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971.
Simon A. Jackson Gregory D. Martin Eugene Aidman Sabina Kleitman 《Metacognition and Learning》2018,13(1):39-56
This article presents the results of a systematic review of the literature surrounding the effects that acute sleep deprivation has on metacognitive monitoring. Metacognitive monitoring refers to the ability to accurately assess one’s own performance and state of knowledge. The mechanism behind this assessment is captured by subjective feelings of confidence concerning the accuracy of our judgments or performance. These judgments influence decision behavior. How well these subjective feelings fit with reality is critical for good decision making. For example, a driver who is overconfident in their ability to remain vigilant after a night without sleep is at risk of having an accident. A learner who is overconfident in their ability to perform well on an exam without sleep is at risk of failing. A break down in metacognitive monitoring might be responsible for the increase in poor decision making observed when people are sleep deprived. Using defined search terms and exclusion criteria, electronic database searches identified ten empirical studies suitable for review. Participants in these studies completed performance-based tasks, typically cognitive, while remaining awake for 28–63 hours. In all studies, metacognitive monitoring was assessed via confidence ratings either pre-, on-, or post-task. Extended wakefulness had a significant negative effect on performance in most studies. Evidencing good monitoring, however, the monitoring estimates such as confidence also tended to decline. Moreover, two critical variables that assess the fit of these estimates to actual performance (bias and discrimination) were mostly unaffected by the number of hours awake. Still, some results indicated that these variables may be affected by substances intended to fight sleep deprivation, such as modafinil. Within the limitations of extant literature (e.g., a sampling bias towards young adult male participants), empirical observations to date converge to suggest that metacognitive monitoring remains largely unaffected by the examined quantities of acute sleep deprivation (up to 63 hours). 相似文献
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In recent years, there has been a significant increase in the provision of formal coach education. However, research has repeatedly demonstrated how coach education has had a limited impact on the learning and development of coach practitioners. To date however, these investigations have avoided female coach populations. Ten women football coaches who had recently completed various association football coach education courses participated in this study. Following the interpretive analysis of 10 semi-structured interviews the findings revealed high levels of gender discrimination and inappropriate cultural practice. The women's experiences are discussed in line with the Bourdieuian notions of social acceptance, symbolic language and power. The women coaches provided a number of recommendations for future coach education provision, which in turn, may help to improve the experiences for those women who participate in the coach education process. 相似文献
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Daniel J. Madigan Andrew P. Hill Paul A. Anstiss Sarah H. Mallinson-Howard Simon Kumar 《European Journal of Sport Science》2018,18(5):713-721
Training distress occurs when athletes fail to cope with physiological and psychological stress and can be an early sign of overtraining syndrome. Recent research has found that perfectionism predicts increases in training distress in junior athletes over time. The current study provides the first empirical test of the possibility that coping tendencies mediate the perfectionism-training distress relationship. Adopting a cross-sectional design, 171 junior athletes (mean age?=?18.1 years) completed self-report measures of perfectionistic strivings, perfectionistic concerns, problem-focused coping, avoidant coping, and training distress. Structural equation modelling revealed that avoidant coping mediated the positive relationship between perfectionistic concerns and training distress, and mediated the negative relationship between perfectionistic strivings and training distress. Problem-focused coping did not mediate any relationships between dimensions of perfectionism and training distress. The findings suggest that the tendency to use coping strategies aimed at avoiding stress may partly explain the relationship between perfectionism and training distress but the tendency to use, or not use, problem-focussed coping does not. 相似文献
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Kirsty Kitto Ben Hicks Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.
Practitioner notes
What is already known about this topic
- ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
- Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
- Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.
What this paper adds
- An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
- A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
- An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.
Implications for practice and/or policy
- Causal models can help us to explicitly specify educational theories in a testable format.
- It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
- Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
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Marina Klimovich Simon P. Tiffin-Richards Tobias Richter 《Journal of Research in Reading》2023,46(2):123-142