Authoring of Adaptive and Adaptable Hypermedia

Authoring of Adaptive Hypermedia has been long considered as secondary to adaptive hypermedia delivery. However, authoring is not trivial at all. There exist some approaches to help authors to build adaptive-hypermedia-based systems, yet there is a strong need for high-level approaches, formalisms and tools that support and facilitate the description of reusable adaptive websites. Only recently have we noticed a shift in interest, as it became clearer that the implementation-oriented approach would forever keep adaptive hypermedia away from the ‘layman’ author. The creator of adaptive hypermedia cannot be expected to know all facets of this process, but can be reasonably trusted to be an expert in one of them. It is therefore necessary to research and establish the components of an adaptive hypermedia system from an authoring perspective, catering for the different author personas that are required. This type of research has proven to lead to a modular view on the adaptive hypermedia

Modeling human behavior in user-adaptive systems: recent advances using soft computing techniques

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application

MOT meets AHA!

MOT (My Online Teacher) is an adaptive hypermedia system (AHS) web-authoring environment. MOT is now being further developed according to the LAOS five-layer adaptation model for adaptive hypermedia and adaptive web-material, containing a domain -, goal -, user -, adaptation – and presentation model. The adaptation itself follows the LAG three-layer granularity structure, figuring direct adaptation techniques and rules, an adaptation language and adaptation strategies. In this paper we shortly describe the theoretical basis of MOT, i.e., LAOS and LAG, and then give some information about the current state of MOT. The purpose of this paper is to show how we plan the design and development of MOT and the well-known system AHA! (Adaptive Hypermedia Architecture), developed at the Technical University of Eindhoven since 1996. We aim especially at the integration with AHA! 2.0. Although AHA! 2.0 represents a progress when compared to the previous versions, a lot of adaptive features that are described by the LAOS and the adaptation granulation model and that are being implemented into MOT are not yet (directly) available. So therefore AHA! can benefit from MOT. On the other hand, AHA! offers a running platform for the adaptation engine, which can benefit MOT in return

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

A Generic Adaptive Model in Adaptive Hypermedia

For adaptive hypermedia there is a strong model in form of the AHAM model and the AHA! system. This model, based on the Dexter Model, however is limited to application in hypermedia systems. In this paper we propose a new Generic Adaptivity Model. This statemachine based model can be used as the basis for adaptation in all kinds of applications

Authoring of adaptive serious games

Game-based approaches to learning are increasingly being recognized as having the potential to stimulate intrinsic motivation amongst learners. Whilst a range of examples of effective serious games exist, creating the high-fidelity content with which to populate a serious game is resource-intensive task. To reduce this resource requirement, research is increasingly exploring means to reuse and repurpose existing games and relevant sources of content. Education has proven a popular application area for Adaptive Hypermedia, as adaptation can offer enriched learning experiences to students. Whilst content to-date has mainly been in the form of rich text, various efforts have been made to integrate Serious Games into Adaptive Hypermedia via run-time adaptation engines. However, there is little in the way of effective integrated authoring and user modeling support for these efforts. This paper explores avenues for effectively integrating serious games into adaptive hypermedia. In particular, we consider authoring and user modeling aspects in addition to integration into run-time adaptation engines, thereby enabling authors to create Adaptive Hypermedia that includes an adaptive game, thus going beyond mere selection of a suitable game and towards an approach with the capability to adapt and respond to the needs of learners and educators

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 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. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

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 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. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

A spiral model for adding automatic, adaptive authoring to adaptive hypermedia

At present a large amount of research exists into the design and implementation of adaptive systems. However, not many target the complex task of authoring in such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck for authoring of adaptive hypermedia. One such solution is the reuse of automatically generated metadata. In our previous work we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT ( My Online Teacher), and a semantic desktop environment, indexed by Beagle++. A prototype, Sesame2MOT Enricher v1, was built based upon this integration approach and evaluated. After the initial evaluations, a web-based prototype was built (web-based Sesame2MOT Enricher v2 application) and integrated in MOT v2, conforming with the findings of the first set of evaluations. This new prototype underwent another evaluation. This paper thus does a synthesis of the approach in general, the initial prototype, with its first evaluations, the improved prototype and the first results from the most recent evaluation round, following the next implementation cycle of the spiral model [Boehm, 88]