This document presents research on the use of social media among students at G.H. Patel PG Institute of Business Management. The objectives are to examine the types of social media used, frequency of usage, purposes for usage, and benefits. A survey was conducted of 150 students, collecting data on their social media usage through questionnaires. Previous research found that students use social media mainly to connect with friends and classmates for studying and entertainment. The findings will provide insights into why social networking sites have become so popular among students.
This study aims to examine factors influencing aspects such as teacher's personality, student's behavior, environmental which has influence student's affective and cognitive. The data were obtained using methods: interview and questionnaire. The random participant has been chosen for interviewed and population has been used for the questionnaire. There were 1585 participants have filled the questionnaire and 24 students have interviewed. Interview data were recorded and analyzed. The results have processed, it was classified according to study programs following the indicator. The research finding shows that: factors from lecturers and teaching assistants got 78 - 81%, academic and non-academic facilities got 74.91% - 80.86% and dormitory as living for students got 69.16% which have a big impact on influencing student's affective and cognitive. There were also issues such as teacher's centered-learning, most of students and class situations can often be uncomfortable.
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This paper reveals the result of 322 distance learners’ perception towards e-learning program
conducted by UiTM. Generally the respondents rated above average for all aspects of distance
learning program irrespective of gender, program of studies, income and occupation. Students’
gender also did not show any difference in their perception. Similarly, semester of studies too, did
not indicate any significant difference except their perception towards lecturer. However
students’ semester of studies showed significant difference towards lecturer, module and
physical. Gender, income and semester of studies did not show any relationship to students’
perception towards all aspects of distance learning program. However students’ program of
studies showed significant relationship towards their perceptions of the program. Students’ CGPA
showed negative relationship with all aspects of distance learning program.
This study aims to examine factors influencing aspects such as teacher's personality, student's behavior, environmental which has influence student's affective and cognitive. The data were obtained using methods: interview and questionnaire. The random participant has been chosen for interviewed and population has been used for the questionnaire. There were 1585 participants have filled the questionnaire and 24 students have interviewed. Interview data were recorded and analyzed. The results have processed, it was classified according to study programs following the indicator. The research finding shows that: factors from lecturers and teaching assistants got 78 - 81%, academic and non-academic facilities got 74.91% - 80.86% and dormitory as living for students got 69.16% which have a big impact on influencing student's affective and cognitive. There were also issues such as teacher's centered-learning, most of students and class situations can often be uncomfortable.
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This paper reveals the result of 322 distance learners’ perception towards e-learning program
conducted by UiTM. Generally the respondents rated above average for all aspects of distance
learning program irrespective of gender, program of studies, income and occupation. Students’
gender also did not show any difference in their perception. Similarly, semester of studies too, did
not indicate any significant difference except their perception towards lecturer. However
students’ semester of studies showed significant difference towards lecturer, module and
physical. Gender, income and semester of studies did not show any relationship to students’
perception towards all aspects of distance learning program. However students’ program of
studies showed significant relationship towards their perceptions of the program. Students’ CGPA
showed negative relationship with all aspects of distance learning program.
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.
Libyan Students' Academic Performance and Ranking in Nursing Informatics - Da...ijdms
Nursing Informatics is becoming a trend in the nursing education sector and health care workforce.
Belonging to the academic performance of the students, steps are necessary to improve it as performance
and retention were becoming a great issue for educators, students and the nation. As the student performs,
all academic measures were recorded into the database system, over the years it accumulated to a large
amount. Data were forgotten, archived at the least. Then came educational data mining, with all its ability.
Unknown and hidden data patterns of Nursing Informatics and accompanying subjects were extracted and
analyzed using the same database grading system of Omar Al-Mukhtar University College of Nursing
known as OMUCON-GSv1. Getting started with mining by employing database management methods and
implementations like Structured Query Language to form a query, filter, pivot table and pivot chart, the
system and the research generated valuable findings. The result of the study showed a favorable academic
performance by the students of nursing and so with the ranking they got for Nursing Informatics. Overall
the OMUCON-GSv1 can generate helpful and meaningful data as it promoted simple educational data
mining. A vital element in the improvement of quality education for the College. Further study and advance
data mining approach were recommended to greatly improve the outcome.
