Manathunga, KHernández-Leo, D2022-03-102022-03-102016-10-26Manathunga K., Hernández-Leo D. (2016) A Multiple Constraints Framework for Collaborative Learning Flow Orchestration. In: Chiu D., Marenzi I., Nanni U., Spaniol M., Temperini M. (eds) Advances in Web-Based Learning – ICWL 2016. ICWL 2016. Lecture Notes in Computer Science, vol 10013. Springer, Cham. https://doi.org/10.1007/978-3-319-47440-3_25978-3-319-47440-3https://rda.sliit.lk/handle/123456789/1564Collaborative 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.enCSCLCollaborative Learning Flow Pattern(s)Macro scriptsJigsawLearning flow orchestrationArticlehttps://doi.org/10.1007/978-3-319-47440-3_25