Observational approaches may limit researchers' ability to comprehensively capture preschool classroom conversations, including the use of wh-words. In the current proof-of-concept study, we present descriptive results using an automated speech recognition (ASR) system coupled with location sensors to quantify teachers' wh-words by preschool teachers in the literacy activity areas of a preschool classroom. Data from two children, one is 5.3 years old with attention-deficit/hyperactivity disorder (ADHD), and another is 5 years old without identified disabilities, along with teachers, were analyzed. We found that the ASR system is a viable solution for automatically quantifying the number of adult wh-words during interactions in preschool classrooms at different time points and locations. This paper reports how an ASR model, coupled with location sensors, quantifies the frequency of wh-words between two-time points and between a child with ADHD and a typically developing child. The results provide a proof of concept that an ASR model, including acoustic and language models, can automate the detection of wh-words in preschool teachers’ classroom speech. However, further research with larger and more diverse samples is required to explore the cost and time implications of scaling up across a variety of settings and populations to inform efficient classwide and individualized data-driven instructional practices.

观察方法可能会限制研究人员全面捕捉学前课堂对话的能力，包括wh词的使用。在当前的概念验证研究中，我们使用自动语音识别（ASR）系统与位置传感器相结合来量化学前班教师在学前教室读写活动区域中使用的 wh 词，从而提供描述性结果。分析了两名儿童的数据，其中一名 5.3 岁患有注意力缺陷/多动障碍 (ADHD)，另一名 5 岁，无明显残疾，与老师一起进行了分析。我们发现，ASR 系统是一种可行的解决方案，可以自动量化学前教室不同时间点和地点互动期间成人 wh 单词的数量。本文报告了 ASR 模型如何与位置传感器相结合，量化两个时间点之间以及 ADHD 儿童和正常发育儿童之间 wh 词的频率。结果提供了概念证明，即 ASR 模型（包括声学模型和语言模型）可以自动检测学前教师课堂演讲中的 wh 词。然而，需要对更大、更多样化的样本进行进一步研究，以探索在各种环境和人群中扩大规模的成本和时间影响，从而为有效的全班和个性化数据驱动的教学实践提供信息。