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.
Predicting Success : An Application of Data Mining Techniques to Student Outc...
This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
or discontinued their education. Operating on over 30,000 student observations, random forests are used
to determine the relative importance of the student characteristics with genetic algorithms to perform
feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter
tuning was also implemented. Overall predictive strength is relatively high as measured by the
Matthews Correlation Coefficient, and both intuitive and novel features which provide support for the
learning model are explored.
An Evaluation of Feature Selection Methods for Positive - Unlabeled Learning ...
Feature Selection is important in the processing of data in domains such
as text because such data can be of very high
dimension. Because in positive
-
unlabeled (PU) learning problems, there are no labeled negative data for training, we need
unsupervised feature selection methods that do not use the class information in the trai
ning documents when selecting features for the
classifier. There are few feature selection methods that are available for use in document classification with PU learning. I
n this paper
we evaluate four unsupervised methods including, collection frequency (
CF), document frequency (DF), collection frequency
-
inverse
document frequency (CF
-
IDF) and term frequency
-
document frequency (TF
-
DF). We found DF most effective in our experiments.
This document summarizes a study conducted by Cambridge ESOL Examinations on the impacts of the IELTS exam. The study explored the effects of the exam on candidates, preparation courses, and receiving institutions. It utilized questionnaires for candidates and teachers, lesson observations, and material reviews to understand test anxiety, motivation, preparation methods and satisfaction. Over 500 candidates from major test-taking regions completed questionnaires providing insights into their experiences, strategies, and attitudes towards IELTS.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
This study examined the ability of a college aptitude test (CAT) and high school GPA to predict students' academic performance in their first year of college, as measured by first-year GPA. The study found that both CAT scores and high school GPA were positively correlated with first-year college GPA. Multiple regression analyses also showed that CAT scores alone were a significant predictor of first-year GPA, explaining 37.3% of variance. Adding high school GPA to the model further improved predictive power, explaining 47% of variance in first-year GPA. The results support the use of both CAT scores and high school GPA in admission decisions and predicting students' future academic performance.
Students academic performance using clustering technique
This document summarizes a study analyzing students' academic performance data. The study collected internal and external marks for 45 students over 5 semesters. It cleaned the data, transforming the marks into sums, and used k-means clustering to group students into 4 categories (excellent, good, fair, poor) for each semester based on their internal and external marks. The analysis found the clusters followed the same performance pattern each semester, with students scoring higher internally also scoring higher externally, indicating a direct relationship between internal and external marks. The study concluded a student's university exam performance can generally be predicted from their internal marks.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
Managers Perceptions towards the Success of E-performance Reporting System
Managers are the key informants in the information system (IS) success measurements. In fact, besides the determinant agents are rarely involved in the assessments, most of the measurements are also often performed by the technical stakeholders of the systems. Therefore, the results may questionable. This study was carried to explain the factors that influence the success of an e-performance reporting system in an Indonesian university by involving ± 70% of the managers (n=66) in the sampled institution. The DeLone and McLean model was adopted and adapted here following the suggestions of the previous meta-analysis studies. The collected data was analyzed using the partial least squares-structural equation modelling (PLS-SEM) for examining the four hypotheses. Despite the findings revealed acceptances of the overall hypotheses, the weak explanation of the user satisfaction variable towards the net benefit one had been the highlighted point. Besides the study limitations, the point may also be the practical and theoretical considerations for the next studies, especially for the IS success studies in Indonesia
A Mamdani Fuzzy Model to Choose Eligible Student EntryTELKOMNIKA JOURNAL
This paper presented about study that have been created a new student choosing system by
using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics
such as human perceptions on choosing of students with some specified criteria. The choosing students
who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the
process can be possible completed execute and can be reduced the time of choose. To accomplish the
process, the fuzzy variable is created by the national final exam scores, report grade, general competency
test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has
been reached 75.63% accuracy.
EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT’S ACADEMIC PERFORMANCE cscpconf
Educational data mining is used to study the data available in the educational field and bring
out the hidden knowledge from it. Classification methods like decision trees, rule mining,
Bayesian network etc can be applied on the educational data for predicting the students
behavior, performance in examination etc. This prediction will help the tutors to identify the
weak students and help them to score better marks. The C4.5 decision tree algorithm is applied
on student’s internal assessment data to predict their performance in the final exam. The
outcome of the decision tree predicted the number of students who are likely to fail or pass. The
result is given to the tutor and steps were taken to improve the performance of the students who
were predicted to fail. After the declaration of the results in the final examination the marks
obtained by the students are fed into the system and the results were analyzed. The comparative
analysis of the results states that the prediction has helped the weaker students to improve and
brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared
with ID3 algorithm and found to be more efficient in terms of the accurately predicting the
outcome of the student and time taken to derive the tree.
Data Analysis and Result Computation (DARC) Algorithm for Tertiary InstitutionsIOSR Journals
The document describes a Data Analysis and Result Computation (DARC) algorithm written in Fortran for analyzing student data and computing examination results in tertiary institutions. DARC takes student information, course data, examination scores, and previous academic records as input. It outputs analysis of student demographics, individual student result sheets showing grades and GPA/CGPA calculations, summaries of academic performance, and logs of courses passed or still outstanding. Testing on sample student data validated DARC's reliability in accurately processing records and computing results for large student populations in a tertiary institution.
Predicting Success : An Application of Data Mining Techniques to Student Outc...IJDKP
This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
or discontinued their education. Operating on over 30,000 student observations, random forests are used
to determine the relative importance of the student characteristics with genetic algorithms to perform
feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter
tuning was also implemented. Overall predictive strength is relatively high as measured by the
Matthews Correlation Coefficient, and both intuitive and novel features which provide support for the
learning model are explored.
An Evaluation of Feature Selection Methods for Positive - Unlabeled Learning ...Editor IJCATR
Feature Selection is important in the processing of data in domains such
as text because such data can be of very high
dimension. Because in positive
-
unlabeled (PU) learning problems, there are no labeled negative data for training, we need
unsupervised feature selection methods that do not use the class information in the trai
ning documents when selecting features for the
classifier. There are few feature selection methods that are available for use in document classification with PU learning. I
n this paper
we evaluate four unsupervised methods including, collection frequency (
CF), document frequency (DF), collection frequency
-
inverse
document frequency (CF
-
IDF) and term frequency
-
document frequency (TF
-
DF). We found DF most effective in our experiments.
goingglobal-session-2-1225-thursday-elt-roger-hawkey-paperJen W
This document summarizes a study conducted by Cambridge ESOL Examinations on the impacts of the IELTS exam. The study explored the effects of the exam on candidates, preparation courses, and receiving institutions. It utilized questionnaires for candidates and teachers, lesson observations, and material reviews to understand test anxiety, motivation, preparation methods and satisfaction. Over 500 candidates from major test-taking regions completed questionnaires providing insights into their experiences, strategies, and attitudes towards IELTS.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
This study examined the ability of a college aptitude test (CAT) and high school GPA to predict students' academic performance in their first year of college, as measured by first-year GPA. The study found that both CAT scores and high school GPA were positively correlated with first-year college GPA. Multiple regression analyses also showed that CAT scores alone were a significant predictor of first-year GPA, explaining 37.3% of variance. Adding high school GPA to the model further improved predictive power, explaining 47% of variance in first-year GPA. The results support the use of both CAT scores and high school GPA in admission decisions and predicting students' future academic performance.
