In education system it is very important to decide learning behavior of students. Today there is huge competition in higher educational institutes. Quality education is essential for facing new educational challenges. Educational Data Mining is useful to classify students according to their knowledge and learning behavior. It helps teachers to implement different teaching methodology as per learning behavior of student. Researcher used Naïve Bayes classification technique on training data set of students. Classification is a supervised learning approach which categorized data into predefined classes. The implementation is carried out using C . Algorithm is implemented on set of multivalued attributes to predict slow learner, average learner and fast learner students. The objective of researcher is to extract hidden knowledge from dataset for prediction of learning behavior of student. Mrs. Varsha. P. Desai "Classification Technique for Predicting Learning Behavior of Student in Higher Education" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Digital Economy and its Impact on Business and Industry , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18697.pdf
http://www.ijtsrd.com/management/business-economics/18697/classification-technique-for-predicting-learning-behavior-of-student-in-higher-education/mrs-varsha-p-desai
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
Educational Data Mining in Program Evaluation: Lessons LearnedKerry Rice
AET 2016 Researchers present findings from a series of data mining studies, primarily examining data mining as part of an innovative triangulated approach in program evaluation. Findings suggest that is it possible to apply EDM techniques in online and blended learning classrooms to identify key variables important to the success of learners. Lessons learned will be shared as well as areas for improving data collection in learning management systems for meaningful analysis and visualization.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
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.
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.
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
Educational Data Mining in Program Evaluation: Lessons LearnedKerry Rice
AET 2016 Researchers present findings from a series of data mining studies, primarily examining data mining as part of an innovative triangulated approach in program evaluation. Findings suggest that is it possible to apply EDM techniques in online and blended learning classrooms to identify key variables important to the success of learners. Lessons learned will be shared as well as areas for improving data collection in learning management systems for meaningful analysis and visualization.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
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.
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.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Research of Influencing Factors of College Students’ Personalized Learning Ba...inventionjournals
Smart learning environment, as a high form of digital learning environment, accelerates the wide spread of personalized learning supported by Information Technology. Based on the literature analysis and Delphi method, this paper constructs a scale of influencing factors of college students’ personalized learning based on smart learning environment. By factors analysis, descriptive statistical analysis, average difference test and regression analysis, this paper obtains four factors that affect college students’ personalized learning based on smart learning environment, i.e. learner factor, teacher factor, learning environment factor and learning resource factor, and explores the relationship among these factors through structural equation model. The purpose of this paper is not only to provide a theoretical basis for further study, but also to provide advice and guidance for the effective launching of personalized learning based on smart learning environment, which helps to stimulate college students’ potential and expertise, teach according to each student's individual differences, and promote the educational reform.
The document summarizes recent developments in technology-supported assessment of self-regulated learning (SRL). It describes widely used self-report survey methods like the Motivated Strategies for Learning Questionnaire and innovative trace-based methods that can automatically collect data on students' learning processes. Trace measures analyze students' online behaviors like note-taking or help seeking. Model tracing matches students' actions to cognitive models of SRL. The computerization of self-evaluation requires students to self-report and compare reports to actual performance. The review finds both established and emerging methods provide new opportunities to understand SRL with technology.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Vitomir Kovanovic
Slides from our presentation at the Seventh National Conference
on Work-Integrated Learning (ACEN’18).
The full paper is available at https://www.researchgate.net/publication/328578409_Examining_the_value_of_learning_analytics_for_supporting_work-integrated_learning
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...Talis
1. The document discusses the use of learning analytics to understand student learning and optimize teaching practices. It describes how analytics can provide insights into student performance, engagement, and retention at various levels from the individual to the institution.
2. Interviews with teachers found they are interested in analytics about student engagement and performance but have concerns about interpreting data. Teachers want analytics to help understand ideal students and provide feedback to improve teaching.
3. A conceptual framework is presented that links learning analytics to learning design to provide context for analyzing educational activities and interactions with resources. Planning questions are also outlined to help educators implement learning analytics.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
This document summarizes a research paper that evaluates the performance of decision tree and clustering techniques using the WEKA data mining tool. The paper uses student academic and performance data to apply decision tree and clustering algorithms and compare the results of each technique. Specifically, it uses WEKA to classify and cluster a dataset containing the marks and percentages of students from educational institutions. The paper aims to determine which technique (decision tree or clustering) provides more accurate and useful results for predicting student performance.
A case of Mbeya University of Science and Technology(MUST)
By;
Dr. Joel S. Mtebe
Director of;
Center for Virtual Learning
University of Dar es Salaam
Tanzania
http://works.bepress.com/mtebe
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.
