| George Siemens is an educator and researcher on learning, networks, analytics and visualization, openness, and organizational effectiveness in digital environments. He is the author of Knowing Knowledge, an exploration of how the context and characteristics of knowledge have changed and what it means to organizations today, and the Handbook of Emerging Technologies for Learning. Knowing Knowledge has been translated into Mandarin, Spanish, Persian, and Hungarian. Siemens is the Associate Director of the Technology Enhanced Knowledge Research Institute at Athabasca University, leading the learning analytics research team. Previously, he was the Associate Director, Research and Development, with the Learning Technologies Centre at University of Manitoba. His PhD, through University of Aberdeen, was on sensemaking and wayfinding in complex information settings. He is a frequent keynote speaker at conferences detailing the influence of technology and media on education, organizations, and society, having presented at conferences in more than 30 countries. His work has been profiled in provincial, national, and international newspapers (including NY Times), radio, and television. His research has received numerous national and international awards. He will receive an honorary doctorate from Universidad de San MartÃn de Porres for his pioneering work in learning, technology, and networks in May 2012. |
| As technology continues to develop in sophistication, domains previously thought to be unique to humans are now threatened. Advances in artificial intelligence suggest that machines can learn and in many cases, outperform humans in tasks involving large quantities of data. For educators and researchers, questions arise as to which attributes related to learning and knowledge development remain uniquely human. What does it mean to be human in a digital age? Equally important is how we will assess and evaluate our learners when more than only their cognitive performance is considered. This talk will explore personal learning graphs - a model for developing and capturing learning that occurs across domains beyond cognitive attributes in formal assessment: metacognitive, affective, and social. The presentation will focus on the need to capture learning that occurs outside of classrooms and a technical model that will enable formal and informal learning to be represented. |
