Wearable Technology for Children’s Educational Purposes
Since September 2018, I have been collaborating with the BodyVis team working on a new wearable computing and electronic textiles (e-textiles) technology for learning about anatomy and physiology. This project is mainly run by Leyla Norooz, a Ph.D candidate from University of Maryland, and Dr. Jon E. Froehlich (PI) and Dr. Tamaea L.Clegg (Co-PI). https://makeabilitylab.cs.washington.edu/project/BodyVis/
We present a new wearable computing and electronic textiles (e-textiles) technology for learning about anatomy and physiology, and for supporting children’s scientific inquiry skills. More specifically, we propose to design, build, and evaluate a set of wearable e-textile shirt prototypes—called BodyVis—that combine embedded sensing and interactive visualization to reveal otherwise “invisible” parts and functions of the human body. As the wearer engages in an activity, physiological phenomena are manifested on the wearable visualization in real-time. In addition, we present a new mixed-reality tool called SharedPhys, which tightly integrates real-time physiological sensing, whole-body interaction, and scientific inquiry to support new forms of embodied interaction and collaborative learning. (Makeabilitylab/BodyVis)
My major contribution in the BodyVis project was in the data collection process and data analysis phase. I co-ran deployment sessions and collected qualitative data (field notes), contributed to protocol iteration, and have been working on the qualitative analysis of transcribed and video data.
- Collected primarily qualitative and some quantitative data
- Qualitative data: field notes taken in situ, written post-session debriefs, video and audio recordings of sessions, daily post-session journal entries completed by children, pre- and post-assessment open-ended questions, focus groups with children post-study
- Quantitative data: pre- and post-assessment true/false and likert scale questions
- Qualitative data analysis: mixed inductive and deductive analysis approach
- Video data: Researcher reviewed debrief and field notes, and one session video, to derive an initial codebook. Two researchers simulacra;taneously coded a second video, updated codebook, then independently coded all videos, developed summaries, and met to discuss data. One researcher wrote a final summary. (This follows Chi’s 8-step process)
- Journal entries, open-ended questions, and focus groups: Two research searchers coded a data subset using an initial codebook, then resolved differences and updated the codebook, coded a second subset, and achieved acceptable inter-rater reliability using Krippendorf’s alpha (at least .80) before coding remaining data.
- Qualitative data analysis: significance of results evaluated via chi-squared and Wilcoxon signed ranks tests (sig. level = 0.05)