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.
|Effective start/end date||9/1/14 → 8/31/16|
- National Science Foundation (National Science Foundation (NSF))
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