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Title: 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.
Authors:
; ; ; ;
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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