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    Evaluating the performance of adaptive learning objects selection and sequencing in adaptive educational hypermedia systems

    Access Status
    Fulltext not available
    Authors
    Karampiperis, P.
    Sampson, Demetrios
    Date
    2009
    Type
    Conference Paper
    
    Metadata
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    Citation
    Karampiperis, P. and Sampson, D. 2009. Evaluating the performance of adaptive learning objects selection and sequencing in adaptive educational hypermedia systems, pp. 316-318.
    Source Title
    Proceedings - 2009 9th IEEE International Conference on Advanced Learning Technologies, ICALT 2009
    DOI
    10.1109/ICALT.2009.134
    ISBN
    9780769537115
    School
    School of Education
    URI
    http://hdl.handle.net/20.500.11937/8414
    Collection
    • Curtin Research Publications
    Abstract

    Adaptive learning objects selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning objects in AEHS, the definition of adaptation behavior, referred to as Adaptation Model, is required. Several efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers either guidance for the direct definition of adaptation rules, or semi-automatic mechanisms for making the design process less demanding via the implicit definition of such rules. The main drawback of the direct definition of adaptation rules is that there can be cases during the run-time execution of AEHS where no adaptation decision can be made, due to inconsistency, and/or insufficiency of the defined adaptation rule sets. The goal of the semi-automatic approaches is to generate a continuous decision function that estimates the desired AEHS response, overcoming the above mentioned problem. To achieve this, they use data from the implicit definition of sample adaptation rules and try to fit the response function on these data. Although such approaches bare the potential to provide efficient Adaptation Models, they still miss a commonly accepted framework for measuring their performance. In this paper, we present our performance evaluation methodology for validating the use of decision-based approaches for adaptive learning objects selection and sequencing in AEHS. © 2009 IEEE.

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