifiers.
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...iosrjce
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.
IRJET - A Study on Student Career PredictionIRJET Journal
This document discusses research on using machine learning techniques to predict student performance and career outcomes. It provides an overview of various studies that have used methods like decision trees, naive Bayes classification, neural networks, and clustering algorithms. The studies aimed to identify factors influencing student performance and predict outcomes like course grades, dropout risk, and placement success. The document also compares the different techniques, finding that deep neural networks and ensemble methods can achieve relatively high prediction accuracy, above 80% in some cases. Overall, the research aims to help educational institutions identify at-risk students and improve student performance.
The factors importance to economization produced cheese mozzarella from cow's...inventy
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.
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.
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.
Design and Analysis of Auger in KAMCO Power Tillerinventy
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.
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.
Gravimetric, mechanical and chemical characterization of different materials used in sewers systems: Polyvinyl chloride (PVC), polypropylene (PP) and high density polyethylene (HDPE), aged in sulfuric acid at 60°C
Mangrove Woods Damage Based On Time of Water Absorption Rate of Woodsinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Common Fixed Theorems Using Random Implicit Iterative Schemesinventy
This document summarizes research on common fixed point theorems using random implicit iterative schemes. It defines random Mann, Ishikawa, and SP iterative schemes. It also defines modified implicit random iterative schemes associated with families of random asymptotically nonexpansive operators. The paper proves the convergence of two random implicit iterative schemes to a random common fixed point. This generalizes previous results and provides new convergence theorems for random operators in Banach spaces.
Numerical modeling of the welding defect influence on fatigue life of the wel...inventy
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.
Research Inventy : International Journal of Engineering and Scienceinventy
esearch 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.
Ethical Issues and Safety in the Use of Clinical Decision Support Systems (CDSS)inventy
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.
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.
Design of Linear Array Transducer Using Ultrasound Simulation Program Field-IIinventy
This document summarizes a study that used the ultrasound simulation program Field II to model and simulate the pressure field generated by a linear array transducer and its propagation through biological tissue. The study designed a 16-element linear array transducer with Field II and simulated its impulse response. It then propagated the acoustic field through a human kidney tissue and observed the pressure profile and beam pattern at the focal point. The study also compared the impulse response, pressure field, beam pattern and detected images produced by linear arrays with 32 elements versus 64 elements. The results demonstrated Field II's ability to simulate ultrasound transducers and propagate fields through tissue.
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.
An empirical review of Motivation as a Constituent to Employees' Retentioninventy
This study investigated the link between motivation and retention and the effect of motivation on retention at different organisational levels. The research linked motivation and high job satisfaction to explore strategies that help in employees' retention and why public sector employees leave with particular reference to Federal Medical Centre (FMC) Owerri. This was achieved by collecting primary data from Federal Medical Centre (FMC) on non-clinical staff/employees (managers and non-managers and secondary data from published materials and the hospital's human resources (HR) data. The findings were tested using employee motivational attributes to prove that motivation plays a crucial role in enhancing employee retention. Motivation was found to be a core factor that determines the level of employee retention among managers and non-managers within the case study organisation. Specifically, it was found out that employees tend to be motivated if they are subjected to performance-based compensation, recognition for good work, and encouraged to pursue individually fulfilling tasks.
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.
Elliptic Curves as Tool for Public Key Cryptographyinventy
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.
Refrigeration forms the basic essence of living comfort. Ejector Expansion Refrigeration Cycle (EERC) is a not so commonly used method of refrigeration. The use of this method is quite understated. It increases the efficiency of the normal refrigeration cycle by almost 16% over the basic cycle by utilising the energy wasted otherwise in the expansion valve in form of expansion process losses. EERC system has high potential which if harnessed properly could prove to be a very efficient method of refrigeration. This paper aims to showcase the real features of this method in a hope that it finds its way out in the commercial industry today.
Spectral studies of praseodymium doped heavy metal borate glass systemsinventy
Praseodymium doped HMO glasses are fabricated with the following compositions using conventional melt quenching technique. The compositions of the glass systems are 12 ZnO + 33 B2O3 + (50-x) PbO + (x+10) CaO + 4 Al2O3 + 1 Pr6O11 where (x = 0,10,20,30 and 40 mol %.). Certain physical properties of these systems have been evaluated and reported. Spectral data for all these systems were recorded for X-ray diffraction, Optical absorption and Fluorescence properties. The Judd-Ofelt intensity parameters Ωλ ( λ = 2,4,6) were evaluated from the spectral data and in turn employed to evaluate the lasing parameters of Pr3+ HMO glass systems such as radiative transition probabilities (A), radiative life-times (τR), branching ratios (βR) absorption cross-sections (σa) and Stimulated emission cross-sections (σe). The experimental and calculated branching ratios (βR) for the lasing transitions 3P0 3H4, 3P0 3H6, and 3P0 3F2 are found to be in good agreement in the present work.
PREDICTING SUCCESS: AN APPLICATION OF DATA MINING TECHNIQUES TO STUDENT OUTCOMESIJDKP
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.
PREDICTING SUCCESS: AN APPLICATION OF DATA MINING TECHNIQUES TO STUDENT OUTCOMESIJDKP
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.
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.
A COMPARATIVE ANALYSIS OF SELECTED STUDIES IN STUDENT PERFORMANCE PREDICTIONIJDKP
This document provides a summary and comparative analysis of 56 studies on predicting student performance published since the 1990s. It finds that while earlier studies used demographic and past academic performance data to predict college success, more recent studies incorporate additional data like online course activities. Most studies were conducted in undergraduate computer science and engineering courses. Prediction types have evolved from binary pass/fail outcomes to more granular predictions of specific grades. Continuous prediction of student progress is now possible using dynamic online data, whereas earlier studies only allowed one-time predictions. Overall predictors and prediction accuracy varied across studies due to different data, algorithms and disciplines, but studies using more data and parameters generally reported higher results.
