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  • Using Collaborative Interactivity Metrics to analyze students’ Problem-Solving Behaviors during STEM+C Computational Modeling Tasks
Title: Using Collaborative Interactivity Metrics to analyze students’ Problem-Solving Behaviors during STEM+C Computational Modeling Tasks
Recently there has been increased development of curriculum and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) learning environments. These environments serve as a catalyst for authentic collaborative problem-solving (CPS) and help students synergistically learn STEM+C content. In this work, we analyzed students’ collaborative problem-solving behaviors as they worked in pairs to construct computational models in kinematics. We leveraged social measures, such as equity and turn-taking, along with a domain-specific measure that quantifies the synergistic interleaving of science and computing concepts in the students’ dialogue to gain a deeper understanding of the relationship between students’ collaborative behaviors and their ability to complete a STEM+C computational modeling task. Our results extend past findings identifying the importance of synergistic dialogue and suggest that while equitable discourse is important for overall task success, fluctuations in equity and turn-taking at the segment level may not have an impact on segment-level task performance. To better understand students’ segment-level behaviors, we identified and characterized groups’ planning, enacting, and reflection behaviors along with monitoring processes they employed to check their progress as they constructed their models. Leveraging Markov Chain (MC) analysis, we identified differences in high- and low-performing groups’ transitions between these phases of students’ activities. We then compared the synergistic, turn-taking, and equity measures for these groups for each one of the MC model states to gain a deeper understanding of how these collaboration behaviors relate to their computational modeling performance. We believe that characterizing differences in collaborative problem-solving behaviors allows us to gain a better understanding of the difficulties students face as they work on their computational modeling tasks.  more » « less
Award ID(s):
2017000
PAR ID:
10468771
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Grieff, S.
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Learning and individual differences
ISSN:
1873-3425
Subject(s) / Keyword(s):
Collaborative problem solving Computational Modeling Synergistic Learning Multimodal Learning Analytics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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