BCC: Collaborative Research: Community Building for Research on Mathematics Learning Using Data-Intensive Sources

BCC：协作研究：使用数据密集型资源进行数学学习研究的社区建设

• 批准号: 1439570
• 负责人: Carolyn Maher
• 金额: $ 60.47万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1439570&HistoricalAwards=false
• 关键词: BCC; Collaborative; Research; Community; Building

Video data is ubiquitous in STEM education research and development. Yet the potential for these data to be used outside the initial project in which they are developed has been limited by the labor intensive methodology to index and categorize video segments in ways that support use by external researchers. This project will build the community to determine the taxonomy by which videos might be characterized. The researchers and a strong advisory board will work with the programmers of inVideo, a tool and software for indexing language and non-language objects in video data sets. Working with video of students and teachers in mathematics classrooms from the Robert B. Davis Institute for Learning, the project will determine which categories of videos from the collection are amenable for indexing by introducing successively more complex video samples. The project will push the boundaries of computer science algorithms for video search, benefiting both the computer science community and the mathematics education research and development communities.There is currently no automated method for accessing the content data locked within a video format that is cost effective and available for education purposes. The researchers in this project will determine whether application of automated indexing to generate high precision and high recall retrievals is possible; what human-augmented annotation generates towards more effective searching; and, advisable practices for obtaining IRB approval to share video resources among a wider community of researchers to facilitate a fine-grained analysis on other data sets. The project will train the audio-to-text translation function of inVideo to recognize speech patterns of people recorded in the videos using translations that currently exist. The second stage of the project will address the process of testing new samples of videos for which human-generated transcripts do not exist. The project will also develop a web interface for inVideo to facilitate transcript editing, time-stamped commenting and tagging by remote users. The results of this study will enhance the utility of educational video to a wide community of researchers.

视频数据无处不在，在STEM教育研发中。然而，这些数据在开发它们的初始项目之外使用的潜力受到劳动密集型方法的限制，以索引和分类视频细分市场，以支持外部研究人员使用的方式。该项目将建立社区，以确定可以通过哪些视频来表征的分类法。研究人员和强大的咨询委员会将与Invideo的程序员合作，Invideo的程序员是一种工具和软件，用于在视频数据集中索引语言和非语言对象。与罗伯特·B·戴维斯（Robert B. Davis）学习研究所的数学课堂学生和老师的视频合作，该项目将通过介绍更复杂的视频样本来确定该集合中哪些视频类别可用于索引。该项目将推动计算机科学算法的界限进行视频搜索，从而使计算机科学界和数学教育研究与发展社区都受益。目前尚无自动化的方法来访问以具有成本效益和可用于教育目的的视频格式锁定的内容数据。该项目的研究人员将确定是否可以应用自动索引来产生高精度和高召回率。人类增强的注释会产生更有效的搜索；以及获得IRB批准的建议实践，以在更广泛的研究人员社区中共享视频资源，以促进对其他数据集的细粒度分析。该项目将使用当前存在的翻译训练Invideo的音频到文本翻译功能，以识别视频中记录的人的语音模式。该项目的第二阶段将解决测试不存在人类生成笔录的新视频样本的过程。 该项目还将为Invideo开发一个Web界面，以促进远程用户的成绩单编辑，时间标记的评论和标记。这项研究的结果将增强对广泛研究人员社区的教育视频的实用性。