IRJET- A Conceptual Framework to Predict Academic Performance of Students usi...IRJET Journal
This document presents a conceptual framework for predicting student academic performance using classification algorithms. The framework uses factors like socioeconomic status, psychological attributes, cognitive attributes, and lifestyle to analyze student performance based on their semester GPA. The document proposes classifying student performance into three classes (first class, second class, third class) based on their first semester GPA. Various classification algorithms like Naive Bayes, random forest, and bagging are evaluated on the student data to identify the best model for predicting performance. The conceptual framework is intended to guide the development of a recommendation system that can help educational institutions identify at-risk students early and improve student outcomes.
Student Performance Prediction via Data Mining & Machine LearningIRJET Journal
This document summarizes research on using data mining and machine learning techniques to predict student performance. Specifically:
- Researchers developed models using decision trees to predict student grades based on past performance, with the goal of helping teachers identify students needing extra support.
- Literature reviews discussed previous research applying techniques like classification and clustering to educational data. The most common task was classification to predict things like course grades or graduation.
- Several studies evaluated different algorithms on student data from universities in India, Saudi Arabia, and Malaysia. The studies aimed to predict outcomes like final GPA based on entrance exam scores and early grades.
- Accuracy of the models varied, with some achieving over 90% accuracy in predicting student performance when trained
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.
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.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various
types of problems. One of the problems is the selection of a proper academic track. The admission of a
student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank
in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
This document discusses using feature selection and classification techniques to predict student performance and recommend an engineering stream for students. It first describes feature selection algorithms like chi-square and correlation-based feature selection to identify relevant attributes from a student data set. It then applies classifiers like NBTree, Naive Bayes, k-nearest neighbor, and multilayer perceptron on the selected features and evaluates their performance. The results show that correlation-based feature selection reduces computation time and improves predictive accuracy for recommending an engineering stream for students.
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 document describes an academic performance analysis system that uses educational data mining techniques. It analyzes student and teacher performance data collected from an engineering college. The system applies the Apriori algorithm and decision tree algorithm to mine patterns in academic data. The Apriori algorithm is used to generate rules based on support, confidence and lift to analyze student performance in different courses. The decision tree algorithm is used to analyze and visualize results for individual students, student groups, and indirectly for teachers. The goal is to identify existing patterns in past student performance data and use it to improve future student and teacher performance.
DIFFERENCE OF PROBABILITY AND INFORMATION ENTROPY FOR SKILLS CLASSIFICATION A...ijaia
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
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.
UNIVERSITY ADMISSION SYSTEMS USING DATA MINING TECHNIQUES TO PREDICT STUDENT ...IRJET Journal
This document summarizes a research study that aimed to predict student performance and support decision making for university admission systems using data mining techniques. The study analyzed data from 2,039 students at a university in Saudi Arabia to compare the predictive power of different data mining classification models (ANN, decision trees, SVM, naive Bayes). It found that a student's score on the pre-admission Scholastic Proficiency Admission Test was the best predictor of their first year GPA. Based on this, the university adjusted its admission criteria to give greater weight to this pre-admission test score. After making this change, the number of students with high GPAs increased while the number with low GPAs decreased.
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.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Similar to Research Inventy : International Journal of Engineering and Science (20)
Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...inventy
The objective of this paper was to investigate experimentally the effect of Evaporative-cooled condenser in a household refrigerator. The experiment was done using HCF134a as the refrigerant. The performance of the household refrigerator with air-cooled and Evaporative-cooled condenser was compared for different load conditions. The results indicate that the refrigerator performance had improved when evaporative-cooled condenser was used instead of air-cooled condenser on all load conditions. Evaporativecooled condenser reduced the energy consumption when compared with the air-cooled condenser. There was also an enhancement in coefficient of performance (COP) when evaporative-cooled condenser was used instead of air-cooled condenser. The Evaporative cooled heat exchanger was designed and the system was modified by retrofitting it, instead of the conventional air-cooled condenser by making drop wise condensation using water and forced circulation over the condenser. From the experimental analysis it is observed that the COP of evaporative cooled system increased by 13.44% compared to that of air cooled system. So the overall efficiency and refrigerating effect is increased. In minimum constructional, maintenance and running cost, the system is much useful for domestic purpose. This study also revealed that combining a evaporative cooled system along with conventional water cooled system under the condition that the defrost water obtained from the freezer is used for drop wise condensation over condenser and water cooled condensation of the condenser at the bottom using remaining defrost water would reduce the power consumption, work done and hence further increase in refrigerating effect of the system. The study has shown that such a system is technically feasible and economically viable
Copper Strip Corrossion Test in Various Aviation Fuelsinventy
This research work takes in to account of corrosiveness test on various aviation fuels in the state of Telengana (India). The purpose of this experiment is to determine the corrosiveness test of fuels. This determination will be accomplished by using copper strip corrosion test by using the copper strip experiment we can determine the corrosive property of the fuel and hence the efficiency of fuel. The research covers the importance of knowing the corrosive property of different petroleum fuels including aviation turbine fuel.
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...inventy
1) The document presents differential identities connecting velocities, pressure, and body force in two-velocity hydrodynamics equations where the pressure in each component is in equilibrium.
2) It summarizes previous work that derived conservation laws and differential equations for two-velocity hydrodynamic systems. Additional conservation laws are derived for these types of systems.
3) The key results are theorems that present differential identities relating the module and direction of a vector field. These identities can be considered additional conservation laws for two-velocity hydrodynamics equations with a single pressure.
