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Socially shared regulation (SSRL) has been recognized as a contributing factor to successful collaborative learning. In this paper, we adopted a process-oriented approach to examine how students deliberate for SSRL through different regulatory triggers in a collaborative learning context. More specifically, this study examines the relationship between different types of regulatory and deliberative characteristics of interactions and then explores their sequential patterns through cognitive and emotional triggers. The study involved ten triads of secondary students (N=30) working on a collaborative learning task. The process mining results showed that following regulatory triggers, groups switched to more metacognitive and socio-emotional interactions as they adopted control strategies, such as defining problems, establishing strategies, and providing social support. This study not only contributes to a better understanding of SSRL by exploring learners’ deliberative negotiation but also presents a novel fine-grain video analysis approach to examine SSRL in collaborative learning.
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Assessing Students’ Knowledge Co-construction Behaviors in a Collaborative Computational Modeling Environment.
Successful knowledge co-construction during collaborative learning requires students to develop a shared conceptual understanding of the domain through effective social interactions. Developing and applying shared understanding of concepts and practices is directly impacted by the prior knowledge that students bring to their interactions. We present a systematic approach to analyze students’ knowledge co-construction processes as they work through a physics curriculum that includes inquiry activities, instructional tasks, and computational model-building activities. Utilizing a combination of students’ activity logs and discourse analysis, we assess how students’ knowledge impacts their knowledge co-construction processes. We hope a better understanding of how students’ co-construction processes develop and the difficulties they face will lead to better adaptive scaffolding of students’ learning and better support for collaborative learning.
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- Award ID(s):
- 2017000
- PAR ID:
- 10348700
- Editor(s):
- Rodrigo, M.M.
- Date Published:
- Journal Name:
- AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.
- Volume:
- 13356
- Page Range / eLocation ID:
- 515-519
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-11647-6_105
