Title:
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A multiple constraints framework for collaborative learning flow orchestration
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Author:
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Manathunga, Kalpani; Hernández Leo, Davinia
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Abstract:
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Paper presented at ICWL 2016, 15th International Conference, Rome, Italy, October 26–29, 2016. |
Abstract:
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Collaborative Learning Flow Patterns (e.g., Jigsaw) offer sound pedagogical strategies to foster fruitful social interactions among learners. The pedagogy behind the patterns involves a set of intrinsic constraints that need to/nbe considered when orchestrating the learning flow. These constraints relate to the organization of the flow (e.g., Jigsaw pattern - a global problem is divided into sub-problems and a constraint is that there need to be at least one expert group working on each sub-problem) and group formation policies (e.g., groups solving the global problem need to have at least one member coming from a different previous expert group). Besides, characteristics of specific learning situations such as learners’ profile and technological tools used provide additional parameters that can be considered as context-related extrinsic constraints relevant to the orchestration (e.g., heterogeneous groups depending on experience or interests). This paper proposes a constraint framework that considers different constraints for orchestration services enabling adaptive computation of orchestration aspects. Substantiation of the framework with a case study demonstrated the feasibility, usefulness and the expressiveness of the framework. |
Abstract:
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This work has been partially funded by the Spanish Ministry of Economy and Competitiveness/n(TIN2014-53199-C3-3-R; MDM-2015-0502). |
Subject(s):
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-CSCL -Collaborative Learning Flow Pattern(s) -Macro scripts -Jigsaw -Learning flow orchestration |
Rights:
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© Springer The final publication is available at Springer via/nhttp://dx.doi.org/10.1007/978-3-319-47440-3_25 |
Document type:
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Book Part Article - Accepted version |
Published by:
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Springer
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