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UC Berkeley, Fall 2020

Addressing Structural Inequality in U.S. Agricultural Higher Education: An Assessment of Pedagogical Practices and Food Systems Coursework at Land-grant Institutions

Partner: Rosalie Z. Fanshel, UC Berkeley, Department of Environmental Science, Policy, and Management, Academic

Overview

Project Description

In the last decade, many U.S. “land-grant” agricultural universities (including UC Berkeley) have turned a reflexive lens on the fact that their institutions marginalize certain community members. These schools have begun to ask how inequities within their food and agriculture education perpetuate food systems injustices in the U.S. more broadly. This research project critically evaluates these nascent institutional efforts by examining how agriculture schools use pedagogical practices to improve equity and inclusion outcomes. Coursework descriptions offer a key insight into the pedagogical methods and types of food systems topics in use at land-grant universities. This project aims to create the first comprehensive database of coursework content at all land-grant institutions. This database will be the first effort in the U.S. to 1) track all recent food and agriculture classes across the land-grant system, and 2) specifically identify pedagogical methods and food justice content. Through content analysis of land-grant university coursework this project seeks to answer the following questions:

  • Which land-grant universities—and how many—currently offer courses that
  • utilize critical pedagogical methods
  • explicitly discuss food systems equity as part of their curriculum?
  • What are the characteristics of the critical, equity-oriented food systems curriculum at land-grant universities?

Expected Deliverable

The data steps are:

  1. Systematically identify the online course catalogues for all 114 land-grant universities.
  2. Create a broad list of keywords that identify the presence of food systems content, equity/justice content, and pedagogical approaches.
  3. Use Python to web-scrape titles and descriptions of food and agricultural coursework across disciplines.
  4. Manually review Python outputs and clean data as necessary.
  5. Quantify the presence of various keywords in association with each land-grant.
  6. Analyze data for patterns, differences, and meanings.

The role of the Discovery Project students in Fall 2020 will be Step 3: To finish building and running Python code for performing the web-scraping of all 114 course catalogues. In spring and summer 2020, DP students completed about 50% of the initial code. The team will work in collaboration with two social science undergraduate students who will be working on Steps 4–6.

What would a successful semester look like to you?

Success would be twofold:

  1. The students stretch their data science skills by applying them to project that address broad “systems” themes of education and food justice, and thus have the opportunity to think and grow in areas outside their primary discipline.
  2. The tasks are accomplished such that we have working data for all 114 schools by the end of the semester.

Additional Skills from ideal candidates

Outstanding communication skills. Comfort with asking questions and seeking help when needed.

Data

Models

Conclusion