Measuring children and young people’s wellbeing in the school context

Although being rooted in the work of ancient Greek philosophers, contemporary research on wellbeing is a relatively new phenomenon. As a term in the literature, wellbeing is often used interchangeably with others, such as happiness, flourishing, enjoying a good life and life satisfaction. Furthermore, the wellbeing of school-aged children is only beginning to be explored with increasing recognition that research conducted on adults cannot be uncritically applied to children and young people. This paper aims to address some of the complexities in conceptualising, and hence assessing, children and young people’s wellbeing by drawing on a recently completed study examining the role of creative initiatives in fostering wellbeing. The new instrument that was developed to capture children and young people’s perceptions of their wellbeing in school is outlined. Data are presented from a survey of 5170 students from 20 primary and 20 secondary schools across England that identify four dimensions of wellbeing. Differences in self-reported wellbeing relating to age, gender and type of school attended (Creative Partnerships versus other schools) are explored. The implications of these findings, particularly differences related to type of school attended, given the focus of this special issue, are considered.     

Promoting Gypsy children's behavioural engagement and school success: Evidence from a four‐wave longitudinal study

Low schooling, high non‐attendance and school dropout rates are critical phenomena within disadvantaged groups, especially among the Gypsy community. For example, in the UK, 10%–25% of Gypsy children do not attend school regularly and have significantly higher levels of overall absence from school (percentage of half‐day sessions missed) than pupils from other ethnic groups. In Portugal, available data on Gypsy children is sparse, yet data from one geographic region of the country reports high school failure (45%) and dropout rates (15%) among this population. The present study assessed the efficacy of a four‐year intervention to promote Gypsy children's behavioural engagement and school success. Gypsy communities were contacted and 30 children participating in the four waves were randomly distributed into control and experimental groups. Every school day throughout four years, 16 children in the experimental group were called at home and invited to go to school. The effectiveness of the intervention was evaluated in four waves (at the end of each of the four school years), assessing behavioural engagement (i.e. school non‐attendance, classroom behaviour) and school achievement (i.e. mathematics achievement, student progression). Findings show the efficacy of the intervention on promoting behaviour engagement and academic success without devaluing Gypsy people's culture.     

Teachers' views on the nature of models

A semi‐structured interview was used in Brazil to enquire into the ‘notion of model’ held by a total sample of 39 science teachers who were: employed in ‘fundamental’ (6–14 years) and ‘medium’ (15–17 years) schools; student science teachers currently doing their practicum; and university science teachers. Seven ‘aspects’ of their notions of a model were identified: the nature of a model, the use to which it can be put, the entities of which it consists, its relative uniqueness, the time span over which it is used, its status in the making of predictions, and the basis for the accreditation of its existence and use. Categories of meaning were identified for each of these aspects. The profiles of teachers' notions of ‘model’ in terms of the aspects and categories were complex, providing no support for the notion of ‘Levels’ in understanding. Teachers with degrees in chemistry or physics had different views about the notion of ‘model’ to those with degrees in biology or with teacher training certificates.     

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.