Advanced middle school classes boost learning but enrollment varies
Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Education
Machine Learning
Summary
Figuring out how advanced classes affect student learning is tricky because students choose whether to enroll, creating unfair comparisons. The authors worked with a detailed dataset from a US school district to study this. They found that advanced English classes have a small positive impact on student achievement. However, some students who would benefit most, like those in the middle achievement range, are less likely to take these classes. The study also found that Black students and those from lower-income backgrounds enroll less often, highlighting opportunities to support underrepresented groups.
What this means in practice
- •For school administrators: Identify students who could benefit most from advanced classes but are unlikely to enroll, enabling targeted outreach or support programs.
- •For educational policy teams: Use enrollment and outcome data to design equitable access policies for advanced coursework to reduce demographic enrollment gaps.
Authors
Nabit Bajwa, Seth B. Hunter, Sanmay Das
Abstract
A fundamentally challenging question in K-12 education is about the effects of taking more advanced or challenging classes. It is particularly complex because students (and/or their parents) choose whether to enroll in these classes, making causal analysis challenging. In this paper, we begin to tackle this question by taking advantage of a novel dataset from a public school system in the US. This dataset records students' course enrollment decisions, prior academic histories, demographics, and subsequent outcomes around the time of a district-wide change that introduced optional open-enrollment advanced middle-school courses in subject areas. This is a rich observational dataset, but enrollment in advanced classes is driven by student characteristics and choices rather than random assignment. This creates a core identification challenge: the same factors that influence enrollment in advanced courses are also predictive of academic outcomes. As a result, simple comparisons between enrolled and non-enrolled students are confounded, and naive estimates may reflect underlying differences in student ability, motivation, or support rather than the impact of coursework itself. Our analysis shows that enrolling in advanced English courses has a net positive but modest effect on student achievement outcomes. However, these benefits are unevenly distributed: some students with relatively large predicted gains ("middle achievers" in prior years) are less likely to enroll than others. Some other groups (e.g. Black students and those with lower socio-economic status) also demonstrate significantly lower propensity to enroll. This gap between predicted benefit and observed enrollment illustrates how careful data analysis can extract actionable insights from large observational datasets, including identifying students who appear well-positioned to benefit but do not select into advanced options.