Issues in Constructivist Early Moral Education

The authors address Goodman's critique of constructivist moral education as articulated by DeVries and Zan (1994) and respond to her proposal that constructivist education be blended with traditional moral education as advocated by Wynne (1991). The authors cite empirical evidence contrary to Goodman's assertion that preschool children are egocentric and incapable of moral reflection. This difference links to dramatically opposed views on the educational implications of Piaget's research and theory. Goodman advocates that teachers should “exploit the child's natural heteronomy” by giving children rules and demanding obedience. The authors cite empirical evidence showing that heteronomous regulation of children leads to a host of undesirable outcomes and that encouragement of children's autonomy leads to positive outcomes. The authors also point out a number of misconceptions of constructivist education as propounded by DeVries and Zan. They discuss Goodman's specific suggestions for blending constructivist and traditional moral education, and conclude that such a blending contradicts considerable research on child development and early education and eliminates critical components of constructivist early moral education.     

The role of school psychology in preventing depression

To advance the role of school psychologists in mental health prevention and wellness activities, in this article we describe (a) a social ecological theory for guiding school‐based prevention research, (b) the role of schools in the development and prevention of depression, (c) a continuum of school‐based support for meeting the needs of children who are currently depressed for preventing future internalizing distress for all children, and (d) social and political factors that must be considered if lasting change is to occur. We conclude with specific considerations for school psychologists. © 2004 Wiley Periodicals, Inc. Psychol Schs 41: 763–775, 2004.     

Developing professional learning for staff working with children with speech,language and communication needs combined with moderate‐to‐severe learning difficulties

This article presents research undertaken as part of a PhD by Carolyn Anderson who is a senior lecturer on the BSc (Hons) in Speech and Language Pathology at the University of Strathclyde. The study explores the professional learning experiences of 49 teachers working in eight schools and units for children with additional support needs in Scotland. In particular, she examines the professional learning experiences of teachers working with children with speech, language and communication needs (SLCN) combined with moderate‐to‐severe learning difficulties. While the teachers under study predominantly engaged with informal professional learning, she observes that they often expressed a lack of confidence in this form of professional development, tending to value formal learning regardless of impact. In response she raises a number of important questions about the role of Initial Teacher and Postgraduate Education and the ways in which schools understand reflective practice.     

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

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.