Collaborative virtual environments (CVEs) hold great potential for people With autism. An exploratory empirical study Was conducted to determine if children and youth With autism could understand basic emotions as represented by a humanoid avatar. Thirty-four participants (ages 7.8—16 years) reported to have autism interacted With a softWare program designed to evaluate their ability to identify and make inferences from facial expressions. Over 90% of the participants accurately recognized emotions displayed by avatar representations. These findings support the optimism that CVEs can be used effectively as an assistive technology, as an educational technology, and as a means of helping address potential theory-of-mind impairments.
This paper reports an investigation of the impact of students' cognitive style on their effective use of educational text-based computer-mediated conferences. The research centres on an empirical study involving students from three courses run by the British Open University. Statistical analysis of the data does not suggest that cognitive style has a strong influence on student participation in the conference, but does suggest that, contrary to expectations, 'imagers' may send more messages to conferences than 'verbalisers'. The data also suggest a possible link between certain cognitive styles and course completion, and that the interaction of different styles within a group, as described by Riding and Rayner's (1998) team roles, may have an indirect influence on task completion.
The drape attributes of fabrics, number of folds, depth of folds and evenness of folds were measured together with the drape coefficient. The relationship between these measurements and the subjective evaluation of the fabric drape were modelled for each end use on a neural network using back propagation, which can correctly predict the grades of 90% of the samples. The relationship between the drape attributes and fabric bending, shear and weight was also modelled using neural networks. It was found that using the natural logarithm of the material property divided first by the weight of the fabric produced the most predictive model. Together, these models provide a powerful predictive tool to determine both the drape attributes and the drape grade from the mechanical properties of a fabric. The accuracy of the prediction of this system was found to be 83% overall. Combining this with a novel feedback system (Stylios and Cheng, in preparation), the drape grade or drape attributes of a fabric can be modified to fit customer requirements and then the changes to the material properties required to achieve them can be determined.
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