A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

In this paper, the authors present their proposal for adaptation of educational contents of learning objects to a particular mobile device and a specific learner. Content adaptation in mobile learning objects implies user adaptation and device adaptation, and requires additional metadata categories in comparison with SCORM 2004. This learning object content model, ALMA (A Learning content Model Adaptation), inherits from the SCORM standard a subset of metadata categories, and extends it with three top level metadata categories for content adaptation, i.e., Knowledge, Use, and Mobile Device Requirements (Castillo & Ayala, 2008). For user adaptation, the authors developed NORIKO (NOn-monotonic Reasoning for Intelligent Knowledge awareness and recommendations On the move), a belief system based on DLV, a programming system based on Answer Set Programming paradigm. For device adaptation the authors designed CARIME (Content Adapter of Resources In Mobile learning Environments), which uses transcoding and transrating to adapt media content to suit the device characteristics.

Download Full-text

Content Adaptation in Mobile Learning Environments

International Journal of Multimedia Data Engineering and Management ◽

10.4018/jmdem.2010100101 ◽

2010 ◽

Vol 1 (4) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Sergio Castillo ◽

Gerardo Ayala

Keyword(s):

Learning Environments ◽

Mobile Learning ◽

Mobile Device ◽

Belief System ◽

Learning Objects ◽

Content Adaptation ◽

Programming System ◽

Knowledge Use ◽

User Adaptation ◽

Content Model

In this paper, the authors present their proposal for adaptation of educational contents of learning objects to a particular mobile device and a specific learner. Content adaptation in mobile learning objects implies user adaptation and device adaptation, and requires additional metadata categories in comparison with SCORM 2004. This learning object content model, ALMA (A Learning content Model Adaptation), inherits from the SCORM standard a subset of metadata categories, and extends it with three top level metadata categories for content adaptation, i.e., Knowledge, Use, and Mobile Device Requirements (Castillo & Ayala, 2008). For user adaptation, the authors developed NORIKO (NOn-monotonic Reasoning for Intelligent Knowledge awareness and recommendations On the move), a belief system based on DLV, a programming system based on Answer Set Programming paradigm. For device adaptation the authors designed CARIME (Content Adapter of Resources In Mobile learning Environments), which uses transcoding and transrating to adapt media content to suit the device characteristics.

Download Full-text

Design and Development of Personalized Learning Environments

Adaptation, Resistance and Access to Instructional Technologies ◽

10.4018/978-1-61692-854-4.ch007 ◽

2011 ◽

pp. 108-117

Author(s):

Raja Maznah Raja Hussain

Keyword(s):

Learning Environments ◽

Teaching And Learning ◽

Personalized Learning ◽

Action Research Project ◽

Pedagogical Approach ◽

Technology Students ◽

Learning Content ◽

Online Tools ◽

The Right

This chapter describes a pedagogical approach to engage students in online learning environments, using XNAMEX Becta’s model for personalized learning and student engagement (PLEaSE). PLEaSE maximizes learning out comes by supporting students at times and in places that are appropriate to their needs and in ways that suit their personal dispositions In this study, students are encouraged to explore, develop, reflect and construct their own knowledge and create their own learning content, while the instructor plays the role of coach and facilitator. This study is part of an ongoing action research project on the Scholarship of Teaching and Learning (SoTL) in Higher Education, whose purpose is to design and develop the Pedagogy of Engagement Integrating Technology (PoEIT) model. PoEIT engages learners in the use of online tools such as forums and blogs while developing their soft skills using Moodle platform. This study shows that with the right integration of pedagogy and technology students can be transformed to become independent learners.

Download Full-text

Activity in a Mobile Learning Environment

Mobile Technologies and Handheld Devices for Ubiquitous Learning ◽

10.4018/978-1-61692-849-0.ch005 ◽

2011 ◽

pp. 58-78

Author(s):

Ray M. Kekwaletswe

Keyword(s):

Learning Environments ◽

Mobile Learning ◽

Social Presence ◽

Learning Task ◽

Personalized Learning ◽

Mobile Technologies ◽

Learning Support ◽

Learning Contexts ◽

Use Context ◽

Mobile Learning Environment

The practical contribution of the chapter is the understanding of activity in mobile learning environments and how learners use awareness to model their actions for the provision of personalized learning support. The chapter is about the advancement of the human-centric approach to personalized learning through enhanced learner-to-learner interaction – where context and social presence awareness is of vital significance to how learners decide and act on a learning task. It is an expedition towards understanding the phenomenon of mobile learning, where personalized learning and support is a result of social awareness activities of learners as they traverse varied learning contexts. Mobile learning, in this chapter, is signified by mobility of learners regardless of mobile technologies. Activity Theory, which draws attention to mediated activity within a social context, is used to explore how mobile learners use context and social presence awareness to facilitate their ubiquitous social interactions.

Download Full-text

Intelligent Mobile Learning Systems for Learners with Style

Tools for Mobile Multimedia Programming and Development - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-4666-4054-2.ch008 ◽

2013 ◽

pp. 131-149 ◽

Cited By ~ 2

Author(s):

Tracey J. Mehigan ◽

Ian Pitt

Keyword(s):

User Interface ◽

Learning Environments ◽

Learning Style ◽

Mobile Learning ◽

Learning Systems ◽

Interface Model ◽

User Models ◽

Learning Content ◽

Research Findings

This chapter discusses the development of intelligent personalized user models for mLearning. Previous research findings are reviewed, indicating that it is possible to identify aspects of a user’s learning style though biometric technologies. A user interface model is presented, designed to intelligently detect the learning-style of individual’s using a mobile learning environments and adapt learning content accordingly. The application of the model to a mLearning system is described.

Download Full-text