Students academic performance using clustering techniquesaniacorreya
This document summarizes a study analyzing students' academic performance data. The study collected internal and external marks for 45 students over 5 semesters. It cleaned the data, transforming the marks into sums, and used k-means clustering to group students into 4 categories (excellent, good, fair, poor) for each semester based on their internal and external marks. The analysis found the clusters followed the same performance pattern each semester, with students scoring higher internally also scoring higher externally, indicating a direct relationship between internal and external marks. The study concluded a student's university exam performance can generally be predicted from their internal marks.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
Managers Perceptions towards the Success of E-performance Reporting SystemTELKOMNIKA JOURNAL
Managers are the key informants in the information system (IS) success measurements. In fact, besides the determinant agents are rarely involved in the assessments, most of the measurements are also often performed by the technical stakeholders of the systems. Therefore, the results may questionable. This study was carried to explain the factors that influence the success of an e-performance reporting system in an Indonesian university by involving ± 70% of the managers (n=66) in the sampled institution. The DeLone and McLean model was adopted and adapted here following the suggestions of the previous meta-analysis studies. The collected data was analyzed using the partial least squares-structural equation modelling (PLS-SEM) for examining the four hypotheses. Despite the findings revealed acceptances of the overall hypotheses, the weak explanation of the user satisfaction variable towards the net benefit one had been the highlighted point. Besides the study limitations, the point may also be the practical and theoretical considerations for the next studies, especially for the IS success studies in Indonesia
The Graduate Aptitude Test in Engineering (GATE) is a national level exam conducted by IITs and IISc Bangalore for admission to postgraduate programs in engineering. It tests the comprehensive understanding of various undergraduate subjects in engineering and science. Candidates need to have a B.E./B.Tech. or equivalent degree to appear for GATE. The exam consists of 65 multiple choice questions across various engineering disciplines, with some questions having negative marking. The GATE score is used for admission to M.Tech programs in IITs and other colleges, as well as for scholarship eligibility. Dedicated preparation and practice of previous year papers is necessary to clear GATE.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
IRJET- Student Performance Analysis System for Higher Secondary EducationIRJET Journal
This document presents a student performance analysis system that was developed to analyze educational data and student performance. The system allows students to log in, enter their details and exam marks. It then provides graphical analysis of student performance individually and overall by subject. Reports can also be generated showing a student's marks, percentage and pass/fail status. The system aims to identify weaker students and help improve their academic results. It was developed using data mining concepts to analyze data from higher secondary students. Future work could expand it to predict student performance and guide them in their education and career paths.
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.
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.
A Longitudinal Study of Undergraduate Performance in Mathematics, an Applicat...iosrjce
Students’ performance in mathematics has been an issue of great concern to most countries,
especially the developing nations. So many programmes have been put in place to improve performances and to
also encourage student to study the course in tertiary institution. In this study we investigate the relationship of
semester, department of a student, age and load unit on marginal mathematics performance o f undergraduate
students. A marginal model was formulated using four working correlation structure where the exchangeable
working correlation structure was selected as the best that models the dataset using quasi information criteria.
The semester, age and load unit were found to be related to the marginal performance in mathematics
A COMPARATIVE ANALYSIS OF SELECTED STUDIES IN STUDENT PERFORMANCE PREDICTIONIJDKP
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.
Influence of Table of Specification on the Construction of Ordinary Level Phy...ijtsrd
This document discusses the importance of using a table of specification when constructing exams, especially for the Ordinary Level Physics exam in Cameroon. It begins by explaining what a table of specification is and how it helps ensure tests are valid and reliable by providing a framework for balancing content coverage, cognitive levels, and question types. The document then discusses how not using a table of specification can negatively impact the validity of exams and student scores. Specifically, it may result in topics being weighted incorrectly and questions not aligning with what was taught. Overall, the document advocates for the use of tables of specification in test construction to improve the quality and meaningfulness of exam results.
A WEB BASED APPLICATION FOR TUTORING SUPPORT IN HIGHER EDUCATION USING EDUCAT...IRJET Journal
This document describes a web-based application for tutoring support in higher education using educational data mining. The application aims to help students select appropriate colleges based on previous college cut-off performances. It uses data mining techniques to predict colleges based on attributes like student aggregate percentage, category, branch, and college information from previous years. The application has three modules - Admin, College, and Student. Colleges can register and provide cut-off details. Students can search for matching colleges based on their profile. The document discusses the literature review, system design, algorithms, and results of the study. It aims to minimize student confusion and help them select colleges without losing admission opportunities.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
IRJET- Using Data Mining to Predict Students PerformanceIRJET Journal
This document describes a study that used logistic regression to predict student performance based on educational data. The researchers collected student data including exam scores, attendance, study hours, family income, etc. from a large dataset. Logistic regression achieved the best prediction accuracy of 82.03% compared to other models like naive bayes, K-nearest neighbor, and multi-layer perceptron. The results indicate that around 230 students would perform poorly, 600 would perform fairly, and 200 would perform well based on the predictive model. This analysis can help identify students needing extra support and help universities improve academic outcomes.
