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This article highlights the emergence of alternative education in PEI and brings to light the complexities underpinning how a child with challenging behaviour is viewed. Through the use of Foucauldian genealogical analysis and critical discourse analysis, this research centres on the discourse of ‘alternative education’ and problematises how alternative education programmes have been put in place as a solution to the problem of the child with challenging behaviour in ‘mainstream’ schools as constituted in the 1990s in PEI, Canada.
Using data generated from educational policies, government documents, and interviews with educators who worked in alternative placements and practitioners who worked with students identified as having problematic behaviour, I propose that alternative education programmes are hybrid programmes emerging from an overlapping of understanding from ‘mainstream’ education and ‘special education’. 相似文献
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.