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Computational modeling has been shown to benefit integrated learning of science and computational thinking (CT), however the mechanics of this synergistic learning are not well understood. In this research, we examine discourse during collaborative computational model building through the lens of a collaborative problem solving framework to gain insights into collaboration and synergistic learning of high school physics and CT. We pilot our novel approach in the context of C2STEM, a designed modeling environment, and examine collaboration and synergistic learning episodes in a video capture of a dyad modeling 2D motion with constant velocities. Our findings exhibit the promise of our approach and lay the foundation for guiding future automated approaches to detecting the synergistic learning of science and CT.
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Relationships Between Body Postures and Collaborative Learning States in an Augmented Reality Study.
In this paper we explore how Kinect body posture sensors can be used to detect group collaboration and learning, in the context of dyad pairs using augmented reality system. We leverage data collected during a study (N = 60 dyads) where participant pairs learned about electromagnetism. Using unsupervised machine learning methods on Kinect body posture sensor data, we contribute a set of dyad states associated with collaboration quality, attitudes toward physics and learning gains.
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- Award ID(s):
- 1748093
- PAR ID:
- 10196320
- Date Published:
- Journal Name:
- International Conference on Artificial Intelligence in Education
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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