Developing collaborative approaches to enhance the professional development of primary mathematics teachers

This research project promoted a collaborative model of professional development between lead teachers from three schools, supported by a project coordinator and a researcher from a local university. Each lead teacher worked with their head teacher to design, lead, and evaluate an innovative, personalised, and school-based mathematics continuing professional development (CPD) programme in their school. University staff helped to facilitate project meetings across the schools and monitored impacts within each school. Professional development meetings, involving all teachers and teaching assistants (TAs) from the schools (n?=?55), were designed to encourage a whole-school approach. The project also provided structured opportunities for the lead teacher to work with colleagues in the classroom, for example, through lesson observation and/or collaborative teaching. The outcomes from this project confirmed that collaborative models of CPD, as opposed to transmission, formal training, and ‘top-down’ models, were welcomed by teachers and head teachers – some of whom reported early indications of improvements in student performance. Commenting on what constitutes the most effective forms of CPD, there was a reiteration of the importance of combining peer and external support through a collaborative process.     

The effects of upper‐secondary education and training systems on skills inequality. A quasi‐cohort analysis using PISA 2000 and the OECD survey of adult skills

Research tells us much about the effects of primary and lower‐secondary schooling on skills inequality, but we know less about the impact of the next stage of education. This article uses a differences‐in‐differences analysis of data on literacy and numeracy skills in PISA 2000 and SAS 2011/12 to assess the contribution of upper‐secondary education and training to inequalities in skills opportunities and outcomes. It finds that greater parity of esteem between academic and vocational tracks, as found in German‐speaking and Scandinavian countries, has some positive effects in mitigating skills inequality. However, the most important factors seem to be high completion rates from long‐cycle upper‐secondary education and training and mandatory provision of Maths and the national language in the curriculum.     

Assessing the impact of football-based health improvement programmes: stay onside,avoid own goals and score with the evaluation!

Health improvement is an important strand of the Premier League’s ‘Creating Chances’ strategy. Through community programmes, professional football clubs offer health-enhancing interventions for a number of different priority groups at risk from a range of lifestyle-related health conditions. However, while national guidance recommends evaluating health improvement interventions, concerns remain about how to do this most effectively. This study aims to investigate the popularity of football-based health improvement schemes and assess the challenges associated with their evaluation. Adapted from existing methodologies, a semi-structured questionnaire was administered to an ‘expert’ sample (n?=?3) of football-led health evaluators. The sample was selected because of their experience and knowledge of performing evaluations of football-led health improvement programmes. Our ‘experts’ offered reasons for the popularity of football settings as channels for health improvement (including the reach of the club badge and the popularity of football), the justification for evaluating such schemes (including confirming effectiveness and efficiency) and the challenges of implementing evaluations (capacity, commitment and capability). Finally, a selection of key considerations for the evaluation of the impact of football-led health improvement programmes (obtaining expert guidance, building capacity and planning for evaluations) are discussed.     

Human and artificial intelligence collaboration for socially shared regulation in learning

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic

• For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
• Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
• It is questionable whether traditional theories of how people learn are useful in the age of AI.

What this paper adds

• Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
• Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
• Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.

Implications for practice and/or policy

• Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
• Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

     

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.