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A Multiple Constraints Framework for Collaborative Learning Flow Orchestration

  • Kalpani ManathungaEmail author
  • Davinia Hernández-Leo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10013)

Abstract

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 be 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.

Keywords

CSCL Collaborative Learning Flow Pattern(s) Macro scripts Jigsaw Learning flow orchestration 

Notes

Acknowledgements.

Special thanks to participants from Escola de Santboi, Spain. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (TIN2014-53199-C3-3-R; MDM-2015-0502).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.ICT DepartmentUniversitat Pompeu FabraBarcelonaSpain

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