We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:


 Artificial Intelligence

 Nonlinear Time Series Analysis

 Web Research 


 Music Technology

 Interactive  Technologies

 Barcelona MedTech

 Natural Language  Processing

 Nonlinear Time Series  Analysis


Wireless Networking

Educational Technologies




Back [PhD thesis] Technology support for scalable and dynamic collaborative learning: a pyramid flow pattern approach

[PhD thesis] Technology support for scalable and dynamic collaborative learning: a pyramid flow pattern approach

Author: Kalpani Manathunga

Supervisor: Davinia Hernández-Leo

Collaborative Learning is the pedagogical approach that considers social interactions as key means to trigger rich learning processes. Collaborative Learning Flow Patterns define best practices to orchestrate collaborative learning activity flow mechanisms (i.e., group formation, roles or resources allocation, phase change). Flow patterns have been experimented and evaluated as effective in small scale settings for decades. Directly applying these pedagogical methods to large learning scenarios is challenging due to the burden that scale represents in the orchestration load or the difficulty of keeping a dynamic meaningful progression when flexible changes are required in a large classroom or in a MOOC. Some attempts have shown positive results, but research around scalable collaborative learning approaches, models and technologies for large classes is scattered. This dissertation conducts a systematic literature review of collaborative learning applications on large classes and analyses the social learning potential of diverse technology-supported spaces in massive courses. Then the dissertation focuses the study on how collaborative learning could address key challenges (i.e., scalability and dynamism) identified in large collaborative learning contexts. Consequently, the thesis proposes a Pyramid flow pattern instantiation, composed of a model with a set of algorithmic rules for flow creation, flow control and flow awareness as well as a PyramidApp authoring and enactment system implementing the model. Experimentation across diverse learning contexts shows that, on one hand, the contributions support meaningful scalable and dynamic collaborative learning and on the other hand, learners and educators perceive the experiences as engaging, with learning values and effective from the perspective of orchestration.

Additional material: