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Hendrix, Maurice
Languages: English
Types: Doctoral thesis
Subjects: LB, QA76
It is well-known that students benefit from personalised attention. However, frequently\ud teachers are unable to provide this, most often due to time constraints. An Adaptive\ud Hypermedia (AH) system can offer a richer learning experience, by giving personalised\ud attention to students. The authoring process, however, is time consuming and cumbersome.\ud Our research explores the two main aspects to authoring of AH: authoring of content and\ud adaptive behaviour. The research proposes possible solutions, to overcome the hurdles\ud towards acceptance of AH in education.\ud Automation methods can help authors, for example, teachers could create linear lessons and\ud our prototype can add content alternatives for adaptation.\ud Creating adaptive behaviour is more complex. Rule-based systems, XML-based conditional\ud inclusion, Semantic Web reasoning and reusable, portable scripting in a programming\ud language have been proposed. These methods all require specialised knowledge. Hence\ud authoring of adaptive behaviour is difficult and teachers cannot be expected to create such\ud strategies. We investigate three ways to address this issue.\ud 1. Reusability: We investigate limitations regarding adaptation engines, which\ud influence the authoring and reuse of adaptation strategies. We propose a metalanguage,\ud as a supplement to the existing LAG adaptation language, showing how\ud it can overcome such limitations.\ud 2. Standardisation: There are no widely accepted standards for AH. The IMSLearning\ud Design (IMS-LD) specification has similar goals to Adaptive\ud Educational Hypermedia (AEH). Investigation shows that IMS-LD is more limited\ud in terms of adaptive behaviour, but the authoring process focuses more on learning\ud sequences and outcomes.\ud 3. Visualisation: Another way is to simplify the authoring process of strategies using\ud a visual tool. We define a reference model and a tool, the Conceptual Adaptation\ud Model (CAM) and GRAPPLE Authoring Tool (GAT), which allow specification\ud of an adaptive course in a graphical way. A key feature is the separation between\ud content, strategy and adaptive course, which increases reusability compared to\ud approaches that combine all factors in one model.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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