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...

Many Web-based educational applications are expected to be used by very different groups of users without the assistance of a human teacher. Accordingly there is a need for systems which can adapt to users with very different backgrounds, prior knowledge of the subject and learning goals. An electronic textbook is one of the most prominent varieties of Web-based educational systems. In this paper we describe an approach for developing adaptive electronic textbooks and present InterBookan authoring tool based on this approach which simplifies the development of adaptive electronic textbooks on the Web.

Abstract:Making ITS available on the World Wide Web (WWW) is a way to integrate the flexibility and intelligence of ITS with world-wide availability of WWW applications. This paper discusses the problems of developing WWW-available ITS and, in particular, the problem of porting existing ITS to a WWW platform. We present the system ELM-ART which is a WWW-based ITS to support learning programming in Lisp. ELM-ART demonstrates how several known ITS technologies can be implemented in WWW context. ITS Technologies and WWW ContextWWW opens new ways of learning for many people. However, most of the existing educational WWW applications use simplest solutions and are much more weak and restricted than existing 'on-site' educational systems and tools. In particular, most WWW educational systems do not use powerful ITS technologies. A promising direction of research is to port these technologies to a WWW platform, thus joining the flexibility and intelligence of ITS with world-wide availability of WWW applications. Most of traditional intelligent techniques applied in ITS can be roughly classified into three groups which we will name as technologies: curriculum sequencing, interactive problem solving support, and intelligent analysis of student solutions. All these technologies are aimed at supporting the "intelligent" duties of the human teacher which can not be supported by traditional non-intelligent tutoring systems. Curriculum sequencing and intelligent analysis of student solutions are the oldest and best-studied technologies in the domain of ITS. Most ITS developed during the first 10 years of ITS history belong to these groups. The technology of interactive problem solving support is a newer one, but it is more "intelligent" and supportive (it helps the student in the most difficult part of the learning process and provides the most valuable support for the teacher in the classroom). It is not surprising that it became a dominating technology during the last 15 years. The WWW context changes the attitudes to traditional ITS techniques . For example, interactive problem solving support currently seems to be a less suitable technology for WWWbased ITS. Vice versa, the two older technologies seem to be very usable and helpful

Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, we address this problem from both modeling and evaluation perspectives.We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states. In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs.We further propose two evaluation metrics tailored towards the variable-number generation. We also introduce a new dataset (ST A C KEX) that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.

Adaptive hypermedia is a new area of research at the crossroads of hypermedia, adaptive systems, and intelligent tutoring systems. Educational hypermedia systems is cirrently the most popular kind of adaptive hypermedia. The goal of this paper is to uncover the secrets of authoring adaptive educational hypermedia. The paper provides a clear structured view on the process of adaptive hypermedia authoring starting from the early design stage. It also reviews a few modern adaptive hypermedia authoring systems that are oriented to educational practitioners.