Advancing Computational Grounded Theory for Audiovisual Data from STEM Classrooms

该提案是为了响应EHR核心研究（ECR）计划公告NSF 19-508而提交的。在STEM教育的基础研究ECR计划提供资金在关键的研究领域是必不可少的，广泛的和持久的。EHR寻求有助于综合，建立和/或扩大以下重点领域研究基础的建议：STEM学习，STEM学习环境，STEM劳动力发展和扩大STEM参与。ECR计划的特点是强调积累强有力的证据，为以下努力提供信息：（a）理解，（B）建立解释理论，（c）建议干预措施（和创新），以应对STEM兴趣，教育，学习，这个EHR核心研究项目正在对视频数据的计算分析进行方法研究，重点是社会和空间在课堂上学习STEM的维度。视频数据很复杂。它们涉及视觉，听觉，空间和时间特征，可以通过几种方式减少。到目前为止，STEM教室视频数据的分析还无法利用计算能力来利用其丰富性。然而，数据科学的最新进展，再加上现有的语音分析方法，使得通过计算从视频中识别重要特征成为可能，同时保持复杂性和细微差别。这些进步将提高研究的可复制性。通过这个项目开发的方法将促进更多的研究人员使用复杂的计算分析与视频数据。这些新方法的应用将有助于提高视频研究的规模和普遍性，并导致新的理论的建立。该研究项目建立在最先进的计算机视觉和语音分析方法的基础上，这些方法对STEM教室中收集的视频数据进行了测试。它是在计算扎根理论方法框架内这样做的，该框架利用了扎根分析方法的解释能力和计算方法的处理能力。具体而言，将产生两种类型的计算分析程序：（a）从STEM教室的视频和音频数据中提取有意义的特征，以及（B）使用这些提取的特征进行探索性模式识别。为了开发这些程序，STEM教室现有的大规模视频数据集将用于测试和完善日益复杂的分析，这些分析也将用于展示这些方法在调查STEM教室的社会和空间维度方面的应用。该项目的重点是整合这些方法，以提高它们的能力，并利用现有的STEM教室的大规模数据集，以便开发的方法可以在现实数据上进行测试。这些数据集足够广泛，可以支持对广泛的研究问题的调查，包括关于学生参与纪律实践的高推理问题。最后，通过配对计算和接地分析方法，该项目正在开发的方法，有可能提高和测试的构建有效性的模式中发现的数据。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This proposal was submitted in response to EHR Core Research (ECR) program announcement NSF 19-508. The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education, learning, and participation.This EHR Core Research project is conducting methodological research on the computational analysis of video data focused on the social and spatial dimensions of STEM learning in classrooms. Video data are complex. They involve visual, acoustic, spatial, and temporal features that can be reduced in several ways. To date, analysis of video data of STEM classrooms has not been able to leverage computational power to take advantage of their richness. However, recent advancements in data science, coupled with existing speech analytics methods, make it possible to computationally identify important features from video in ways that preserve complexity and nuance. These advancements will improve research replicability. The methods developed through this project will facilitate use of sophisticated computational analysis with video data by more researchers. Application of these new methods will help increase the scale and generalizability of video research and lead to the building of new theory. This research project builds on state-of-the-art computer vision and speech analytics methods tested on video data collected in STEM classrooms. It does so within a computational grounded theory methodological framework, which leverages the interpretive power of grounded analytical approaches with the processing power of computational methods. Specifically, two types of computational analysis procedures will be produced: (a) extracting meaningful features from video and audio data of STEM classrooms, and (b) conducting exploratory pattern identification using these extracted features. To develop these procedures, existing large-scale video datasets of STEM classrooms will be used to test and refine increasingly sophisticated analyses, which will also be used to demonstrate the application of these methods to investigate the social and spatial dimensions of STEM classrooms. The project focuses on integrating these methods to improve their power and leverages existing large-scale datasets of STEM classrooms, such that the methods developed can be tested on realistic data. The datasets are extensive enough to support the investigation of a wide range of research questions, including high-inference questions about students' participation in disciplinary practices. Finally, by pairing computational and grounded analytical methods, the project is developing methods that have the potential to enhance and test construct validity of the patterns found in the data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.