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Quantifying Engagement in Preschool Classrooms - Conversational Turn-Taking & Topic Initiations

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Conference

2022 ASEE Gulf Southwest Annual Conference

Location

Prairie View, Texas

Publication Date

March 16, 2022

Start Date

March 16, 2022

End Date

March 18, 2022

Tagged Topic

Diversity

Page Count

6

DOI

10.18260/1-2--39202

Permanent URL

https://peer.asee.org/39202

Download Count

353

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Paper Authors

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Sarah Tao Center for Robust Speech Systems (CRSS), University of Texas at Dallas

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Sarah Tao is an post-baccalaureate electrical engineering undergraduate at the University of Texas at Dallas. She is a former educator with 7 years of teaching experience originally from Houston, Texas.

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Satwik Dutta University of Texas at Dallas Orcid 16x16 orcid.org/0000-0001-5880-7454

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Satwik is a second-year Ph.D. candidate in Electrical Engineering and and Louis Beecherl, Jr. Graduate Fellow at the University of Texas at Dallas pursuing research in speech processing. He graduated with an MS-EE degree from UT Dallas in Spring 2020. Currently, he is working to develop speech processing algorithms to analyze child-adult interactions in pre-school classrooms, thereby understanding speech and language development in children.

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Yagmur N/A Seven

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Dwight Irvin

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Jay Buzhardt University of Kansas

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As an Associate Research Professor at the University of Kansas, Dr. Buzhardt’s research interests focus on developing and testing technology solutions to support data-driven intervention decision making in early childhood education. At Juniper Gardens Children’s Project (JGCP), he leads the Technology Innovation Development & Research (TIDR) Lab, which is a hybrid of onsite fulltime application developers and externally contracted developers, where online and mobile applications are designed, developed, tested, and maintained for nearly all JGCP interventions that utilize technology. Through grants funded through OSEP and IES and led by Dr. Buzhardt, the TIDR Lab developed and currently maintains the MOD and IGDI platform where it is hosted. Additionally, Dr. Buzhardt has led or co-led 10 federal grants from the Department of Education (5 from Office of Special Education Programs, 5 from Institute of Education Sciences) and four from the National Institute on Disability, Independent Living, and Rehabilitation Research. He currently directs a project funded by the Institute of Education Sciences to develop a web application that guides educators' data-driven intervention decision making. He also leads a $2.5M project funded by the Office of Special Education Programs to develop and test strategies and applications grounded in Implementation Science to scale-up sustained use of data-driven decision-making practices by infant-toddler service providers. He recently completed a 2nd successful RCT of the MOD across four states to test web-based decision-making support vs. self-guided decision making in Early Head Start home visiting settings. Other relevant projects include investigations of the construct and predictive validity of infant-toddler IGDI assessments, development of web-based professional development for elementary educators, and a current NSF-funded project to develop technology to automatically measure child and adult language in preschool and informal learning contexts.

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John H. L. Hansen University of Texas at Dallas Orcid 16x16 orcid.org/0000-0003-1382-9929

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John H.L. Hansen, received Ph.D. & M.S. degrees from Georgia Institute of Technology, and B.S.E.E. degree from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 2005, where he is Associate Dean for Research, Prof. of Electrical & Computer Engineering, and holds a joint appointment in School of Behavioral & Brain Sciences (Speech & Hearing). At UTDallas, he established Center for Robust Speech Systems (CRSS). He is an ISCA Fellow, IEEE Fellow, past TC-Chair of IEEE Signal Proc. Society, Speech & Language Proc. Tech. Comm.(SLTC), and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He currently serves as President of ISCA (Inter. Speech Comm. Assoc.). He has supervised 92 PhD/MS thesis candidates, was recipient of 2020 UT-Dallas Provost’s Award for Grad. Research Mentoring, 2005 Univ. Colorado Teacher Recognition Award, and author/co-author of +750 journal/conference papers in the field of speech/language/hearing processing & technology.

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Abstract

Adult-child interaction is an important component for language development in young children. However, such development varies based on the quality and quantity of these conversations. Teachers responsible for the language acquisition of their students have a vested interest in improving such conversation in their classrooms. Advancements in speech technology and natural language processing can be used as an effective tool by teachers in pre-school classrooms, to acquire large amounts of conversational data, receive feedback from automated conversational analysis, and use it to improve and amend their teaching methods. Measuring engagement among pre-school children and teachers is a challenging task and not well defined. Conversational turn-taking and topic initiations are two factors of conversational engagement. In this study, we focus on developing criteria to measure conversational turn-taking and topic initiation during adult-child interactions in preschool environments. More specifically, our goal is to create measures and visualizations which can reflect mutual conversational engagement for children/students and teachers/adults. A conversational turn is when an adult speaks, and a child follows, or vice versa, with no longer than 5 seconds in between. Any sound is counted as a response. Topic initiation refers to the statement that prompts the first conversational turn in an adult-child verbal exchange on a certain subject. If the topic spoken about changed, or more than 5 seconds went by without a response, a new topic initiation was noted. Child initiations refer to a child beginning a conversation while adult initiations refer to an adult beginning a conversation. A total of 5, 30-minute recordings and transcripts of adult-child interaction sessions were analyzed – out of which 3 were of typically developing children and 2 were of children with speech or language delays. Conversational turns were used to track the length of a conversation, but not to track vocabulary complexity, as a turn includes any sound as a response and speaking words is not necessary for a turn to occur. This was most noticeable in children with speech delays, as their responses were usually less than 5 words. Adult conversation initiation was found to promote more conversational turns, but their effectiveness was mitigated by the lower average target PMLU score used to measure children’s noun complexity. Counting vocabulary produced by children does not distinguish between repeated use of words or identify complexity of the words used. We used topic initiations and conversational turns as indicators of conversational quality. The next iterative of this problem is to deploy various solutions from speech and language processing technology to automate the measurements. Counting conversational turns, conversation initiations, or vocabulary alone is not enough to judge the quality of a conversation and track language acquisition. It is necessary to use a combination of the three and include a measurement of the complexity of vocabulary.

Tao, S., & Dutta, S., & Seven, Y. N., & Irvin, D., & Buzhardt, J., & Hansen, J. H. L. (2022, March), Quantifying Engagement in Preschool Classrooms - Conversational Turn-Taking & Topic Initiations Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas. 10.18260/1-2--39202

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