Automated Data Integration, Cleaning and Analysis Using Data Mining and SPSS ...CSCJournals
Students’ performance plays major role in determining the quality of our education system. Sijil Pelajaran Malaysia (SPM) is a public examination compulsory to be taken by Form 5 students in Malaysia. The performance gap is not only a school and classroom issue but also a national issue that must be addressed properly. This study aims to integrate, clean and analysis through automated data mining techniques. Using data mining techniques is one of the processes of transferring raw data from current educational system to meaningful information that can be used to help the school community to make a right decision to achieve much better results. This proved DM provides means to assist both educators and students, and improve the quality of education. The result and findings in the study show that automated system will give the same result compare with manual system of integration and analysis and also could be used by the management to make faster and more efficient decision in order to map or plan efficient teaching approach for students in the future.
The article one about Tutors’ Views on the Utilization of E-learning System in Architectural Educationc critique and the article 2 about BELL /CESSNA BREAK GROUND
Article Review. "Retaining Experts:Administrators' views on Retention Incentives and Older Employees" Moon T.C., Beck S., & Laudicina R.J., Clin Lab Sci 2014;27(3):162
Assignment in fulfillment of MBA, subject: Human Resource Management by Santhy Govindasamy, The Open University Malaysia
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.
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
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
An Analysis of Behavioral Intention toward Actual Usage of Open Source Softwa...IJAEMSJORNAL
This study focused on analyzing behavioral intention toward the actual usage of open source software in private universities in Tanzania. Questionnaires were used to collect quantitative data in two private universities namely Iringa University and Ruaha Catholic University. Stratified sampling technique was utilized to ensure sample representativeness among two universities where simple random sampling was used to draw a sample from each stratum during the survey. Finding Using Structural Equation Modeling indicated that performance expectancy (source code production and software localization) and social factor (Vendor, internet services provider and lecturer) have a significant influence toward behavioral intention while effort expectancy was found to be insignificant. In addition the behavioral intention was found to be significant toward student’s actual usage of open source software in Universities. This study recommended that for students to develop behavioral intention toward OSS actual usage, internet service provider have to increase the level of internet services that can assist the university communities to access and download open source software. In addition, to increase actual use, open source software vendors and lecturer or experts have to make sure that their software source code is free for distribution and localization, this will increase self-motivation and interest of the students toward actual usage of open source software.
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.
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
Effective non verbal communication in the business communicationugik sugiharto
non verbal communication is a message which send from us, whether intentionally or unaware, but it gives impact to our communication especially in business communication
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.
Libyan Students' Academic Performance and Ranking in Nursing Informatics - Da...ijdms
Nursing Informatics is becoming a trend in the nursing education sector and health care workforce.
Belonging to the academic performance of the students, steps are necessary to improve it as performance
and retention were becoming a great issue for educators, students and the nation. As the student performs,
all academic measures were recorded into the database system, over the years it accumulated to a large
amount. Data were forgotten, archived at the least. Then came educational data mining, with all its ability.
Unknown and hidden data patterns of Nursing Informatics and accompanying subjects were extracted and
analyzed using the same database grading system of Omar Al-Mukhtar University College of Nursing
known as OMUCON-GSv1. Getting started with mining by employing database management methods and
implementations like Structured Query Language to form a query, filter, pivot table and pivot chart, the
system and the research generated valuable findings. The result of the study showed a favorable academic
performance by the students of nursing and so with the ranking they got for Nursing Informatics. Overall
the OMUCON-GSv1 can generate helpful and meaningful data as it promoted simple educational data
mining. A vital element in the improvement of quality education for the College. Further study and advance
data mining approach were recommended to greatly improve the outcome.
Automated Data Integration, Cleaning and Analysis Using Data Mining and SPSS ...CSCJournals
Students’ performance plays major role in determining the quality of our education system. Sijil Pelajaran Malaysia (SPM) is a public examination compulsory to be taken by Form 5 students in Malaysia. The performance gap is not only a school and classroom issue but also a national issue that must be addressed properly. This study aims to integrate, clean and analysis through automated data mining techniques. Using data mining techniques is one of the processes of transferring raw data from current educational system to meaningful information that can be used to help the school community to make a right decision to achieve much better results. This proved DM provides means to assist both educators and students, and improve the quality of education. The result and findings in the study show that automated system will give the same result compare with manual system of integration and analysis and also could be used by the management to make faster and more efficient decision in order to map or plan efficient teaching approach for students in the future.