Educational Data Mining in relation to Educational Statistics of NepalRoshan Bhandari
This is the final presentation done at the Institute of Engineering Pulchowk Engineering Campus. We have applied data mining techniques like Regression Analysis, Clustering, to find the problems in education of Nepal. We had collaborated with Depart of Education of Nepal for Data. We came up with a suggestive term called "Educational Development Index" to find the relative development status of a district.
To read the complete report of our research please check here:-
http://flipkarma.com/project/educational-data-mining-in-relation-to-educational/
27_06_2019 Wolfgang Greller, from University of Teacher Education (Viena), on...eMadrid network
This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify learning patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, properly using learner data for SRL requires skills like data literacy, critical thinking, and ensuring ethical and responsible use of student information. Educators need competencies in areas such as data protection, privacy, and using social context to help learners apply analytics insights.
Learning Analytics for Self-Regulated Learning (2019)Wolfgang Greller
This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, effectively using learner data for SRL requires competencies like data literacy, critical thinking, and ensuring ethical and responsible use of student data.
The document discusses using Twitter tweets to assess personality. It proposes predicting a user's personality using their public tweets on Twitter based on the DISC framework of dominance, influence, steadiness and compliance. It finds that analyzing word frequencies in a user's tweets can indicate which DISC categories are most dominant. The study aims to develop a useful profiling tool and discusses how personality assessment could benefit fields like marketing, IT and anthropology. It concludes by noting this is an initial attempt and future work could expand the analysis by including location data and interest parameters.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document proposes a tiered architecture to transform institutional knowledge in higher educational institutions into institutional intelligence through knowledge management and data mining techniques.
2) A three-phase approach is used: phase 1 identifies functional domains and performance indicators through interviews; phase 2 proposes the tiered architecture; phase 3 will model the architecture using KM and data mining methods.
3) The architecture aims to better access, analyze, and utilize institutional knowledge to extract relationships and patterns to enhance performance, decision making, and processes in higher education.
Identifying the Key Factors of Training Technical School and College Teachers...ijtsrd
According to the Bangladesh Bureau of Statistics BBS , the literacy rate in Bangladesh is increasing day by day. But, Its not acceptable to our present day. The role model of an education system is a teacher or instructor. Proper education can improve our literacy rate and also be a huge change for our future Digital Bangladesh. This enhancement is only possible to highly trained instructors or teachers. In order to improve an organizations training process, it’s important to assess how instructors are trained their students. This research has worked on identifying the key factors of training Technical School and College teachers in Bangladesh. The proposed work is conducted by Data Mining and Machine Learning. The methods of this experiment are Data Processing, Data Mining, and Analysis and Evaluation. Filtering our data is completed by using the Data Processing method. After that, the datasets are trained and tested by the Data Mining and Machine Learning tools. Finally, the experimental results are evaluated and analyzed by the different assessment tools. The accuracy of our trained models are 0.97 , 0.97 , 0.96 , 0.96 , 0.96 , 0.96 , 0.94 , 0.93 , 0.93 , 0.92 , 0.91 , 0.33 , 0.22 using the Logistic Regression, Extra Trees Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, SVM Linear Kernel, Ada Boost Classifier, K Neighbors Classifier, Linear Discriminant Analysis, Decision Tree Classifier, Ridge Classifier, Quadratic Discriminant Analysis, Naive Bayes, respectively. As a result, the Logistic Regression does accurately identify and classify the key factors of training Technical School and College teachers. The Logistic Regression model accuracy is 0.97 which gives better accuracy than other machine learning algorithms. Md. Mehedi Hasan | Md. Imran Ali | Nakib Aman Turzo | Golam Rabbani "Identifying the Key Factors of Training Technical School and College Teachers in Bangladesh Using Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47901.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/47901/identifying-the-key-factors-of-training-technical-school-and-college-teachers-in-bangladesh-using-data-mining/md-mehedi-hasan
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Research of Influencing Factors of College Students’ Personalized Learning Ba...inventionjournals
Smart learning environment, as a high form of digital learning environment, accelerates the wide spread of personalized learning supported by Information Technology. Based on the literature analysis and Delphi method, this paper constructs a scale of influencing factors of college students’ personalized learning based on smart learning environment. By factors analysis, descriptive statistical analysis, average difference test and regression analysis, this paper obtains four factors that affect college students’ personalized learning based on smart learning environment, i.e. learner factor, teacher factor, learning environment factor and learning resource factor, and explores the relationship among these factors through structural equation model. The purpose of this paper is not only to provide a theoretical basis for further study, but also to provide advice and guidance for the effective launching of personalized learning based on smart learning environment, which helps to stimulate college students’ potential and expertise, teach according to each student's individual differences, and promote the educational reform.
