About Me

I design online modules or MOOClets which adaptively improve and personalize people’s education in complex real-world environments, by aligning tests of scientific hypotheses with practical improvements.

I combine cognitive science behavioral experiments and computational models for learning and high-level cognition, with human-computer interaction design of practical online technology which simultaneously supports students in learning, instructors in teaching, and scientists in

Examples include increasing motivation and reflective problem-solving while solving mathematics exercises on Khan Academy, strategies for self-questioning that enhance learning from videos in MOOCs, and digital tools to provide in-the-moment guidance to students in applying management concepts from courses to everyday interactions with people. You can contact me at joseph_jay_williams AT harvard DOT edu.

    In this blend of scientific research and applications I collaborate and consult with domain/topic experts, instructors, designers, and researchers from diverse disciplines like education, psychology, human-computer interaction and machine learning. 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, and experience as an ed-tech consultant and science & technology advisor. 

I am currently a Research Fellow at HarvardX, the online learning research and development component at Harvard. 

I was previously a postdoc at Stanford University in the Graduate School of Education and Lytics Lab. I received my PhD from UC Berkeley's Psychology Department in Experimental and Computational Cognitive Science. 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.