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Title: Defining adaptive learning design templates for combining design and runtime adaptation in aLFanet
Authors: Boticario, Jesús
Santos, Olga
Barrera, Carmen
Gaudioso, Elena
Hernandez, Felix
Rodriguez, Antonio
Van Rosmalen, Peter
Koper, Rob
Keywords: adaptation
machine learning
Issue Date: 25-Jun-2004
Abstract: Adaptive features are expected to improve the effectiveness of the learning process in online learning. Nevertheless, most current adaptive sys-tems do not deal with combining design and runtime adaptations. To take ad-vantage of this combination a new adaptive iLMS based on standards, called aLFanet, is being developed. The system includes: (i) an authoring tool to de-velop courses IMS-LD compliant, (ii) an adaptive engine based on a multi-agent architecture which is intended to cope with several adaptive tasks for various types of users (learners, authors and tutors), (iii) a set of advanced pedagogical scenarios that combine design and runtime adaptations to make the authoring of these type of adaptive courses feasible. In this paper we focus on the types of adaptations and the process we have defined to facilitate the con-struction of the adaptive scenarios.
Appears in Collections:1. LN: Publications and Preprints
Keur der Wetenschap

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