SlideShare une entreprise Scribd logo
1  sur  5
Télécharger pour lire hors ligne
International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656
www.ijcat.com 331
Ontology based design for M- learning system
M.Thangaraj
Department of Computer Science
Madurai Kamaraj University, Madurai
Tamil Nadu, India
S.Vanathi
Department of Computer Science
Govt. Arts College, Melur
Tamil Nadu, India
Abstract: Information and communication Technology is a gateway through which large population of students has been addressed.
Mobile learning technology the latest arrival highly changing the way the students learn, interact, access up to data information. It
mainly satisfies the current and future generation which needs information at the earliest rather than later few touches. The World
Wide Web acts as an interface in E- learning as well as in mobile learning (M-learning) environments. It supports and facilitates
the delivery of teaching and learning materials. M - l e a r n i n g provides quality educational content with the help of semantic
web technologies like Ontology. This study presents Mobile Learning framework for making efficient learning with a case study on
cyber security.
Keywords: Information and Communication Technology; Ontology; M-learning; E-learning; cyber security.
1. INTRODUCTION
Education is a process which enables a person’s holistic
development of personality through knowledge acquisition.
Learning environment has the influence on knowledge
improvement or knowledge enhancement [8]. Teaching
learning processes delivered through mobile devices are based
on wired and wireless communication technologies. Mobile
learning creates a new learning environment using handheld
devices by interfacing world wide web and the learner [10].
Mobile learning, recent form of distant learning is an extension
of E-learning application is shown in Figure 1, which also has
the use of audio, visual, cognitive, cooperative and interactive
contents delivered via smart digital electronic devices in an
attempt to create a direct, dynamic, ongoing learning
environment. Such form of learning enables the individual
learner to move freely in the learning material, at the same time
can access to knowledge sources wherever and whenever the
learner desires.
In the world of education and training there exist many
definitions for mobilE-learning. Among them ADL defines
mobile learning or “M- Learning” as the use of handheld
computing devices to provide access to learning content and
information resources.
Thus M-learning overcomes the time and distance barriers of
traditional learning and cost barrier of E-learning.
Figure 1. Various Dimensions of latest learning/ Evolution of
distant learning
Ontology is a conceptual system a semantic account, which
expresses a meta-level specification [5], and it owns the
features like expressiveness, extensibility, interoperability,
sharing and re-usability.
The various directions of research in ontology based M-learning
are discussed as related work in section 2. Section 3,
describes the Modified Intelligent Recommender System
for Effective E-learning Environment Architecture,
which encompasses various activities of M-learning in
detail. Section 4 explains evaluation and experimental
results on the efficiency of the system. Section 5, concludes
and guides future work.
International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656
www.ijcat.com 332
2. RELATED WORK
The changes in the educational scenario are wider and
fast. Mobile learning is a personalized as well as interactive
mode of learning. The implementation of mobile learning using
android OS is illustrated in [3].
The meaning of mobile learning, its key issues,
solutions and the knowledge transformation due to the
development of mobile learning are clearly explained in [6].
E-learning fulfils the user context with the help of
semantic web technology like ontology. Ontology based
applications improve the understanding of concepts, critical
thinking and creative thinking using the semantic learning
environment is discussed [9].
IRS-EEE presents the effective E-learning
environment using concept oriented, ontological content
management. The essentials of semantic web are emphasized in
[11].
The cost and portability while using laptops for E-
learning are high. This paper tries to overcome the cost and time
barriers.
3. MODIFIED IRS-EEE
ARCHITECTURE
The Modified IRS-EEE Architecture (Figure 2) is
based on the semantic structure, promises a powerful approach
to satisfy the M-learning requirements. This Semantic
architecture of M-learning has four layers such as User Layer
(UL), Service Layer (SL), Content Management Layer (CML)
and Database Layer (DBL).
The semantic based IRS-EEE architecture [11] is structured
to satisfy E-learning requirements, of the user which is
tailored to deliver the content through mobile devices result
into the modified IRS-Architecture is an extension of the
IRS-EEE. This architecture has four layers such as User Layer
(UL), Service Layer (SL), Content Management Layer (CML)
and Database Layer (DBL).
1. User layer
The two types of users of this layer are
Provider and student .This system revolves around the
users. The user profile is stored and updated whenever
needed.
A. Data Provider
The role of provider in this system is to
monitor user’s profile, presenting the content, adding
and updating the contents, questions, assessment of
outcome and learner’s performance and tracking the
performance of the entire system. Provider has the
facility to convert the web pages into compatible
mobile device content.
B. Learner
The learner is the next stage user. The
learners are the Persons/Students using advanced
cellular phones or smart phones having the link to
internet. Using such phones is regarded as more
trendy, fashionable and prestigious among the young
education by means of mobile devices is gaining
efficiency in its design methods.
Figure 2. Modified IRS-EEE Architecture
C. Mobile devices
Typical examples of mobile devices include
smart phones, tablets, laptops, and personal media
players. We limit our implementation in smart phones
and tablets only because they are highly portable (than
laptop etc.,) and easy to handle. These mobiles
devices have the property to produce unique
educational affordances: portability, social
interactivity, context sensitivity, connectivity and
individuality [4].
International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656
www.ijcat.com 333
2. Service Layer
A. Profile management
The function of the Profile management
module is to maintain all the related information
about the learners, and there interactions etc. it
also, updates and keeps track of the consistency of
data by proper updating process.
B. Mobile course delivery
Presently mobile learning is regarded as a
core pedagogical activity in higher institution of
learning and the instructional technology insist the
content transmitted through mobile technology is
mostly social, to a lesser extent and economic [6]. The
content of a particular concept or sub-concept is
observed and learned by the learner. An effective
learning environment is provided with the help of
ontology and sub-ontology based concepts. The
learners should concentrate an assessment before
beginning to learn a concept known to be pretest.
C. Evaluation and Feedback Provider
Once the student has finished the course,
