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1. Do Preschool Peers Create a Sustaining Environment in Kindergarten? Evidence from the ECLS-K:2011

   https://doi.org/10.1177/23328584251318306  

   Shea, Zhiling Meng; Zhang, Qing; Jenkins, Jade Marcus; Watts, Tyler W (January 2025, AERA Open)

   Using the Early Childhood Longitudinal Study - Kindergarten Cohort 2011, we examine (i) whether more preschool attendees in a kindergarten classroom relate to higher academic and social skills for children who did not go to preschool, (ii) whether more preschool attendees in the classroom moderate the benefits of preschool attendance (child N = 11,360; class N = 2,460; 67% White; 51% males; M_age= 5.6 years), and (iii) whether more preschool attendees in the classroom relate to teachers’ perceptions of children’s skills and their instructional content. In contrast to prior analyses using the 1998 cohort of the ECLS-K, we found no evidence of an association between the classroom percentage of preschool-attending peers and children’s academic, executive function, and behavioral and prosocial skills. However, we found that the percentage of preschool peers was associated with teachers’ perceptions of children’s reading skills and teachers’ instructional time spent on advanced reading content. 

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2. Teacher-Student learning based Acoustic Models for Robust Speech Recognition in Naturalistic Childhood Classroom settings

   Kothalkar, P.V. (February 2023, Speech communication)

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with both teachers and classmates. Early childhood researchers recognize the importance in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of child interactions. Recently, large language model-based speech technologies have performed well on conversational speech recognition. In this regard, we assess performance of such models on the wide dynamic scenario of early childhood classroom settings. This study investigates an alternate Deep Learning-based Teacher-Student learning solution for recognizing adult speech within preschool interactions. Our proposed adapted model achieves the best F1-score for recognizing most frequent 400 words on test sets for both classrooms. Additionally, F1-scores for alternate word groups provides a breakdown of performance across relevant language-based word-categories. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics from this study can also be used for broad-based feedback for teachers. 

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3. Measuring Frequency of Child-directed WH-Question Words for Alternate Preschool Locations using Speech Recognition and Location Tracking Technologies

   https://doi.org/10.1145/3461615.3485440  

   Kothalkar, P.; Datla, S.; Dutta, S.; Hansen, J.H.L.; Seven, Y.; Irvin, D.; Buzhardt J. (October 2021, ACM ICMI - WoCBU-2021: Workshop on Bridging Social Sciences and AI for Understanding Child Behavior)

   Speech and language development in children are crucial for ensuring effective skills in their long-term learning ability. A child’s vocabulary size at the time of entry into kindergarten is an early indicator of their learning ability to read and potential long-term success in school. The preschool classroom is thus a promising venue for assessing growth in young children by measuring their interactions with teachers as well as classmates. However, to date limited studies have explored such naturalistic audio communications. Automatic Speech Recognition (ASR) technologies provide an opportunity for ’Early Childhood’ researchers to obtain knowledge through automatic analysis of naturalistic classroom recordings in measuring such interactions. For this purpose, 208 hours of audio recordings across 48 daylong sessions are collected in a childcare learning center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. Approximately 29 hours of adult speech and 26 hours of child speech is segmented using manual transcriptions provided by CRSS transcription team. Traditional as well as End-to-End ASR models are trained on adult/child speech data subset. Factorized Time Delay Neural Network provides a best Word-Error-Rate (WER) of 35.05% on the adult subset of the test set. End-to-End transformer models achieve 63.5% WER on the child subset of the test data. Next, bar plots demonstrating the frequency of WH-question words in Science vs. Reading activity areas of the preschool are presented for sessions in the test set. It is suggested that learning spaces could be configured to encourage greater adult-child conversational engagement given such speech/audio assessment strategies. 

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4. End-to-end Child-Adult Speech Diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model

   Kothalkar, P.V.; Buzhardt, J.; Irvin, D.; Hansen, J.H.L. (August 2022, The journal of the Acoustical Society of America)

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs. controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates an alternate Deep Learning-based diarization solution for segmenting classroom interactions of 3-5 year old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our proposed ResNet model achieves a best F1-score of ∼71.0% on data from two classrooms, based on dev and test sets of each classroom. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs. child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations. 

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5. Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation

   https://doi.org/10.1121/10.0024353  

   Kothalkar, Prasanna V; Hansen, John_H L; Irvin, Dwight; Buzhardt, Jay (February 2024, The Journal of the Acoustical Society of America)

   Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3–5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations. 

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