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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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Adult Education at Biological Field Stations: Building Capacity for Science Learning
An understanding of science concepts is important for living in modern society. Supporting adults’ science learning can be particularly challenging because most adults no longer attend formal educational institutions where access and opportunities are facilitated by teachers and school-sponsored programs. Biological field stations (BFSs) are a newly recognized educational venue that hold considerable intrinsic value for adult science education. In this study, we conducted a survey of 223 U.S. BFSs about their nonformal and informal educational outreach programs for adults. Results show BFSs offer a wide variety of science learning programs for adults, focused heavily on experiential learning to engage learners. These experiences promote interactions with the natural environment and are perceived to increase participants’ knowledge and skills. This study has implications for how adult educators can better support the professional development of science educators at BFSs and enrich the general public's science learning.
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- PAR ID:
- 10540638
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Adult Education Quarterly
- Volume:
- 72
- Issue:
- 3
- ISSN:
- 0741-7136
- Format(s):
- Medium: X Size: p. 284-307
- Size(s):
- p. 284-307
- Sponsoring Org:
- National Science Foundation
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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 ∼78.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with Automatic Speech Recognition-based resegmentation 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.more » « less
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