The document summarizes recent developments in technology-supported assessment of self-regulated learning (SRL). It describes widely used self-report survey methods like the Motivated Strategies for Learning Questionnaire and innovative trace-based methods that can automatically collect data on students' learning processes. Trace measures analyze students' online behaviors like note-taking or help seeking. Model tracing matches students' actions to cognitive models of SRL. The computerization of self-evaluation requires students to self-report and compare reports to actual performance. The review finds both established and emerging methods provide new opportunities to understand SRL with technology.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Vitomir Kovanovic
Slides from our presentation at the Seventh National Conference
on Work-Integrated Learning (ACEN’18).
The full paper is available at https://www.researchgate.net/publication/328578409_Examining_the_value_of_learning_analytics_for_supporting_work-integrated_learning
Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insigh...Talis
1. The document discusses the use of learning analytics to understand student learning and optimize teaching practices. It describes how analytics can provide insights into student performance, engagement, and retention at various levels from the individual to the institution.
2. Interviews with teachers found they are interested in analytics about student engagement and performance but have concerns about interpreting data. Teachers want analytics to help understand ideal students and provide feedback to improve teaching.
3. A conceptual framework is presented that links learning analytics to learning design to provide context for analyzing educational activities and interactions with resources. Planning questions are also outlined to help educators implement learning analytics.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
This document summarizes a research paper that evaluates the performance of decision tree and clustering techniques using the WEKA data mining tool. The paper uses student academic and performance data to apply decision tree and clustering algorithms and compare the results of each technique. Specifically, it uses WEKA to classify and cluster a dataset containing the marks and percentages of students from educational institutions. The paper aims to determine which technique (decision tree or clustering) provides more accurate and useful results for predicting student performance.
A case of Mbeya University of Science and Technology(MUST)
By;
Dr. Joel S. Mtebe
Director of;
Center for Virtual Learning
University of Dar es Salaam
Tanzania
http://works.bepress.com/mtebe
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.
Educational Data Mining in relation to Educational Statistics of NepalRoshan Bhandari
This is the final presentation done at the Institute of Engineering Pulchowk Engineering Campus. We have applied data mining techniques like Regression Analysis, Clustering, to find the problems in education of Nepal. We had collaborated with Depart of Education of Nepal for Data. We came up with a suggestive term called "Educational Development Index" to find the relative development status of a district.
To read the complete report of our research please check here:-
http://flipkarma.com/project/educational-data-mining-in-relation-to-educational/
27_06_2019 Wolfgang Greller, from University of Teacher Education (Viena), on...eMadrid network
This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify learning patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, properly using learner data for SRL requires skills like data literacy, critical thinking, and ensuring ethical and responsible use of student information. Educators need competencies in areas such as data protection, privacy, and using social context to help learners apply analytics insights.
Learning Analytics for Self-Regulated Learning (2019)Wolfgang Greller
This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, effectively using learner data for SRL requires competencies like data literacy, critical thinking, and ensuring ethical and responsible use of student data.
The document discusses using Twitter tweets to assess personality. It proposes predicting a user's personality using their public tweets on Twitter based on the DISC framework of dominance, influence, steadiness and compliance. It finds that analyzing word frequencies in a user's tweets can indicate which DISC categories are most dominant. The study aims to develop a useful profiling tool and discusses how personality assessment could benefit fields like marketing, IT and anthropology. It concludes by noting this is an initial attempt and future work could expand the analysis by including location data and interest parameters.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document proposes a tiered architecture to transform institutional knowledge in higher educational institutions into institutional intelligence through knowledge management and data mining techniques.
2) A three-phase approach is used: phase 1 identifies functional domains and performance indicators through interviews; phase 2 proposes the tiered architecture; phase 3 will model the architecture using KM and data mining methods.
3) The architecture aims to better access, analyze, and utilize institutional knowledge to extract relationships and patterns to enhance performance, decision making, and processes in higher education.
