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861.
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