We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository  as well as at the UPF e-repository  . Below a non-exhaustive list of datasets representative of the research in the Department.

As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)

 

 

Back Manathunga K, Hernández-Leo D. A Multiple Constraints Framework for Collaborative Learning Flow Orchestration. Advances in Web-Based Learning – ICWL 2016. 15th International Conference

Manathunga K, Hernández-Leo D. A Multiple Constraints Framework for Collaborative Learning Flow Orchestration. Advances in Web-Based Learning – ICWL 2016. 15th International Conference

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.

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