The Learning Transfer Big Picture     

George Hall  Mike Smith  Carolyn Dare 《Performance Improvement》2014,53(10):6-11

Do not forget learning transfer while crafting your learning and development initiatives. A research‐driven model allows your organization to adapt quickly and make real‐time adjustments to your learning strategy.  相似文献   

Using learning sets to support UK delivery of off-shore learning in Africa     

Michelle Blackburn 《Action Learning: Research and Practice》2014,11(1):81-87

This account of practice focuses on the delivery of Action Learning Sets in Swaziland and Malawi as part of a UK university's remote Master's degree teaching programme. It draws upon the experience of an Academic delivering the programme and the efforts made to refine the approach to action learning given time, understanding and resource challenges. The outcome of limited research on the benefits of Action Learning Sets is shared within this context.  相似文献   

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics     

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.

What this paper adds

• 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.

Implications for practice and/or policy

• 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.

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