he/she would take up a post-test. During the test
multiple-choice questions related to the topic are
presented in slides and answers are gathered using
SMS facilities of the mobile phones of the students
that are illustrated in Figure 3.The results are
compared with pretest to assess the improvement of
the user [1] and the skills gained by the user. Test
results are Tracked and stored to assess their
improvement, and it is used as a measure of input of
M-learning. It provides the facility that the learner can
give feedback about the presentation of the content
and about the ease of access.
Figure 3. Evaluation and feedback process
The design of CML presents and preserves the hierarchical
course structure with their semantic relationship between the
concepts.
3. Database layer
The database components of user layer
contain all the required information about learners. This
layer maintains the databases with users’ profile, ontology
based content, test score and tracking status of the every
learner.
4. EVALUATION AND EXPERIMENTAL
RESULTS
The mobile platforms targeted for the study included
android, Smartphone platforms provide a mobile web browser
with support for HTML5. These direct-touch Smartphone
platforms are specifically targeted as they provide a superior
user experience compared to non-touch mobile devices. In
addition, we need to provide the best possible user experience
on mobile to see its confirmation.
The course on CYBER SECURITY is selected as sample course
for M-learning to provide awareness of Information Security
and give an exposure as a spectrum of security activities,
methods, methodologies, and procedure. Our M-learning course
includes the topics like security principles, threats, attacks,
security models, security policies, overview of authentication,
encryption, and certification, security detection, business risk
analysis, protection of information assets.
This course is developed and pilot-tested. For this study,
Bachelor of Science in computer science students are chosen
from different colleges which are geographically distributed
from urban, semi-urban and rural colleges of Madurai Kamaraj
University and they have participated in this study with the
condition that
1. Learner should have knowledge of computers.
2. Learner should have smart phones or tablet Pc
linked with internet and sizable storage.
Once the learner enters into modified IRS-EEE he/she has to
sign-in and enter his profile. The admin has approved the learner
.The learner selects the course. Before the course content is
presented there is pretest. The selected course is divided into
blocks of related concepts. After careful learning of the concepts
to estimate their capture before the course MCQ (Multiple
Choice Questions) is given. MCQ assessment is one of the best
and often used Formative Assessment tool [7] post-test is
conducted to measure the knowledge improvement in the
domain and the efficiency of the system.
The efficiency of the system is measured using the
parameters like ease of interactivity, independency, instant
access, storage and maintenance which are measured against
Learners satisfaction in percentage. The results of the study
shows the acceptance of mobile learning that is following
shown in Figure 4.
International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656
www.ijcat.com 334
Figure 4. Comparisons of E-learning and M-learning
Compared to traditional learning environment E-learning
changes the environment from classroom to computers that to
be transferred to just a touch in M-learning.
The new generation prefers much than E-learning for its
features like cost effectiveness. Personalization but the system
maintenance is less in E-learning than M-learning which is
shown in Figure 5.
Figure 5. Student Access mobile vs laptops
5. CONCLUSION
As computer and Internet become essential tools for
education; technology becomes more mobile, affordable,
effective and easy to use. This offers many opportunities to
widen participation and access to ICT, particularly the Internet
(infoDev, 2010). Mobile devices such as phones and PDAs are
much more affordable than desktop computers, and therefore
represent a less expensive access to the internet (even if the
cost of the connection may be higher) (InfoDev, 2010). The
introduction of the Tablet PC can now access mobile internet
with much functionality than desktop computers.
Mobile devices can be used anywhere, anytime,
including at home, on the train, in hotels-this is invaluable for
work-based training.
It is much easier to accommodate several mobile
devices in a classroom than several desktop computers.
Future mobile technologies may be able to present textbooks,
create database, aid in library utilization, and foster contextual
learning. Mobile learning has become an integral part of
education in many parts of the world, and new research
advances in design and implementation will ensure its
increasing importance.
6. REFERENCE
[1] Fernando A. Mikic Fonte, Juan C. Burguillo-
Rial,Martin Llamas-Nistal, and David Fernandez-
Hermida “A BDI based Intelligent Tutoring
Module for the E-learning Platform INES”
40th
ASEE/IEEE frontiers in education Conference,
October 27-30,2010, Washington, DC.
[2] Giselher H. J. R, “An Educational Taxonomy for
Learning Objects”, Third IEEE International
Conference on Advanced Learning Technologies
(ICALT’03), pp250, 2003.
[3] Hafizul Fahri Hanafi, Khairulanuar Samsudin,’’
Mobile learning Environment System(MLES)”,
The case of Android based Learning Application
on Undergraduates’ Learning (IJACSA)
International Journal of Advanced Computer
Science and Applications Vol.3, No 3, 2012.
[4] Klopfer, E., and Squire, K. (2008),’’Environmental
Detectives: the development of an augmented
reality platform for environmental simulations’’.
Educational Technology Research and
Development.
[5] Ljiljana Stojanovic, Steffen Stab, Rudi
Studer,”E-learning based on the Semantic Web”,
The Electronic JournalofE-
learningvolume4.issue 2, pp111=118.
[6] Mohamed Osman M. El-Hussein and Johannes C.
Cronje, “Defining Mobile Learning in the Higher
Education Landscape, Educational Technology
&Society’’, 13 (3), 12–21.(2010).
[7] Noorminshah lahad, Georgios A. Dafoulas, Maya
Milankovic-Atkinson, Alan Murphy, ”E-learning
in Developing Countries in Suggesting a
Methodology for Enabling Computer Aided
Assessment” IEEE International Conference on
Advanced Learning Technology(ICALT) 2004.
[8] Shaileshkumar K. Patel et al, “Semantic Web
Technology and Ontology designing for E-learning
Environments’’, International Journal of
Computer Science and Information Technologies,
(IJCSIT), Vol. 6 (1) , 2015, 48-51.
[9] Sohaib Ahmed and David Parsons, “An ontology-
Driven Mobile Web Application for Science
Enquiry Based Learning”, The 7th International
International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656
www.ijcat.com 335
Conference on Information Technology and
Applications (ICITA 2011).
[10] Tayseer Andrawes Saleem, “Mobile learning
technology: A new step in E-learning, Journal of
Theoretical and Applied Information Technology”,
31st
December 20011, Vol 34 No.2, ISSN: 1992-
8645.
[11] Thangaraj M, Vanathi S, “ An Intelligent
Recommender system for Effective E learning
Environment, International Journal of Engineering
Research and Technology (IJERT) ISSN: 2278-
0181,Vol 3 Issue 11, November-2014.

Contenu connexe

Tendances

A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...
A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...
A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...IJSEA
 
IoT-based students interaction framework using attention-scoring assessment i...
IoT-based students interaction framework using attention-scoring assessment i...IoT-based students interaction framework using attention-scoring assessment i...
IoT-based students interaction framework using attention-scoring assessment i...eraser Juan José Calderón
 
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...eraser Juan José Calderón
 
Development of a mobile learning application to support e learning and analys...
Development of a mobile learning application to support e learning and analys...Development of a mobile learning application to support e learning and analys...
Development of a mobile learning application to support e learning and analys...ijmnct
 
Supervised learning techniques for virtual military training
Supervised learning techniques for virtual military trainingSupervised learning techniques for virtual military training
Supervised learning techniques for virtual military trainingElena Susnea
 
E Learning In Modern Education System .
E Learning In Modern Education System .E Learning In Modern Education System .
E Learning In Modern Education System .Assignment Studio
 
Mobile learning architecture using fog computing and adaptive data streaming
Mobile learning architecture using fog computing and adaptive data streamingMobile learning architecture using fog computing and adaptive data streaming
Mobile learning architecture using fog computing and adaptive data streamingTELKOMNIKA JOURNAL
 
RISKS AND REMEDIES IN E-LEARNING SYSTEM
RISKS AND REMEDIES IN E-LEARNING SYSTEMRISKS AND REMEDIES IN E-LEARNING SYSTEM
RISKS AND REMEDIES IN E-LEARNING SYSTEMIJNSA Journal
 
Design strategies for mobile language learning effectiveness using hybrid mcd...
Design strategies for mobile language learning effectiveness using hybrid mcd...Design strategies for mobile language learning effectiveness using hybrid mcd...
Design strategies for mobile language learning effectiveness using hybrid mcd...Alexander Decker
 
Review of monitoring tools for e learning platforms
Review of monitoring tools for e learning platformsReview of monitoring tools for e learning platforms
Review of monitoring tools for e learning platformsijcsit
 
Design and Implementation of Efficient Search Methodology for Content-Based R...
Design and Implementation of Efficient Search Methodology for Content-Based R...Design and Implementation of Efficient Search Methodology for Content-Based R...
Design and Implementation of Efficient Search Methodology for Content-Based R...IDES Editor
 
Computer application in content areas
Computer application in content areasComputer application in content areas
Computer application in content areaszulfiqaralibehan
 
Utilization of Information Technology Among Higher Educational Institutions R...
Utilization of Information Technology Among Higher Educational Institutions R...Utilization of Information Technology Among Higher Educational Institutions R...
Utilization of Information Technology Among Higher Educational Institutions R...Jo Balucanag - Bitonio
 
Paper id 2320144
Paper id 2320144Paper id 2320144
Paper id 2320144IJRAT
 

Tendances (18)

A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...
A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...
A PARADIGM FOR THE APPLICATION OF CLOUD COMPUTING IN MOBILE INTELLIGENT TUTOR...
 
IoT-based students interaction framework using attention-scoring assessment i...
IoT-based students interaction framework using attention-scoring assessment i...IoT-based students interaction framework using attention-scoring assessment i...
IoT-based students interaction framework using attention-scoring assessment i...
 
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...
Multimedia based IoT-centric smart framework for eLearning paradigm Muhammad ...
 
Cv hasnain acad12
Cv hasnain acad12Cv hasnain acad12
Cv hasnain acad12
 
Development of a mobile learning application to support e learning and analys...
Development of a mobile learning application to support e learning and analys...Development of a mobile learning application to support e learning and analys...
Development of a mobile learning application to support e learning and analys...
 
Supervised learning techniques for virtual military training
Supervised learning techniques for virtual military trainingSupervised learning techniques for virtual military training
Supervised learning techniques for virtual military training
 
E Learning In Modern Education System .
E Learning In Modern Education System .E Learning In Modern Education System .
E Learning In Modern Education System .
 
Mobile learning architecture using fog computing and adaptive data streaming
Mobile learning architecture using fog computing and adaptive data streamingMobile learning architecture using fog computing and adaptive data streaming
Mobile learning architecture using fog computing and adaptive data streaming
 
RISKS AND REMEDIES IN E-LEARNING SYSTEM
RISKS AND REMEDIES IN E-LEARNING SYSTEMRISKS AND REMEDIES IN E-LEARNING SYSTEM
RISKS AND REMEDIES IN E-LEARNING SYSTEM
 
Design strategies for mobile language learning effectiveness using hybrid mcd...
Design strategies for mobile language learning effectiveness using hybrid mcd...Design strategies for mobile language learning effectiveness using hybrid mcd...
Design strategies for mobile language learning effectiveness using hybrid mcd...
 
Review of monitoring tools for e learning platforms
Review of monitoring tools for e learning platformsReview of monitoring tools for e learning platforms
Review of monitoring tools for e learning platforms
 
01357477
0135747701357477
01357477
 
Design and Implementation of Efficient Search Methodology for Content-Based R...
Design and Implementation of Efficient Search Methodology for Content-Based R...Design and Implementation of Efficient Search Methodology for Content-Based R...
Design and Implementation of Efficient Search Methodology for Content-Based R...
 
Applying Semantic Web Technologies to Services of e-learning System
Applying Semantic Web Technologies to Services of e-learning SystemApplying Semantic Web Technologies to Services of e-learning System
Applying Semantic Web Technologies to Services of e-learning System
 
Computer application in content areas
Computer application in content areasComputer application in content areas
Computer application in content areas
 
Utilization of Information Technology Among Higher Educational Institutions R...
Utilization of Information Technology Among Higher Educational Institutions R...Utilization of Information Technology Among Higher Educational Institutions R...
Utilization of Information Technology Among Higher Educational Institutions R...
 
EdTech
EdTechEdTech
EdTech
 
Paper id 2320144
Paper id 2320144Paper id 2320144
Paper id 2320144
 

En vedette (14)

Cisco Mobile Innovations 2013
Cisco Mobile Innovations 2013Cisco Mobile Innovations 2013
Cisco Mobile Innovations 2013
 
Big datafordevelopment un-globalpulsejune2012
Big datafordevelopment un-globalpulsejune2012Big datafordevelopment un-globalpulsejune2012
Big datafordevelopment un-globalpulsejune2012
 
Why Care About UX
Why Care About UXWhy Care About UX
Why Care About UX
 
Sub1547
Sub1547Sub1547
Sub1547
 
Sub1560
Sub1560Sub1560
Sub1560
 
Making of Better Doctors
Making of Better Doctors Making of Better Doctors
Making of Better Doctors
 
Dream Demolisher
Dream DemolisherDream Demolisher
Dream Demolisher
 
Xperience Life A Start Up
Xperience Life A Start UpXperience Life A Start Up
Xperience Life A Start Up
 
New AgriCenter Program Overview Farm Chemicals Mag
New AgriCenter Program Overview Farm Chemicals MagNew AgriCenter Program Overview Farm Chemicals Mag
New AgriCenter Program Overview Farm Chemicals Mag
 
Ijsea04031015
Ijsea04031015Ijsea04031015
Ijsea04031015
 
Industry Ambassadors To China, International Facilities Management, Washingt...
 Industry Ambassadors To China, International Facilities Management, Washingt... Industry Ambassadors To China, International Facilities Management, Washingt...
Industry Ambassadors To China, International Facilities Management, Washingt...
 
Audio 4
Audio 4Audio 4
Audio 4
 
Cool Tools for Recruiting HRevolution 2011
Cool Tools for Recruiting HRevolution 2011Cool Tools for Recruiting HRevolution 2011
Cool Tools for Recruiting HRevolution 2011
 
Cisco 1760
Cisco 1760Cisco 1760
Cisco 1760
 

Similaire à Ijcatr04041023

M-Learning
M-LearningM-Learning
M-Learningbutest
 
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITS
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITSAN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITS
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITSijistjournal
 
An analysis of Mobile Learning Implementation in Shinas College of Technology...
An analysis of Mobile Learning Implementation in Shinas College of Technology...An analysis of Mobile Learning Implementation in Shinas College of Technology...
An analysis of Mobile Learning Implementation in Shinas College of Technology...ijcnes
 
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICESQOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICESijfcstjournal
 
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdf
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdfThe_Effectiveness_of_Using_Mobile_Learning_Techniq.pdf
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdfGlyHabines
 
Big data integration for transition from e-learning to smart learning framework
Big data integration for transition from e-learning to smart learning framework Big data integration for transition from e-learning to smart learning framework
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
 
Collaborative learning assistant for android
Collaborative learning assistant for androidCollaborative learning assistant for android
Collaborative learning assistant for androidJPINFOTECH JAYAPRAKASH
 
Solving The Problem of Adaptive E-Learning By Using Social Networks
Solving The Problem of Adaptive E-Learning By Using Social NetworksSolving The Problem of Adaptive E-Learning By Using Social Networks
Solving The Problem of Adaptive E-Learning By Using Social NetworksEswar Publications
 
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...Carrie Cox
 
A Literature Survey on Mobile-Learning Management Systems
A Literature Survey on Mobile-Learning Management SystemsA Literature Survey on Mobile-Learning Management Systems
A Literature Survey on Mobile-Learning Management SystemsAM Publications
 
A Survey on E-Learning System with Data Mining
A Survey on E-Learning System with Data MiningA Survey on E-Learning System with Data Mining
A Survey on E-Learning System with Data MiningIIRindia
 
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATION
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATIONEFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATION
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATIONIAEME Publication
 
Recent Trends in E-Learning and Technologies
Recent Trends in E-Learning and Technologies Recent Trends in E-Learning and Technologies
Recent Trends in E-Learning and Technologies IIJSRJournal
 
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...IJAEMSJORNAL
 
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...eraser Juan José Calderón
 

Similaire à Ijcatr04041023 (20)

M-Learning
M-LearningM-Learning
M-Learning
 
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITS
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITSAN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITS
AN OVERVIEW OF CLOUD COMPUTING FOR E-LEARNING WITH ITS KEY BENEFITS
 
An analysis of Mobile Learning Implementation in Shinas College of Technology...
An analysis of Mobile Learning Implementation in Shinas College of Technology...An analysis of Mobile Learning Implementation in Shinas College of Technology...
An analysis of Mobile Learning Implementation in Shinas College of Technology...
 
