#LAK17 - it's all about multimodality

Originally posted on lilab.eu

The 2017 edition of the Learning Analytics & Knowledge conference beat all the previous records with 344 submissions from 1000 authors and 415 participants, the acceptance rate of the full paper was 34%.

Multimodality is the main focus

The trending topic of #LAK17 is undoubtedly multimodality. Two keynotes out of three Sanna Jarvela and Sydney D'Mello focus on multimodal data for learning. The topic is also reflected in many studies presented during the parallel presentations. Continue reading

Digital Learning and Media Literacy - #LLLWeek16

digital-learning-headerDuring the Lifelong Learning Week 2016 organised by the Lifelong Learning Platform, the second Digital Learning working group meeting took place at MundoJ, bringing representatives of the European Commission, Digital Europe and a number of other NGOs to discuss and share experience on the topic of Digital Learning. The objective of this discussion was to find a common ground and find possible project ideas. I was asked to make an introduction and overview on the topic: this was my contribution.

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When talking about - digital - technology in education people seem polarised between two opposite standpoints. On one side there are the cyber-optimists who believe that technology is the quick-fix for all the problems education is facing in 21st Century. On the other side, the cyber-frightened who perceive technology as highly dangerous and rather prefer to stick to the traditional practices that are widely tested and recognised.

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Visual Learning Pulse - Master Thesis


 

Project title
Visual Learning Pulse: Flow Prediction and Feedback in Self-regulated Learning

Abstract
Visual Learning Pulse is a Master thesis research project developed in cooperation with the Welten
Institute, the Research Centre for Learning, Teaching and Technology at the Open University of the Netherlands, and partially financed by the European project Learning Analytics Community Exchange (LACE). Visual Learning Pulse explores whether physiological and physical data such as heart rate, step count and weather data if correlated with learning activity data can be used to predict learning success in self-regulated learning settings.

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