Visual Learning Pulse: Flow Prediction and Feedback in Self-regulated Learning
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.
Knowledge exchange among researchers not necessarily should happen in formal scientific conferences but can also include less formal and more fun moments. It is in this spirit that the Joint Technology Enhanced Learning Summer School was ideated, to give a space to PhDs and young researchers for peer learning, to exchange views, practices and receive feedback. And of course also have a break from work and have fun. This is what JTEL was about, and I admit I really liked it as it foster cooperation and knowledge sharing for the researcher working in the technology enhanced learning field.
This year's JTEL took place in Estonia from the 19th to the 24th of June, close to the cold Baltic sea. As usual on dimstudio.org, this post wraps up the highlights of with 10 tweets. Continue reading
Written for Lilab.eu
The first episode in of Big Data in Education introduced the opportunities arising by programmatically collecting and analysing educational data. The second episode detailed the Dimensions of Education Data, the so-called input space of the Big Data in Education. As anticipated before, this session talks about learning outcomes measurement, or namely how to transform learning performance and assessment indicators to take into account when deploying Big Data techniques in Education.
But if we now know where to collect data, why bother about the output at all? The output space is as important as the input as most of the supervised Big Data techniques consists in model or pattern discovering, through which is possible to perform automatic predictions or classifications.
Continue the reading at lilab.eu
Last week 6th Learning Analytics & Knowledge Conference 2016 (LAK16) took place in the beautiful city Edinburgh, Scotland.
The conference has seen a record-breaking number of attendees and submissions which are collected in the proceedings.
I took part in the pre-conference and the conference sessions with the delegation of the Open University in The Netherlands.
In this post the highlights of my participation to the conference by selecting 10 tweets.