M-Learners Performance Using Intelligence and Adaptive E-Learning Classify th...IRJET Journal
This document discusses using machine learning classification algorithms to predict student performance based on educational data. It compares the performance of five classification algorithms - J48, Naive Bayes, Bayes Net, Backpropagation Network, and Radial Basis Function Network - in predicting student academic achievement using attributes like demographic information, test scores, and academic factors. The experiment found that the Radial Basis Function Network algorithm achieved the highest accuracy, correctly classifying 100% of instances, compared to 75-95% accuracy for the other algorithms. Convolutional neural networks are also discussed as a powerful tool for image and language processing in educational data mining.
IRJET- Analysis of Student Performance using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to analyze student performance data and predict student outcomes. It begins with an abstract describing how educational data has become important for supporting student success. It then discusses prior related work applying classification algorithms like decision trees to predict student grades or performance. The document goes on to describe applying various classification algorithms like J48 decision trees, K-nearest neighbors, and others to student data and comparing their performance at predicting outcomes. It discusses preprocessing the data with k-means clustering before classification. The goal is to identify at-risk students early to better support them.
An empirical study on assessment of co attainment for a diploma courseIAEME Publication
This document discusses a study on assessing course outcome attainment for a diploma course in Applied Physics. It describes the background and methodology used to assess the attainment of course outcomes for the Applied Physics course. Assessment was conducted for 60 students in their first year of a diploma program. The methodology uses data from student marks in final exams, tests, assignments, and projects. A computerized system was developed to expedite the analysis process. The findings are then used for continuous quality improvement of the course and program.
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.
IRJET- Evaluation Technique of Student Performance in various CoursesIRJET Journal
The document proposes a system to evaluate student performance in various courses using techniques like machine learning. It discusses challenges in predicting student performance and developing a model that incorporates students' academic records and evolving progress. The proposed system aims to track student academic and extracurricular information to predict suitable courses and analyze growth.
IRJET- Performance for Student Higher Education using Decision Tree to Predic...IRJET Journal
This document discusses using decision trees to predict career decisions for 12th grade students in India. It first provides background on the challenges in the Indian education system and how data mining can help improve decision making. It then reviews previous studies applying various data mining techniques like decision trees and random forests to predict student performance. The paper proposes using a decision tree approach on student data to distinguish slow and fast learners and help students make better career choices based on their interests and skills. The decision tree approach achieved 80% accuracy in predicting student career decisions, helping students choose appropriate paths.
This document discusses using machine learning to predict student performance in online learning environments. It reviews studies that have examined online course data to predict student outcomes using machine learning techniques. The studies identified features of online courses used for prediction, outputs of prediction models, methodologies for feature extraction, evaluation metrics, and challenges. Machine learning algorithms commonly used in the studies include logistic regression, naive Bayes, decision trees, AdaBoost, k-nearest neighbor, and neural networks. The document provides an in-depth analysis of different machine learning models and their effectiveness in predicting student certificate acquisition, grades, and students at risk of failure.
ISSN 2454-535X
International Journal of Mechanical Engineering Research and Technology aims to provide the best possible service to authors of original research articles, and the fairest system of peer review.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published Quarterly offers fast publication schedule whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites the process All submitted research articles are subjected to the immediate rapid screening by editors consultation with Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
Data mining to predict academic performance. Ranjith Gowda
This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.
IRJET- Teaching Learning Practices for Metrology & Quality Control Subject in...IRJET Journal
1. The document discusses teaching and learning practices for the Metrology and Quality Control subject in an outcome-based education system.
2. It outlines the program educational objectives, program outcomes, and course outcomes for the subject and describes how they are mapped and assessed.
3. Internal evaluations of students including unit tests, assignments, and exams are used to measure course outcome attainment, with lower attainment found for two course outcomes, leading to corrective actions being taken like industrial visits and expert lectures.
This document describes a school bus tracking and security system that uses face recognition, GPS, and notification technologies. The system uses a camera to identify students as they board and exit the bus. A GPS module tracks the bus location and uploads coordinates to a database. Parents and school administrators can access this information through a mobile app to track students. When a student's face is recognized, a notification is sent to the parents. The system aims to increase student safety by monitoring their locations and notifying parents when they enter or exit the bus.
