Hypermedia Lab
The non-linear connection of information nodes (structure of hypertext) is characteristical for the presentation of information with hypermedia. In addition, single nodes provide multimedia components (e.g. text, image, animation, video) by offering different representational codes (e.g. verbal, pictorial) through different sensory modes (e.g. visual, auditory). These representational codes can be used interactively. Given the fact of their interdependence, hypermedia provide various possibilities of self-directed learning.
In hypermedia-based learning environments the learner is granted a high degree of control: Scores of media-based decisions are made by the learner through navigating the learning environment (e.g., concerning the selection and sequencing of triggered information chunks, representational codes or sensory modes). Regarding the hypertext structure, users have to autonomously arrange meaningful information out of the different presentation modes. The user therefore has to select information that is adapted to his standard of knowledge and ambition. The central issue in designing hypermedia is to link media - e.g., text, image, animation or audio - in a way which supports the deep understanding of contents, without cognitively overloading the learner by the variety of information and presentation modes.
The Hypermedia Lab is interested in this issue and follows two main areas of research:
Learner control and networked information presentation
Research in this group focuses on navigation and exploration behavior in hypermedia environments, especially in regard to strategies of adaptive selection, sequencing, evaluation, and processing of retrieved "information entities" that consist of different representational codes and different sensory modalities. On a theoretical basis, we are interested in the interaction of cognitive resource limitations (e.g., working memory, modality-specific processing channels), additional cognitive demands of hypermedia navigation (e.g., orientation, evaluation, or control), user characteristics (e.g., epistemological and domain specific beliefs, attitudes), and design options for instructional materials. Findings on utilization patterns and deficits when interacting with hypermedia are used to design support devices that could be relevant especially in informal settings. One emphasis of the research unit is placed on a Dutch-German research cooperation on "Affordances for learning in multimedia learning environments". A second emphasis is placed on the investigation of information evaluation processes during internet search, particularly on the acquisition of basic knowledge about controversially discussed scientific topics (e.g., nano- or biotechnology).
Interactive multimedia components
In this group the focus is on the instructional design and the adaptive use of interactive multimedia components, which are used in hypermedia-based environments. Here especially the integration of auditory information and dynamic-interactive visualizations in hypermedia-based environments comes to the fore. The design and use of those multimedia components is analysed in regard to cognitive load, attentional control by cueing and the linkage of verbal and pictorial information. Besides the measurement of cognitive load and learning outcomes, the observation of the processing of interactive multimedia components with eye movement analyses plays an important role. A focal point in this research project is the transfer of principles of designing multimedia components - extracted from laboratory experiments - to realistic appliance settings (e.g. school, expositions) and to test their validity in these contexts. Another focus lies on the interdisciplinary conception and evaluation of dynamic multimedia components dealing with marine biology. In close collaboration with computer scientists, designers and biologists, 3-D-animations are developed addressing pupils and students as well as visitors of aquariums. The animations are optimized for different innovative input and output-interfaces (e.g., mobile end device, paper-PDAs, acceleration sensors, identification of gestures).