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...inventy
People’s lives are increasingly centred on work; they spend at least one-third of their time within the organisations that employ them. Investigating the factors that interfere with employees’ well-being and the organisational environment is becoming an increasing concern in organisations. This article identifies the criteria of the quality of life (QoL), quality of working life (QWL) and organisational climate instruments to point out their similarities. For bibliographic construction and data research, articles were sought in national and international journals, books and dissertations/articles in SciELO, Science Direct, Medline and Pub Med databases. The results show direct relationships amongst QoL, QWL and organisational climate instruments. The relationship between QoL and QWL instruments is based on fair compensation, social interaction, organisational communication, working conditions and functional capacity. QWL and organisational climate instruments are related through social interaction and interfaces. QoL and organisational climate instruments are related based on social interaction, organisational communication, and work conditions.
A Study of Automated Decision Making Systemsinventy
The decision making process of many operations are dependent on analysing very large data sets, previous decisions and their results. The information generated from the large data sets are used as an input for making decisions. Since the decisions to be taken in day to day operations are expanding, the time taken for manual decision making is also expanding. In order to reduce the time, cost and to increase the efficiency and accuracy, which are the most important things for customer satisfaction, many organisations are adopting the automated decision making systems. This paper is about the technologies used for automated decision making systems and the areas in which automated decisions systems works more efficiently and accurately.
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...inventy
The mechanism of heterogeneous nucleation of β-form L-glutamic acid was deeply investigated in cooling crystallization. The present study found that the β-form crystals were epitaxially grown on the α-form crystals and they were preferably crystallized on the (011) and (001) surfaces instead of the (111) surfaces of α- form crystals. This result was explained via the molecular simulation. The molecular simulation indicated that the different surfaces of α-form crystals provided different functional groups, resulting in different sites for the heterogeneous nucleation of β-form crystals. Here, the functional group were COO- , C=O and O-H on the (011) and (001) surfaces of α-form crystals, respectively, while it was the NH3 + on the (111) surfaces of α-form crystals. As such, the degree of lattice matching (E) between the β-form crystals and the various surfaces of α- form crystal was distinguished, where the degree of lattice matching (E) between the β-form crystals and the (011), (001) and (111) surfaces of α-form crystal were estimated as 5.30, 5.25 and 2.39, respectively, implying that the (011) and (001) surfaces of α-form crystal were more favorable to generate the heterogeneous nucleation of β-form crystals than the (111) surfaces of α-form crystal
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...inventy
In recent decades, polymers have undergone a remarkable historical development and their use has been greatly imposed by gradually dethroning most of the secular materials. These polymer materials have always distinguished themselves by their simple shaping and inexpensive price, their versatility, lightness, and chemical stability but despite their massive use in everyday life as well as in advanced technologies. Generally, these materials still not understood which requires a thorough knowledge of their chemical, physical, rheological and mechanical properties. This paper, we study the mechanical behavior of an amorphous polymer: Acrylonitrile Butadiene Styrene “ABS” by means of uniaxial tensile testing on pierced test pieces with different notch lengths ranging between 1 to 14mm.The proposed approach consists in analyzing the evolution of the global geometry of the obtained strain curves by taking into account the zones and characteristic points of these curves as well as the effect of the damage on the mechanical behavior of the polymer ABS, in order to visualize the evolution of the damage by a static model
Application of Kennelly’model of Running Performances to Elite Endurance Runn...inventy
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Development and Application of a Failure Monitoring System by Using the Vibra...inventy
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Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...inventy
Industrial polluting effluents containing heavy metals are of serious environmental concern in India. Chromium is frequently used in industries like electroplating, metal finishing, cooling towers, dyes, paints, anodizing and leather tanning and is found as traces in effluents finding their way to natural water bodies causing hazardous toxicity to the health of humans, animals and aquatic lives directly or indirectly. Many methods for the removal of Chromium such as chemical reduction, precipitation, ion exchange, electrochemical reduction, evaporation, reverse osmosis and adsorption using activated carbon etc. have been reported but all being expensive and complicated to operate. Experimental practices reveal that adsorption by agricultural and horticultural wastes are quite simple, inexpensive and efficient method. Agra is famous for Potato farming, a lot of discarded potato waste from cold storages is thrown along road side drains causing solid waste generated which either creates solid waste disposal problem or otherwise it finds way to Yamuna river resulting high BOD and posing a serious threat to the aquatic environment. For developing countries like India adsorption studies using discarded potato (Solanum tuberosum) waste from cold storages (DPWC) a solid waste as low cost adsorbent for Chromium removal was dual beneficial i.e., an ideal solution to these solid wastes disposal problem of Agra and removal of Chromium from tannery effluents and thereby saving aquatic life from Chromium contamination in Yamuna river. Keeping this in view batch experiments were designed to study the feasibility of discarded potato waste from cold storages to remove chromium (VI) from the aqueous solutions. During the study various affecting parameters, such as pH, adsorbent does, initial concentration, temperature, contact time, adsorbent grain size and start up agitation speed were optimized as 5.0, 10-20 g/l, 50 mg/l, 250C, 135 minutes, average size and 80 rpm respectively on chromium removal efficiency. Various Isotherms such as Langmuir, Freundlich, Tempkin also fitted suitably and various corresponding constants determined from these Isotherms favor and support the adsorption. Thermodynamic constants ∆G, ∆H and ∆S were found to be 0.267 KJ/mole, 0.288 KJ/mole and 0.0013 KJ/mole respectively.
