What is the problem to which interactive multimedia is the solution?

This is something of an unusual paper. It serves as both the reason for and the result of a small number of leading academics in the field, coming together to focus on the question that serves as the title to this paper: What is the problem to which interactive multimedia is the solution? Each of the authors addresses this question from their own viewpoint, offering informed insights into the development, implementation and evaluation of multimedia. The result of their collective work was also the focus of a Western Australian Institute of Educational Research seminar, convened at Edith Cowan University on 18 October, 1994. The question posed is deliberately rhetorical - it is asked to allow those represented here to consider what they think are the significant issues in the fast-growing field of multimedia. More directly, the question is also asked here because nobody else has considered it worth asking: for many multimedia is done because it is technically possible, not because it offers anything that is of value or provides the solution to a particular problem. The question, then, is answered in various ways by each of the authors involved and each, in their own way, consider a range of fundamental issues concerning the nature, place and use of multimedia - both in education and in society generally. By way of an introduction, the following provides a unifying context for the various contributions made here

Hypermedia learning and prior knowledge: Domain expertise vs. system expertise

Prior knowledge is often argued to be an important determinant in hypermedia learning, and may be thought of as including two important elements: domain expertise and system expertise. However, there has been a lack of research considering these issues together. In an attempt to address this shortcoming, this paper presents a study that examines how domain expertise and system expertise influence students’ learning performance in, and perceptions of, a hypermedia system. The results indicate that participants with lower domain knowledge show a greater improvement in their learning performance than those with higher domain knowledge. Furthermore, those who enjoy using the Web more are likely to have positive perceptions of non-linear interaction. Discussions on how to accommodate the different needs of students with varying levels of prior knowledge are provided based on the results

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)

Agents for Distributed Multimedia Information Management

This paper discusses the role of agents in a distributed multimedia information system (DMIS) engineered according to the principles of open hypermedia. It is based on the new generation of Microcosm, an open hypermedia system developed by the Multimedia Research Group at the University of Southampton. Microcosm provides a framework for supporting the three major roles of agents within open information systems: resource discovery, information integrity and navigation assistance. We present Microcosm and its agents, and discuss our current research in applying agent technology in this framework

The relationship between web enjoyment and student perceptions and learning using a web-based tutorial

Web enjoyment has been regarded as a component of system experience. However, there has been little targeted research considering the role of web enjoyment alone in student learning using web-based systems. To address this gap, this study aims to examine the influence of web enjoyment on learning performance and perceptions by controlling system experience as a variable in the study. 74 students participated in the study, using a web-based tutorial covering subject matter in the area of 'Computation and algorithms'. Their learning performance was assessed with a pre-test and a post-test and their learning perceptions were evaluated with a questionnaire. The results indicated that there are positive relationships between the levels of web enjoyment and perceived usefulness and non-linear navigation for users with similar, significant levels of system experience. The implications of these findings in relation to web-based learning are explored and ways in which the needs of students who report different levels of web enjoyment might be met are discussed

Towards a Framework for Developing Mobile Agents for Managing Distributed Information Resources

Distributed information management tools allow users to author, disseminate, discover and manage information within large-scale networked environments, such as the Internet. Agent technology provides the flexibility and scalability necessary to develop such distributed information management applications. We present a layered organisation that is shared by the specific applications that we build. Within this organisation we describe an architecture where mobile agents can move across distributed environments, integrate with local resources and other mobile agents, and communicate their results back to the user

Reviews

Brian Clegg, Mining The Internet — Information Gathering and Research on the Net, Kogan Page: London, 1999. ISBN: 0–7494–3025–7. Paperback, 147 pages, £9.99

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