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

Navigation in hypermedia learning systems: Experts vs. novices

With the advancement of Web technology, hypermedia learning systems are becoming more widespread in educational settings. Hypermedia learning systems present course content with non-sequential formats, so students are required to develop learning paths by themselves. Yet, empirical evidence indicates that not all students can benefit from hypermedia learning. Research into individual differences suggests that prior knowledge has significant effects on student learning in hypermedia systems, with experts and novices showing different preferences to the use of hypermedia learning systems and requiring different levels of navigation support. It is therefore essential to develop a mechanism to help designers understand the needs of experts and novices. To address this issue, this paper presents a framework to illustrate the needs of students with different levels of prior knowledge by analyzing the findings of previous research. The overall aim of this framework is to integrate students’ prior knowledge into the design of hypermedia learning systems. Finally, implications for the design of hypermedia learning systems are discussed

A model for hypermedia learning environments based on electronic books

Designers of hypermedia learning environments could take advantage of a theoretical scheme which takes into account various kinds of learning activities and solves some of the problems associated with them. In this paper, we present a model which inherits a number of characteristics from hypermedia and electronic books. It can provide designers with the tools for creating hypermedia learning systems, by allowing the elements and functions involved in the definition of a specific application to be formally represented A practical example, CESAR, a hypermedia learning environment for hearing‐impaired children, is presented, and some conclusions derived from the use of the model are also shown

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Applying digital content management to support localisation

The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM

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)

Continuous use of authoring for adaptive educational hypermedia : a long-term case study

Adaptive educational hypermedia allows lessons to be personalized according to the needs of the learner. However, to achieve this, content must be split into stand-alone fragments that can be processed by a course personalization engine. Authoring content for this process is still a difficult activity, and it is essential for the popularization of adaptive educational hypermedia that authoring is simplified, so that the various stakeholders in the educational process, students, teachers, administrators, etc. can easily work with such systems. Thus, real-world testing with these stakeholders is essential. In this paper we describe recent extensions and improvements we have implemented in the My Online Teacher MOT3.0 adaptation authoring tool set, based on an initial set of short-term evaluations, and then focus on describing a long-term usage and assessment of the system

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

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

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