The linguistic environment of the classroom is influential to young children’s language development. To date, however, literature on the linguistic environment of child-care centers has largely examined teacher practices or children’s aggregate environment, overlooking the child’s first-person experiences and differentiated experiences within the classroom. In this study we used a new method in the educational setting that captures the learner’s perspective: head-mounted cameras. Thirteen children in one preschool classroom wore a head-mounted camera to capture their first-person experiences in one morning session, including interactions with others and the features of the child-directed speech (CDS) addressed to them. Results revealed that, from children’s personal view, the linguistic environment of the classroom is more dynamic from what previous studies have reported. Children interacted for longer with their teachers than their peers and heard more CDS from them, but for some children peers served as an additional source of language. Further, our analysis highlighted within-classroom variability in language experiences in terms of the properties of the CDS addressed to target children and how they were exposed to this input over time. Results are discussed with respect to peer influence on children’s learning, heterogeneity in learning opportunities in classrooms, and the variability of the linguistic environment over time.

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.

In the last decade, a growing number of schools have begun implementing dual language education (DLE), and studies have shown evidence of the benefits of DLE for elementary education students. However, existing research syntheses do not focus on DLE in the early years (pre-Kindergarten and Kindergarten), considering young bilingual children’s development and learning characteristics. In this paper, a novel conceptual framework is used to explore the extant literature on DLE in the early years moving beyond Bronfenbrenner’s ecological systems theory to consider additional characteristics relating to bilingual children’s development and learning. A systematic literature review was conducted following a rigorous procedure, resulting in nine studies that met the inclusion criteria. Information about each study was coded and analyzed. The results describe the studies’ sample characteristics, research design, and findings organized by students’ academic skills (i.e., language, literacy, and mathematics), dual language classroom practices, and parents’ perceptions of DLE. This paper highlights current knowledge of DLE programs in the early years, identifies gaps, and offers recommendations for future research, policy, and practice.

Research Findings: Conversing abstract concepts boost children’s language learning. Despite the numerous studies on the linguistic environment of early childhood education settings (ECE), most of this work disregards contextual factors that may influence abstract conversations and omits characteristics of children’s verbal participation in these interactions. We examined how preschool classroom contexts influenced the abstraction level of children’s conversations and how the context and conversational partners’ language influenced children’s verbal participation. We analyzed 2,928 utterances in 453 natural teacher and peer conversations during a one-week period in a preschool classroom. Bayesian analysis showed that children and teachers used significantly more abstract talk in small-group activities coded as math and science than other coded settings. Moreover, Bayesian analysis revealed that the abstraction level of conversational partners’ turns influenced that of children’s talk beyond the characteristics of the context. Interestingly, this association was moderated by the partner (teacher or peer). Practice or Policy: The inconsistent association between measurements of ECE quality and children’s outcomes invites practitioners to reevaluate what these measurements should assess. This study suggests the need to pay greater attention to the features of ECE settings that are conducive to children’s development, such as learning materials, grouping, and peer interactions.

Purpose: This study examines the accuracy of Interaction Detection in Early Childhood Settings (IDEAS), a program that automatically transcribes audio files and estimates linguistic units relevant to speech-language therapy, including part-of-speech units that represent features of language complexity, such as adjectives and coordinating conjunctions. Method: Forty-five video-recorded speech-language therapy sessions involving 27 speech-language pathologists (SLPs) and 56 children were used. The F measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy). Results: The high F measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy. Conclusions: IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children's language growth.

This study examined the dimensionality of caregivers’ child-directed talk and the association of child directed talk and children’s language skills at age of 3 years in a sample of 63 low-SES dyads in low-SES homes. Exploratory factor analysis identified that caregivers’ child directed talk is characterized by two dimensions: quantity and complexity. The quantity factor consisted of utterances per minute, total number of words, and type-token ratio, whereas the complexity factor included mean length of utterance and the percentage of complex utterances. After controlling for covariates (e.g. child age, race, maternal education, and language used at home), CDT complexity was significantly related to children’s receptive and expressive language skills, however, this association was not significant for CDT quantity. These results have important implications for intervention development.