Identifying the Key Factors of Training Technical School and College Teachers...ijtsrd
According to the Bangladesh Bureau of Statistics BBS , the literacy rate in Bangladesh is increasing day by day. But, Its not acceptable to our present day. The role model of an education system is a teacher or instructor. Proper education can improve our literacy rate and also be a huge change for our future Digital Bangladesh. This enhancement is only possible to highly trained instructors or teachers. In order to improve an organizations training process, it’s important to assess how instructors are trained their students. This research has worked on identifying the key factors of training Technical School and College teachers in Bangladesh. The proposed work is conducted by Data Mining and Machine Learning. The methods of this experiment are Data Processing, Data Mining, and Analysis and Evaluation. Filtering our data is completed by using the Data Processing method. After that, the datasets are trained and tested by the Data Mining and Machine Learning tools. Finally, the experimental results are evaluated and analyzed by the different assessment tools. The accuracy of our trained models are 0.97 , 0.97 , 0.96 , 0.96 , 0.96 , 0.96 , 0.94 , 0.93 , 0.93 , 0.92 , 0.91 , 0.33 , 0.22 using the Logistic Regression, Extra Trees Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, SVM Linear Kernel, Ada Boost Classifier, K Neighbors Classifier, Linear Discriminant Analysis, Decision Tree Classifier, Ridge Classifier, Quadratic Discriminant Analysis, Naive Bayes, respectively. As a result, the Logistic Regression does accurately identify and classify the key factors of training Technical School and College teachers. The Logistic Regression model accuracy is 0.97 which gives better accuracy than other machine learning algorithms. Md. Mehedi Hasan | Md. Imran Ali | Nakib Aman Turzo | Golam Rabbani "Identifying the Key Factors of Training Technical School and College Teachers in Bangladesh Using Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47901.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/47901/identifying-the-key-factors-of-training-technical-school-and-college-teachers-in-bangladesh-using-data-mining/md-mehedi-hasan
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
Performance Evaluation of Feature Selection Algorithms in Educational Data Mi...IIRindia
Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
AI-Learning style prediction for primary educationIRJET Journal
This document presents research on developing an AI model to predict learning styles for primary school students in an online learning environment. The researchers created an AI model within an online learning portal that recommends educational materials tailored to each student's preferred learning style. They developed a novel AI strategy using collaborative filtering to enable the model to independently predict learning styles. The model was tested on Indonesian elementary school students and showed improved performance over traditional matrix factorization-based models. The researchers conclude that accounting for individual learning preferences through personalized recommendations can improve student engagement and test scores with online learning platforms.
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.
The document describes a case study that uses educational data mining techniques to analyze graduate student data from the College of Science and Technology in Khanyounis, Palestine from 1993-2007. The data includes 18 attributes for 3360 students. After preprocessing, association rules, classification, clustering, and outlier detection are applied. Association rules found relationships between attributes like poor grades and average performance. Classification rules predicted grade based on attributes like secondary school type and GPA. Clustering grouped students and outlier detection found anomalous cases. The results provide insights to improve student performance and academic decision making.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Data mining approach to predict academic performance of studentsBOHRInternationalJou1
Powerful data mining techniques are available in a variety of educational fields. Educational research is
advancing rapidly due to the vast amount of student data that can be used to create insightful patterns
related to student learning. Educational data mining is a tool that helps universities assess and identify student
performance. Well-known classification techniques have been widely used to determine student success in
data mining. A decisive and growing exploration area in educational data mining (EDM) is predicting student
academic performance. This area uses data mining and automaton learning approaches to extract data from
education repositories. According to relevant research, there are several academic performance prediction
methods aimed at improving administrative and teaching staff in academic institutions. In the put-forwarded
approach, the collected data set is preprocessed to ensure data quality and labeled student education data
is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve
(ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic
models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of
OVA of 98–99.6%
This document provides a systematic review of educational data mining (EDM) techniques and their applications. It discusses how EDM can be used to extract hidden information from large student data repositories using clustering, classification, prediction, and recommendation algorithms. These algorithms help group similar students, categorize students, predict student outcomes, and suggest courses. The document also reviews literature applying these EDM techniques and outlines future work on semantic and opinion mining to improve adaptive learning systems.
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
IRJET - Recommendation of Branch of Engineering using Machine LearningIRJET Journal
This document describes a machine learning system that recommends engineering branches to students based on their scores. It uses K-nearest neighbors and collaborative filtering techniques. The system aims to help students select an engineering branch that matches their abilities and reduces confusion. It analyzes student data like marks to make personalized recommendations. The document reviews similar existing recommendation systems and the techniques they use. The proposed system seeks to guide students towards suitable engineering fields and reduce the workload on counselors.
This document discusses using data mining techniques to analyze faculty performance at an engineering college in India. It proposes analyzing 4 parameters - student complaints, feedback, results, and reviews - to evaluate faculty instead of just 2 parameters (feedback and results) used previously. It will use opinion mining to analyze faculty performance and calculate scores. The system will collect data, preprocess it, apply a KNN algorithm to the 4 parameters to calculate scores for each faculty, sum the scores, classify results using rule-based classification, and analyze outcomes by subject and class. It reviews related work applying educational data mining and concludes the multiple classifier approach is better, and future work could consider more parameters and expand to all college branches and departments.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
This document discusses using data mining techniques like classification and clustering algorithms to analyze how technology can improve student performance. It provides an overview of several research papers on this topic, including how they selected data sets and technologies. Specifically, it examines the role of classification algorithms in learning data mining and discusses papers that used algorithms like Naive Bayes, J48, and support vector machines to analyze student performance data. It also discusses the use of clustering algorithms for grouping students and analyzing their learning. In general, the document analyzes how data mining can help evaluate the impact of technologies on student learning and performance.
