To support preschool children’s learning about data in an applied way that allows children to leverage their existing mathematical knowledge (i.e. counting, sorting, classifying, comparing) and apply it to answering authentic, developmentally appropriate research questions with data. To accomplish this ultimate goal, a design-based research approach [1] was used to develop and test a classroom-based preschool intervention that includes hands-on, play-based investigations with a digital app that supports and scaffolds the investigation process for teachers and children. This formative study was part of a codesign process with teachers to elicit feedback on the extent to which the series of investigations focused on data collection and analysis (DCA) and the teacher-facing app were (a) developmentally appropriate, (b) aligned with current preschool curricula and routines, (c) feasible to implement, and (d) included design elements and technology affordances teachers felt were useful and anticipated to promote learning. Researchers conducted in-depth interviews (n=10) and an online survey (n=19) with preschool teachers. Findings suggest that teaching preschoolers how to collect and analyze data in a hands-on, play-based, and developmentally appropriate way is feasible and desirable for preschool teachers. Specifically, teachers reported that the initial conceptualization of the investigations were developmentally appropriate, aligned with existing curricular activities and goals, was adaptable for the age and developmental readiness of young children, and that the affordances of the technology are likely to allow preschool children to engage meaningfully in data collection, visualization, and analysis. Findings also suggest that this approach to supporting preschool teachers and children to learn about and conduct DCA merits further study to ensure productive curricular implementation that positively influences preschoolers’ learning. These findings were used to revise the investigations and app, which showed positive outcomes when used in classrooms [2], which add to the scant literature on DCA learning for pre-schoolers and provides insights into the best ways to integrate technology into the classroom.
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Automatic Measurement of Teachers' Talk: Indicators of Location and Quality in Science Activities
A key goal of Next Generation Science Standards is to promote interest and
exploration of natural phenomena. In preschool settings, teachers prompt
exploration by asking questions, encouraging informal exploration and experimentation. To date, live or offline video observation has
been the sole way to capture the quality of teacher question asking in the pre-k
classroom (e.g., Sanders et al., 2016). To date, Automatic Speech Recognition
(ASR) has not been used to measure the content/quality of teacher talk. Here, we used ASR to quantify preschool teachers’ use of keywords that promote
student exploration and inquiry.
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- Award ID(s):
- 2016725
- PAR ID:
- 10180041
- Date Published:
- Journal Name:
- CRIEI-2020: Conf. on Research Innovations in Early Intervention
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The use of wh-words, including wh-questions and wh-clauses, can be linguistically, conceptually, and interactively challenging to preschoolers. Young children develop mastery of wh-words as they formulate and hear these words during daily interactions in contexts such as preschool classrooms. Observational approaches limit researchers' ability to comprehensively capture the classroom conversations, including wh-words. In the current study, we report the results of the first study using the automated speech recognition (ASR) system coupled with location sensors designed to quantify teachers' wh-words in the literacy activity areas of a preschool classroom. We found that the ASR system is a viable solution to automatically quantify the number of adult wh-words used in preschool classrooms. Our findings demonstrated that the most frequently used adult wh-word type was "what." Classroom adults used more wh-words during time point 1 compared to time point 2. Lastly, a child at risk for developmental delays heard more wh-words per minute than a typically developing child. Future research is warranted to further improve the effortsmore » « less
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Abstract Background What and how teachers learn through teaching without external guidance has long been of interest to researchers. Yet limited research has been conducted to investigate how learning through teaching occurs. The microgenetic approach (Siegler and Crowley, American Psychologist 46:606–620, 1991) has been useful in identifying the process of student learning. Using this approach, we investigated the development of teacher knowledge through teaching as well as which factors hinder or promote such development.
Results Our findings suggest that teachers developed various components of teacher knowledge through teaching without external professional guidance. Further, we found that the extent to which teachers gained content-free or content-specific knowledge through teaching depended on their robust understanding of the concept being taught (i.e., content knowledge), the cognitive demand of the tasks used in teaching, and the lesson structure chosen (i.e., student centered vs. teacher centered).
Conclusions In this study, we explored teacher learning through teaching and identified the sources leading to such learning. Our findings underscore the importance of teachers’ robust understanding of the content being taught, the tasks used in teaching, and a lesson structure that promotes teachers’ learning through teaching on their own.
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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.more » « less
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null (Ed.)Speech and language development in children is crucial for ensuring optimal outcomes in their long term development and life-long educational journey. A child’s vocabulary size at the time of kindergarten entry is an early indicator of learning to read and potential long-term success in school. The preschool classroom is thus a promising venue for monitoring growth in young children by measuring their interactions with teachers and classmates. Automatic Speech Recognition (ASR) technologies provide the ability for ‘Early Childhood’ researchers for automatically analyzing naturalistic recordings in these settings. For this purpose, data are collected in a high-quality childcare center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. A preliminary task for ASR of daylong audio recordings would involve diarization, i.e., segmenting speech into smaller parts for identifying ‘who spoke when.’ This study investigates a Deep Learning-based diarization system for classroom interactions of 3-5-year-old children. However, the focus is on ’speaker group’ diarization, which includes classifying speech segments as being from adults or children from across multiple classrooms. SincNet based diarization systems achieve utterance level Diarization Error Rate of 19.1%. Utterance level speaker group confusion matrices also show promising, balanced results. These diarization systems have potential applications in developing metrics for adult-to-child or child-to-child rapid conversational turns in a naturalistic noisy early childhood setting. Such technical advancements will also help teachers better and more efficiently quantify and understand their interactions with children, make changes as needed, and monitor the impact of those changes.more » « less
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Accepted Manuscript1.0
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