Research


Improving Online Learning Through Course Design: A Microeconomic Approach (Click Here for Draft)

Online education has expanded dramatically over the past two decades, yet significant learning challenges remain. In light of these, my paper provides the first microeconomic analysis to examine how the quality of online university courses can be enhanced through course design. First, I gather rich data covering 3,700 undergraduates at a large public university taking an online introductory programming course that has a cumulative structure. The data allow me to monitor students' study time precisely and to characterize important dimensions of heterogeneity: student attentiveness and whether they are forward-looking. I then conduct two randomized interventions that nudge students to utilize an online discussion board more fully and to complete online assignments. I find that an additional 4.5 weeks of discussion board utilization increases final exam grades by 0.07 SD and completing one extra online assignment (out of 10 in total) raises final grades by 0.18 SD. I then develop and estimate a behavioural model of student effort supply, credibly identifying the marginal benefits and costs of effort at each stage of the cumulative learning process using the two field experiments. The estimated model allows me to explore the efficacy of changing assignment grading weights to improve student learning. In contrast to the actual (equally-weighted) grading scheme, simulated weights that maximize learning are decreasing across assignments, serving to increase effort by myopic students early in the course when they acquire foundational skills. My course-design approach is applicable more generally in other online and traditional course settings.

Provision of Online Public Goods: Evidence From a Peer Discussion Board (2020)

Understanding Gender Gaps in STEM Specialization (2020), with Robert McMillan and Linda Wang

The Impact of Information Disclosure on Student Course Selection (2018), with Robert McMillan and Linda Wang