Big Data and Advanced Analytics For Improving Teaching Practices In 2023 | Fu...Future Education Magazine
Here are 7 ways of big data and advanced analytics to improve teaching practices: 1. Data Sources in Education 2. The Role of Big Data in Education 3. Advanced Analytics in Education 4. Assessing Teaching Practices with Data 5. Enhancing Teaching Practices with Data
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
Similar to Classification Technique for Predicting Learning Behavior of Student in Higher Education (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Classification Technique for Predicting Learning Behavior of Student in Higher Education
1. International Journal of Trend in
International Open Access Journal
ISSN No: 2456
INTERNATIONAL CON
ITS IMPACT ON BUSINESS AND INDUSTRY
Organised By: V. P. Institute of Management Studies & Research, Sangli
@ IJTSRD | Available Online @ www.ijtsrd.com
Classification Technique
Student
Assistant Professor, V. P. Institute of Management Studies & Research, Sangli, Maharashtra, India
Affiliated to Shivaji University, Kolhapur
ABSTRACT
In education system it is very important to decide
learning behavior of students. Today there is huge
competition in higher educational institutes. Quality
education is essential for facing new educational
challenges. Educational Data Mining is useful to
classify students according to their knowledge and
learning behavior. It helps teachers to implement
different teaching methodology as per learning
behavior of student. Researcher used Naïve Bayes
classification technique on training data set of
students. Classification is a supervised learning
approach which categorized data into predefined
classes. The implementation is carried out using C#.
Algorithm is implemented on set of multivalued
attributes to predict slow learner, average learner and
fast learner students. The objective of researcher is to
extract hidden knowledge from dataset for prediction
of learning behavior of student.
KEYWORD: Training Dataset, Supervised,
Unsupervised, Machine learning, Data Mining.
I. INTRODUCTION
Data Mining is a process of discovering knowledge
from database. It is a technique to identify patterns
and determine relationship between objects in dataset.
Data mining motivates various applications in
machine learning to learn from data. It consists of
many algorithms which are based on supervised and
unsupervised learning. There are different techniques
of data mining like classification, clustering,
predictive analysis, association rule mining, sequence
mining, graph mining, regression and time series
analysis etc. Selection and implementation of best
International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
ISSN No: 2456 - 6470 | Conference Issue – ICDEBI
INTERNATIONAL CONFERENCE ON DIGITAL ECONOMY AND
TS IMPACT ON BUSINESS AND INDUSTRY
Organised By: V. P. Institute of Management Studies & Research, Sangli
www.ijtsrd.com | Conference Issue: ICDEBI-2018 |
Classification Technique for Predicting Learning Behavior
Student in Higher Education
Mrs. Varsha. P. Desai
V. P. Institute of Management Studies & Research, Sangli, Maharashtra, India
Affiliated to Shivaji University, Kolhapur, Maharashtra, India
In education system it is very important to decide
learning behavior of students. Today there is huge
competition in higher educational institutes. Quality
education is essential for facing new educational
challenges. Educational Data Mining is useful to
lassify students according to their knowledge and
learning behavior. It helps teachers to implement
different teaching methodology as per learning
behavior of student. Researcher used Naïve Bayes
classification technique on training data set of
lassification is a supervised learning
approach which categorized data into predefined
classes. The implementation is carried out using C#.
Algorithm is implemented on set of multivalued
attributes to predict slow learner, average learner and
students. The objective of researcher is to
extract hidden knowledge from dataset for prediction
Training Dataset, Supervised,
Unsupervised, Machine learning, Data Mining.
discovering knowledge
from database. It is a technique to identify patterns
and determine relationship between objects in dataset.
Data mining motivates various applications in
machine learning to learn from data. It consists of
based on supervised and
unsupervised learning. There are different techniques
of data mining like classification, clustering,
predictive analysis, association rule mining, sequence
mining, graph mining, regression and time series
d implementation of best
suitable algorithm for getting optimum solution to the
problem is a challenging task in data mining.
Data mining plays vital role in education system.
Predicting learning behavior of student is very critical
process. Learning behavior of student depend of
different factors like gender, family background,
location, age, interest, strength, weakness, culture,
curriculum etc. Today education system creates
tremendous carrier opportunities in the front of
students. It is challenging work for teacher to provide
education as per student need and interest. Learning
student behavior is very essential for getting better
teaching outcome as well as student’s satisfaction. A
Classification technique in data mining helps teachers
to predict student behavior and selecting appropriate
teaching methodology to enhance teaching and
learning process.
