Continuous measurement of dynamic classroom social interactions

Abstract

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children’s interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children’s location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children’s velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.

Mining Exceptional Social Behaviour

“…This means that boys follow other boys and girls follow other girls, much more than what would be expected. We note that these subgroups have much higher score than the others, which goes in line with the observations already discussed in [26]. This seems to confirm the homophily regarding the gender, meaning that children interact more with children of the same gender.…”

“…ProSoc the higher the score the highest the social skills are Conduct a high value represents behaviour problems Emotion the higher the score the more emotional difficulties Peer high scores indicate that the child has issues making friends Hyper the higher the more hyperactive the child is The other dataset, playgroundB [26] has the position of 14 children (8 girls) around 5 years-old and socio-demographic attributes, such as gender and age. The data was collected through a real time location system that used UWB sensors.…”

“…In addition, deliberately gathering data from social and ubiquitous environments through sensors (of proximity or geo-localization) is also being used to study the behaviour of people without interfering with their actions [16], e. g., movement data [23], on a more naturalistic basis. More specifically, it was applied to the domain of social interactions [26], where the authors analyze the properties (mainly focusing on gender phenomena) of five-year-old kids' social interactions.…”

“…One dataset, playgroundA [16], has the geographic position of 18 children over time, in 10 different sessions and personal attributes (gender, age, emotional stability etc). The other dataset, playgroundB [26] has the position of 16 children and sociodemographic attributes, such as gender and age. The results were mostly expected, when analyzed by experts [26] in the domain, and similar between the two datasets.…”

“…The other dataset, playgroundB [26] has the position of 16 children and sociodemographic attributes, such as gender and age. The results were mostly expected, when analyzed by experts [26] in the domain, and similar between the two datasets. This shows it is a valid approach.…”