We show that a Reinforcement Learning (RL) model produces better learning gains using fewer educational activities than a linear assignment condition, and produces similar learning gains to a self-directed condition using fewer educational activities and with lower dropout rates.

College and university students submit millions of course reviews each year, yet these instruments are only rarely leveraged for scientific inquiry. This paper examines 11,255 reviews submitted to computer science courses to illustrate how such inquiry might proceed.

The authors combine qualitative and quantitative techniques to observe how 55,016 applicants to a highly selective public university narrate their cases for admission.

The authors attended discussions and interviewed instructors in Stanford’s Computer Science Department to identify successful approaches and problem areas in the rapid transition to online learning.

Using digital trace data to observe this process at scale at a private research university, together with qualitative student interviews, we provide a novel empirical study of course consideration as part of the course selection process.

The US federal government has serially called upon colleges and universities to assist the nation in moments of national crisis. This brief outlines an ambitious plan to enlist the postsecondary sector in helping millions of Americans get back to work in the wake of the pandemic and equip them for ongoing prosperity in a highly dynamic economy.