II. Literature Review:
Researcher has gone through previous research related
to classification techniques in data mining. It is
observed that, Naïve Bayes classification algorithm is
used for student’s performance classification. Web
mining and multifactor analysis technique is
implemented for prediction [3]
forest and Naïve Bayes theorem is used for
classification of student behavior
results of all three algorithms and it is found that
Naïve Bayes method gives better results than other
classification techniques.[4]
Naïve Bays algorithm is
implemented for slow Lerner prediction using python
and accuracy is compared using WEKA data mining
tool.
Research and Development (IJTSRD)
www.ijtsrd.com
ICDEBI-2018
FERENCE ON DIGITAL ECONOMY AND
TS IMPACT ON BUSINESS AND INDUSTRY
Organised By: V. P. Institute of Management Studies & Research, Sangli
| Oct 2018 Page: 163
g Learning Behavior of
V. P. Institute of Management Studies & Research, Sangli, Maharashtra, India
suitable algorithm for getting optimum solution to the
problem is a challenging task in data mining.
Data mining plays vital role in education system.
Predicting learning behavior of student is very critical
havior of student depend of
different factors like gender, family background,
location, age, interest, strength, weakness, culture,
curriculum etc. Today education system creates
tremendous carrier opportunities in the front of
work for teacher to provide
education as per student need and interest. Learning
student behavior is very essential for getting better
teaching outcome as well as student’s satisfaction. A
Classification technique in data mining helps teachers
tudent behavior and selecting appropriate
teaching methodology to enhance teaching and
Researcher has gone through previous research related
to classification techniques in data mining. It is
classification algorithm is
used for student’s performance classification. Web
mining and multifactor analysis technique is
. Decision tree, Random
forest and Naïve Bayes theorem is used for
classification of student behavior. Researcher evaluate
results of all three algorithms and it is found that
Naïve Bayes method gives better results than other
Naïve Bays algorithm is
implemented for slow Lerner prediction using python
using WEKA data mining
2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
According to literature review it is found that Naïve
Bayes is suitable classification algorithm for multi
attribute analysis. It is essential to develop user
friendly application which useful in any education
sector. Researcher developed application using C# for
predicting learning behavior of student by
implementing Naïve Bayes theorem.
III. Classification Techniques:
Classification is a supervised learning method where
data is divided into different categories or classes.
The objective of classification to predict target class
for given dataset. There are various techniques of
classification like decision tree, Naïve Bayes
classifier, nearest neighbor approach, artificial neural
network these are important techniques of
classification. Accuracy of target prediction is
depends upon selection of classification technique. In
many real life situations classification is
fundamentally probabilistic, it is uncertain to which
class record is belong.[1]
IV. Naïve Bayes Classifier:
Bayesian classification is based on Bayes theorem.
The posterior probability of the class that a record
belongs to is an approximated using prior probability
which drawn from training dataset. Classification
model estimate the likelihood of the record belonging
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Conference Issue: ICDEBI-2018 |
According to literature review it is found that Naïve
Bayes is suitable classification algorithm for multi
attribute analysis. It is essential to develop user
friendly application which useful in any education
cher developed application using C# for
predicting learning behavior of student by
Classification is a supervised learning method where
data is divided into different categories or classes.
e objective of classification to predict target class
for given dataset. There are various techniques of
classification like decision tree, Naïve Bayes
classifier, nearest neighbor approach, artificial neural
network these are important techniques of
ification. Accuracy of target prediction is
depends upon selection of classification technique. In
many real life situations classification is
fundamentally probabilistic, it is uncertain to which
n classification is based on Bayes theorem.
The posterior probability of the class that a record
belongs to is an approximated using prior probability
which drawn from training dataset. Classification
model estimate the likelihood of the record belonging
to each class. The class with highest prevents for Y to
happen when events for X probability becomes the
class label for the record.[2]
Definition of Bayes Theorem:
variables X and Y, each of them taking a specific
value corresponds to a random event. A conditional
probability P(X/Y) represents the probability of
events for Y to happen when event for X have already
occurred.[2]
P(X/Y) = P(X/Y).P(Y)
P(X)
P(Y/X) = P(X/Y).P(Y)
P(Y)
V. Training Dataset:
Following table shows training dataset of MCA I year
student dataset. Here researcher is interested to predict
learning behavior of student from given training
dataset using Naïve Bayes algorithm. Student data
consists of different attributes like Gender, Area,
SSC_Medium, SSC_Percentage, HSC_faculty,
Math_At_HSC,Graduation_Marks,Admission_Type,
Entrance_Rank,ParentsIncome,,Attendan
cation_Skill, Learning_Behavior (Class Label) etc.
