In r4ds/bookclub-dsieur: Data Science in Education Using R Book Club

Walkthrough 3: Using School-Level Aggregate Data to Illuminate Educational Inequities

Learning objectives:

This chapter explores what aggregate data is, and how to access, clean, and explore it.

Slides

r knitr::include_url("./R/2021-03-17/Ch9_walkthrough3.html")

Meeting Videos

Cohort 1

r knitr::include_url("https://www.youtube.com/embed/Z5KRaOgW0sk")

Meeting chat log

00:26:06    Ryan Woodbury:  Here's a bit of history and potential future directions for FRL metric: https://dataqualitycampaign.org/resource/accurate-student-poverty-data-is-crucial-to-supporting-all-students/
00:30:14    Isabella Velásquez: here's another reference on FRPL; Urban Institute is coming up with an alternative measure (disclosure: my coworker is funding this project): https://www.urban.org/features/measuring-student-poverty-dishing-alternatives-free-and-reduced-price-lunch
00:31:39    Isabella Velásquez: also want to share my favorite tweet :) https://twitter.com/andrewheiss/status/1021944992351186944?s=21
00:36:34    Ronak Patel:    Thanks for sharing those articles!