The article one about Tutors’ Views on the Utilization of E-learning System in Architectural Educationc critique and the article 2 about BELL /CESSNA BREAK GROUND
Article Review. "Retaining Experts:Administrators' views on Retention Incentives and Older Employees" Moon T.C., Beck S., & Laudicina R.J., Clin Lab Sci 2014;27(3):162
Assignment in fulfillment of MBA, subject: Human Resource Management by Santhy Govindasamy, The Open University Malaysia
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.
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
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
An Analysis of Behavioral Intention toward Actual Usage of Open Source Softwa...IJAEMSJORNAL
This study focused on analyzing behavioral intention toward the actual usage of open source software in private universities in Tanzania. Questionnaires were used to collect quantitative data in two private universities namely Iringa University and Ruaha Catholic University. Stratified sampling technique was utilized to ensure sample representativeness among two universities where simple random sampling was used to draw a sample from each stratum during the survey. Finding Using Structural Equation Modeling indicated that performance expectancy (source code production and software localization) and social factor (Vendor, internet services provider and lecturer) have a significant influence toward behavioral intention while effort expectancy was found to be insignificant. In addition the behavioral intention was found to be significant toward student’s actual usage of open source software in Universities. This study recommended that for students to develop behavioral intention toward OSS actual usage, internet service provider have to increase the level of internet services that can assist the university communities to access and download open source software. In addition, to increase actual use, open source software vendors and lecturer or experts have to make sure that their software source code is free for distribution and localization, this will increase self-motivation and interest of the students toward actual usage of open source software.
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.
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
Effective non verbal communication in the business communicationugik sugiharto
non verbal communication is a message which send from us, whether intentionally or unaware, but it gives impact to our communication especially in business communication
Non-Verbal Communication PowerPoint PPT Content Modern SampleAndrew Schwartz
163 slides include: what is non-verbal communication and what it includes, the categories within non-verbal communication, non-verbal behaviors, highlighting non-verbal statistics, tips to understand non-verbal communication, the 65 body areas displaying non-verbal communication behaviors, analyzing non-verbal communication, understanding eye access cues, how to detect lies, non-verbal communication trivia: time, space, voice, touch, objects, how to's and more.
(Communication & PR) When Books Undergo Heart Transplant: Beating Beyond the ...Mark Raygan Garcia
Used during a whole-day workshop I conducted this year for the librarians of the Robert B. & Metta J. Silliman University Lbrary System. Portions of this presentation were extracted from the presentation I gave as speaker to the regional conference of the Philippine Librarians Association, Inc. at the University of San Carlos in Cebu City some 2 years ago.
FREE TRAINING SLIDES FOR NON-VERBAL BUSINESS COMMUNICATIONS- For more free training, tips and tools - check us out at: www.tek-infovision.com Email me at: bam@tek-infovision.com
"The Influence of Online Studies and Information using Learning Analytics"Fahmi Ahmed
This research will help people with inadequate knowledge to get
a better understanding of online study or e-learning. Through this
study, the social impact of online users or learners can be
increased, and the users can have a clear idea of online study. In
this research, the graphs will be presented according to country,
gender, age, online resources, etc. showing the impact of online
study and information on online users. The learners will get an
understandable knowledge of the type of sources, what is their
purpose, and resources people can use in online study. From this,
the learners will get a guide or path that how easily they can learn
online for study in a more flexible way. The outcomes are
visualized using the R language and Tableau with pre-processed
data.
Predicting students’ intention to continue business courses on online platfor...Samsul Alam
The objective of this study was to analyze the intention of a University's business department students to continue their studies on e-learning platforms during the ongoing COVID-19 pandemic. To this end, a questionnaire was developed to collect primary data from students in business fields. The study took into account more than 285 respondents from two different universities and relied on the expectation confirmation model (ECM) theory and the structural equation model. The partial least squares (SEM-PLS) method was used to analyze the data. The results of the study showed that task skills (TS) and task challenges (TC) were significant for the enjoyment (EN) of the students which in turn had a positive effect on the satisfaction levels. Confirmation (CON) had an impact on the post adoption perceived usefulness (PAPU), which was deemed positive for student satisfaction (SAT). The SAT and psychological safety (PS) of online learning platforms were found to positively influence the continuance intention (CI) on e-learning platforms. Finally, both SAT and PS of online learning platforms were observed to positively influence CI on e-learning platforms. Further research in this area could be useful in making decisions about promoting educational programs based on e-learning. The researchers recommend that academicians and policymakers must ensure appropriate arrangements for teaching on e-learning platforms.
