Please use this identifier to cite or link to this item:
https://repository.isls.org//handle/1/7312
Title: | Automated Tracking of Student Activities in a Makerspace Using Motion Sensor Data |
Authors: | Sung, Gahyun Yoo, Tyler Chng, Edwin Yang, Stephanie Schneider, Bertrand |
Keywords: | CSCL |
Issue Date: | Jun-2021 |
Publisher: | International Society of the Learning Sciences |
Citation: | Sung, G., Yoo, T., Chng, E., Yang, S., & Schneider, B. (2021). Automated Tracking of Student Activities in a Makerspace Using Motion Sensor Data. In Hmelo-Silver, C. E., De Wever, B., & Oshima, J. (Eds.), Proceedings of the 14th International Conference on Computer-Supported Collaborative Learning - CSCL 2021 (pp. 185-188). Bochum, Germany: International Society of the Learning Sciences. |
Abstract: | Learning inside makerspaces can be difficult to track and support. Using Kinect data collected from students enrolled in a course for making, we explore ways to track student learning trajectories in an automated way. Namely, by transforming our data into a set of action sequences that span a semester, we are able to find that discouraged students display a statistically distinct type of activity pattern already in the first two weeks. Generating metrics on makerspace use, we also find that time spent alone and the number of transitions between stations are significant indicators for discouragement and motivation levels. We argue that high-frequency location data could provide an accessible, meaningful overview of student learning in a makerspace to all stakeholders, and conclude with limitations and future directions. |
URI: | https://doi.dx.org/10.22318/cscl2021.185 https://repository.isls.org//handle/1/7312 |
Appears in Collections: | ISLS Annual Meeting 2021 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
185-188.pdf | 472.71 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.