Content Adaptation in Mobile Learning Environments

Content Adaptation in Mobile Learning Environments

Sergio Castillo (Universidad de las Américas Puebla, México) and Gerardo Ayala (Universidad de las Américas Puebla, México)
Copyright: © 2012 |Pages: 16
DOI: 10.4018/978-1-4666-1791-9.ch014
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Abstract

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.
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Armoleo (Architecture For Mobile Learning Objects)

ARMOLEO is our proposal for the design, development and use of learning objects aimed to be used in mobile learning environments. With ARMOLEO we have proposed three models for the design and use of LOs in mobile learning environments, based on their respective learning strategies and the required awareness support (Ayala & Castillo, 2008):

  • 1.

    Personalization model, based on Personalization Learning and supporting Knowledge Awareness.

  • 2.

    Interaction model, based on Situated Learning and supporting Context Awareness, and

  • 3.

    Collaboration model, based on Collaborative Learning and supporting Social and Knowledge Awareness.

The architecture (see Figure 1) is composed by the following components:

Figure 1.

ARMOLEO components

  • The deductive database of Learner models,

  • Database for Device’s Profiles,

  • MLOs repository, composed by ALMA Packages and Metadata database,

  • Database for Collaboration scripts,

  • Learner and Device identifier,

  • NORIKO, the learner’s models manager,

  • MLOs selector,

  • CARIME, the MLOs device adapter,

  • Collaboration Script selector, and

  • ALMA Packager.

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