Effect of Various External and Internal Factors on the Carrier Mobility in n-...inventy
The effect of various external (temperature, electric field, light) and intracrystalline (doping, initial resistivity) factors on the mobility of carriers in layered n-InSe semiconductor experimentally have been investigated. Scientific explanations of the results are proposed
Transient flow analysis for horizontal axial upper-wind turbineinventy
This study is to carry out a transient flow field analysis on the condition that the wind turbine is working to generate turbine, the wind turbine operating conditions change over time, Purpose of this study is try to find out the rule from the wind turbine changing over time . In transient analysis, the wind velocity on inlet boundary and rotation speed in the rotor field will change over time, and an analytical process is provided that can be used for future reference. At present, the wind turbine model is designed on the concept of upwind horizontal axis type. The computer engineering software GH Bladed is used to obtain the relationship between the rotor velocity and the wind turbine. Then the ANSYS engineering software is used to calculate the stress and strain distribution in the blades over time. From the analytical result, the relationship between the stress distribution in the blades and the rotor velocity is got to be used as a reference for future wind turbine structural optimization.
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...inventy
Wind tunnel tests are being performed routinely around the world for designing tall buildings but the advent of powerful computational tools will make time-history analysis for wind more common in near future. As the duration of wind storms ranges from tens of minutes to hours while earthquake durations are typically less than a three to four minutes, the choice of a time step size (Δt) for wind studies needs to be much larger both to reduce the computational time and to save disk space. As the error in any numerical solution of the equation of motion is dependent on step size (Δt), careful investigations on the choice of numerical integration methods for wind analyses are necessary. From a wide variety of integration methods available, it was decided to investigate three methods that seem appropriate for 3D-time history analysis of tall buildings for wind. These are modal time history analysis, the Hilber-Hughes-Taylor (HHT) method or α-method with α=- 0.1, and the Newmark method with β=0.25 and γ=0.5 ( i.e., trapezoidal rule). SAP2000, a common structural analysis software tool, and a 64-story structure are used to conduct all the analyses in this paper. A boundary layer wind tunnel (BLWT) pressure time history measured at 120 locations around the building envelope of a similar structure is used for the analyses. Analyses performed with both the HHT and Newmark-method considering P-delta effects show that second order effects have a considerable impact on both displacement and acceleration response. This result shows that it is necessary to account P-delta effect for wind analysis of tall buildings. As the direct integration time history analysis required very large computation times and very large computer physical memory for a wind duration of hours, a modal analysis with reduced stiffness is considered as a good alternative. For that purpose, a non-linear static analysis of the structure with a load combination of 1.0D + 1.0L is performed in SAP2000 and the reduced stiffness of the structure after the analysis is used to conduct an eigenvalue analysis to extract the mode shapes and frequencies of this structure. Then the first 20- modes are used to perform a modal time history analysis for wind load. The result shows that the responses from modal analysis with “20-mode (reduced stiffness)” are comparable with that from the P-Δ analyses of Newmark-method
Impacts of Demand Side Management on System Reliability Evaluationinventy
This summary provides an overview of the impacts of demand side management (DSM) techniques on power system reliability in Saudi Arabia:
1. DSM techniques like load shifting can improve power system reliability by transferring load from peak to off-peak periods, reducing peak demand and allowing generators to operate more efficiently.
2. The study models load shifting and adding renewable energy sources to the Riyadh power system and calculates reliability indices like loss of load probability (LOLP) and expected energy not served (EENS) to analyze the impacts on reliability.
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Reliability Evaluation of Riyadh System Incorporating Renewable Generationinventy
In this paper, the experience of Saudi Electricity Company (SEC) in analyzing the generation adequacy for Year 2013 is presented. This analysis is conducted by calculating several reliability indices for Riyadh system hourly load during all four seasonal periods. The reliability indices are gauged against the international utility practice. SEC also plans to introduce renewable energy into the network in order to secure the environmental standards and reduce fuel costs of conventional generation. Thus, the reliability improvement due to different integration levels of Solar and Wind generating sources has also been investigated. The capacity value provided by these variable renewable energy sources (VERs) to reliably meet the system load has been calculated using effective load carrying capability (ELCC) technique with a loss of load expectancy metric.
The effect of reduced pressure acetylene plasma treatment on physical charact...inventy
The capacitors are increasingly being used as energy storage devicesin various power systems. The scientists of the world are tryingto maximize the electrical capacity of the supercapacitors. To achieve this purpose, numerous method sare used: the surface activation of electrodes, the surface etching using the electronbeam, the electrode etching with variousgasplasma, etc. The purpose of this work is toresearch how the properties of carbon electrodes depend on the plasma parameters at whichtheywere formed. The largest surface area ofcarbonelectrodeof47.25m2 /gis obtainedat 15 ofAr/C2H2gasratio. Meanwhile, theSEMimages show that the disruption of structures with low bond energies and the formation of new onesare taking place when the carbon electrodes are etched at acetylene plasma and placed on carbon electrode. The measurements of capacitance showthat capacitors with affectedelectrodes have about10-15% highercapacity than those not treated with acetyleneplasma.
Experimental Investigation of Mini Cooler cum Freezerinventy
In general cases the refrigerator could be converted into an air conditioner by attaching a fan. Thus a cooler as well as freezer is obtained in a single set up. The freezer can be converted to an air conditioner when the outside air is allowed to flow beside the cooling coil and is forced outside by an exhaust fan. In this case a mini scale cooler cum freezer using R134a as refrigerant was fabricated and tested In our mini project work we had designed, fabricated and experimentally analysed a mini cooler cum freezer. From the observations and calculations, the results of mini cooler cum freezer are obtained and are compared.
Growth and Magnetic properties of MnGeP2 thin filmsinventy
We have successfully grown MnGeP2 thin films on GaAs (100) substrate. A ferromagnetic transition near 320 K has been observed by temperature dependent magnetization and resistance measurements. Field dependent magnetization experiments have shown that the coercive fields at 5, 250, and 300 K are 3870, 1380 and 155 Oe, respectively. Magnetoresistance and Hall measurements have displayed that hole conduction is dominant in MnGeP2. PACS: 75.50.Pp, 75.70.-i, 85.70.-w, 73.50.-h
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
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Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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Research Inventy : International Journal of Engineering and Science
1. Research Inventy: International Journal Of Engineering And Science
Vol.3, Issue 11 (November 2013), PP 07-14
Issn(e): 2278-4721, Issn(p):2319-6483, Www.Researchinventy.Com
Risk Status Prediction and Modelling Of Students’
Academicachievement - A Fuzzy Logic Approach
Adeleke Raheem Ajiboye1, Ruzaini Abdullah Arshah2, Hongwu Qin3
1,2,3,
Faculty Of Computer Systems And Software Engineering
Universiti Malaysia Pahang, Malaysia.
