873 resultados para Driver feedback


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This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.

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In multimedia retrieval, a query is typically interactively refined towards the ‘optimal’ answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. In this paper, we introduce a new approach called SaveRF (Save random accesses in Relevance Feedback) for iterative relevance feedback search. SaveRF predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses can be saved, hence reducing the number of random accesses. In addition, efficient scan on the overlap before the search starts also tightens the search space with smaller pruning radius. We implemented SaveRF and our experimental study on real life data sets show that it can reduce the I/O cost significantly.

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This paper investigates an on-going study into the nature of the discourse teacher educators (TEds) and student-teachers (Ts) use during postobservation feedback, and explores the feedback discourse in terms of the impact it may have on teacher learning and change. Two types of feedback, confirmatory and corrective, are briefly discussed, and the former is then made the focus of the paper in its role as a potential instigator of teacher learning and change.

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A central component in pre-service teacher training is teaching practice and feedback. In some cases, feedback results in disquiet and tension (Brandt, 2008). Many researchers attribute this tension to the incompatibility of the assessment and development roles that the trainer must perform. The research reported on here, however, suggests that tension may also be rooted in a difference in expectation amongst trainers and trainees about the purpose and performance of feedback. This can result in trainees not playing by the rules of the game (Roberts & Sarangi, 2001) either because they do not understand them or because they wish to challenge them.