BackgroundOutdoor social participation in the school playground is crucial for children's socio‐emotional and cognitive development. Yet, many children with disabilities in mainstream educational settings are not socially included within their peer group. We examined whether loose‐parts‐play (LPP), a common and cost‐effective intervention that changes the playground play environment to enhance child‐led free play, can promote social participation for children with and without disabilities.MethodForty‐two primary school children, out of whom three had hearing loss or autism, were assessed for two baseline and four intervention sessions. We applied a mixed‐method design, combining advanced sensors methodology, observations, peer nominations, self‐reports, qualitative field notes and an interview with the playground teachers.ResultsFindings indicated for all children a decrease during the intervention in social interactions and social play and no change in network centrality. Children without disabilities displayed also an increase in solitude play and in the diversity of interacting partners. Enjoyment of LPP was high for all children, yet children with disabilities did not benefit socially from the intervention and became even more isolated compared with baseline level.ConclusionsSocial participation in the schoolyard of children with and without disabilities did not improve during LPP in a mainstream setting. Findings emphasize the need to consider the social needs of children with disabilities when designing playground interventions and to re‐think about LPP philosophy and practices to adapt them to inclusive settings and goals.

(1) Many children in schoolyards are excluded from social interactions with peers on a daily basis. For these excluded children, schoolyard environments often contain features that hinder, rather than facilitate, their participation. These features may include lack of appropriate play equipment, overcrowded areas, or insufficient supervision. These can generate negative situations, especially for children with special needs—such as attention deficit or autism—which includes 10% of children worldwide. All children need to be able to participate in their social environment in order to engage in social learning and development. For children living with a condition that limits access to social learning, barriers to schoolyard participation can further inhibit this. Given that much physical development also occurs as a result of schoolyard play, excluded children may also be at risk for reduced physical development. (2) However, empirically examining schoolyard environments in order to understand existing obstacles to participation requires huge amounts of detailed, precise information about play behaviour, movement, and social interactions of children in a given environment from different layers around the child (physical, social, and cultural). Recruiting this information has typically been exceedingly difficult and too expensive. In this preliminary study, we present a novel sensor data-driven approach for gathering information on social interactions and apply it, in light of schoolyard affordances and individual effectivities, to examine to what extent the schoolyard environment affects children’s movements and social behaviours. We collected and analysed sensor data from 150 children (aged 5–15 years) at two primary special education schools in the Netherlands using a global positioning system tracker, proximity tags, and Multi-Motion Receivers to measure locations, face-to-face interactions, and activities. Results show strong potential for this data-driven approach to examine the triad of physical, social, and cultural affordances in schoolyards. (3) First, we found strong potential in using our sensor data-driven approach for collecting data from individuals and their interactions with the schoolyard environment. Second, using this approach, we identified and discussed three schoolyard affordances (physical, social, and cultural) in our sample data. Third, we discussed factors that significantly impact children’s movement and social behaviours in schoolyards: schoolyard capacity, social use of space, and individual differences. Better knowledge on the impact of these factors could help identify limitations in existing schoolyard designs and inform school officials, policymakers, supervisory authorities, and designers about current problems and practical solutions. This data-driven approach could play a crucial role in collecting information that will help identify factors involved in children’s effective movements and social behaviour.

The authors request the following corrections because the changes made according to the second round of the review process were not included in the original publication [...]

Detecting and analyzing group behavior from spatiotemporal trajectories is an interesting topic in various domains, such as autonomous driving, urban computing, and social sciences. This paper revisits the group detection problem from spatio-temporal trajectories and proposes "WavenetNRI", a graph neural network (GNN) based method. The proposed WavenetNRI extends the previously proposed neural relational inference (NRI) method (an unsupervised learning approach for inferring interactions from observational data) in two directions:(1) symmetric edge features and edge updating processes are applied to generate symmetric edge representations corresponding to the symmetric binary group relationships; (2) a gated dilated residual causal convolutional (GD-RCC) block is adopted to capture both short and long dependency of the edge feature sequences. We evaluated the performance of the proposed model on three simulation datasets and three real-world pedestrian datasets, using the Group Mitre metric to measure the quality of the predicted groups. We compared WavenetNRI with four baseline methods, including two clustering-based and two classification-based methods. In these experiments, NRI and WavenetNRI outperformed all other baselines on the group-interaction simulation datasets, while NRI performed slightly better than WavenetNRI. On the pedestrian datasets, the WavenetNRI outperformed other classification-based baselines. However, it did not compete against the clustering-based methods. Our ablation study showed that while both proposed changes cannot be effective at the same time, either of them can improve the performance of the original NRI on one dataset type.