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Adult-child interaction is an important component for language development in young children. 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 amend their teaching methods. Measuring engagement among pre-school children and teachers is a challenging task and not well defined. In this study, we focus on developing criteria to measure conversational turn-taking and topic initiation during adult-child interactions in preschool environments. However, 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. The next iterative of this problem is to deploy various solutions from speech and language processing technology to automate these measurements. * (2022 ASEE Best Student Paper Award Winner) 

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.