Fuzzy User Modeling for Adaptation in Educational Hypermedia

Kavčič, Alenka

doi:10.1109/tsmcc.2004.833294

Cited by 94 publications

(41 citation statements)

References 10 publications

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“…An even faster approach to navigate to specific KE and associated educational content can be provided by a visual representation of the domain model (also known as a domain map), which is used in AEH systems such as AES-CS (Triantafillou, Pomportis & Demetriadis, 2003). The enhanced concept-based hyperspace approach was used to create relatively large AEH systems with quite straightforward structure, and allows for a number of adaptation techniques (Kavcic, 2004;Papanikolaou et al, 2003;Steinacker et al, 2001). …”

Section: Figure 2 An Enhanced Concept-based Hyperspacementioning

confidence: 99%

“…Hiding technology can be used to hide links to pages representing KEs, which have prerequisites not yet learned (Brusilovsky & Pesin, 1998;Kavcic, 2004) or which do not belong to the current educational goal Papanikolaou et al, 2003).…”

Section: Figure 2 An Enhanced Concept-based Hyperspacementioning

confidence: 99%

“…It increased student non-sequential navigation (i.e., use of links beyond "back" and "continue") and helped students who followed the system's guidance to gain better knowledge of the subject. The prerequisite-based "traffic light" annotation approach introduced originally in ELM-ART (Weber & Brusilovsky, 2001) and popularized by InterBook, was later successfully applied in a number of other systems (Carmona et al, 2002;Henze & Nejdl, 2001;Kavcic, 2004).…”

Section: Page Indexing: the Case Of Interbookmentioning

confidence: 99%

“…Adaptive hypermedia (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 in order 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).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Hypermedia for Education and Training

Brusilovsky¹

2012

Adaptive Technologies for Training and Education

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Section: Figure 2 An Enhanced Concept-based Hyperspacementioning

confidence: 99%

Section: Figure 2 An Enhanced Concept-based Hyperspacementioning

confidence: 99%

Section: Page Indexing: the Case Of Interbookmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Hypermedia for Education and Training

Brusilovsky¹

2012

Adaptive Technologies for Training and Education

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“…However, in the field of adaptive hypermedia there are few studies that report the use of approximate reasoning techniques (notable exceptions are [82] for BNs and [99] for FL). Probably, the reason is that researchers are devoting much more attention to other relevant aspects such as foundation and core techniques (data mining, meta-data, semantic web, intelligent web agents, web services, etc.)…”

Section: Uncertainty-based User Modeling For Adaptive Hypermedia and mentioning

confidence: 99%

User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

645

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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