The impact of loose-parts-play on schoolyard social participation of children with and without disabilities: A case study.

BACKGROUND: Outdoor 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. METHOD: Forty-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. RESULTS: Findings 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. CONCLUSIONS: Social 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.

Social connectedness at the playground before and after COVID-19 school closure.

Social connectedness at school is crucial to children's development, yet very little is known about the way it has been affected by school closures during COVID-19 pandemic. We compared pre-post lockdown levels of social connectedness at a school playground in forty-three primary school-aged children, using wearable sensors, observations, peer nominations and self-reports. Upon school reopening, findings from sensors and peer nominations indicated increases in children's interaction time, network diversity and network centrality. Group observations indicated a decrease in no-play social interactions and an increase in children's involvement in social play. Explorative analyses did not reveal relations between changes in peer connectedness and pre-lockdown levels of peer connectedness or social contact during the lockdown period. Findings pointed at the role of recess in contributing to children's social well-being and the importance of attending to their social needs upon reopening.

Correction: Nasri et al. A Novel Data-Driven Approach to Examine Children's Movements and Social Behaviour in Schoolyard Environments. Children 2022, 9, 1177.

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 [...].

A Novel Data-Driven Approach to Examine Children's Movements and Social Behaviour in Schoolyard Environments.

(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.

VPint: value propagation-based spatial interpolation.

Given the common problem of missing data in real-world applications from various fields, such as remote sensing, ecology and meteorology, the interpolation of missing spatial and spatio-temporal data can be of tremendous value. Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from (a) a trade-off between modelling local or global spatial interaction, (b) the assumption there is only one possible path between two points, and (c) the assumption of homogeneity of intermediate locations between points. Addressing these issues, we propose a value propagation-based spatial interpolation method called VPint, inspired by Markov reward processes (MRPs), and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant. Both these interpolation variants operate locally, while implicitly accounting for global spatial relationships in the entire system through recursion. We evaluated our proposed methods by comparing the mean absolute error, root mean squared error, peak signal-to-noise ratio and structural similarity of interpolated grid cells to those of 8 common baselines. Our analysis involved detailed experiments on a synthetic and two real-world datasets, as well as experiments on convergence and scalability. Empirical results demonstrate the competitive advantage of VPint on randomly missing data, where it performed better than baselines in terms of mean absolute error and structural similarity, as well as spatially clustered missing data, where it performed best on 2 out of 3 datasets.

Identifying Stops and Moves in WiFi Tracking Data.

There are multiple methods for tracking individuals, but the classical ones such as using GPS or video surveillance systems do not scale or have large costs. The need for large-scale tracking, for thousands or even millions of individuals, over large areas such as cities, requires the use of alternative techniques. WiFi tracking is a scalable solution that has gained attention recently. This method permits unobtrusive tracking of large crowds, at a reduced cost. However, extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the dependence on signals generated by the tracked device, which are irregular and sparse. To facilitate further data analysis, we can partition individual trajectories into periods of stops and moves. This abstraction level is fundamental, and it opens the way for answering complex questions about visited locations or even social behavior. Determining stops and movements has been previously addressed for tracking data gathered using GPS. GPS trajectories have higher positional accuracy at a fixed, higher frequency as compared to trajectories obtained through WiFi. However, even with the increase in accuracy, the problem, of separating traces in periods of stops and movements, remains similar to the one we encountered for WiFi tracking. In this paper, we study three algorithms for determining stops and movements for GPS-based datasets and explore their applicability to WiFi-based data. We propose possible improvements to the best-performing algorithm considering the specifics of WiFi tracking data.

Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review.

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.