Interpreting Video Data: Introducing a "Significance Measure" to Layer Description.

Abstract

This paper proposes a new approach for researchers who analyze video data, recommending that data be layered in as many ways as possible as they are selected, coded, and annotated. Although video has become an important source of data over the past decade, the problem facing researchers is that interpreting video is fundamentally different from interpreting text. Given its multi-grained nature, how do we put together and then make sense of the chunked clusters of video? Furthermore, how do we share our views about our video data with our colleagues who may be using part or all of the same set of data? To address these questions, I will: 1) examine some of the theoretical issues underlying the inherent complexity of working with video data, 2) describe the video ethnography of a particular graduate student user who is working with video data, and then, 3) explain the use of a tool called a Significance which allows users to layer or weigh the relative importance of topics. In other words, as researchers rate the significance of their data within a particular video data analysis environment called Learning Constellations 2.0, they add layers or thickness to raw data. The conclusion of this paper is that layered structures will enable us to see which data are most significant, and for whom, to the total body of data. Using a Significant Measure will support the work of colleagues who share the same video (and text) by building layered collaborative interpretations.