Data Scraping and AI/ML Research on Education Policy and Philanthropies

Mentor Areas

The research assistant will join a research team which includes Pam Grossman (Professor of Education, Penn GSE), Maya Kaul (Postdoctoral Scholar, Penn GSE), and Sarah Schneider Kavanagh (Associate Professor, Penn GSE). The broader project of which this study is a part of is focused on identifying the policies and practices necessary to help cultivate a thriving K-12 teaching profession. The research mentors bring diverse expertise in education scholarship, especially teacher education, education policy, and qualitative and mixed methods research. 

Description:

We are interested in hiring a research assistant to join a study focused on the role of private philanthropies/foundations in shaping the teaching profession and teaching policy between 1985-2025. The broader study draws on artifacts, interviews, and public data to analyse how the priorities and grant-making areas of major foundations in education evolved over these four decades. In the next step of this study, we are hoping to construct a robust dataset of grant-level data from 10-15 major foundations, drawing on public grant-making data (i.e., tax documents) we have been collecting. 

To so do, we are looking for someone who can help us with one (or ideally both) of the following two tasks:

1. Scraping large PDFs (i.e., tax documents) to construct a dataset using Python. This will involve scraping large documents and constructing a CSV dataset from the resulting data.
2. Applying AI/ML techniques to analyze the resulting dataset. We will have an excel sheet with thousands of grants with short descriptions of each grant, and want to explore ways to automate classifying the grants into different topical areas. 

If you have the skills to only assist with only one of the tasks, we encourage you to still apply. 

Preferred Qualifications

The ideal candidate wouldn’t need a substantive background in education, but would bring a strong background in coding/data cleaning using Python. It is a plus if you have any background with social network analysis, but not required.