来自观测数据的可操作见解：K-12教育中高级课程案例

中文摘要 AI 辅助

K-12教育中一个根本性的挑战性问题涉及选修更多高级或挑战性课程的影响。由于学生（及其家长）自行选择是否注册这些课程，这一问题尤为复杂，使得因果分析颇具难度。在本文中，我们利用美国一个公立学校系统的新颖数据集开始着手解决这一问题。该数据集记录了学生在一次全区范围变革前后的课程注册决策、先前学业历史、人口统计信息及后续结果，此次变革在学科领域引入了可选性的开放式注册中学高级课程。这是一个丰富的观测数据集，但高级课程的注册受学生特征和选择驱动，而非随机分配。这构成了一个核心识别挑战：影响高级课程注册的相同因素也预测学业成果。因此，注册与未注册学生之间的简单比较存在混杂，朴素估计可能反映学生能力、动机或支持方面的潜在差异，而非课程本身的影响。我们的分析表明，注册高级英语课程对学生学业成果具有净正面但适度的效应。然而，这些益处分布不均：一些预测增益相对较大的学生（前几年的“中等成就者”）注册的可能性低于其他学生。其他一些群体（如黑人学生和社会经济地位较低的学生）也表现出显著较低的注册倾向。预测益处与观察到的注册之间的这种差距说明，仔细的数据分析如何能从大型观测数据集中提取可操作的见解，包括识别那些看似有条件受益但未选择高级选项的学生。

英文摘要

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.

发表机构

• George Mason University（乔治梅森大学）
• Virginia Tech（弗吉尼亚理工大学）

机构由 AI 辅助整理，请以论文原文为准。