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Title: Understanding Students’ Model Building Strategies Through Discourse Analysis
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.
Authors:
; ; ;
Award ID(s):
1640199
Publication Date:
NSF-PAR ID:
10110540
Journal Name:
International Conference on Artificial Intelligence in Education (AIED) 2019.
Sponsoring Org:
National Science Foundation
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