The benefits of computational model building in STEM domains are well documented yet the synergistic learning processes that lead to the effective learning gains are not fully understood. In this paper, we analyze the discussions between students working collaboratively to build computational models to solve physics problems. From this collaborative discourse, we identify strategies that impact their model building and learning processes.
Analyzing Students’ Synergistic Learning Processes in Physics and CT by Collaborative Discourse Analysis
The introduction of computational modeling into science curricula has been shown to benefit students’ learning, however the synergistic learning processes that contribute to these benefits are not fully understood. We study students’ synergistic learning of physics and computational thinking (CT) through their actions and collaborative discourse as they develop computational models in a visual block-structured environment. We adopt a case study approach to analyze students synergistic learning processes related to stopping conditions, initialization, and debugging episodes. Our findings show a pattern of evolving sophistication in synergistic reasoning for model-building activities.
- Award ID(s):
- 1640199
- Publication Date:
- NSF-PAR ID:
- 10110535
- Journal Name:
- Computer-supported collaborative learning
- ISSN:
- 1573-4552
- Sponsoring Org:
- National Science Foundation
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