The Use of Case Study Competitions to Prepare Students for the World of Work

As we continue into the new millennium, it is imperative that educational institutions equip graduates with the knowledge and skills that are increasingly needed and valued by business and industry. In this article, the authors argue that the case study approach and, specifically, case study competitions constitute an ideal pedagogical strategy for achieving this objective in an effective and efficient manner, with resulting benefits for both students and employers

Identifying walking trips from GPS and accelerometer data in adolescent females

Background: Studies that have combined accelerometers and global positioning systems (GPS) to identify walking have done so in carefully controlled conditions. This study tested algorithms for identifying walking trips from accelerometer and GPS data in free-living conditions. The study also assessed the accuracy of the locations where walking occurred compared with what participants reported in a diary. Methods: A convenience sample of high school females was recruited (N = 42) in 2007. Participants wore a GPS unit and an accelerometer, and recorded their out-of-school travel for 6 days. Split-sample validation was used to examine agreement in the daily and total number of walking trips with Kappa statistics and count regression models, while agreement in locations visited by walking was examined with geographic information systems. Results: Agreement varied based on the parameters of the algorithm, with algorithms exhibiting moderate to substantial agreement with self-reported daily (Kappa = 0.33-0.48) and weekly (Kappa = 0.41-0.64) walking trips. Comparison of reported locations reached by walking and GPS data suggest that reported locations are accurate. Conclusions: The use of GPS and accelerometers is promising for assessing the number of walking trips and the walking locations of adolescent females

Evolution of aquatic insect behaviours across a gradient of disturbance predictability

Natural disturbance regimes—cycles of fire, flood, drought or other events—range from highly predictable (disturbances occur regularly in time or in concert with a proximate cue) to highly unpredictable. While theory predicts how populations should evolve under different degrees of disturbance predictability, there is little empirical evidence of how this occurs in nature. Here, we demonstrate local adaptation in populations of an aquatic insect occupying sites along a natural gradient of disturbance predictability, where predictability was defined as the ability of a proximate cue (rainfall) to signal a disturbance (flash flood). In controlled behavioural experiments, populations from predictable environments responded to rainfall events by quickly exiting the water and moving sufficiently far from the stream to escape flash floods. By contrast, populations from less predictable environments had longer response times and lower response rates, reflecting the uncertainty inherent to these environments. Analysis with signal detection theory showed that for 13 out of 15 populations, observed response times were an optimal compromise between the competing risks of abandoning versus remaining in the stream, mediated by the rainfall–flood correlation of the local environment. Our study provides the first demonstration that populations can evolve in response to differences in disturbance predictability, and provides evidence that populations can adapt to among-stream differences in flow regime