Scraping scholarship data from Scholarship America with Minexa.ai

Scholarship America maintains one of the largest publicly browsable scholarship databases in the US. The browse page at scholarshipamerica.org lists hundreds of programs with award amounts, deadlines, eligible institutions, and geographic scope. If you are building a scholarship aggregator, doing financial aid research, or just trying to track what programs are open and when, that data is useful but locked inside paginated HTML.

This article walks through how to pull it out using the Minexa.ai Chrome extension without writing any scraping code.

What the page looks like

The browse page at scholarshipamerica.org/students/browse-scholarships/ renders a paginated list. Each entry includes a program name, award amount, deadline, eligible institution types, and a location tag. The pagination uses a URL pattern (?_paged=2, ?_paged=3, etc.), which Minexa detects automatically.

Opening Minexa on the page

Once the extension is installed and you are on the browse page, click the Minexa icon. The popup asks you to confirm you are on the right page.

After confirming, Minexa scans the page and surfaces the pagination it found.

No configuration needed here. Hit Continue.

Choosing scraping depth

This is a decision point most scraping tools skip entirely. Minexa asks whether you want to scrape the list only, or the list plus the detail page behind each link.

For most research use cases, the list data is sufficient. If you need full program descriptions or eligibility criteria from each individual scholarship page, the detail option handles that in the same run.

Automatic container and field detection

Minexa highlights the repeating container it identified. You do not click on individual fields. It finds all data points inside the container on its own and presents them for review.

This is useful when you are not sure what fields are available. You see everything Minexa found before committing to a run.

Video walkthrough

Watch full video walkthrough

Sample output

Here is a cleaned sample from the extracted JSON (meta fields and field prefixes removed):

[
  {
    "scholarship_program_name": "#RAREis Scholarship Fund",
    "award_amount": "$5,000",
    "event_date_time": "April 28, 2026 3:00 PM CT",
    "eligible_institutions": "Community or Technical College, Four-Year University, Graduate School",
    "location": "National",
    "status": "Closed",
    "program_link": "https://scholarshipamerica.org/scholarship/rareis/"
  },
  {
    "scholarship_program_name": "Amazon Future Engineer Scholarship",
    "award_amount": "Up to $40,000",
    "event_date_time": "January 26, 2026 3:00 PM CT",
    "eligible_institutions": "Community or Technical College, Four-Year University",
    "location": "National",
    "status": "Closed",
    "program_link": "https://scholarshipamerica.org/scholarship/amazonfutureengineer/"
  }
]

Each row is one scholarship. Fields map directly to what is on the page.

Working with the data in Python

import json

with open('scholarships.json', 'r') as f:
    data = json.load(f)

for item in data:
    name = item.get('scholarship_program_name', '')
    amount = item.get('award_amount', 'TBD')
    deadline = item.get('event_date_time', '')
    location = item.get('location', '')
    print(f"{name} | {amount} | {deadline} | {location}")

The export from Minexa is already structured, so there is no parsing or cleanup step before this runs.

Scheduling and export options

Once the scraper is configured, you can schedule it to run on a recurring basis and push results directly to Google Sheets. For a scholarship database that updates seasonally, this means your dataset stays current without manual re-runs.