BigBasket encashing the Demonetisation: A big opportunityIJSRED
1. BigBasket is India's largest online grocery retailer, launched in 2011 when online grocery shopping was still nascent.
2. During India's 2016 demonetization, when cash was scarce, online grocery saw a major boost as consumers turned to sites like BigBasket for contactless digital payments.
3. However, BigBasket faced challenges in meeting consumer expectations for quick delivery while expanding partnerships with local vendors for fresh produce during this surge in demand.
Quantitative and Qualitative Analysis of Plant Leaf DiseaseIJSRED
This document discusses a technique for detecting plant leaf diseases using image processing. It begins with an introduction to plant pathology and the importance of identifying plant diseases. Common plant leaf diseases like Alternaria Alternata, Anthracnose, Bacterial blight, and Cercospora Leaf Spot are described along with their symptoms. The existing methods of disease identification are discussed. The proposed method uses various image processing techniques like filtering, histogram equalization, k-means clustering, and Gray Level Co-occurrence Matrix (GLCM) feature extraction to detect diseases. Image quality is then assessed to identify the affected regions of the leaf.
DC Fast Charger and Battery Management System for Electric VehiclesIJSRED
This document discusses the development of a DC fast charger and battery management system for electric vehicles. It aims to reduce charging times for EVs by designing an efficient charging mechanism. A PIC microcontroller controls the charging voltage and a battery management system monitors battery temperature, voltage, current and provides notifications. The system uses a step-down transformer, rectifier, voltage regulators and temperature sensor to charge lithium-ion batteries safely and quickly, while the battery management system protects the batteries from overcharging or overheating. Faster charging times through more charging stations could encourage greater adoption of electric vehicles.
France has experienced steady economic growth through policies that develop human capital and innovation. It has a highly organized education system that has increased enrollments over time, particularly in tertiary education. France also invests heavily in research and development, ranking highly in patents and innovative organizations. Infrastructure investment has also increased tangible capital stock. Additionally, factors like political stability, rule of law, and low corruption create an environment conducive to business investment and growth. Major events like the French Revolution helped shape France culturally, legally and technologically in ways that still influence its growth path today.
This document describes an acquisition system designed to make the examination process more efficient. The system uses a Raspberry Pi to control various hardware components including an RFID reader, rack and pinion assembly, and motor. It is intended to reduce the time and effort required of staff to distribute exam materials by automating the process. When examiners scan their RFID tags, the system verifies their identity and allows them to retrieve the appropriate exam bundles via a motorized rack and pinion assembly. The goal is to minimize manual labor and speed up exam distribution using an automated hardware and software solution controlled by a Raspberry Pi microcontroller.
Parallelization of Graceful Labeling Using Open MPIJSRED
This document summarizes research on parallelizing the graceful graph labeling problem using OpenMP on multi-core processors. It introduces the concepts of parallelization, multi-core architecture, and OpenMP. An algorithm is designed to parallelize graceful labeling by distributing graph vertices across processor cores. Execution time and speedup are measured for graphs of increasing size, showing improved speedup and reduced time with parallelization. Results show consistent performance gains as graph size increases due to better utilization of the multi-core architecture.
Study of Phenotypic Plasticity of Fruits of Luffa Acutangula Var. AmaraIJSRED
This study examines the phenotypic plasticity of fruits in the plant Luffa acutangula var. amara across different locations in Sindhudurg district, Maharashtra, India. The study found that the plant exhibited plasticity in growth cycle, flowering season, leaf shape, and fruit size depending on location. Maximum fruit weights and sizes were recorded at Talebazar village, while minimum sizes were found at Dahibav village. The variation in fruit morphology is an adaptation to the different environmental conditions at each site.
Understanding Architecture of Internet of ThingsIJSRED
The document discusses the architecture of the Internet of Things (IoT). It begins by introducing IoT and its key components. It then discusses three traditional IoT architectures: (1) a three-layer architecture consisting of a perception, network and application layer; (2) the TCP/IP four-layer model; and (3) the Telecommunications Management Network's five-layer logical layered architecture. The document proposes a new five-layer IoT architecture combining aspects of these models. The five layers are the business, application, processing, transport and perception layers. The perception layer collects data via sensors while the business layer manages the overall enterprise.
