Can teaching be taught? Improving teachers' pedagogical skills at scale in rural Peru
教学能否被教会?秘鲁农村教师教学技能的大规模提升AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要明确利用随机分配评估教育项目的处理效应,并讨论教师流动引起的跨校溢出,直接涉及因果推断与干扰。
教学能否被教会?秘鲁农村教师教学技能的大规模提升AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
We evaluate the impact of a large‐scale teacher coaching program in Peru, a context with high teacher turnover, on teachers' pedagogical skills and student learning. Previous studies find that small‐scale coaching programs can improve teaching of reading and science in developing countries. However, scaling up can reduce programs' effectiveness, and teacher turnover can erode compliance and cause spillovers onto non‐program schools. We develop a framework that defines different treatment effects when teacher turnover is present, and explains which effects can be estimated. We evaluate this teacher coaching program, exploiting random assignment of that program's expansion to 3797 rural schools in 2016. After two years, teachers assigned to the program increased their aggregate pedagogical skills by 0.20 standard deviations. The program also increased student learning; after 1 year, Grade 2 students' mathematics and reading scores increased by 0.106 and 0.075 standard deviations (of the distributions of those test scores), respectively. After three years, the cumulative effect increases slightly, to 0.114 and 0.100, respectively. One reason why these impacts are low is that some uncoached teachers moved into treated schools in years 2 and 3. Following our framework, we estimate that the impacts on students of having a “fully” coached teacher for all three years are 0.18 and 0.16 standard deviations for mathematics and reading comprehension, respectively.
社会网络数据中处理效应的识别与估计:当社会连接存在混杂时
In observational network data, the adoption of a treatment and the formation of social ties often depend on the same characteristics that affect outcomes. We propose a generalized propensity score for each unit's effective treatment, built from a model for treatment assignment and a dyadic model for network formation, which may include latent node heterogeneity. The score is marginalized over the network given covariates rather than conditioned on the realized network, and…
复杂(准)实验设计下的简单标准误
We develop standard errors for estimates of causal effects in settings where the distribution of treatment exposures is known but potentially exhibits complicated dependence patterns across units. Such dependence arises, for example, in settings with spillovers/interference across units or when the treatment assignment is spatially correlated. Our variance estimator generalizes several properties of the Neyman variance estimator for completely randomized experiments to…
边处理与节点结果的网络实验
We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment…
Comparing dependence structures between two related multivariate time series is of fundamental interest in many scientific applications, where changes may occur in both directed temporal interactions and contemporaneous connectivity. Modeling each time series by a vector autoregressive (VAR) model, we propose a new framework for estimation and inference of differential Granger causality (DiffGC) and differential network (DiffNet) structures in high dimensions. The proposed…
存在干扰时合成控制的稳健回归方法
Synthetic control methods are widely used for policy evaluation, but most existing approaches rule out interference among units, compromising validity when such effects are present. We develop a framework that accommodates contaminated donor pools and unknown interference patterns through two stages: factor-model adjustment for unobserved confounding, followed by robust regression in which direct and interference effects appear as a sparse outlier component. We study two…
随机实验的一般设计型框架与估计器
We describe a widely applicable design-based framework for drawing causal inference in randomized experiments. Causal effects are defined as linear functionals evaluated at unit-level potential outcome functions. Assumptions about the potential outcome functions are encoded as function spaces. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions that previously could not be investigated with design-based…