20,763 research outputs found
Factors influencing learner driver experiences [Road Safety Grant Report 2009-003]
When compared with more experienced drivers, new drivers have a higher crash risk. This study examined the experiences of learner drivers in Queensland and New South Wales in order to develop an understanding of the factors that influenced them while learning to drive. This will enable the development of more effective licensing systems. The research was informed by a number of heoretical perspectives, particularly social learning theory. Participants were recruited from driver licensing centres as soon as they passed their practical driving test to attain a provisional licence. Of those approached, 392 new drivers from capital cities and regional locations in Queensland and New South Wales completed a 35 minute telephone interview that collected information on a range of personal, social, environmental and socio-demographic factors. Participants were obtaining their licence before several changes to the licensing systems in both Queensland and New South Wales were made in 2007. Several implications for countermeasure development resulted from this research. These included ensuring licensing authorities carefully consider mandating a minimum number of hour of practice as it may inadvertently suppress the amount of practice that some learners obtain. Licensing authorities should consider the use of logbooks for learner drivers, even if there is no minimum amount of supervised practice required as it may assist learners and their supervisors structure their practice more effectively. This research also found that the confidence of learner drivers increases between when they first obtain their learner licence and when they obtain their provisional licence. This is an important issue requiring further attention by licensing authorities
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On the adequacy of current empirical evaluations of formal models of categorization
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)
An open learner model for trainee pilots
This paper investigates the potential for simple open learner models for highly motivated, independent learners, using the example of trainee pilots. In particular we consider whether such users access their learner model to help them identify their current knowledge level, areas of difficulty and specific misconceptions, to help them plan their immediate learning activities; and whether they find a longer‐term planning aid useful. The Flight Club open learner model was deployed in a UK flight school over four weeks. Results suggest that motivated users such as trainee pilots will use a system with a simple open learner model, and are interested in consulting their learner model information both to facilitate planning over time, and to understand their current knowledge state. We discuss the extent to which our findings may be relevant to learners in other contexts
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
ML decision-aid systems are increasingly common on the web, but their
successful integration relies on people trusting them appropriately: they
should use the system to fill in gaps in their ability, but recognize signals
that the system might be incorrect. We measured how people's trust in ML
recommendations differs by expertise and with more system information through a
task-based study of 175 adults. We used two tasks that are difficult for
humans: comparing large crowd sizes and identifying similar-looking animals.
Our results provide three key insights: (1) People trust incorrect ML
recommendations for tasks that they perform correctly the majority of the time,
even if they have high prior knowledge about ML or are given information
indicating the system is not confident in its prediction; (2) Four different
types of system information all increased people's trust in recommendations;
and (3) Math and logic skills may be as important as ML for decision-makers
working with ML recommendations.Comment: 10 page
Beyond ECDL: basic and advanced IT skills for the new library professional
This paper reports on a new multimedia-centred ICT module, called Fundamentals of Information and Communication Technology (FICT) for Postgraduate Information and Library Studies students at the Graduate School of Informatics at Strathclyde University. It had radical aims (introducing novel ICT skill content in a progressive manner, encouraging deep learning and self-directed study) and used a weekly survey and a post-module survey to investigate its operation. Skills learnt were compared to skills required during student placement in libraries. Conclusions are drawn as to its success in matching the needs of future library professionals
You can go your own way: effectiveness of participant-driven versus experimenter-driven processing strategies in memory training and transfer
Cognitive training programs that instruct specific strategies frequently
show limited transfer. Open-ended approaches can achieve greater transfer, but may fail to benefit many older adults due to age deficits in self-initiated processing. We examined whether a compromise that encourages effort at encoding without an experimenter-prescribed strategy might yield better results. Older adults completed memory training under conditions that either (1) mandated a specific strategy to increase deep, associative encoding, (2) attempted to suppress such encoding by mandating rote rehearsal, or (3) encouraged time and effort toward encoding but allowed for strategy choice. The experimenter-enforced associative encoding strategy succeeded in creating integrated representations of studied items, but training-task progress was related to pre-existing ability. Independent of condition assignment, self-reported deep encoding was associated with positive training and transfer effects, suggesting that the most beneficial outcomes occur when environmental support guiding effort is provided but participants generate their own strategies
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