Twelve hundred and fifty college students starting introductory courses in thirteen academic disciplines were asked to predict their grade in the course. Results showed that overall, males predicted higher grades for themselves than did females (p <.001). This held true for entering freshmen as well as for those with previous college experience. The phenomena was noted in 26 of 37 classes tested, including 7 of 9 in the natural sciences, 11 of 13 in the social sciences, but only 8 of 15 in the humanities. Sex of the instructor was irrelevant, raising the question of whether female instructors as role models have the positive effect upon women students that has been claimed. The differences found were slight, but persistent. Both sexes predicted very high grades. The data suggest that sex differences in prediction were not based on a female sense of incompetence, but upon a greater willingness among males to make highly positive predictions.
In this paper, a basic set of motion relations capturing specific prototypical movements of vehicles on US road networks are introduced. Vehicle positional data collected from a geosensor network and stored in a spatio-temporal database serve as the basis for computing the relations that include isBehind, inFrontOf, driveBeside, and passBy. Relational SQL queries are used to derive the relations, returning information about pairs of moving objects and their relative positions. This information provides additional user contexts for binary vehicle patterns relative to a reference object. A framework for the kinds of moving objects that participate in these relations is supplied through an associated TransportationDevice ontology. Depending on the class of moving object, a relation such as isBehind captures scenarios that are facilitating or inhibiting with respect to the movement of traffic. For example, if a police car is known to be behind an automobile, the automobile typically slows to correspond with the legal speed limit. In this work, we show how linking the spatio-temporal database to an ontology can augment and extend the motion relation information, providing multi-granular perspectives of moving vehicles.
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