ABSTRACT: Several students usually fall victims of low grade point at the end of their first year in the
institution of higher learning and some were even withdrawn due to their unacceptable grade point average
(GPA); this could be prevented if necessary measures were taken at the appropriate time. In this paper, a model
using fuzzy logic approach to predict the risk status of students based on some predictive factors is proposed.
Some basic information that has some correlations with students’ academic achievement and other predictive
variables were modelled, the simulated model shows some degree of risk associated with their past academic
achievement. The result of this study would enable the teacher to pay more attention to student’s weaknesses
and could also help school management in decision making, especially for the purpose of giving scholarship to
talented students whose risk of failure was found to be very low; while students identified as having high risk of
failure, could be counselled and motivated with a view to improving their learning ability.
KEYWORDS: fuzzy logic, academic achievement, prediction and risk status.
I.
INTRODUCTION
In the institution of higher learning, several students are found of having low grades while they are in
first year; findings reported in [5] revealed that, stress during this period is associated with overall academic
adjustment and low GPA. Modeling of student’s achievement is a useful tool for both educators and students, as
this can help to have better understanding of student’s weakness and bring about enhancement [8]. First year
students needs some form of monitoring especially as regards to their academic performance. Modelling the
past academic achievement in order to establish the risk of students’ failure based on some information they
earlier submitted for admission purposes is a step in the right direction, though, a challenging task. According to
[10], working with uncertain information makes estimation with the actual number value difficult, but this could
be easily understood if done with natural language. Fuzzy logic technique (FLT) provides efficient and feasible
solutions by following the input output system represented in Fig 2. The knowledge of fuzzy logic is most
suitable according to [11], when modelling of human evaluation is needed. Also, in [19], it was reported that
FLT is the most important technique to handle imprecision and uncertainty. It is of paramount importance to
evaluate students’ achievement after the students complete their registration as such exercise would enable the
teachers to offer assistance to them for better performance. Knowing fully well that education is very essential
and inevitable for the upliftment and progress of a nation [1], all hands must always be on deck to make it
promising.
The objective of this paper was to explore the students’ academic achievement of newly admitted
students with a view to classifying their risk status using fuzzy logic technique. The rest of this paper is
organised as follows: Some related works reported in literature on prediction of student’s performance were
discussed in the next section; in section 3, we discussed briefly about fuzzy logic concept and its basic
operations; while in section 4, we present the design, analysed the method used and display our results;
discussion of results is in section 5 and the whole work is concluded in section 6.
II.
RELATED WORKS
Several classification algorithms have been applied to predict students’ academic achievements, in the
process, the levels of accuracies were measured; however, most of these methods are subjective. It is important
to predict students’ performance in order to differentiate between the fast learners and slow learners as observed
in [9]. It was revealed in their findings that, students’ academic performance should not depend on their own
efforts alone, relevant predictive factors were also identified. A comparative analysis of techniques for
predicting academic performance was proposed in [2], models were constructed using weka tool, a very high
accuracy was reported and diverse grading systems was identified as the difficulty encountered in the course of
applying the technique to international students. The academic predictors were measured in [3] to determine
7
2. Risk Status Prediction And Modelling...
their accuracy and efforts were made at establishing their level of reliability most especially at discriminating
from success and failure cases of classifier or predictive model. Research conducted in [5] shows that matureage students achieved higher final degree GPA compared to young undergraduates. Though, this may be
environment specific.
Achievement evaluation model reported in [4] proposed Radial Basis Function Neural Network and
similarity filter to evaluate learning achievement, three phases that can reduce bias assessment were identified,
these include: selection of important feature attributes to enhance classification performance, using of minimal
entropy principle approach to fuzzify the quantitative data, model construction and accuracy evaluation.
Genetic fuzzy approach was proposed in [19] to identify students’ skills. The idea to combine the two
techniques was to explore soft computing techniques that support learning and evolution. Rules for identifying
some intelligence were generated for the achievement and powerful classification of human capabilities.Also, in
a survey carried out on Fuzzy Inference-Based student evaluation methods in [17], five different evaluation
methods capable of unveiling students’ achievement were identified; these include: Fuzzy Classification, Baiand-Chen’s Method, Saleh-and-Kim’s Method, Fuzzy Rule Interpolation and Rasmani-and-Shen’s Method. It
was concluded that Fuzzy inference based solutions offer a transparency result due to the humanly interpretable
rules.Evaluation of students’ performance using data-driven fuzzy rule was proposed in [6], the approach was
reported to perform Norm-Referenced Evaluation which produced new and informative scores based on several
information retrieved from data. It was concluded that the findings was meant to help strengthen the system that
is commonly in use, as the approach was intended to provide additional information for decision making.
III.