Table 1: Training Dataset:
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 | IF: 4.101
| Oct 2018 Page: 164
o each class. The class with highest prevents for Y to
happen when events for X probability becomes the
Definition of Bayes Theorem: Given two random
variables X and Y, each of them taking a specific
value corresponds to a random event. A conditional
probability P(X/Y) represents the probability of
events for Y to happen when event for X have already
Following table shows training dataset of MCA I year
student dataset. Here researcher is interested to predict
of student from given training
dataset using Naïve Bayes algorithm. Student data
consists of different attributes like Gender, Area,
SSC_Medium, SSC_Percentage, HSC_faculty,
Math_At_HSC,Graduation_Marks,Admission_Type,
Entrance_Rank,ParentsIncome,,Attendance,Communi
cation_Skill, Learning_Behavior (Class Label) etc.
3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
VI. Student related Variables:
VII. Data Pre-processing:
Data was pre-processed by performing following
operations [3]
:
1. Converting all fields to categories.
2. Features combine to reduce dimensionality.
3. Missing values are replaced by frequently
occurring values.
VIII. Algorithm:
1. Import dataset into Sqlserver
2. Find probability of each class.
3. Select parameter set as per input requirement.
4. For each input record:
i. For each attribute:
A. Entities are divided into different categories
according to categorical data.
B. Probability is calculated from training dataset.
5. For each attribute in testing dataset
i. For each attribute:
A. Calculate probability and classify the data
accordingly
B. Return the diagnosis parameter and calculated
probability of each class [4]
.
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Conference Issue: ICDEBI-2018 |
by performing following
Features combine to reduce dimensionality.
Missing values are replaced by frequently
Select parameter set as per input requirement.
Entities are divided into different categories
Probability is calculated from training dataset.
Calculate probability and classify the data
Return the diagnosis parameter and calculated
C. Compare class wise probability value and
Return final classification which has highest
probability.
IX. Implementation of algorithm:
Here Naïve Bayes algorithm is implemented on above
dataset. C# is used for stepwise implementation of
algorithm and predicting data for unknown
tuple/record.
Algorithm is implemented to predict learning
behavior of student with following known attribute
values:
X= Gender=M, Area=Rural, SSC_Medium=English,
SSC_Percentage=Poor, HSC_Faculty=Commerce,
HSC_percentage=Good, Maths_At_HSC=Yes,
Graduation_Marks:Poor, Admission_Type=MC,
Entrance_Rank=Good, parents_Income
Attendance=Average, Communicaton_Skill=Good.
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 | IF: 4.101
| Oct 2018 Page: 165
Compare class wise probability value and
Return final classification which has highest
Implementation of algorithm:
Here Naïve Bayes algorithm is implemented on above
dataset. C# is used for stepwise implementation of
algorithm and predicting data for unknown
Algorithm is implemented to predict learning
dent with following known attribute
X= Gender=M, Area=Rural, SSC_Medium=English,
SSC_Percentage=Poor, HSC_Faculty=Commerce,
HSC_percentage=Good, Maths_At_HSC=Yes,
Graduation_Marks:Poor, Admission_Type=MC,
Entrance_Rank=Good, parents_Income=Low,
Attendance=Average, Communicaton_Skill=Good.
4. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
In above problem there are three classes:
C1: Learning Behavior Slow
C2: Learning Behavior Fast,
C3: Learning Behavior Average.
Here we need to predict whether X belongs to which
class.
P(X/C1)=0.33*0.33*0.33*0.33*0.66*0.33*1*0.33*0.
66*0.66*0.33*0.33*0.66=2.66
P(X/C2)=0.66*0.33*0.66*0.33*0.66*0.33*
0.33*0.33* 0.33*1*0.33*0.33*0.33=1.33
P(X/C3)=0.75*0.75*0.25*0.5*0.25*0.25*0.25*0.5*0.
25*0.25*0.5*0.75*0.25=3.21
P(X/C1)*P(C1)=2.66*0.3=0.798
P(X/C2)*P(C2)=1.33*0.3=0.399
P(X/C3)*P(C3)=3.21*0.4=1.284
P(X/C3)*P(C3) gives highest probability so X
belongs to class C3.
According to Naïve Bayes theorem it is predicted that
given tuple X belongs to class C3. Which means that
there is highest probability that student is Fast Lerner.
X. Finding:
Implementation of Naïve Bayes theorem using C# we
can find out Fast, Slow and Average learners.
Conclusion:
Naïve bays theorem is implemented using C# to
determine Slow Learner, Average Lerner and Fast
Learner. This application is useful in education
system to categories student according to their
learning behavior. Proposed application is very user
friendly and applicable for any higher education
sector. It helps teachers to implement different
teaching and learning techniques for providing quality
education to the students. Successful implementation
of this model will improve overall result and learning
interest among students.
