research designs adaptive systems for online content, like intelligent lessons that emulate your favorite teacher in personalizing explanations. This computational cognitive science approach requires integrating my research in psychology & education, human-computer interaction, and statistical machine learning.
To make any static website become intelligently adaptive, I use powerful systems for randomized A/B experiments. Examples range from adaptive explanations for how to solve math problems, to self-personalizing emails to change people's behavior. My systems continually crowdsource new 'A' and 'B' designs from psychological scientists, using randomized comparisons to evaluate how helpful these alternative designs are, for people with different profiles. Algorithms from statistical machine learning use this data for real-time enhancement and personalization, by changing which designs are presented to future users. By enabling scientific discoveries from each user to directly impact engineering improvements for the next, my work provides intelligent content that never stops improving.
Below, my TEDxPortofSpain talk explains how I use this approach in education, using MOOClets to intelligently adapt explanations for how to solve math problems.
You can contact me at : email@example.com
In blending scientific research and applications I collaborate and consult with domain/topic experts, instructors, designers, and researchers from diverse disciplines like education, psychology, and computer science. I draw on theories and methodology from research I have done, as well as synthesizing findings from other scientists, work on behavior change, reviews of evidence-based best practices for teaching and learning, practical experience as a statistics tutor, evaluations of educational technology products & authoring tools for e-learning, experience as an ed-tech consultant and science & technology advisor, and my own active development of interactive learning and behavior change resources.
I am currently a Research Fellow at HarvardX, the online learning research and development component of Harvard University. I also work closely with Neil Heffernan at WPI using the ASSISTments authoring and online mathematics exercise platform, leading the advisory board for an NSF Software Infrastructure grant that crowdsources randomized controlled trials from the broader scientific community.
I was previously a postdoc at Stanford University in the Graduate School of Education and Lytics Lab, working with the Office of the Vice Provost for Online Learning and Candace Thille's Open Learning Initiative. I received my PhD in Experimental and Computational Cognitive Science from UC Berkeley's Psychology Department. I worked with Tania Lombrozo to investigate why prompting people to explain "why?" helps learning, and with Tom Griffiths on using Bayesian statistics and methods from machine learning to characterize learning, reasoning, and judgment.