E-Learning Systems Content Adaptation Frameworks and Techniques

2009 ◽  
pp. 460-468
Author(s):  
Tiong T. Goh ◽  
Kinshuk ◽  
Kinshuk
Keyword(s):  
Mobile Devices ◽  
Web Applications ◽  
Learning Systems ◽  
Personal Digital Assistants ◽  
Content Adaptation ◽  
Web Content ◽  
E Learning ◽  
Increasing Demand ◽  
Service Agreement ◽  
Smart Mobile

Content adaptation is defined as the process of selection, generation, or modification of content which include text, images, audio, video, navigation, interaction, any object within a Web page, and associate service agreement (Forte, Claudino, de Souza, do Prado, & Santana, 2007) to suit user’s context (TellaSonera, 2004). With the proliferation of mobile devices such as personal digital assistants (PDA) and smart mobile phones which have the capability of accessing the Internet anytime and anywhere, there is an increasing demand for content adaptation to provide these devices with appropriate content that is aesthetically pleasant, easy to navigate, and achieve satisfying user experiences. This article first provides an overview of frameworks and techniques in Web content adaptation that are being developed to extend Web applications to non-desktop platforms. After describing general adaptation techniques, this article focuses particularly on the adaptation requirements for e-learning systems, especially when they are accessed through mobile devices.

Web Content Adaptation Frameworks and Techniques

2011 ◽  
pp. 1070-1077
Author(s):  
Tiong-Thye Goh ◽  
Kinshuk
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Web Application ◽  
Learning Systems ◽  
Personal Digital Assistants ◽  
Content Adaptation ◽  
Web Pages ◽  
Web Content ◽  
Application Access ◽  
The Web

Most Web pages are designed with desktop platform access in mind, but with the proliferation of mobile devices such as Personal Digital Assistants (PDAs) and mobile phones, accessing Web pages through a variety of devices without proper content adaptation can result in an aesthetically unpleasant, un-navigable and, in most cases, unsatisfying experience. This article provides an overview of approaches in Web content adaptation framework and techniques being developed to extend the Web application access to non-desktop platforms. After describing general adaptation techniques, the article focuses particularly on the adaptation requirements of learning systems, especially when they are accessed through mobile devices.

Mobile Learning

2011 ◽  
pp. 103-128
Author(s):  
Marija Zelic
Keyword(s):  
Mobile Learning ◽  
Relevant Literature ◽  
Learning Systems ◽  
Personal Digital Assistants ◽  
Desktop Computers ◽  
Efficiency And Effectiveness ◽  
E Learning ◽  
New Ideas ◽  
Access To Knowledge

Mobile learning, as the “portable and personal” fashion of e-learning, is intended to enhance the efficiency and effectiveness of learning in the context of handheld terminals. Most present-day learning systems run on desktop computers and are not designed for use on mobile devices such as mobile phones, smart phones, Personal Digital Assistants, etc. Mobile learning systems aim to improve the quality of learning by providing mobile learners with an easy, contextualized and ubiquitous access to knowledge. This chapter gives an overview of the current state of knowledge and research in the m-learning domain, describes issues and problems pertinent to mobile learning and offers our approach to solving these problems in the form of a mobile intelligent tutoring model we are currently developing. Given the present absence of relevant literature and referent material we think that this chapter provides developers with some new ideas.

Adaptive Device Context Based Mobile Learning Systems

2011 ◽  
Vol 9 (1) ◽  
pp. 44-56 ◽  
Author(s):  
Haitao Pu ◽  
Jinjiao Lin ◽  
Yanwei Song ◽  
Fasheng Liu
Keyword(s):  
Mobile Computing ◽  
Information Transmission ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Learning Systems ◽  
Learning System ◽  
Transmission Parameters ◽  
E Learning ◽  
Contextual Environment ◽  
Mobile Learning System

Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for mobile devices to adapt to e-learning is presented. To provide device-independence mobile learning services, a context-aware mobile learning approach is proposed. Firstly, the formal definitions of contexts and their influence on mobile learning services, including device contexts NCxt, matrix of information transmission parameters S, the degree of influence of the context NCxt on information transmission parameters Q, and adaptation coefficient E, are given. By using this approach, the mobile learning system is constructed. In an example using this approach, the authors detect the contextual environment of mobile computing and adapt the mobile learning services to the learners’ devices automatically.

