LAK2013

:), I'm reading this book at the moment.

About the Course: In this eleven-week course you'll learn about the tools used by scientists to understand complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks. You'll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology. There are no prerequisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.

Imagine when these families come into contact with MOOC-provided learning experiences/content; a domain of the MOOC discussion I haven't heard explored with empirical data before ...

If we can do a tricorder for health, then we can do one for learning? That would be like a 'killer app' for learning analytics?

Presents the Learning Analytics Acceptance Model (LAAM) of factors influencing the beliefs of educators concerning the adoption a learning analytics tool.

"One thing is certain - as learning materials digitise and the popularity of Moocs grows, we will see a very different model of education unfold. This model will draw on data and insight, as much as faculty knowledge and experience, to ensure that education materials and learning processes are as effective as possible."

"we need to worry more about which models and which data have been chosen in the first place, why that process is successful when it is, and - most importantly - who gets to decide what data is collected and what models are trained."

Yes! Who's getting to ask the questions, make the decisions, choose the models, all important aspects that don't often get discussed. Who isn't part of the dialogue and which questions aren't getting asked? Which data isn't being gathered? Thanks for emphasizing this.