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
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
Campus Recruitment is the process done by the corporate sectors to selecting eligible students pursuing graduate in educational Institute. Nowadays E- Campus Recruitment has become an emerging trend in the recruitment process. Some of the companies follow this Recruitment process for the purpose of mass recruitment by reducing their cost, time and receiving proper response. The present study makes an attempt in understanding E-campus recruitment process and the factors influencing the students to select the campus recruitment.
THE RATIONALE AND FRAMEWORK FOR EVALUATING THE ICT USE IN TEACHER EDUCATION D...DrGavisiddappa Angadi
This study investigated that the quantity of computer and ICT use in teacher education institutions is less and it is mainly focused on the learning of ICT skills which takes optimum time computers are used. The framework is then improved towards a new framework that can be effectively used to evaluate information and communication technology use in pre-service teacher education. In this study the independent variables are derived from computer use within the institution, some other extraneous factor may have impact on the results. Those factors may be computer use at home, friends home and cyber or internet centers by the respondents. The study concludes that the reality rhetoric gap of the impact of ICTs in teacher education institutions be evaluated from periodically to ensure that the quality with program objectives are met. The result shows that there is an urgent need to conduct intensive training to all the teacher educators in the colleges of education. The curriculum developers should develop global content for serving the local needs and make it to available to all the colleges of education online as well as offline content or blended learning modules. Several factors have been cited as responsible for low quantity of computer use in colleges of education. Some of these factors are; attitude towards new technologies, poor management, lack of local content serving local needs, shortage of equipments.
The focus of this study is to seek the relevance of investing in Information Technology (IT) by the students. The research takes into account 50 students studying at different disciplines at Dhaka University. The respondents were visited randomly to get the relevant data. The result of the study suggests that students’ academic quality and knowledge enhancement have a relationship with investment in IT though the relationship is not significant. The result of hypothesis testing shows that students those have invested in personal computer and internet secure comparatively higher cumulative grade point average (CGPA) rather than those who haven’t invested on these IT tools. But the likelihood of investing higher amount in IT will pay-off better CGPA is not found thus there is no association of good result and investing heavily on IT. However, the findings of this exploratory study offer insights that the money invested in IT for academic purpose is more advantageous than otherwise be invested especially for those students whose academic curriculum mainly decorated in accordance with the modern up-to-date era of Information Technology. Eventually, this study will help concerned students, guardians and academicians understanding how important IT is for student’s academic performance.
Higher education institutions now a days are operating in an increasingly complex and
competitive environment. The application of innovation is a must for sustaining its competitive advantage.
Institution leaders are using data management and analytics to question the status quo and develop effective
solutions. Achieving these insights and information requires not a single report from a single system, but
rather the ability to access, share, and explore institution-wide data that can be transformed into meaningful
insights at every level of the institution. Consequently, institutions are facing problems in providing necessary
information technology support for fulfilling excellence in performance. More specifically, the best practices
of big data management and analytics need to be considered within higher education institutions. Therefore,
the study aimed at investigating big data and analytics, in terms of: (1) definition; (2) its most important
principles; (3) models; and (4) benefits of its use to fulfill performance excellence in higher education
institutions. This involves shedding light on big data and analytics models and the possibility of its use in
higher education institutions, and exploring the effect of using big data and analytics in achieving performance
excellence. To reach these objectives, the researcher employed a qualitative research methodology for
collecting and analyzing data. The study concluded the most important result, that there is a significant
relationship between big data and analytics and excellence of performance as big data management and
analytics mainly aims at achieving tasks quickly with the least effort and cost. These positive results support
the use of big data and analytics in institutions and improving knowledge in this field and providing a practical
guide adaptable to the institution structure. This paper also identifies the role of big data and analytics in
institutions of higher education worldwide and outlines the implementation challenges and opportunities in the
education industry.
