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Computer-Assisted Video Analysis Methods for Understanding Underrepresented Student Participation and Learning in Collaborative Learning Environments

用于了解协作学习环境中代表性不足的学生参与和学习的计算机辅助视频分析方法

基本信息

批准号：
1842220
负责人：
Marios Pattichis
金额：
$ 30万
依托单位：
University of New Mexico
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842220&HistoricalAwards=false
关键词：
Computer Assisted Video Analysis Methods

项目摘要

Research that seeks to understand classroom interactions often relies on video recordings of classrooms so that researchers can document and analyze what teachers and students are doing in the learning environment. When studies are large scale, this analysis is challenging in part because it is time-consuming to review and code large quantities of video. For example, hundreds of hours of videotaped interaction between students working in an after-school program for advancing computational thinking and engineering learning for Latino/a students. This project is exploring the use of computer-assisted methods for video analysis to support manual coding by researchers. The project is adapting procedures used for computer-aided diagnosis systems for medical systems. The computer-assisted process creates summaries that can then be used by researchers to identify critical events and to describe patterns of activities in the classroom such as students talking to each other or writing during a small group project. Creating the summaries requires analyzing video for facial recognition, motion, color and object identification. The project will investigate what parts of student participation and teaching can be analyzed using computer-assisted video analysis. This project is supported by NSF's EHR Core Research (ECR) program, the STEM+C program and the AISL program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. The project is funded by the STEM+Computing program, which seeks to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within disciplinary STEM teaching and learning in early childhood education through high school (preK-12). As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.The video analysis systems will provide video summarizations for specific activities which will allow researchers to use these results to quantify student participation and document teaching practices that support student learning. This will support the analysis of large volumes of video data that are often time-consuming to analyze. The video analysis system will identify objects in the scene and then use measures of distances between objects and other tracking methods to code different activities (e.g., typing, talking, interaction between the student and a facilitator). The two groups of research questions are as follows. (1) How can human review of digital videos benefit from computer-assisted video analysis methods? Which aspects of video summarization (e.g., detected activities) can help reduce the time it takes to review the videos? Beyond audio analytics, what types of future research in video summarization can help reduce the time that it takes to review videos? (2) How can we quantify student participation using computer-assisted video analysis methods? What aspects of student participation can be accurately measures by computer-assisted video analysis methods? The video to be used for this study is drawn from a project focused on engineering and computational thinking learning for Latino/a students in an after-school setting. Hundreds of hours of video are available to be reviewed and analyzed to design and refine the system. The resulting coding will also help document patterns of engagement in the learning environment.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.

试图了解课堂互动的研究通常依赖于教室的视频记录，以便研究人员可以记录和分析教师和学生在学习环境中所做的事情。当研究是大规模的，这种分析是具有挑战性的，部分原因是它是耗时的审查和编码大量的视频。例如，数百小时的录像互动的学生在课后计划工作，以推进计算思维和工程学习的拉丁美洲/学生。这个项目正在探索使用计算机辅助方法进行视频分析，以支持研究人员的手工编码。该项目正在调整用于医疗系统的计算机辅助诊断系统的程序。计算机辅助过程创建摘要，然后研究人员可以使用这些摘要来识别关键事件并描述课堂活动的模式，例如学生在小组项目中相互交谈或写作。创建摘要需要分析视频以进行面部识别、运动、颜色和对象识别。该项目将调查学生参与和教学的哪些部分可以使用计算机辅助视频分析进行分析。该项目由NSF的EHR核心研究（ECR）计划，STEM+C计划和AISL计划支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。该项目由STEM+Computing计划资助，旨在通过将计算思维和计算活动应用于学科STEM教学和学习中，以应对计算STEM领域的新挑战，这些教学和学习贯穿于幼儿教育到高中（preK-12）。作为其整体战略的一部分，以加强在非正式环境中的学习，推进非正式STEM学习（AISL）计划旨在推进新的方法，并以证据为基础的理解，在非正式环境中的STEM学习的设计和开发。这包括提供多种途径，以扩大获得和参与STEM学习经验，推进非正式环境中STEM学习的创新研究和评估，视频分析系统将为特定活动提供视频摘要，这将使研究人员能够使用这些结果来量化学生的参与程度，并记录支持的教学实践。学生学习。这将支持对大量视频数据的分析，这些数据的分析通常很耗时。视频分析系统将识别场景中的对象，然后使用对象之间的距离的测量和其他跟踪方法来编码不同的活动（例如，打字、谈话、学生和辅导员之间的互动）。两组研究问题如下。(1)人类对数字视频的审查如何从计算机辅助视频分析方法中受益？视频摘要的哪些方面（例如，检测到的活动）可以帮助减少查看视频所需的时间？除了音频分析，未来在视频摘要方面的哪些研究可以帮助减少查看视频所需的时间？(2)我们如何使用计算机辅助视频分析方法来量化学生的参与？学生参与的哪些方面可以通过计算机辅助视频分析方法准确测量？用于本研究的视频是从一个项目中提取的工程和计算思维学习的拉丁美洲/学生在课后设置。数百小时的视频可供审查和分析，以设计和完善系统。由此产生的编码还将有助于记录学习环境中的参与模式。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Person Detection in Collaborative Group Learning Environments Using Multiple Representations

DOI：
10.1109/ieeeconf53345.2021.9723388
发表时间：
2021-10
期刊：
2021 55th Asilomar Conference on Signals, Systems, and Computers
影响因子：
0
作者：
Wenjing Shi;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva
通讯作者：
Wenjing Shi;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva

Fast Hand Detection in Collaborative Learning Environments

协作学习环境中的快速手部检测

DOI：
10.1007/978-3-030-89128-2_43
发表时间：
2021
期刊：
CAIP 2021: Computer Analysis of Images and Patterns
影响因子：
0
作者：
Teeparthi, S.;Jatla, V.;Pattichis, M.S.;Celedón-Pattichis, S.;Carlos LópezLeiva, C.
通讯作者：
Carlos LópezLeiva, C.

Facial Recognition in Collaborative Learning Videos