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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This content will become publicly available on September 15, 2024
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
- NSF-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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Practitioner notes What is already known about this topic
Student turn taking can provide information about the nature of student discussions and collaboration.
Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis.
TAs have little training in how to productively intervene with students about collaborative skills.
What this paper adds
TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills.
TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion.
Student turn overlap was higher without the presence of TAs.
Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group.
Implications for practice and/or policy
TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support.
TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake.
Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs.
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