FUZZY LOGIC
Fuzzy logic (FL) can be described as logic of fuzzy sets [13]. It is an area of soft computing that
enables a computer system to reason with uncertainty [16]. The concept was initially formalized by Lofti Zadeh
in his seminar 1965 paper “Fuzzy sets”. A fuzzy set is distinct from a crisp or Boolean set because it allows its
elements to have a degree of membership i.e the characteristics function of a fuzzy set can have values between
0 and 1 [15]. The core of a fuzzy set is its membership function: a surface or line that defines the relationship
between a value in the set’s domain and its degree of membership [13].Fuzzy logic according to [12] has two
different meanings: It can be referred to as a logical system which may be viewed as an extension and
generalization of classical multi-valued logics. In a wider sense, FL is almost synonymous with the theory of
fuzzy sets as any field X and any theory Y can be fuzzified by replacing the concept of a crisp set in X and Y by
that of a fuzzy set [12]. The fuzzy linguistics variable “risk” can be categorized as: very low, low, medium, high
and very high. Each category is called a linguistic modifier. The modifier can take its degree of membership
from [0, 1] as shown in figure 3. The scales on this figure are used to distinguish the prediction of students’
academic achievement risk (very low risk, low risk, medium risk, high risk and very high risk).
IV.
DESIGN AND METHODS
4.1 Dataset and Data Preparation
Getting rid of errors and outliers that may be present in the data are parts of pre-processing task that
should be done to make the data suitable for modelling. In many real-world applications, most especially in
cases when it involves huge amounts of data, the subset of cases with complete data may be relatively small.
Errors can result from data entry mistakes, transposing digits or specifying invalid dates; too much noise can
result in poor quality models [17]. The dataset was collected from a university in north central, Nigeria. The
data comprised of 37 records of candidates that were offered admission to study Computer Science. TABLE 1
contains the predictive variables, while Fig. 1 shows data preparation using rapidminer tool.
Table 1: The predictive variables
S/NO
1
INPUT
VARIABLE
SSRS
DESCRIPTION
OPTIONS
2
NSSSE
3
EM
Secondary
School
Result
Strength
Number of sittings in secondary
school examination
Entry mode
A1;A2;A3;B2
;B3;C4;C5;C6
1 attempt ;
2 attempts
Utme1
Remedial 2
Literate 1
Illiterate 2
Private 1
4
PLS
Parent literacy status
5
OSSA
Ownership of secondary school
8
VALUES
OBTAINABLE
1-7
1-2
1-2
1-2
1-2
3. Risk Status Prediction And Modelling...
6
LSSA
7
SOJE
attended
Location of secondary school
attended
Score obtained in Unified
Tertiary
Matriculation
Examination.
Public 2
Town 1; City
2; Village 3
Above 250 1
221 – 250 2
200 – 220 3
Below 200
4
1-3
1-4
Figure 1: Data cleaning
Table 2 Transformed students’ data
As shown in TABLE 2, the individual student’s data were transformed based on the options and obtainable
values in TABLE 1.
4.2 Analysis of fuzzy set structure and operations
If X is a collection of objects denoted generically by x, then a “fuzzy set” A in X is defined as a set of ordered
pairs [15]:
A = {x, μx) | x Є X}………………… (1)
where μx) is called membership function for the fuzzy set A which maps each element of X to a
membership value between 0 and 1. Element x may have full, partial or no membership in A. Its degree of
membership would be considered to be full if μ = 1; partial, if μx) lies between 0 and 1 i.e 0 <
x)
9
4. Risk Status Prediction And Modelling...
μx) < 1; and no membershipexist if μ
x) = 0. s illustrated in Fig. 3, a fuzzy set is formed when a
A
linguistic variable combines with a linguistic modifier (i.e. very low_risk,low_risk, high_risk, medium_risk
etc). Each linguistic modifier is linked to a numerical value on a scale ranges from 0 to 9 that represents the
academic achievement risk. Also, each element represents a corresponding value of a degree of membership in
the universe of discourse. Fuzzy sets can be manipulated using one of the four standard fuzzy set operations:
union, intersection, complementation, and implication operations [14]. Though, the set operations discussed
here are often used, fuzzy set operations are not limited to this four. A fuzzy set union is performed by applying
the maximum (Max) function to the elements of two sets, for instance,
let μx) = {1,
3,5,8,9} and μBy) = {1,
7,4,8,9}
the union of fuzzy set C = A B; it follows that:
μCz) = μ(x) μB (y) = Max { μA(x) , μB (y)}
A
μCz) = {1,
7,5,8,9}.
The intersection of two sets can be determined by applying the minimum (Min) function:
μx) μBy)= Min { μA(x) , μB (y)} = {0,4,1,0,0}
Complement of a set is can be computed by subtracting each element of the set from its maximum possible
value:
μĀx) = {9 μx) = {8,
6,4,1,0}
The implication function decides if a particular set is true, to what extent can we conclude the other set can be
said to be true? To illustrate implication operation, we can compute:
μĀ μB (q) = μĀx) μy)
μĀ μB (q) = {8,6,4,1,0} {1,7,4,8,9} = {8,7,4,8,9}
4.3 Proposed model for academic achievement risk status
Due to vagueness in grading educational system, according to [18], the use of fuzzy theory provide
better models of subjective judgment. This approach essentially involves three main tasks: fuzzification,
inference and defuzzification as represented in Fig. 2. Excerpt of 37 records from the data collected were
modelled and a fuzzy set A was formed. The set takes its values from {X} in a closed interval [0,1]. From
equation 1 and degree of membership in Fig. 3,
.fA(x) = {0.1, 0.2, 0.4, 0.5, 0.8, 1}
Knowledge
based
Input
Fuzzifier
Defuzzifier
Inference
Output
There are different forms of membership functions, here we used trapezoidal to illustrate the membership
function. According to [15], a trapezoidal membership function is specified by four parameters {a,b,c,d} as
shown in equation 2:
Figure 2 Input /Output of a fuzzy logic system
10
5. Risk Status Prediction And Modelling...
Very low
Figure 3
Fuzzy set structure for risk status
The parameters {a,b,c,d} with a < b < = c < d, determine the x coordinates of the four corners of the underlying
high
trapezoidal membership function.
Trapezoid (x; a,b,c,d) =
0,
(x-a) / (b-a),
x <= a.
a <= x <= b.
