Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767118
Title: A visual adaptive authoring framework for adaptive hypermedia
Author: Khan, Javed Arif
ISNI:       0000 0004 7657 8722
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2018
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Abstract:
In a linear hypermedia system, all users are offered a standard series of hyperlinks. Adaptive Hypermedia (AH) tailors what the user sees to the user's goals, abilities, interests, knowledge and preferences. Adaptive Hypermedia is said to be the answer to the 'lost in hyperspace' phenomenon, where the user has too many hyperlinks to choose from, and has little knowledge to select the most appropriate hyperlink. AH offers a selection of links and content that is most appropriate to the current user. In an Adaptive Educational Hypermedia (AEH) course, a student's learning experiences can be personalised using a User Model (UM), which could include information such as the student's knowledge level, preferences and culture. Beside these basic components, a Goal Model (GM) can represent the goal the users should meet and a Domain Model (DM) would represent the knowledge domain. Adaptive strategies are sets of adaptive rules that can be applied to these models, to allow the personalisation of the course for students, according to their needs. From the many interacting elements, it is clear that the authoring process is a bottleneck in the adaptive course creation, which needs to be improved in terms of interoperability, usability and reuse of the adaptive behaviour (strategies). Authoring of Adaptive Hypermedia is considered to be difficult and time consuming. There is great scope for improving authoring tools in Adaptive Educational Hypermedia system, to aid already burdened authors to create adaptive courses easily. Adaptation specifications are very useful in creating adaptive behaviours, to support the needs of a group of learners. Authors often lack the time or the skills needed to create new adaptation specifications from scratch. Creating an adaptation specification requires the author to know and remember the programming language syntax, which places a knowledge barrier for the author. LAG is a complete and useful programming language, which, however, is considered too complex for authors to deal with directly. This thesis thus proposes a visual framework (LAGBlocks) for the LAG adaptation language and an authoring tool (VASE) to utilise the proposed visual framework, to create adaptive specifications, by manipulating visual elements. It is shown that the VASE authoring tool along with the visual framework enables authors to create adaptive specifications with ease and assist authors in creating adaptive specifications which promote the "separation of concern". The VASE authoring tool offers code completeness, correctness at design time, and also allows for adaptive strategies to be used within other tools for adaptive hypermedia. The goal is thus to make adaptive specifications easier, to create and to share for authors with little or no programming knowledge and experience. This thesis looks at three aspects of authoring in adaptive educational hypermedia systems. The first aspect of the thesis is concerned with problems faced by the author of an adaptive hypermedia system; the second aspect is concerned with describing the findings gathered from investigating the previously developed authoring tools; and the final aspect of the thesis is concerned with the proposal, the implementation and the evaluation of a new authoring tool that improves the authoring process for authors with different knowledge, background and experience. The purpose of the new tool, VASE, is to enable authors to create adaptive strategies in a puzzle-building manner; moreover, the created adaptive strategies could be used within (are compatible with) other systems in adaptive hypermedia, which use the LAG programming language.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.767118  DOI: Not available
Keywords: QA76 Electronic computers. Computer science. Computer software
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