A COMPARATIVE ANALYSIS OF SELECTED STUDIES IN STUDENT PERFORMANCE PREDICTIONIJDKP
Predicting student success has long been an interest of institutions of higher education as well as
organisations responsible for preparing high-stake, standardised tests administered at national and
international levels. This study discusses how performance prediction studies have evolved from those that
use demographic data and high school grades to predict success in college to those that utilise
sophisticated data collected in non-traditional educational platforms to predict end-of-course performance
and to those that show how student progress can be tracked in a continuous manner. A total of 56 studies
published since the nineties are discussed. Views on strengths and weaknesses as well as observed
opportunities for improvement are presented. The consistently high results reported in many of the studies
shall convince the reader that automated solutions to the problem of predicting student progress and
performance can either be tailored for specific settings or can be adopted from similar settings in which
they have been utilized successfully. A recommendation on how to build upon recent success is provided
This proposed system will help in consulting the career opportunities to the students after 10th, 12th or graduation for their bright future and will show the recent industrial trends in that particular profession. In this system we will be working on real time web-based application which will provide students forum for discussion, real time job updates from industry, different industrial events nearby places, live chat with the professional experts. User can apply for the jobs. Database management, real time system and web-based languages will be used design this application. This proposed system will provide the direct communication platform for students with the industry. This system will help the students or employees to build the professional career, resume according to the format approved by industry. User can update and share their documents and experiences with the industry. This system will provide automated verification system with the help of network security. Priyanka Bodke | Nikita Kale | Sneha Jha | Vaishnavi Joshi"Real Time Application for Career Guidance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11525.pdf http://www.ijtsrd.com/engineering/computer-engineering/11525/real-time-application-for-career-guidance/priyanka-bodke
Predictive and Statistical Analyses for Academic Advisory Supportijcsit
The ability to recognize students’ weakness and solving any problem may confront them in timely fashion is
always a target of all educational institutions. This study was designed to explore how can predictive and
statistical analysis support the academic advisor’s work mainly in analysis students’ progress. The sample
consisted of a total of 249 undergraduate students; 46% of them were Female and 54% Male. A one-way
analysis of variance (ANOVA) and t-test were conducted to analysis if there was different behaviour in
registering courses. Predictive data mining is used for support advisor in decision making. Several
classification techniques with 10-fold Cross-validation were applied. Among of them, C4.5 constitutes the
best agreement among the finding results.
Assessing the Readiness to Adopt E-Learning among Industrial Training Institu...IOSRJBM
E-learning is using computer and Internet to learn part of or full course whether it is in school, college or in any educational training. The use of e-learning has gained momentum in recent years contrasting to the under valuations done in the previous years. This study was undertaken to assess the readiness of the Industrial Training Institute (ITI) students to adopt e-learning platform. The constructs attitude, perceived ease of use, perceived benefits and behavioural intention from Technology Adoption Model are studied to attain the objective. It is a descriptive research and the population used for this study is Industrial Training Institutes (ITI) in Tamil Nadu. The data was collected from sample of 267 students. The findings of the research infer that the ITI students are willing to adopt the e-learning platform.
This study is aiming to investigate influencing factor of entrepreneurship collaborative learning performances by using Partial Least Squares Structural Equation Modeling approach (PLS-SEM). A hypothetical conceptual model for improving entrepreneurship learning performance develop based on the previous study, which composed of four enablers called university’s vision and mission, entrepreneurial background lecturer, strong culture and rewards system.The methodology used to test the conceptual model was delivering the questionnaire survey to 72 (seventy two) lecturers in business field. The unit analysis of this study is the universities, both public and private universities in South of Tangerang. Sample was selected using simple random sampling. The questionnaires were developed from the past studies in similar area of entrepreneurship in higher education, before distributed to the respondents. The findings from this study provide insight to construction that the relationships of variable vision and mission to effectiveness have path coefficient value 0.195 and the lecture background is 0.040. Meanwhile, reward and culture shows stronger influence to Entrepreneurship collaborative Learning Effectiveness with the patch coefficient values are 0.296 and 0.335.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Research on use of social media among students of GHPIBM, Vallabh Vidyanagar
1. Presented By: Presented To:
Bijay Sunuwar (15006) Dr. Darshana
Dave
Hemali Parikh (14046)
Pratik Vagadiya(14050)
Shikha Karamchandani (14065)
“USE OF SOCIAL MEDIAAMONG G.H.
PATEL PG INSTITUTE OF BUSINESS
MANAGEMENT STUDENTS”
2. ABSTRACT
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management2
In the context of today’s electronic media, social
networking sites have come to mean individuals,
using the Internet and web application to
communicate in previously impossible ways.
This is largely the result of a culture-wide paradigm
shift in the uses and possibilities of the internet itself.
This study examines the Use of Social Media among
Students of GH Patel PG Institute of Business
Management.
The main objectives of the study are to find out the
reasons why students use Social Media, to identify
students’ perception of Social Media and to find out
the frequency of Social Media usage.
3. 3/23/2017
G.H. Patel Postgraduate Institute of Business
Management3
A descriptive survey design was adopted.
Around 150 students from this institute are selected for
the study.
Questionnaire will be used as an instrument for data
collection.
The result of other major research articles related to this
topic reveals that mostly all the student were using the
social networking sites in interaction with friends,
connecting to their class mates for online study and for
discussing serious national issues and watching movies
etc.
4. 3/23/2017
G.H. Patel Postgraduate Institute of Business
Management4
Similarly, they spend more than five hours using Social
Media. Findings of the study indicate that using Social
Media has positively influenced their lifestyles.
And greatly helped them to achieve academic
excellence.
These findings provide implications for future research
on why these social networking sites have gained
popularity.
5. OBJECTIVES
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management5
To ascertain the various categories of social networking
sites used by GH Patel Post Graduate Institute of
Business Management Students.
To examine the extent and frequency of usage of social
networking sites by GH Patel Post Graduate Institute of
Business Management Students.
To examine their purposes of using social networking
sites, to determine the benefits of using social networking
sites.
To identify how students of Kaduna Polytechnic, Nigeria
perceive social media.
6. Literature Reviews
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management6
(Martin, 2008)
College students have great interest in social media. For the
purpose of this study, social media was defined as Facebook,
YouTube, Blogs, Twitter, MySpace or LinkedIn.Although,
providing a detailed perspective on social media use among
university students and underscoring that such use can produce
both positive and negative consequences.
(Jacobsen, & Forste, 2011)
Almost 25 percent of students’ time on the Internet is now spent
on social networking websites. Facebook is the most used social
network by college students, followed by YouTube and Twitter.
Moreover, Facebook alone reports that it now has 500 active
million users, 50% of whom log on every day.
7. Research Design
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management7
Sources of data
Primary Data: Structured questionnaires
Secondary Data:
− Secondary method of data collection is considered
specifically for the purpose of literature review.
− Reference books and academic journals were
consulted.
− The internet was used to collect data.
− Past research survey data was used to help create
the questionnaire used to collect primary data.
8. 3/23/2017
G.H. Patel Postgraduate Institute of Business
Management8
Sampling Design
The population for this research study consists of
students who are currently studying in GH Patel Post
Graduate Institute of Business Management .
We will be opting for the convenient sampling because of
the budget and time constraints.
Sample Size
Sample size consists of 150 respondents (GH Patel Post
Graduate Institute of Business Management students.)
Data Management and Analysis
The data collected from the questionnaires will then be
coded and entered into the computer system. Software
programs like Excel will be used.
After processing data, descriptive as well as inferential
analysis will be done.
9. 3/23/2017
G.H. Patel Postgraduate Institute of Business
Management9
Scheduling
Proposal Preparation days: 5
Data collection days: 8
Data entry and data analysis: 5 days
Report finalization: 3 days
Total: 21 days
Budgeting
Printing Expense: 40 pages @ Rs 1 each= Rs 40
Photocopy cost: 400 pages @ Rs 1 = Rs 400
Miscellaneous Expenses = Rs 360
Total = Rs 1000
10. Questionnaire
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management10
Questionnaire is the set of formalized questions
for obtaining information from the respondents
Open ended and close ended questions are used
Different types of questions include dichotomous,
multiple choice and rating scale
Personal questions are placed on the last section
11. Significance and Scope
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management11
With the increase in purchasing power and
fashion consciousness the demand for the
cosmetics products are increasing
In semi urban markets people are becoming
aware about varieties of cosmetics
Know about the preference, perception of women
regarding cosmetics
Know about the demand for products
Brand loyalty
12. Bibliogrpahy
3/23/2017
G.H. Patel Postgraduate Institute of Business
Management12
http://www.ripublication.com/gjmbs_spl/gjmbsv3n
7_17.pdf
http://www.ukessays.com/essays/marketing/cons
umer-behavior-of-cosmetics-among-college-
female-students-marketing-
essay.php#ixzz3Uvr7AQXM
http://www.ccsenet.org/journal/index.php/ijms/arti
cle/viewFile/10386/7413
http://theglobaljournals.com/paripex/file.php?val=
May_2014_1400155726_f3d33_49.pdf
http://globalvisionpub.com/globaljournalmanager/
pdf/1390558193.pdf