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICESQOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES
 
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdf
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdfThe_Effectiveness_of_Using_Mobile_Learning_Techniq.pdf
The_Effectiveness_of_Using_Mobile_Learning_Techniq.pdf
 
Big data integration for transition from e-learning to smart learning framework
Big data integration for transition from e-learning to smart learning framework Big data integration for transition from e-learning to smart learning framework
Big data integration for transition from e-learning to smart learning framework
 
Collaborative learning assistant for android
Collaborative learning assistant for androidCollaborative learning assistant for android
Collaborative learning assistant for android
 
Solving The Problem of Adaptive E-Learning By Using Social Networks
Solving The Problem of Adaptive E-Learning By Using Social NetworksSolving The Problem of Adaptive E-Learning By Using Social Networks
Solving The Problem of Adaptive E-Learning By Using Social Networks
 
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...
Application Of Cloud Computing In Teaching And Learning In A Post Graduate Pr...
 
Online assignment
Online assignmentOnline assignment
Online assignment
 
Online Assignment
Online AssignmentOnline Assignment
Online Assignment
 
A Literature Survey on Mobile-Learning Management Systems
A Literature Survey on Mobile-Learning Management SystemsA Literature Survey on Mobile-Learning Management Systems
A Literature Survey on Mobile-Learning Management Systems
 
4213ijsea05
4213ijsea054213ijsea05
4213ijsea05
 
A Survey on E-Learning System with Data Mining
A Survey on E-Learning System with Data MiningA Survey on E-Learning System with Data Mining
A Survey on E-Learning System with Data Mining
 
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATION
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATIONEFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATION
EFFECT OF CLOUD BASED MOBILE LEARNING ON ENGINEERING EDUCATION
 
Recent Trends in E-Learning and Technologies
Recent Trends in E-Learning and Technologies Recent Trends in E-Learning and Technologies
Recent Trends in E-Learning and Technologies
 
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...
 
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...
E-Learning: Challenges and Research Opportunities Using Machine Learning & Da...
 
Ijsrdv6 i120151
Ijsrdv6 i120151Ijsrdv6 i120151
Ijsrdv6 i120151
 
Qualitative research
Qualitative researchQualitative research
Qualitative research
 

Plus de Editor IJCATR

Text Mining in Digital Libraries using OKAPI BM25 Model
 Text Mining in Digital Libraries using OKAPI BM25 Model Text Mining in Digital Libraries using OKAPI BM25 Model
Text Mining in Digital Libraries using OKAPI BM25 ModelEditor IJCATR
 
Green Computing, eco trends, climate change, e-waste and eco-friendly
Green Computing, eco trends, climate change, e-waste and eco-friendlyGreen Computing, eco trends, climate change, e-waste and eco-friendly
Green Computing, eco trends, climate change, e-waste and eco-friendlyEditor IJCATR
 
Policies for Green Computing and E-Waste in Nigeria
 Policies for Green Computing and E-Waste in Nigeria Policies for Green Computing and E-Waste in Nigeria
Policies for Green Computing and E-Waste in NigeriaEditor IJCATR
 
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Editor IJCATR
 
Optimum Location of DG Units Considering Operation Conditions
Optimum Location of DG Units Considering Operation ConditionsOptimum Location of DG Units Considering Operation Conditions
Optimum Location of DG Units Considering Operation ConditionsEditor IJCATR
 
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Editor IJCATR
 
Web Scraping for Estimating new Record from Source Site
Web Scraping for Estimating new Record from Source SiteWeb Scraping for Estimating new Record from Source Site
Web Scraping for Estimating new Record from Source SiteEditor IJCATR
 
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...
 Evaluating Semantic Similarity between Biomedical Concepts/Classes through S... Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...Editor IJCATR
 
Semantic Similarity Measures between Terms in the Biomedical Domain within f...
 Semantic Similarity Measures between Terms in the Biomedical Domain within f... Semantic Similarity Measures between Terms in the Biomedical Domain within f...
Semantic Similarity Measures between Terms in the Biomedical Domain within f...Editor IJCATR
 
A Strategy for Improving the Performance of Small Files in Openstack Swift
 A Strategy for Improving the Performance of Small Files in Openstack Swift  A Strategy for Improving the Performance of Small Files in Openstack Swift
A Strategy for Improving the Performance of Small Files in Openstack Swift Editor IJCATR
 
Integrated System for Vehicle Clearance and Registration
Integrated System for Vehicle Clearance and RegistrationIntegrated System for Vehicle Clearance and Registration
Integrated System for Vehicle Clearance and RegistrationEditor IJCATR
 
Assessment of the Efficiency of Customer Order Management System: A Case Stu...
 Assessment of the Efficiency of Customer Order Management System: A Case Stu... Assessment of the Efficiency of Customer Order Management System: A Case Stu...
Assessment of the Efficiency of Customer Order Management System: A Case Stu...Editor IJCATR
 
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Editor IJCATR
 
Security in Software Defined Networks (SDN): Challenges and Research Opportun...
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Security in Software Defined Networks (SDN): Challenges and Research Opportun...
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Editor IJCATR
 
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Measure the Similarity of Complaint Document Using Cosine Similarity Based on...
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Editor IJCATR
 
Hangul Recognition Using Support Vector Machine
Hangul Recognition Using Support Vector MachineHangul Recognition Using Support Vector Machine
Hangul Recognition Using Support Vector MachineEditor IJCATR
 
Application of 3D Printing in Education
Application of 3D Printing in EducationApplication of 3D Printing in Education
Application of 3D Printing in EducationEditor IJCATR
 
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Editor IJCATR
 
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Comparative analysis on Void Node Removal Routing algorithms for Underwater W...
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Editor IJCATR
 
Decay Property for Solutions to Plate Type Equations with Variable Coefficients
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsDecay Property for Solutions to Plate Type Equations with Variable Coefficients
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsEditor IJCATR
 

Plus de Editor IJCATR (20)

Text Mining in Digital Libraries using OKAPI BM25 Model
 Text Mining in Digital Libraries using OKAPI BM25 Model Text Mining in Digital Libraries using OKAPI BM25 Model
Text Mining in Digital Libraries using OKAPI BM25 Model
 
Green Computing, eco trends, climate change, e-waste and eco-friendly
Green Computing, eco trends, climate change, e-waste and eco-friendlyGreen Computing, eco trends, climate change, e-waste and eco-friendly
Green Computing, eco trends, climate change, e-waste and eco-friendly
 
Policies for Green Computing and E-Waste in Nigeria
 Policies for Green Computing and E-Waste in Nigeria Policies for Green Computing and E-Waste in Nigeria
Policies for Green Computing and E-Waste in Nigeria
 
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...
 