1,
(d-x) / (d-c),
b <= x <= c.
……….(2)
c <= x <= d.
0,
d <= x.
Table 3 Predictive variables and degree of membership
Predictive variable
SSRS
NSSSE
EM
PLS
OSSA
LSSA
SOJE
Membership value fB(y)
0.8
0.5
0.4
0.6
0.7
0.6
1.0
Representation (y)
V1
V2
V3
V4
V5
V6
V7
37 students were considered in this research and the researchers evaluated 7 predictive factors on which
predictions were based. From the data displayed in TABLE 3, a fuzzy set B was formed and it takes its values
from the closed interval [0,1]. Also from equation 1,
B = {y, μy) | y Є Y}………………… (3)
11
6. Risk Status Prediction And Modelling...
fB(y) = {0.8, 0.5, 0.4, 0.6, 0.7, 0.6, 1.0} as shown in TABLE 3. The table also shows the membership values
assigned to each predictive variable which translates to its predictive relevance. Linguistic variables were
mapped to corresponding fuzzy values which results to another set as shown in equation 4:
C = {y, μy) | y Є Y} ……………
……...(4)
fC(y) = {0.1, 0.2, 0.5, 0.8, 1.0} as shown in TABLE 4.
Table 4 Fuzzy linguistic variables and membership values
Linguistic variables
Very low risk
Low risk
Medium risk
High risk
Very high risk
Fuzzy values
0 <= x<= 2
1 <= x<= 3
2 <= x<= 5
4 <= x<= 7
6 <= x<= 9
Relative importance
1.0
0.8
0.5
0.2
0.1
As shown in Fig. 3 and TABLE 4, the five fuzzy sets can be interpreted as follows:
Very low risk : {1|1.0, 2|0.8, 3|0.0, 4|0.0, 5|0.0, 6|0.0, 7|0.0, 8|0.0, 9|0.0}
Low risk : {1|0.6, 2|0.8, 3|0.5, 4|0.0, 5|0.0, 6|0.0, 7|0.0, 8|0.0, 9|0.0}
Medium risk : {1|0.0, 2|0.3, 3|0.5, 4|0.4, 5|0.0, 6|0.0, 7|0.0, 8|0.0, 9|0.0}
High risk : {1|0.0, 2|0.0, 3|0.0, 4|0.4, 5|0.4, 6|0.2, 7|0.2, 8|0.0, 9|0.0}
Very high risk : {1|0.0, 2|0.0, 3|0.0, 4|0.0, 5|0.0, 6|0.2, 7|0.2, 8|0.1, 9|0.1}
The technique of fuzzy set addresses the representation of parameters using linguistic variables [7], it also
provides dynamic framework to handle qualitative information especially when quantitative seems
inappropriate. Through the process of fuzzification, we find the membership value of all the input values in
TABLE 2, these values were transformed to form another set as shown in TABLE 5. From TABLE 5, the
researchers formed 37 fuzzy sets fc1(y), fc2(y)………fc37(y) that takes its membership values from [0,1]. This
process of reduction otherwise known as defuzzification [13] produced the final single scaler results shown in
TABLE 6, the table displayed the risk status of all the students (37 cases).
fc1(y) = {0.5, 1.0, 0.8, 1.0, 0.8, 0.5, 1.0}
fc2(y) = {0.8, 1.0, 0.8, 0.8, 0.8, 1.0, 1.0}
---fc37(y) = {0.5, 0.8, 0.8, 0.5, 0.8, 0.8, 1.0}
Table 5
CASE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
V1
0.5
0.8
0.8
0.5
0.5
0.8
0.5
0.8
0.8
0.5
0.8
0.2
0.8
0.5
0.2
0.5
0.5
0.5
0.8
0.5
0.5
0.5
V2
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
The academic achievements of all the 37 students
V3
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.5
0.8
0.8
0.5
0.8
0.8
0.8
0.8
0.5
0.5
0.8
0.8
0.8
0.5
V4
1.0
0.8
1.0
1.0
1.0
0.8
1.0
1.0
1.0
1.0
1.0
0.8
1.0
0.8
0.8
0.8
1.0
1.0
1.0
1.0
1.0
0.8
12
V5
0.8
0.8
0.8
1.0
0.8
0.8
0.8
0.8
0.8
0.8
1.0
0.8
0.8
1.0
1.0
0.8
0.8
0.8
1.0
1.0
1.0
1.0
V6
0.5
1.0
1.0
0.8
0.8
0.5
1.0
0.8
0.5
1.0
0.8
1.0
0.5
1.0
0.8
1.0
1.0
1.0
1.0
0.8
0.8
0.8
V7
1.0
1.0
1.0
1.0
1.0
0.8
1.0
1.0
0.8
1.0
1.0
1.0
1.0
0.8
1.0
1.0
1.0
1.0
1.0
0.8
0.8
1.0
7. Risk Status Prediction And Modelling...
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
0.5
0.2
0.5
0.5
0.2
0.8
0.5
0.5
0.2
0.5
0.2
0.2
0.5
0.5
0.5
1.0
0.8
1.0
1.0
1.0
0.8
1.0
1.0
0.8
0.8
0.8
0.8
1.0
1.0
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.5
0.8
0.8
0.8
0.8
0.8
0.8
1.0
0.5
1.0
0.5
0.5
1.0
1.0
1.0
1.0
1.0
0.5
1.0
1.0
0.5
0.5
1.0
0.8
0.8
1.0
0.8
0.8
0.8
0.8
1.0
0.8
0.8
1.0
0.8
0.8
0.8
0.8
0.8
0.8
0.8
1.0
0.5
0.8
0.8
0.8
1.0
0.5
0.8
0.8
0.8
0.8
1.0
0.8
1.0
1.0
1.0
0.8
1.0
1.0
0.8
1.0
0.8
0.8
0.8
0.8
1.0
By applying the Min function to the degree of membership displayed in TABLE 5, we arrived at the decision on
the risk status of individual case as shown in TABLE 6.
Table 6
CASE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
VALUES
0.5
0.8
0.8
0.5
0.8
0.5
0.5
0.8
0.5
0.5
0.8
0.2
0.5
0.5
0.2
0.5
0.5
0.5
0.8
The risk status of all the students
RISK STATUS
medium
low
low
medium
low
medium
medium
low
medium
medium
low
high
medium
medium
high
medium
medium
medium
low
V.
CASE
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
VALUES
0.5
0.5
0.5
0.5
0.2
0.5
0.5
0.2
0.5
0.5
0.5
0.2
0.5
0.2
0.2
0.5
0.5
0.5
RISK STATUS
medium
medium
medium
medium
high
medium
medium
high
medium
medium
medium
high
medium
high
high
medium
medium
medium
DISCUSSION OF RESULTS
TABLE 6 shows the risk status of all the 37 students.The analysis of the results revealed three clusters
of students as regards their risk status. Information from the result also shows that, 24 of the students were
predicted to have medium risk; 6 of the students were predicted to have low risk; there is no need to exercise
any fear about the future performance on these category of students. However, 7 students were predicted to
have high risk; this category of students deserves special attention so that they can cope well with their studies.
Generally, cases with values above 0.5 have satisfactory academic achievement, while cases with values less
than 0.5 needs to sit up and make extra efforts to meet the challenges ahead.
VI.
CONCLUSION
This research adds to the rationale for having prior knowledge about the academic achievement of all
the newly admitted and registered students, at the earliest possible time of their studentship, with a view to
determining their strengths and weaknesses.The researchers modelled the transformed input predictive variables
using the approach of fuzzy logic. The various methods used to predict student’s performance were discussed;
the risk status of students of Computer Science department which comprised of 37 records were predicted in
this research, the researchers would extend the technique to cover many departments across faculties in
subsequent research. The technique of fuzzy logic applied in this research shows its capability of handling
uncertainty.The results segmented the students according to their risk status, the model can be applied to predict
13
8. Risk Status Prediction And Modelling...
the academic performance of all applicants seeking admission to Nigerian institutions of higher learning and the
technique used can be generalized to make similar prediction in any institution outside Nigeria. Exploring
students’ achievement at the early stage of their studies would help the teacher to pay special attention to
students predicted to have high risk of failure and render needed assistance to them when it matters most.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
B.K. Bhardwaj and S. Pal, Data Mining: A prediction for performance improvement using classification, International Journal
of Computer Science and Information Security, Vol. 9(4), 2011.
V. Juana-Maria and F. Manuel, How does one assess the accuracy of academic success predictors? ROC analysis applied to
university entrance factors, International Journal of Mathematical Education in Science and Technology, Vol. 39 (3), 2008, pp.
325–340.
S. Michael, Hardiness commitment, gender and age differentiate university academic
performance, British Journal of
Educational Psychology, 79, 2009, pp.189–204.
C. Ching-Hsue, et al., A new e-learning achievement evaluation model based on RBF-NN and simulation filter, Neural
Computing & Application; 20, 2011, 659–669.
L.J. Friedlander, G.J. Reid, N. Shupak, & R. Cribbie, Social support, self-esteemand stress as predictors of adjustment to
university among first-year undergraduates, Journal of College Student Development,48(3), 2007, 259-274.
K.A. Rasmani and Q. Shen, Data-driven fuzzy rule generation and its application for student academic performance
evaluation; Springer, Applied Intelligence, 25, 2006, 305–319.
F. Herrera and E. Herrera-Viedma, Linguisticss decision analysis: Steps for solving decision problem under linguistics
information, Fuzzy sets and system management, 115, 2000, 67-82.
P. Cortez, and A. Silva, Using data mining to predict secondary school student performance, A Proceedings of 5th Annual
Future Business Technology Conference, Porto, 2008.
N.T. Nghe, P. Janecek, and P. Haddawy, A Comparative Analysis ofTechniques for Predicting Academic Performance, 37th
ASEE/IEEE Frontiers in Education Conference, 2007.
M. Delgado, F. Herrera, E. Herrera-Viedma, L. Martinez, Combining numerical and linguistic information in group decision
making, Information Sciences, 107, 1998, 177–194.
K. Cengiz, Fuzzy Engineering Economics with Applications (Springer-Verlag Berlin Heidelberg, 2008).
J.K. George, and B. Yuan, Fuzzy sets and Fuzzy logic: Theory and Applications (Printice-Hall of India private limited, New
Delhi, 2008).
E. Cox, Fuzzy modelling and genetic algorithms for data mining and exploration (Morgan Kaufman, 2005).
J.M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions ( Prentice-Hall Publishing
Company, 2001).
O. Castillo, P. Melin, Type-2 Fuzzy logic: Theory and Applications (Springer-Verlag, Berlin Heidelberg, 2008).
O. Castillo, P. Melin,. Soft Computing for Control of non-linear dynamic systems
(Springer, Heidelberg, 2001).
Z. C. Johanyák, Survey on Five Fuzzy Inference-Based Student Evaluation Methods; I.J. Rudas et al. (Eds.): Computational
Intelligence in Engineering (SCI 313, Springer-Verlag Berlin Heidelberg, 2010) pp. 219–228.
R. Biswas . An application of fuzzy sets in students’ evaluation. Fuzzy Sets System 74, 1995, 187–194.
K. Mankad, P.S. Sajja, and R. Akerkar, An automatic evolution of rules to identify students’ multiple intelligence N.
Meghanathan et al. (Eds): (CCSIT, Springer-Verlag Berlin Heidelberg, 2011).
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