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)

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Profile transformation in mobile technology based educational systems : a thesis presented in partial fulfillment of the requirements for the degree of Master of Information Science in Information Systems at Massey University, Palmerston North, New Zealand

In order to meet the learning needs from various types of students, computer aided education systems try to include new methods to provide personalized education to every student. From the early 1970s, a lot of adaptive educational systems have been created to provide training on a variety of subjects. Combined with the Internet, the adaptive educational systems have become web-based and even more popular. Recently, the development of mobile technology has made the web-based adaptive educational systems accessible through mobile phones. It is necessary that the students can also receive adaptive educational contents on mobile phones. This research project investigated the possible student's preference differences between Personal Computer (PC) and mobile phone, and then proposed a student profile transformation framework to address such differences. This research project conducted two surveys on the student profile transformation between PC and mobile phone. A demo web-based educational system that could be accessed from both PC and mobile phone was also developed for participants of the surveys to give more real and precise responses. Based on Felder-Silverman Learning Style Theory (Felder, 1993; Felder & Silverman, 1988) and the results of the surveys, this thesis proposes a student profile template and a student profile transformation framework, which both fully considered the influences of device capabilities and locations on students' preferences on mobile phones. Furthermore, the proposed framework integrates a solution for unsupported preferences and preference conflicts. By implementing the proposed template and framework, the students' preference changes between PC and mobile phone are automatically updated according to various device capabilities and locations, and then the students can receive adaptive educational contents that meet their updated preferences

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

SemWeB Semantic Web Browser – Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks

Imagine a Web browser that can understand the context of a Web page and recommends related semantic hyperlinks in any Web domain. In addition, imagine this browser also understands your browsing needs and personalizes information for you. The aim of our research is to achieve this in open Web environment using Semantic Web technologies and adaptive hypermedia techniques. In this paper, we discuss a novel Semantic Web browser, SemWeB, which utilizes linked data for context-based hyperlink recommendation and uses a behavior-based and an ontology-driven user modeling architecture for personalization on Web documents. The aim of this research is to bring the gap between the technology and user needs using Semantic Web technologies in Web browsing

The design and implementation of an adaptive e-learning system

This paper describes the design and implementation of an adaptive e-learning system that provides a template for different learning materials as well as a student model that incorporates five distinct student characteristics as an aid to learning: primary characteristics are prior knowledge, learning style and the presence or absence of animated multimedia aids (multimedia mode); secondary characteristics include page background preference and link colour preference. The use of multimedia artefacts as a student characteristic has not previously been implemented or evaluated. The system development consists of a requirements analysis, design and implementation. The design models including use case diagrams, conceptual design, sequence diagrams, navigation design and presentation design are expressed using Unified Modelling Language (UML). The adaptive e-learning system was developed in a template implemented using Java Servlets, XHTML, XML, JavaScript and HTML. The template is a domain-independent adaptive e-learning system that has functions of both adaptivity and adaptability

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Adaptive Educational Hypermedia based on Multiple Student Characteristics

The learning process in Adaptive Educational Hypermedia (AEH) environments is complex and may be influenced by aspects of the student, including prior knowledge, learning styles, experience and preferences. Current AEH environments, however, are limited to processing only a small number of student characteristics. This paper discusses the development of an AEH system which includes a student model that can simultaneously take into account multiple student characteristics. The student model will be developed to use stereotypes, overlays and perturbation techniques. Keywords: adaptive educational hypermedia, multiple characteristics, student model

Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

Abstract. Teacher and students can use WWW as a limitless source of learning material for nearly any subject. Yet, such abundance of content comes with the problem of finding the right piece at the right time. Conventional adaptive educational systems cannot support personalized access to open-corpus learning material as they rely on manually constructed content models. This paper presents an approach to this problem that does not require intervention from a human expert. The approach has been implemented in an adaptive system that recommends students supplementary reading material and adaptively annotates it. The results of the evaluation experiment have demonstrated several significant effects of using the system on students ’ learning