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