My research focuses on online and technology-enriched education, especially at large scale, and especially in computer science.

Students: I’m happy to talk about getting you involved in our research, but in the thick of the semester I don’t usually have cycles to onboard new folks. I suggest coming to our open group meetings, Fridays 12-1pm in the BiD Lab (360 Hearst Mining), and seeing what opportunities arise. Good times to talk about things like this are right after the ends of semesters, with a view to getting started before the subsequent semester or summer.

Prof. Marti Hearst and I co-founded the successful ACM Learning@Scale conference to attract interdisciplinary work in this area.)

My background and previous projects (see left) are in Internet systems, programming systems, and applied machine learning; I bring these ideas to bear on CS education at scale.

You can read about our work and download our papers on the ACE Lab website.

Besides the fact that this area is a good fit for the current exploding demand for computing education, it’s both rich because of scale and rich despite scale:

Rich because of scale: with large cohorts of students (online or in the classroom), you can not only gather and analyze much richer datasets, but also use student work products in new ways, such as using some students’ work to give hints to other students.

Rich despite scale: At scale, what mechanisms increase students’ sense of engagement and of being part of a cohort despite the fact they may never meet their peers in person, and how can we help instructors better understand what’s happening in such large cohorts?

Delivered as a service: With online learning technology taking the form of Web services accessed by standard clients such as browsers, the potential exists for reducing research results to practice much more quickly and “baking in” best practices directly into the software.

Current Graduate Students and Committees (see also Alumni)