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681.
Enda F. Whyte Chris Richter Siobhan O’connor Kieran A. Moran 《Journal of sports sciences》2018,36(8):889-900
We investigated the effects of high intensity, intermittent exercise (HIIP) and anticipation on trunk, pelvic and lower limb biomechanics during a crossover cutting manoeuvre. Twenty-eight male, varsity athletes performed crossover cutting manoeuvres in anticipated and unanticipated conditions pre- and post-HIIP. Kinematic and kinetic variables were captured using a motion analysis system. Statistical parametric mapping (repeated-measures ANOVA) was used to identify differences in biomechanical patterns. Results demonstrated that both unanticipation and fatigue (HIIP) altered the biomechanics of the crossover cutting manoeuvre, whereas no interactions effects were observed. Unanticipation resulted in less trunk and pelvic side flexion in the direction of cut (d = 0.70 – 0.79). This led to increased hip abductor and external rotator moments and increased knee extensor and valgus moments with small effects (d = 0.24–0.42), potentially increasing ACL strain. The HIIP resulted in trivial to small effects only with a decrease in internal knee rotator and extensor moment and decreased knee power absorption (d = 0.35), reducing potential ACL strain. The effect of trunk and hip control exercises in unanticipated conditions on the crossover cutting manoeuvre should be investigated with a view to refining ACL injury prevention programmes. 相似文献
682.
Andy Nguyen Sanna Järvelä Carolyn Rosé Hanna Järvenoja Jonna Malmberg 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):293-312
Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.
Practitioner notes
What is already known about this topic- Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
- It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
- Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.
- Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
- Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
- Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
- Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.
- Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
- This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.