Virtual Reality (VR) has been used for some time for training various skills. The results obtained are generally very reassuring, suggesting that Virtual Environments (VEs) are an effective new kind of educational tool. There are some, however, who argue that there are cases in which a 2D approach would achieve the same training effect. The literature suggests that the key features that distinguish VR from other training approaches is the sense of presence, which provides a first-person experience of the world. Usually, real world learning is multisensory and gives ownership and control over the experience, increases learner motivation, and triggers the construction of knowledge. Despite technical limitations, a VE is the most effective form of information technology for providing multisensory experience including visual, auditory, and to some extend haptic and tactile cues. The sense of presence ensures that the perceived experience is interpreted as being real and makes it likely that skills learned in the VE will be transferred to the real world. We argue that for training time-limited decision-making skills, the learner should also have an opportunity to reflect on actions/strategies to improve performance. Therefore a virtual environment for training should also provide support for students to reflect on their performance. This paper describes a prototype training system that aims to support both construction of knowledge and cognitive learning. It is also intended to trigger a sense of presence as well as provide support mechanisms, not available in the real world, that the students can exercise to reflect on the training experience.
One of the main differences between novice and expert problem solving in physics is that novices mostly construct problem representations from objects and events in the experimental situation, whereas experts construct representations closer to theoretical terms and entities. A main difficulty in physics is in interrelating these two levels, i.e. in modelling.Relatively little research has been done on this problem, most work in AI, psychology and physics education having concentrated on how students use representations in problem solving, rather than on the complex process of how they consmlct them. We present a study that aims to explore how students construct models for energy storage, transformation and transfers in simple experimental situations involving electricity and mechanics. The study involved detailed analysis of problem solving dialogues produced by pairs of students, and AI modelling of these processes. We present successively more refined models that are capable of generating ideal solutions, solutions for individual students for a single task, then models for individuals across different tasks. The students' construction of energy models can be modelled in terms of the simplest process of modelling-establishing term to term relations between elements of the object/event 'world' and the theory/model world, with underlying linear causal reasoning. Nevertheless, our model is unable to take into account more sophisticated modelling processes in students. In conclusion we therefore describe future work on the development of a new model that could take such processes into account.
This paper looks at the particular role which diagrammatic representations, and external representations more generally, play within an educational context. In particular, it considers the way in which the demands on diagrammatic representational systems in educational settings differ with respect to other settings (e.g. professional): in some instances, these demands are increased, while in others, the demands are markedly different.The paper considers three key issues: the question of whether diagrams make certain tasks easier (and whether this is desirable from an educational point of view), the generalisation and transfer of diagrammatic skills once learnt, and the possible problems associated with simultaneously learning domain knowledge and a novel representational system. The paper then considers a number of sub-issues, and concludes by highlighting areas of particular interest for future AI research.
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