This document describes a project report submitted by three students for their bachelor's degree. The report outlines the development of a smart shopping cart system that utilizes RFID and Zigbee technologies. The smart cart is intended to enhance the shopping experience for customers by automatically billing items as they are added to the cart, providing real-time stock levels, and reducing checkout times. The system aims to benefit both customers through a more personalized shopping experience and retailers by improving stock management and reducing shoplifting. The document includes sections on requirements, system design, implementation, results and discussion, and conclusions.
An Emperical Study of Learning How Soft Skills is Essential for Management St...IJSRED
This document discusses an empirical study on the importance of soft skills for management students' careers. It finds that while hard skills and academic performance were once prioritized by employers, soft skills like communication, teamwork, and emotional intelligence are now essential for success. The study surveyed 50 management students and faculty in Bangalore to understand how well soft skills training is incorporated and its benefits. It determined that soft skills like communication are crucial as they influence interactions and job performance. However, older teaching methods do not sufficiently develop these skills. Integrating soft skills training into courses could better prepare students for today's work challenges.
The document describes a proposed smart canteen management system that uses various technologies like a web application, barcode scanner, and thermal printer to automate the food ordering process. The system aims to reduce wait times for students and avoid food wastage by allowing online ordering and monitoring stock. A barcode scanner will be used to identify students during ordering and payment. Thermal printers will generate receipts. The system is expected to reduce workload for staff and provide detailed sales reports for management.
This document discusses Gandhi's concept of trusteeship as an alternative economic system. It summarizes that Gandhi did not distinguish between economics and ethics, and based trusteeship on religious ideas like non-possession and truth as well as Western ideas like stewardship. Trusteeship aimed to persuade wealthy property owners to hold wealth in trust for the benefit of society rather than personal gain. It was meant as a non-violent alternative to capitalism and communism that eliminated class conflict through cooperation and trust between rich and poor. The document provides background on the philosophical and religious influences on Gandhi's views before explaining the key aspects of his theory of trusteeship.
Impacts of a New Spatial Variable on a Black Hole Metric SolutionIJSRED
This document discusses the impacts of introducing a new spatial variable in black hole metrics. It begins by summarizing Einstein and Rosen's 1935 paper which introduced a variable ρ = r - 2M in the Schwarzschild metric to remove the singularity. The document then introduces a similar new variable p = r - 2√M and analyzes how this impacts the Schwarzschild metric. Specifically, it notes that this new variable allows for negative radii values and multiple asymptotic regions beyond just two, introducing concepts of probability and imaginary spatial coordinates. Overall, the document explores how different mathematical variables can impact theoretical physics concepts like wormholes.
A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledg...IJSRED
This document summarizes a study that assessed the effectiveness of a planned teaching program on mothers' knowledge of preventing acute respiratory infections in children under 5. 50 mothers were surveyed before and after the program. Before, 36% had moderate knowledge, 62% had inadequate knowledge, and 2% had adequate knowledge. After, 34% had moderate knowledge, 0% had inadequate knowledge, and 66% had adequate knowledge, showing the program improved mothers' knowledge. The study found no significant association between mothers' knowledge and factors like age, education, or family type.
This document describes a proposed ingenuous Trafalgar contrivition system to improve traffic flow and emergency vehicle access. The system uses embedded technologies like a Raspberry Pi, RF transmitter and receiver, and an Android app. When an emergency vehicle is detected approaching a traffic light, the system will open the lights on its path without disrupting other signals. The app will also help identify hit-and-run vehicles through a brief tracking period. The goal is to reduce traffic congestion and response times to save lives.
This document discusses a proposed system called the Farmer's Analytical Assistant, which aims to help farmers in India maximize crop yields through predictive analysis and recommendations. It analyzes agricultural data on factors like soil properties, rainfall, and past crop performance using machine learning techniques to predict optimal crops for different regions based on the environmental conditions. The proposed system would allow farmers to input local data, receive personalized yield predictions and crop suggestions, and get advice from experts online. The methodology section describes how climate/rainfall and soil data is collected and analyzed using machine learning models to provide crop recommendations. The goal is to improve upon traditional crop selection methods and help increase farmers' incomes.
