A Glass Box Approach to Adaptive Hypermedia

Höök, Kristina; Karlgren, Jussi; Wærn, Annika; Dahlbäck, Nils; Jansson, Carl Gustaf; Karlgren, Klas; Lemaire, Benoı̂t

doi:10.1007/978-94-017-0617-9_6

Cited by 23 publications

(36 citation statements)

References 12 publications

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“…Tsandilas and schraefel (2004) suggest sliders as a way for the user to control fragment adaptation. Höök (1996) explored adaptive stretchtext -a specific kind of hypertext where both the user and the system can decide which fragments are hidden or visible.…”

Section: Fragment Indexing: the Case Of Adaptsmentioning

confidence: 99%

Adaptive Hypermedia for Education and Training

Brusilovsky¹

2012

Adaptive Technologies for Training and Education

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Section: Fragment Indexing: the Case Of Adaptsmentioning

confidence: 99%

Adaptive Hypermedia for Education and Training

Brusilovsky¹

2012

Adaptive Technologies for Training and Education

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“…To support more complex personalisation, this thesis follows Höök's "black box in a glass box" approach (2000; Höök et al 1996): arguing what is important for scrutinisation is the ability to provide the end user with an understanding of the overall result of the personalisation and how it might be changed to achieve a desired affect, rather than an in-depth understanding of the algorithm. To support this, SASY/ATML allows the content author to provide explanations for complex personalisation rules.…”

Section: Complexity Of Adaptive Responsementioning

confidence: 99%

“…For example, Amazon (www.amazon.com), an AH recommender system that recommends products to consumers, asks users to rate items to indicate their interest in that type of item and provide additional preferences directly by setting up a user profile. A less reliable method of obtaining input to the user model is by observing certain actions performed by the user, such as clicking a link to obtain detail about a topic, as evidence from the user is interested in the topic, as in systems like POP (Höök et al, 1996). 9 Description taken from http://en.wikipedia.org/wiki/Ubiquitous_computing, accessed 14 th Jan 2006.…”

Section: Input For User Model Acquisitionmentioning

confidence: 99%

A Scrutable Adaptive Hypertext

Czarkowski

Kay

2002

Lecture Notes in Computer Science

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Fuelled by the popularity and uptake of the World Wide Web since the 1990s, many researchers and commercial vendors have focussed on Adaptive Hypermedia Systems as an effective mechanism for disseminating personalised information and services. Such systems store information about the user, such as their goals, interests and background, and use this to provide a personalised response to the user. This technology has been applied to a number of contexts such as education systems, e-commerce applications, information search and retrieval systems.As an increasing number of systems collect and store personal information about their users to provide a personalised service, legislation around the world increasingly requires that users have access to view and modify their personal data. The spirit of such legislation is that the user should be able to understand how personal information about them is used. There literature has reported benefits of allowing users to access and understand data collected about them, particularly in the context of supporting learning through reflection. Although researchers have experimented with open user models, typically the personalisation is inscrutable: the user has little or no visibility in to the adaptation process. When the adaptation produces unexpected results, the user may be left confused with no mechanism for understanding why the system did what it did or how to correct it.This thesis is the next step, giving users the ability to see what has been personalised and why. In the context of personalised hypermedia, this thesis describes the first research to go beyond open, or even scrutable user models; it makes the adaptivity and associated processes open to the user and controllable. The novelty of this work is that a user of an adaptive hypertext system might ask How was this page personalised to me? and is able to see just how their user model affected what they saw in the hypertext document. With an understanding of the personalisation process and the ability to control it, the user is able to steer the personalisation to suit their changing needs, and help improve the accuracy of the user model.Developing an interface to support the scrutinisation of an adaptive hypertext is difficult. Users may not scrutinise often as it is a distraction from their main task. But when users need to scrutinise, perhaps to correct a system misconception, they need to easily find and access the scrutinisation tools.Ideally, the tools should not require any training and users should be able to use them effectively without prior experience or if have not used them for a long time, since this is how users are likely to scrutinise in practice.The contributions of thesis are: (1) SASY/ATML, a domain independent, reusable framework for creation and delivery of scrutable adaptive hypertext; (2)a toolkit of graphical tools that allow the user to scrutinise, or inspect and understand what personalisation occurred and control it; (3) evaluation of 4 the scrutinisation tools and (4) a set of g...

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“…This fires the adaptation rules that refer to possible user goals specified in the catalogue. The adaptation rules can, for example, recommend some pages to the user [23], focus user attention on a subset of the hyperspace [69; 198], or adapt content of the selected page [88].The goal/task recognition process is difficult and not precise in general. It is especially difficult in AH and other Web-based system where the flow of information from the user (bandwidth) required by user modeling components is thinner than in traditional desktop systems.…”

mentioning

confidence: 99%

“…Finally, several recent projects explored the use of data mining technologies to identify the current user task in an expected sequence of tasks and to provide personalized task-level support [87; 94]. More information about the use of data mining technologies for Web personalization ns provided is Chapter 3 of this book [135].A popular example of goal adaptation is provided by the PUSH system [88]. This system has a small catalogue of user goals and adapts the presentation of each selected page to the current goal.…”

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confidence: 99%

User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

638

416

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Abstract. One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling. IntroductionAdaptive hypermedia and other adaptive Web systems (AWS) belong to the class of user-adaptive software systems [174]. One distinctive feature of an adaptive system is a user model. The user model is a representation of information about an individual user that is essential for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. For example, when the user searches for relevant information, the system can adaptively select and prioritize the most relevant items (see Chapter 6 of this book [125]). When the user navigates from one item to another, the system can manipulate the links (e.g., hide, sort, annotate) to provide adaptive navigation support (see Chapter 8 of this book [21]). When the user reaches a particular page, the system can present the content adaptively (see Chapter 13 of this book [28]). To create and maintain an up-to-date user model, an adaptive system collects data for the user model from various sources that may include implicitly observing user interaction and explicitly requesting direct input from the user. This process is known as user modeling. User modeling and adaptation are two sides of the same coin. The amount and the nature of the information represented in the user model depend to a large extent on the kind of adaptation effect that the system has to deliver. 4 P. Brusilovsky and E. Millán As mentioned in the introduction, Chapters 1 to 5 of this book are focused mostly on the modeling side of personalization, while the remaining chapters focus mostly on the adaptation side. Chapters 1 and 2 are specifically devoted to user models and user modeling. Beyond this, user modeling issues are discussed at different levels of detail in several other chapters. This chapter attempts to complement the remaining chapters in two ways. First, it provides an overview (a "big picture") of the user modeling side referring readers when necessary to additional information in chapters within this book. Second, it attempts to complement other chapters by presenting aspects that are either not covered in other chapters or covered insufficiently.To envision the big picture, this chapter follows Sleeman [175] who suggested classifying user models by the nature and form of information contained in the model as well as the methods of working with it. Following hi...

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A Glass Box Approach to Adaptive Hypermedia

Cited by 23 publications

References 12 publications

Adaptive Hypermedia for Education and Training

Adaptive Hypermedia for Education and Training

A Scrutable Adaptive Hypertext

User Models for Adaptive Hypermedia and Adaptive Educational Systems

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