The past decade has solidified and advanced two important tracks in helping researchers understand learning: neuroscience and big data. Sophisticated imaging techniques allow insight into the functioning of the human brain that was until recently unimaginable. Small controlled studies are laying a foundation for a new science of learning.
In contrast, big data, often generated in technological environments, presents researchers with fuzzier and messier data than what is common in neuroscience. The large N, however, offers tantalising insights into the social, affective, and meta-cognitive aspects of learning as it happens in authentic work and school settings.
In this presentation Professor George Siemens (Director of the LINK Research Lab at the University of Texas, Arlington) explores the methodological differences that underpin the neuroscience and big data (learning analytics) frameworks of learning research and suggest ways in which they might contribute to future educational models.
FOR MORE INFORMATION AND SPEAKER'S BIOGRAPHY SEE THIS PAGE: http://sydney.edu.au/sydney_ideas/lectures/2016/professor_george_siemens.shtml
