Evaluating the accuracy of automated processing of child and adult language production in preschool classrooms.

Young children's language and social development is influenced by the linguistic environment of their classrooms, including their interactions with teachers and peers. Measurement of the classroom linguistic environment typically relies on observational methods, often providing limited 'snapshots' of children's interactions, from which broad generalizations are made. Recent technological advances, including artificial intelligence, provide opportunities to capture children's interactions using continuous recordings representing much longer durations of time. The goal of the present study was to evaluate the accuracy of the Interaction Detection in Early Childhood Settings (IDEAS) system on 13 automated indices of language output using recordings collected from 19 children and three teachers over two weeks in an urban preschool classroom. The accuracy of language outputs processed via IDEAS were compared to ground truth via linear correlations and median absolute relative error. Findings indicate high correlations between IDEAS and ground truth data on measures of teacher and child speech, and relatively low error rates on the majority of IDEAS language output measures. Study findings indicate that IDEAS may provide a useful measurement tool for advancing knowledge about children's classroom experiences and their role in shaping development.

Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

The present study explored whether a tool for automatic detection and recognition of interactions and child-directed speech (CDS) in preschool classrooms could be developed, validated, and applied to non-coded video recordings representing children's classroom experiences. Using first-person video recordings collected by 13 preschool children during a morning in their classrooms, we extracted high-level audiovisual features from recordings using automatic speech recognition and computer vision services from a cloud computing provider. Using manual coding for interactions and transcriptions of CDS as reference, we trained and tested supervised classifiers and linear mappings to measure five variables of interest. We show that the supervised classifiers trained with speech activity, proximity, and high-level facial features achieve adequate accuracy in detecting interactions. Furthermore, in combination with an automatic speech recognition service, the supervised classifier achieved error rates for CDS measures that are in line with other open-source automatic decoding tools in early childhood settings. Finally, we demonstrate our tool's applicability by using it to automatically code and transcribe children's interactions and CDS exposure vertically within a classroom day (morning to afternoon) and horizontally over time (fall to winter). Developing and scaling tools for automatized capture of children's interactions with others in the preschool classroom, as well as exposure to CDS, may revolutionize scientific efforts to identify precise mechanisms that foster young children's language development.

A Control-Theoretic Assessment of Interventions During Drinking Events.

This paper employs control-theoretic tools to provide guidelines for in-situ interventions aimed at reducing high-risk alcohol consumption at drinking events. A dynamical directed network model of a drinking event with external intervention, suitable for mathematical analysis and parameter estimation using field data is proposed, with insights from pharmacokinetics and psychology. Later, a characterization of a bound on blood alcohol content (BAC) trajectories is obtained via Lyapunov stability analysis, and structural controllability guarantees are obtained via a graph-theoretic method. We use the degree of controllability, given to be the trace of the system's controllability Gramian, as a metric to compare the viability of network nodes for intervention based on theoretic and heuristic centrality measures. Results of numerical examples of bars and parties, informed by field data, and the stability and controllability results, suggest that intervening in the environment in wet bars, while targeting influential individuals with high alcohol consumption motivations in private parties efficiently yield lower peak BAC levels in individuals at the drinking events.