Does regular online testing enhance student learning in the numerical sciences? Robust evidence from a large data set

Abstract While a number of studies have been conducted on the impact of online assessment and teaching methods on student learning, the field does not seem settled around the promised benefits of such approaches. It is argued that the reason for this state of affairs is that few studies have been able to control for a number of confounding factors in student performance. We report on the introduction of a regular (every 3 weeks) low‐mark online assessment tool in a large, first‐year business mathematics course at the University of New South Wales, a major Australian university. Using a retrospective regression methodology together with a very large and rich data set, we test the proposition that exposure to the online assessment instrument enhances student learning. Significantly, we are able to control for prior student aptitude, in‐course mastery, gender and even effort via a voluntary class attendance proxy. Furthermore, the study incorporates two large, and statistically diverse cohorts as well as manipulations in the model tested to robustly examine the outcomes. Our central result is that higher exposure to the online instrument robustly leads to higher student learning, all else being equal. Various implications for online assessment design, implementation and targeting are also discussed.