Jonna Malmberg, a Post-Doctoral researcher from the SLAM project is visiting at the School of Informatics, University of Edinburgh, Scotland. The research visit will strengthen already existing networks with Prof. Dragan Gasevic and his research team.
Dragan Gasevic is a Professor and the Chair in Learning Analytics and Informatics in the Schools of Education and Informatics at the University of Edinburgh since February 2015.
Since SLAM research project builds in for interdisciplinary research, Dragan Gasevic and his team can provide and add valuable methodological knowledge in terms of how to process multichannel data that consists of physiological reactions and their associations for self-regulated learning. Specifically, they focus of investigating how machine learning methods can self-regulated in the context of collaboration.
During the research visit, Jonna has been working intensely with Postgraduate Student Oliver Fincham, who is a member of Institute for Adaptive and Neural Computation and Markus Hörmann. He is a research fellow at the chair of Teaching and Learning with Digital Media at the University of Technology Munich and works closely with Prof Maria Bannert.
Since SLAM research project builds in for interdisciplinary research, Dragan Gasevic and his team can provide and add valuable methodological knowledge in terms of how to process multichannel data that consists of physiological reactions and their associations for self-regulated learning. Specifically, they focus of investigating how machine learning methods can self-regulated in the context of collaboration.
During the research visit, Jonna has been working intensely with Postgraduate Student Oliver Fincham, who is a member of Institute for Adaptive and Neural Computation and Markus Hörmann. He is a research fellow at the chair of Teaching and Learning with Digital Media at the University of Technology Munich and works closely with Prof Maria Bannert.
The research visit at the beautiful and old city of Edinburgh provides Jonna a great opportunity to learn about state of the art research projects dealing with, for example Natural Language Processing (NLP) and machine-learning methods that can definitely be useful and valuable for the progress of the SLAM research project.