Optimum Location of DG Units Considering Operation Conditions
Optimum Location of DG Units Considering Operation ConditionsOptimum Location of DG Units Considering Operation Conditions
Optimum Location of DG Units Considering Operation Conditions
 
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...
 
Web Scraping for Estimating new Record from Source Site
Web Scraping for Estimating new Record from Source SiteWeb Scraping for Estimating new Record from Source Site
Web Scraping for Estimating new Record from Source Site
 
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...
 Evaluating Semantic Similarity between Biomedical Concepts/Classes through S... Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...
 
Semantic Similarity Measures between Terms in the Biomedical Domain within f...
 Semantic Similarity Measures between Terms in the Biomedical Domain within f... Semantic Similarity Measures between Terms in the Biomedical Domain within f...
Semantic Similarity Measures between Terms in the Biomedical Domain within f...
 
A Strategy for Improving the Performance of Small Files in Openstack Swift
 A Strategy for Improving the Performance of Small Files in Openstack Swift  A Strategy for Improving the Performance of Small Files in Openstack Swift
A Strategy for Improving the Performance of Small Files in Openstack Swift
 
Integrated System for Vehicle Clearance and Registration
Integrated System for Vehicle Clearance and RegistrationIntegrated System for Vehicle Clearance and Registration
Integrated System for Vehicle Clearance and Registration
 
Assessment of the Efficiency of Customer Order Management System: A Case Stu...
 Assessment of the Efficiency of Customer Order Management System: A Case Stu... Assessment of the Efficiency of Customer Order Management System: A Case Stu...
Assessment of the Efficiency of Customer Order Management System: A Case Stu...
 
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*
 
Security in Software Defined Networks (SDN): Challenges and Research Opportun...
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Security in Software Defined Networks (SDN): Challenges and Research Opportun...
Security in Software Defined Networks (SDN): Challenges and Research Opportun...
 
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Measure the Similarity of Complaint Document Using Cosine Similarity Based on...
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...
 
Hangul Recognition Using Support Vector Machine
Hangul Recognition Using Support Vector MachineHangul Recognition Using Support Vector Machine
Hangul Recognition Using Support Vector Machine
 
Application of 3D Printing in Education
Application of 3D Printing in EducationApplication of 3D Printing in Education
Application of 3D Printing in Education
 
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...
 
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Comparative analysis on Void Node Removal Routing algorithms for Underwater W...
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...
 
Decay Property for Solutions to Plate Type Equations with Variable Coefficients
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsDecay Property for Solutions to Plate Type Equations with Variable Coefficients
Decay Property for Solutions to Plate Type Equations with Variable Coefficients
 

Dernier

Solving The Right Triangles PowerPoint 2.ppt
Solving The Right Triangles PowerPoint 2.pptSolving The Right Triangles PowerPoint 2.ppt
Solving The Right Triangles PowerPoint 2.pptJasonTagapanGulla
 
8251 universal synchronous asynchronous receiver transmitter
8251 universal synchronous asynchronous receiver transmitter8251 universal synchronous asynchronous receiver transmitter
8251 universal synchronous asynchronous receiver transmitterShivangiSharma879191
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfme23b1001
 
Class 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm SystemClass 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm Systemirfanmechengr
 
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)Dr SOUNDIRARAJ N
 
An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...Chandu841456
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxk795866
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionMebane Rash
 
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvWork Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvLewisJB
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfAsst.prof M.Gokilavani
 
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfCCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfAsst.prof M.Gokilavani
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catcherssdickerson1
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEroselinkalist12
 
Transport layer issues and challenges - Guide
Transport layer issues and challenges - GuideTransport layer issues and challenges - Guide
Transport layer issues and challenges - GuideGOPINATHS437943
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...VICTOR MAESTRE RAMIREZ
 
computer application and construction management
computer application and construction managementcomputer application and construction management
computer application and construction managementMariconPadriquez1
 
Past, Present and Future of Generative AI
Past, Present and Future of Generative AIPast, Present and Future of Generative AI
Past, Present and Future of Generative AIabhishek36461
 
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsync
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsyncWhy does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsync
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsyncssuser2ae721
 

Dernier (20)

Solving The Right Triangles PowerPoint 2.ppt
Solving The Right Triangles PowerPoint 2.pptSolving The Right Triangles PowerPoint 2.ppt
Solving The Right Triangles PowerPoint 2.ppt
 
8251 universal synchronous asynchronous receiver transmitter
8251 universal synchronous asynchronous receiver transmitter8251 universal synchronous asynchronous receiver transmitter
8251 universal synchronous asynchronous receiver transmitter
 
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdf
 
Class 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm SystemClass 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm System
 
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
 
An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of Action
 
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvWork Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvv
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
 
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfCCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
 
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
 
Transport layer issues and challenges - Guide
Transport layer issues and challenges - GuideTransport layer issues and challenges - Guide
Transport layer issues and challenges - Guide
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...
 
computer application and construction management
computer application and construction managementcomputer application and construction management
computer application and construction management
 
Past, Present and Future of Generative AI
Past, Present and Future of Generative AIPast, Present and Future of Generative AI
Past, Present and Future of Generative AI
 
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsync
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsyncWhy does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsync
Why does (not) Kafka need fsync: Eliminating tail latency spikes caused by fsync
 

