Pme Paul De Bra

AHA! An open Adaptive Hypermedia Architecture

Abstract Hypermedia applications generate comprehension and orientation problems due to their rich link structure. Adaptive hypermedia tries to alleviate these problems by ensuring that the links that are offered and the content of the information pages are adapted to each individual user. This is done by maintaining a user model. Most adaptive hypermedia systems are aimed at one specific application. They provide an engine for maintaining the user model and for adapting content and link structure. They use a fixed screen layout that may include windows (HTML frames) for an annotated table of contents, an overview of known or missing knowledge, etc. Such systems are typically closed and difficult to reuse for very different applications. We present AHA, an open Adaptive Hypermedia Architecture that is suitable for many different applications. This paper concentrates on the adaptive hypermedia engine, which maintains the user model and which filters content pages and link structures accordingly. The engine...

Adaptive hypermedia: from systems to framework

The navigational freedom in conventional hypermedia applications leads to comprehension and orientation problems (Nielsen 1990). Adaptive hypermedia attempts to overcome these problems by adapting the presentation of information and the overall link structure, based on a user model. This paper introduces a framework for adaptive hypermedia systems (AHS). It briefly describes some popular methods and techniques for adaptation. Examples and evaluations of existing AHS are used to illustrate the potential benefits of using adaptation in hypermedia applications.

AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques

A hypermedia application offers its users much freedom to navigate through a large hyperspace. Adaptive hypermedia (AH) offers personalized content, presentation, and navigation support. Many adaptive hypermedia systems (AHS) are tightly integrated with one specific application and/or use a limited number of techniques and methods. This makes it difficult to capture all of them in one generic model. In this paper we examine adaptation questions stated in the very beginning of the AH era and elaborate on their recent interpretations. We will reconsider design issues for application independent generic AHS, review open questions of system extensibility introduced in adjacent research fields and try to come up with an up-to-date taxonomy of adaptation techniques and an extensive set of requirements for a new adaptive system reference model or architecture, to be developed in the future.

Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors

We introduce a methodology to improve Adaptive Systems for Web-Based Education. This methodology uses evolutionary algorithms as a data mining method for discovering interesting relationships in students’ usage data. Such knowledge may be very useful for teachers and course authors to select the most appropriate modifications to improve the effectiveness of the course. We use Grammar-Based Genetic Programming (GBGP) with multi-objective optimization techniques to discover prediction rules. We present a specific data mining tool that can help non-experts in data mining carry out the complete rule discovery process, and demonstrate its utility by applying it to an adaptive Linux course that we developed.

Creating and delivering adaptive courses with AHA

AHA! is an Open Source adaptive hypermedia platform, resulting from 10 years of experience with creating, using and improving on-line adaptive courses and presentations. This paper focuses on some recent additions to AHA! that are especially important for adaptive educational applications, namely stable presentations, adaptive link (icon) annotations and adaptive link destinations. We not only describe the technical aspects of these parts of AHA! but also illustrate their use in educational applications. We describe some fundamental limitations of Web-based adaptive applications, and show how AHA! deals with them in order to provide adaptation to prerequisite relationships in the way one would expect.

Adaptive web-based educational hypermedia

This chapter describes recent and ongoing research to automatically personalize a learning experience through adaptive educational hypermedia. The Web has made it possible to give a very large audience access to the same learning material. Rather than offering several versions of learning material about a certain subject, for different types of learners, adaptive educational hypermedia offers personalized learning material without the need to know a detailed classification of users before starting the learning process. We describe different approaches to making a learning experience personalized, all using adaptive hypermedia technology. We include research on authoring for adaptive learning material (the AIMS and MOT projects) and research on modeling adaptive educational applications (the LAOS project). We also cover some of our ongoing work on the AHA! system, which has been used mostly for educational hypermedia but has the potential to be used in very different application areas as well.

Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA!, offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model, CAM, which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools.

Adaptation Control in Adaptive Hypermedia Systems

A hypermedia application offers its users a lot of freedom to navigate through a large hyperspace, described by a domain model. Adaptive hypermedia systems (AHS) aim at overcoming possible navigation and comprehension problems by providing adaptive navigation support and adaptive content. The adaptation is based on a user model that represents relevant aspects about the user. In this paper, we concentrate on the adaptation engine (AE) that is responsible for performing the adaptation according to the adaptation rules specified in the adaptation model. We analyze the dependencies between the authoring process and the functionality of the adaptation engine. From this we conclude how the authoring process can be simplified by a more powerful AE. In particular, a well-designed AE should be general purpose (i.e., not application domain specific) and should guarantee that the interpretation of the rules is deterministic, always terminates and produces the results desired by the author.

Sufficient Conditions for Well-Behaved Adaptive Hypermedia Systems

We focus on well-behaved Adaptive Hypermedia Systems, which means the adaptation engine that executes adaptation rules always terminates and produces predictable (confluent) adaptation results. Unfortunately termination and confluence are undecidable in general. In this paper we discuss sufficient conditions to help authors to write adaptation rules that satisfy termination and confluence.

Embedding information retrieval in adaptive hypermedia: IR meets AHA!

This paper concentrates on the retrieval aspect in adaptive hypermedia (AH). Traditionally, AH research concentrates on applications that are ‘closed’, in the sense that they assume fixed content elements. Certain applications ask for an extension of the contents considered, with data obtained through information retrieval (IR). This paper addresses this issue of ‘opening up’ AH applications, and gives insight into research that applies techniques from IR and from the Semantic Web (SW) for the embedding of IR in AH. We look at this issue in the context of an abstract reference model (AHAM) and a concrete implementation framework (AHA!). The goal of this research is to define a framework for AH with extended IR functionality. We address the relevant issues for this framework, characterized by the application of concepts from the SW paradigm leading to an enriched notion of concept relevancy.