Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology,and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem-solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices.
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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.
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- Award ID(s):
- 2017000
- PAR ID:
- 10468771
- 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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