Ijcatr04041023

  • 1. International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656 www.ijcat.com 331 Ontology based design for M- learning system M.Thangaraj Department of Computer Science Madurai Kamaraj University, Madurai Tamil Nadu, India S.Vanathi Department of Computer Science Govt. Arts College, Melur Tamil Nadu, India Abstract: Information and communication Technology is a gateway through which large population of students has been addressed. Mobile learning technology the latest arrival highly changing the way the students learn, interact, access up to data information. It mainly satisfies the current and future generation which needs information at the earliest rather than later few touches. The World Wide Web acts as an interface in E- learning as well as in mobile learning (M-learning) environments. It supports and facilitates the delivery of teaching and learning materials. M - l e a r n i n g provides quality educational content with the help of semantic web technologies like Ontology. This study presents Mobile Learning framework for making efficient learning with a case study on cyber security. Keywords: Information and Communication Technology; Ontology; M-learning; E-learning; cyber security. 1. INTRODUCTION Education is a process which enables a person’s holistic development of personality through knowledge acquisition. Learning environment has the influence on knowledge improvement or knowledge enhancement [8]. Teaching learning processes delivered through mobile devices are based on wired and wireless communication technologies. Mobile learning creates a new learning environment using handheld devices by interfacing world wide web and the learner [10]. Mobile learning, recent form of distant learning is an extension of E-learning application is shown in Figure 1, which also has the use of audio, visual, cognitive, cooperative and interactive contents delivered via smart digital electronic devices in an attempt to create a direct, dynamic, ongoing learning environment. Such form of learning enables the individual learner to move freely in the learning material, at the same time can access to knowledge sources wherever and whenever the learner desires. In the world of education and training there exist many definitions for mobilE-learning. Among them ADL defines mobile learning or “M- Learning” as the use of handheld computing devices to provide access to learning content and information resources. Thus M-learning overcomes the time and distance barriers of traditional learning and cost barrier of E-learning. Figure 1. Various Dimensions of latest learning/ Evolution of distant learning Ontology is a conceptual system a semantic account, which expresses a meta-level specification [5], and it owns the features like expressiveness, extensibility, interoperability, sharing and re-usability. The various directions of research in ontology based M-learning are discussed as related work in section 2. Section 3, describes the Modified Intelligent Recommender System for Effective E-learning Environment Architecture, which encompasses various activities of M-learning in detail. Section 4 explains evaluation and experimental results on the efficiency of the system. Section 5, concludes and guides future work.
  • 2. International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656 www.ijcat.com 332 2. RELATED WORK The changes in the educational scenario are wider and fast. Mobile learning is a personalized as well as interactive mode of learning. The implementation of mobile learning using android OS is illustrated in [3]. The meaning of mobile learning, its key issues, solutions and the knowledge transformation due to the development of mobile learning are clearly explained in [6]. E-learning fulfils the user context with the help of semantic web technology like ontology. Ontology based applications improve the understanding of concepts, critical thinking and creative thinking using the semantic learning environment is discussed [9]. IRS-EEE presents the effective E-learning environment using concept oriented, ontological content management. The essentials of semantic web are emphasized in [11]. The cost and portability while using laptops for E- learning are high. This paper tries to overcome the cost and time barriers. 3. MODIFIED IRS-EEE ARCHITECTURE The Modified IRS-EEE Architecture (Figure 2) is based on the semantic structure, promises a powerful approach to satisfy the M-learning requirements. This Semantic architecture of M-learning has four layers such as User Layer (UL), Service Layer (SL), Content Management Layer (CML) and Database Layer (DBL). The semantic based IRS-EEE architecture [11] is structured to satisfy E-learning requirements, of the user which is tailored to deliver the content through mobile devices result into the modified IRS-Architecture is an extension of the IRS-EEE. This architecture has four layers such as User Layer (UL), Service Layer (SL), Content Management Layer (CML) and Database Layer (DBL). 1. User layer The two types of users of this layer are Provider and student .This system revolves around the users. The user profile is stored and updated whenever needed. A. Data Provider The role of provider in this system is to monitor user’s profile, presenting the content, adding and updating the contents, questions, assessment of outcome and learner’s performance and tracking the performance of the entire system. Provider has the facility to convert the web pages into compatible mobile device content. B. Learner The learner is the next stage user. The learners are the Persons/Students using advanced cellular phones or smart phones having the link to internet. Using such phones is regarded as more trendy, fashionable and prestigious among the young education by means of mobile devices is gaining efficiency in its design methods. Figure 2. Modified IRS-EEE Architecture C. Mobile devices Typical examples of mobile devices include smart phones, tablets, laptops, and personal media players. We limit our implementation in smart phones and tablets only because they are highly portable (than laptop etc.,) and easy to handle. These mobiles devices have the property to produce unique educational affordances: portability, social interactivity, context sensitivity, connectivity and individuality [4].
