The StorySpinner Sculptural Reader

This demo is of a hypertext reading system called StorySpinner. It follows the sculptural hypertext methodology and has been used as a test bed for experimenting with the authoring of narrative flow in automatically generated stories. Readers are able to select and read one of two available stories. Reading a story involves selecting tarot cards which are mapped to chunks of story text based on possible interpretations of the cards and information concerning current story state

StorySpinner: Controlling Narrative Pace in Hyperfiction

This paper describes the StorySpinner system, a sculptural hypertext reader used as a test bed for experimenting with the authoring of narrative flow in automatically generated stories. An overview of the system is presented along with discussion and conclusions arising from initial user trials

Teaching new media composition studies in a lifelong learning context

Governmental proposals for lifelong learning, and the role of Information and Learning Technologies/Information Communication Technologies (ILT/ICT) in this, idealistically proclaim that ILT/ICT empowers learners. A number of important governmental funding initiatives have recently been extended to the development of ILT in further education, which provides a particularly appropriate environment for lifelong learning. Yet little emphasis is given to more problematic research findings that students may be ‘disarmed’ in the process of learning to use technology. In the current global shift towards new forms of multimedia literacy, it is important to recognize human diversity by carrying out research focusing on the actual problems students face in adapting to Web‐based technology as a new authoring medium. A case study into multimedia creative composition carried out with FE students in 1996–9 found that students tend to experience a problematic but potentially useful period of ‘creative mess’ when authoring in multimedia, and that ‘scaffolding’ strategies can be useful in overcoming this. Such strategies can empower students to derive benefits from multimedia composition if close attention is given to the setting up of the learning environment: a teachers’ model for supporting novice hypermedia authors in further education is proposed, to assist teachers to understand and support the learning processes students may undergo in dynamic composition using new media technology

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

Building multi-layer social knowledge maps with google maps API

Google Maps is an intuitive online-map service which changes people's way of navigation on Geo-maps. People can explore the maps in a multi-layer fashion in order to avoid information overloading. This paper reports an innovative approach to extend the "power" of Google Maps to adaptive learning. We have designed and implemented a navigator for multi-layer social knowledge maps, namely ProgressiveZoom, with Google Maps API. In our demonstration, the knowledge maps are built from the Interactive System Design (ISD) course at the School of Information Science, University of Pittsburgh. Students can read the textbooks and reflect their individual and social learning progress in a context of pedagogical hierarchical structure

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)