Responsible Artificial Intelligence for Education and Human Support
I am a professor at the German University of Digital Science and associate researcher at the DIPF - Lebniz Institute.
My research focuses on AI in Education, human-AI interaction, Learning analytics and Multimodality.
I'm available to work together.
Last week I presented in a free webinar on our new MSc Advanced Digital Reality at the German UDS
The characteristics of this master are
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%.
Submission stats #lak17 well done to our phds to be part of it with full papers @m_a_s_c @dimstudi0 pic.twitter.com/Uqb5qJP4ci
— HDrachsler (@HDrachsler) March 15, 2017
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.
While sitting on my train back from Brussels after the European Education and Training & Youth Forum 2016 (ETYForum) I thought it would have been useful to debrief my general feeling of frustration and disillusion I realised I had after the event.
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
Written for Lilab.eu
Like in many other fields like healthcare, retail, telecommunications and natural science, Big Data and Analytics have become a new hype in Education and Learning under the umbrella name of "Learning Analytics". As technology becomes ubiquitous and more accessible, as most of the learning time is now spent on Massive Online Open Courses, vast quantities of data are continuously generated and stored in IT systems. These data offer unprecedented opportunities for researchers to analyse and understand several different aspects of learning and education. This data-driven approach is shaking the traditional paradigms of educational research.
Continue reading on lilab.eu