Functions of Forensic Engineering Investigator in IndiaIJSRED
Forensic engineering involves applying engineering principles and methodologies to answer legal questions, especially regarding accidents and failures. A forensic engineer investigates failures through failure analysis and root cause analysis to determine how and why something failed. The engineer must be familiar with relevant codes and standards, understand eyewitness testimony, apply the scientific method to reconstruct events, and report findings clearly to assist courts. A forensic engineering investigation follows the scientific method to methodically analyze evidence and test hypotheses to determine the cause and circumstances of a failure or accident.
GSM Based Smart Helmet with Sensors for Accident Prevention and Intellectual ...IJSRED
1) The document describes a smart helmet system for motorbike riders that aims to improve safety.
2) The smart helmet contains sensors to detect accidents and whether the rider is wearing the helmet or intoxicated. It can send alerts with location data via GSM if an accident is detected.
3) The system is intended to only allow ignition of the motorbike if the rider is wearing the smart helmet, to encourage helmet usage and prevent accidents. It integrates sensors in the helmet with controls on the motorbike.
Conservation of Taksar through Economic RegenerationPriyankaKarn3
This was our 9th Sem Design Studio Project, introduced as Conservation of Taksar Bazar, Bhojpur, an ancient city famous for Taksar- Making Coins. Taksar Bazaar has a civilization of Newars shifted from Patan, with huge socio-economic and cultural significance having a settlement of about 300 years. But in the present scenario, Taksar Bazar has lost its charm and importance, due to various reasons like, migration, unemployment, shift of economic activities to Bhojpur and many more. The scenario was so pityful that when we went to make inventories, take survey and study the site, the people and the context, we barely found any youth of our age! Many houses were vacant, the earthquake devasted and ruined heritages.
Conservation of those heritages, ancient marvels,a nd history was in dire need, so we proposed the Conservation of Taksar through economic regeneration because the lack of economy was the main reason for the people to leave the settlement and the reason for the overall declination.
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
Exploring Deep Learning Models for Image Recognition: A Comparative Reviewsipij
Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision,
enabling computers or other computing devices to identify and categorize objects within images. Among
numerous fields of life, food processing is an important area, in which image processing plays a vital role,
both for producers and consumers. This study focuses on the binary classification of strawberries, where
images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we
aim to determine the effectiveness of different models in identifying whether an image contains
strawberries. This research has practical applications in fields such as agriculture and quality control. We
compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks
(CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by
MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis,
our results demonstrate that CNN outperforms the other models in this task. In the future, the deep
learning models can be evaluated on a richer and larger number of images (datasets) for better/improved
results.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
An Internet Protocol address (IP address) is a logical numeric address that is assigned to every single computer, printer, switch, router, tablets, smartphones or any other device that is part of a TCP/IP-based network.
Types of IP address-
Dynamic means "constantly changing “ .dynamic IP addresses aren't more powerful, but they can change.
Static means staying the same. Static. Stand. Stable. Yes, static IP addresses don't change.
Most IP addresses assigned today by Internet Service Providers are dynamic IP addresses. It's more cost effective for the ISP and you.
Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas.
Neste vídeo, trago os pontos chave do encontro, como:
- AI Assistant para uso junto com a SPL
- SPL2 para uso em Data Pipelines
- Ingest Processor
- Enterprise Security 8.0 (Maior atualização deste seu release)
- Federated Analytics
- Integração com Cisco XDR e Cisto Talos
- E muito mais.
Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções.
https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf
Nosso grupo oficial da Splunk:
https://usergroups.splunk.com/sao-paulo-splunk-user-group/
Response & Safe AI at Summer School of AI at IIITHIIIT Hyderabad
Talk covering Guardrails , Jailbreak, What is an alignment problem? RLHF, EU AI Act, Machine & Graph unlearning, Bias, Inconsistency, Probing, Interpretability, Bias
Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...IJAEMSJORNAL
This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.
Development of Chatbot Using AI/ML Technologiesmaisnampibarel
The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses.
Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology.
The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.