Jun 16

ANR DyLNet — Language Dynamics, Linguistic Learning, and Sociability at Preschool: Benefits of Wireless Proximity Sensors in Collecting Big Data

Because preschool is the first step in a child’s school career, it is necessary to understand how children from different social backgrounds integrate and adapt to it. Oral language plays a key role in this process: it is the mean and result of socialization at school and is the “vital condition for the success of all pupils” (BOEN, 26/03/2015). Children integrate and adapt at school by communicating. At the same time, school socialization increases the opportunities to communicate with peers and with the adults responsible for the children, promotes learning and strengthens linguistic skills. A virtuous circle – or a spiral of failure – may therefore become established between children’s sociability, oral communication and learning at school. Social inequalities are a key factor in this chain since, as of age 2, children from different backgrounds do not exhibit the same level of language skills and do not all use, to the same extent, the linguistic codes that are encouraged at school. These early differences, which are transmitted within the family, have given rise to numerous studies that have revealed the influence of the nature and quantity of the speech addressed to children in different social environments. However, these works tell us little about the influence of peers, which may modulate the impact of the family given that peer groups give rise to a certain social mix, in particular in the school context. School attendance therefore introduces a new factor into the equation, especially when the academic group is socially mixed or through the speech produced by the teachers. The aim of DyLNet is to observe and characterize the relations between child socialization and oral language learning during the preschool period by means of an innovative multidisciplinary approach that combines work in the fields of language acquisition, sociolinguistics and network science. It will be implemented through the 3-year follow-up of all the children (200) and teaching staff at a socially mixed preschool. The social interactions between individuals will be recorded using wireless sensor technology which will record inter-individual proximity data at 5 second intervals. These sensors will be worn for one week every month for a period of 3 years. We will monitor the children’s language development on the basis of their results in general language tests and the recording of their social use of language in natural interactions, through microphones implemented on the sensors. Finally, the children’s social profiles will be identified by means of questionnaires sent to their families. Thanks to the analytical (detection of communities) and modeling (data driven multi-agent models) power of the network science, the social interaction data will be matched against the children’s linguistic performances and sociolinguistic usage. The task, in particular, will be to examine the influence of the children’s social relations on their language development (if individuals stay in the same peer community between two observation times, does the linguistic distance between them falls over the same period?) and, equally, the influence of language on these social relations (if two individuals belong to the same linguistic group at time T, does the probability that they will be in the same peer community increase at time T+n?). We shall also examine the interactions between the pupils and the teaching staff – teachers and classroom assistants – in order to observe whether their frequency has an impact on the children’s language development. Finally, DyLNet will result in the provision to the scientific community of a database indicating the relations between the recorded interaction frequencies and the language descriptions of a broad school community of children and adults followed up over three years.