Abstract
Author(s): Tucker, L; Scherr, RE; Zickler, T; Mazur, E | Abstract: Large-scale audiovisual data that measure group learning are time consuming to collect and analyze. As an initial step towards scaling qualitative classroom observation, we qualitatively coded classroom video using an established coding scheme with and without its audio cues. We find that interrater reliability is as high when using visual data only - without audio - as when using both visual and audio data to code. Also, interrater reliability is high when comparing use of visual and audio data to visual-only data. We see a small bias to code interactions as group discussion when visual and audio data are used compared with video-only data. This work establishes that meaningful educational observation can be made through visual information alone. Further, it suggests that after initial work to create a coding scheme and validate it in each environment, computer-automated visual coding could drastically increase the breadth of qualitative studies and allow for meaningful educational analysis on a far greater scale.
Highlights
Classroom observation is an important means of measuring learning behaviors that students and instructors may not accurately self-report in surveys
We find that interrater reliability is as high when using visual data only—without audio—as when using both visual and audio data to code
Though we study video from university physics recitation sections, the general nature of the worksheet activity and visual coding elements suggest that this research is not science specific; we expect visual-only coding should generalize to other coding schemes, and coding schemes beyond epistemology
Summary
Classroom observation is an important means of measuring learning behaviors that students and instructors may not accurately self-report in surveys. Analysis of video collected during an activity allows for measure of in-themoment behavioral response to the classroom environment without bias from retrospection, which can affect recall of emotion [1,2], focus [3], and how time was spent [4,5]. We base our work on the Scherr and Hammer coding scheme, which uses group behavioral clusters that indicate an epistemological frame [13]. These behavioral clusters are identified by speech, other audible cues, and body language that occur simultaneously among group members. Groups usually transition between different behavioral clusters in unison, allowing behavioral labeling to be done for the group as a whole rather than for each individual [13]
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