scholarly journals Web content adaptation for mobile device: A fuzzy-based approach

2012 ◽  
pp. 102-122
Keyword(s):  
Content Analysis ◽  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Internet ◽  
Experimental Results ◽  
Content Adaptation ◽  
Web Content

While HTML will continue to be used to develop Web content, how to effectively and efficiently transform HTML-based content automatically into formats suitable for mobile devices remains a challenge. In this paper, we introduce a concept of coherence set and propose an algorithm to automatically identify and detect coherence sets based on quantified similarity between adjacent presentation groups. Experimental results demonstrate that our method enhances Web content analysis and adaptation on the mobile Internet.

scholarly journals Impacts of mobile technologies, systems and resources on language learning: A systematic review of selected journal publications from 2007-2016

2018 ◽  
pp. 375-388 ◽  
Keyword(s):  
Language Learning ◽  
Mobile Devices ◽  
Learning Strategies ◽  
Mobile Learning ◽  
Learning Systems ◽  
Mobile Technologies ◽  
Personal Digital Assistants ◽  
Tablet Pcs ◽  
Mobile Assisted Language Learning ◽  
Future Work

This study examined the mobile-assisted language learning studies published from 2007 to 2016 in selected journals from the aspects of adopted mobile devices, mobile learning systems/resources, and the benefits and challenges of utilizing mobile devices or learning systems/resources. The results revealed that the traditional mobile devices (e.g., Personal Digital Assistants, PDAs) and the current popular mobile devices (e.g., smartphones and tablet PCs) were frequently adopted for language learning in different time periods, while wearable devices have not been adopted by any language learning research so far. In addition, most of the studies used researcher-developed learning systems/resources, while the use of educational affordances of free applications or resources needs to be promoted. Furthermore, the abundant benefits of using mobile devices or mobile learning systems/resources for language acquisition were found in many studies, such as providing substantial chances for learning, and providing or building authentic environments for learners’ meaningful knowledge construction; on the other hand, the studies also reported several challenges (e.g., insufficient practice time and the lack of effective learning strategies) to be overcome in the future. Finally, several suggestions are provided for researchers or practitioners to conduct their future work.

Adaptive Device Context Based Mobile Learning Systems

2014 ◽  
pp. 43-54
Author(s):  
Haitao Pu ◽  
Jinjiao Lin ◽  
Yanwei Song ◽  
Fasheng Liu
Keyword(s):  
Mobile Computing ◽  
Information Transmission ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Traditional Education ◽  
Learning Systems ◽  
Transmission Parameters ◽  
E Learning ◽  
Contextual Environment ◽  
Mobile Learning System

Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for mobile devices to adapt to e-learning is presented. To provide device-independence mobile learning services, a context-aware mobile learning approach is proposed. Firstly, the formal definitions of contexts and their influence on mobile learning services, including device contexts NCxt, matrix of information transmission parameters S, the degree of influence of the context NCxt on information transmission parameters Q, and adaptation coefficient E, are given. By using this approach, the mobile learning system is constructed. In an example using this approach, the authors detect the contextual environment of mobile computing and adapt the mobile learning services to the learners’ devices automatically.

The Preliminary Investigation of the Factors that Influence the E-Learning Adoption in Higher Education Institutes

2010 ◽  
Vol 8 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Maen Al-hawari ◽  
Sanaa Al-halabi
Keyword(s):  
High Performance ◽  
Preliminary Investigation ◽  
Learning Systems ◽  
Educational Institutions ◽  
Traditional Learning ◽  
Web Content ◽  
Analysis Software ◽  
E Learning ◽  
And Performance ◽  
Learning Factors

Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the authors focus on the preliminary investigation of factors that influence e-learning adoption in Jordan. As a pioneer country for e-learning systems in the Middle East, an investigation has been completed for one of Jordan’s universities that has implemented e-learning. Factors are defined through the analysis of unstructured interviews with developers and users of the e-learning systems, and Leximancer content analysis software is used to analyze the interview’s content. Main factors include Internet, legislations, human factors, and Web content.

Sign in / Sign up

Export Citation Format

Share Document