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Conference Issue: ICDEBI-2018 |
In above problem there are three classes:
Here we need to predict whether X belongs to which
0.33*0.33*0.33*0.66*0.33*1*0.33*0.
P(X/C2)=0.66*0.33*0.66*0.33*0.66*0.33*
0.33*0.33* 0.33*1*0.33*0.33*0.33=1.33
P(X/C3)=0.75*0.75*0.25*0.5*0.25*0.25*0.25*0.5*0.
P(X/C3)*P(C3) gives highest probability so X
According to Naïve Bayes theorem it is predicted that
given tuple X belongs to class C3. Which means that
ent is Fast Lerner.
Implementation of Naïve Bayes theorem using C# we
can find out Fast, Slow and Average learners.
Naïve bays theorem is implemented using C# to
determine Slow Learner, Average Lerner and Fast
Learner. This application is useful in education
system to categories student according to their
learning behavior. Proposed application is very user
applicable for any higher education
sector. It helps teachers to implement different
teaching and learning techniques for providing quality
education to the students. Successful implementation
of this model will improve overall result and learning
REFERENCES:
1. Jiawei Han and Micheline Kamber,
Concepts and Techniques
0535-8.
2. Hongbo Du, Data Mining Technique,
81-315-1955-4.
3. K. Prasada Rao, M. V. P. Chandra Sekhara Rao,
B. Ramesh, Predicting Learning Behavior of
Students using Classification Techniques,
International Journal of Computer Applications
(0975 – 8887) Volume 139
4. Swati and Rajinder Kaur,
Classification for the Slow Learner Prediction
over Various Class of Student Dataset,
Journal of Science and Technology,
DOI: 10.17485/ijst/2016/v9i48/103651, December
2016, ISSN (Online): 0974
5. Swati and Rajinder Kaur, Multifactor Naïve Bayes
Classification For The Slow Learner Prediction
Over Multicass Student Dataset,
Journal on Computational Science & Applications
(IJCSA) Vol.6, No. 4, August 2016
6. Shiwani Rana*, Roopali Garg,
Prediction using Multi-
Classification Algorithm,
Information Technology, UIET, Panjab
University, Chandigarh, India. 02 December
2016.
7. R. Kohavi, “Scaling up the accuracy of Naïve
Bayes classifiers: a decision
International Conference on K
Discovery and Data Mining (KDD 96), ACM,
Aug. 1996, pp. 202-207.
8. C. G. Nespereira, E. Elhariri, N. El
Vilas, and R. P. D. Redondo,
based classification approach for predicting
student’s performance in blended lear
International Conference on Advanced Intelligent
System and Informatics (AISI 15), Springer, Nov.
2015, pp. 47-56.
9. Sudha M, Kumaravel A. Performance comparison
based on attribute selection tools for data mining.
Indian Journal of Science and
Nov; 7(S7):1–5.
10. Weka, University of Waikato, New Zealand,
http://www.cs.waikato.ac.nz/ml/weka/
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 | IF: 4.101
| Oct 2018 Page: 166
Jiawei Han and Micheline Kamber, Data Mining
Concepts and Techniques ISBN-978-81-312-
, Data Mining Technique, ISBN-978-
P. Chandra Sekhara Rao,
Predicting Learning Behavior of
Students using Classification Techniques,
International Journal of Computer Applications
8887) Volume 139 – No.7, April 2016.
and Rajinder Kaur, Using Factor
Classification for the Slow Learner Prediction
r Various Class of Student Dataset, Indian
Journal of Science and Technology, Vol 9(48),
DOI: 10.17485/ijst/2016/v9i48/103651, December
0974-564.
, Multifactor Naïve Bayes
Classification For The Slow Learner Prediction
Over Multicass Student Dataset, International
Journal on Computational Science & Applications
(IJCSA) Vol.6, No. 4, August 2016
Shiwani Rana*, Roopali Garg, Slow Learner
-Variate Naïve Bayes
Classification Algorithm, Department of
Information Technology, UIET, Panjab
University, Chandigarh, India. 02 December
Scaling up the accuracy of Naïve
Bayes classifiers: a decision-tree hybrid," Proc.
International Conference on Knowledge
Discovery and Data Mining (KDD 96), ACM,
C. G. Nespereira, E. Elhariri, N. El-Bendary, A. F.
Vilas, and R. P. D. Redondo, “Machine learning
based classification approach for predicting
student’s performance in blended learning,” Proc.
International Conference on Advanced Intelligent
System and Informatics (AISI 15), Springer, Nov.
Performance comparison
based on attribute selection tools for data mining.
Indian Journal of Science and Technology. 2014
Weka, University of Waikato, New Zealand,
http://www.cs.waikato.ac.nz/ml/weka/