KIPP Philadelphia Public Schools, Philadelphia, PA
This summer, I was a Data and Analytics intern at KIPP Philadelphia Public Schools, a branch of the broader KIPP Charter School network that spans many regions throughout the United States.
The network emphasizes the necessity of serving its students equitably and justly, and throughout my internship, I observed KIPP Philadelphia’s efforts to achieve these ideals. One such effort was a session on Data Equity that I attended during KIPP’s annual Data Retreat. It highlighted ways that data analysts can support or undermine equitable decision-making through their work. These lessons will serve me well into the future, whether I am creating or consuming data analyses.
Aside from Data Retreat sessions, my internship also began with an exploration of education policy. I read about the history of education policy in the U.S. and Pennsylvania’s charter school regulations; I also attended KIPP Philadelphia meetings throughout the summer, where I witnessed the role of data analysis and education policy in shaping decisions.
As for my contributions to the KIPP Philadelphia data team, I worked on various projects throughout the summer, three of which I would like to highlight. During my first project, I helped analyze the effects of remote learning on KIPP Philadelphia students. Our results informed decisions concerning what adjustments to make for the upcoming school year. One such adjustment is the adoption of a service called DeansList, a web-based implementation of MTSS (Multi-tiered System of Supports), which facilitate the delivery of emotional and educational resources to the students that need them. MTSS should help address the tolls of remote learning, but this is only possible if its usage is monitored and driven by evidence, which itself requires data.
Thus, my second project was to write software that would facilitate the ongoing collection of this data. Using a Python script I created, the required data is retrieved from the DeansList GraphQL API and added to KIPP Philadelphia’s SQL database. In the same spirit of easing data-driven decision-making, my final project was to automate part of KIPP Philadelphia’s annual review of its assessment cutoffs. These cutoffs translate student performance on e-learning platforms to proficiency levels on Pennsylvania state examinations so that students requiring additional resources can be identified and supported.
These projects were excellent learning opportunities and provided me with my first experiences performing database programming and writing substantial R code. Additionally, throughout my final project, I read many online materials concerning predictive modeling. Because I was fascinated by this material, I plan to explore these topics more deeply in courses that I take this year. Specific courses that I am considering for this purpose are Causal Inference, Machine Learning: Algorithms and Applications, and Computational Biology. Depending upon my experiences in these courses, I might pursue a Computer Science thesis on a related topic.
Given how much I learned, I would like to thank my supervisor, Mike MacArthur, for providing me with guidance and exploratory freedom throughout my internship. I am also grateful to the ’68 Center for Career Exploration and Jeffrey Hines ’77 for creating this opportunity.