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Chaitanya Ekanadham leads a team of data scientists and analysts developing the core models that power Knewton's adaptive learning products. He first became interested in the process of learning (by machines and humans) during his undergraduate years at Stanford, where he majored in Symbolic Systems and Math & Computational Science. He went on to complete his doctorate in applied math at the Courant Institute (NYU), where he worked on computational models of adaptation in the retina and methods for neural data analysis.
Pre-requisites(if any background is required): none
Preparation(such as reading materials): Knewton's technical white paper (http://www.knewton.com/wp-content/uploads/knewton-technical-white-paper-201501.pdf)
I'll talk about the Knewton data science team's vision of representing learning experiences, and go into a few specific ways we do that, such as content graphing and measuring student ability.