  • 3. International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656 www.ijcat.com 333 2. Service Layer A. Profile management The function of the Profile management module is to maintain all the related information about the learners, and there interactions etc. it also, updates and keeps track of the consistency of data by proper updating process. B. Mobile course delivery Presently mobile learning is regarded as a core pedagogical activity in higher institution of learning and the instructional technology insist the content transmitted through mobile technology is mostly social, to a lesser extent and economic [6]. The content of a particular concept or sub-concept is observed and learned by the learner. An effective learning environment is provided with the help of ontology and sub-ontology based concepts. The learners should concentrate an assessment before beginning to learn a concept known to be pretest. C. Evaluation and Feedback Provider Once the student has finished the course, he/she would take up a post-test. During the test multiple-choice questions related to the topic are presented in slides and answers are gathered using SMS facilities of the mobile phones of the students that are illustrated in Figure 3.The results are compared with pretest to assess the improvement of the user [1] and the skills gained by the user. Test results are Tracked and stored to assess their improvement, and it is used as a measure of input of M-learning. It provides the facility that the learner can give feedback about the presentation of the content and about the ease of access. Figure 3. Evaluation and feedback process The design of CML presents and preserves the hierarchical course structure with their semantic relationship between the concepts. 3. Database layer The database components of user layer contain all the required information about learners. This layer maintains the databases with users’ profile, ontology based content, test score and tracking status of the every learner. 4. EVALUATION AND EXPERIMENTAL RESULTS The mobile platforms targeted for the study included android, Smartphone platforms provide a mobile web browser with support for HTML5. These direct-touch Smartphone platforms are specifically targeted as they provide a superior user experience compared to non-touch mobile devices. In addition, we need to provide the best possible user experience on mobile to see its confirmation. The course on CYBER SECURITY is selected as sample course for M-learning to provide awareness of Information Security and give an exposure as a spectrum of security activities, methods, methodologies, and procedure. Our M-learning course includes the topics like security principles, threats, attacks, security models, security policies, overview of authentication, encryption, and certification, security detection, business risk analysis, protection of information assets. This course is developed and pilot-tested. For this study, Bachelor of Science in computer science students are chosen from different colleges which are geographically distributed from urban, semi-urban and rural colleges of Madurai Kamaraj University and they have participated in this study with the condition that 1. Learner should have knowledge of computers. 2. Learner should have smart phones or tablet Pc linked with internet and sizable storage. Once the learner enters into modified IRS-EEE he/she has to sign-in and enter his profile. The admin has approved the learner .The learner selects the course. Before the course content is presented there is pretest. The selected course is divided into blocks of related concepts. After careful learning of the concepts to estimate their capture before the course MCQ (Multiple Choice Questions) is given. MCQ assessment is one of the best and often used Formative Assessment tool [7] post-test is conducted to measure the knowledge improvement in the domain and the efficiency of the system. The efficiency of the system is measured using the parameters like ease of interactivity, independency, instant access, storage and maintenance which are measured against Learners satisfaction in percentage. The results of the study shows the acceptance of mobile learning that is following shown in Figure 4.
  • 4. International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656 www.ijcat.com 334 Figure 4. Comparisons of E-learning and M-learning Compared to traditional learning environment E-learning changes the environment from classroom to computers that to be transferred to just a touch in M-learning. The new generation prefers much than E-learning for its features like cost effectiveness. Personalization but the system maintenance is less in E-learning than M-learning which is shown in Figure 5. Figure 5. Student Access mobile vs laptops 5. CONCLUSION As computer and Internet become essential tools for education; technology becomes more mobile, affordable, effective and easy to use. This offers many opportunities to widen participation and access to ICT, particularly the Internet (infoDev, 2010). Mobile devices such as phones and PDAs are much more affordable than desktop computers, and therefore represent a less expensive access to the internet (even if the cost of the connection may be higher) (InfoDev, 2010). The introduction of the Tablet PC can now access mobile internet with much functionality than desktop computers. Mobile devices can be used anywhere, anytime, including at home, on the train, in hotels-this is invaluable for work-based training. It is much easier to accommodate several mobile devices in a classroom than several desktop computers. Future mobile technologies may be able to present textbooks, create database, aid in library utilization, and foster contextual learning. Mobile learning has become an integral part of education in many parts of the world, and new research advances in design and implementation will ensure its increasing importance. 6. REFERENCE [1] Fernando A. Mikic Fonte, Juan C. Burguillo- Rial,Martin Llamas-Nistal, and David Fernandez- Hermida “A BDI based Intelligent Tutoring Module for the E-learning Platform INES” 40th ASEE/IEEE frontiers in education Conference, October 27-30,2010, Washington, DC. [2] Giselher H. J. R, “An Educational Taxonomy for Learning Objects”, Third IEEE International Conference on Advanced Learning Technologies (ICALT’03), pp250, 2003. [3] Hafizul Fahri Hanafi, Khairulanuar Samsudin,’’ Mobile learning Environment System(MLES)”, The case of Android based Learning Application on Undergraduates’ Learning (IJACSA) International Journal of Advanced Computer Science and Applications Vol.3, No 3, 2012. [4] Klopfer, E., and Squire, K. (2008),’’Environmental Detectives: the development of an augmented reality platform for environmental simulations’’. Educational Technology Research and Development. [5] Ljiljana Stojanovic, Steffen Stab, Rudi Studer,”E-learning based on the Semantic Web”, The Electronic JournalofE- learningvolume4.issue 2, pp111=118. [6] Mohamed Osman M. El-Hussein and Johannes C. Cronje, “Defining Mobile Learning in the Higher Education Landscape, Educational Technology &Society’’, 13 (3), 12–21.(2010). [7] Noorminshah lahad, Georgios A. Dafoulas, Maya Milankovic-Atkinson, Alan Murphy, ”E-learning in Developing Countries in Suggesting a Methodology for Enabling Computer Aided Assessment” IEEE International Conference on Advanced Learning Technology(ICALT) 2004. [8] Shaileshkumar K. Patel et al, “Semantic Web Technology and Ontology designing for E-learning Environments’’, International Journal of Computer Science and Information Technologies, (IJCSIT), Vol. 6 (1) , 2015, 48-51. [9] Sohaib Ahmed and David Parsons, “An ontology- Driven Mobile Web Application for Science Enquiry Based Learning”, The 7th International
  • 5. International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 331 - 335, 2015, ISSN:- 2319–8656 www.ijcat.com 335 Conference on Information Technology and Applications (ICITA 2011). [10] Tayseer Andrawes Saleem, “Mobile learning technology: A new step in E-learning, Journal of Theoretical and Applied Information Technology”, 31st December 20011, Vol 34 No.2, ISSN: 1992- 8645. [11] Thangaraj M, Vanathi S, “ An Intelligent Recommender system for Effective E learning Environment, International Journal of Engineering Research and Technology (IJERT) ISSN: 2278- 0181,Vol 3 Issue 11, November-2014.