Course Scraper

CLI tool that scrapes classes.tcu.edu for semester course offerings. Generates offerings-{term}.json files used by Engelina and Ada, the degree-planning wizards in the Advising Ecosystem.

Usage

node scrape.mjs --term <term> [--subjects <list>] [--attribute <attr>] [--out <file>]

Options

Flag	Short	Description
--term	-t	Term name or code (e.g. "Spring 2026" or "4263")
--subjects	-s	Comma-separated subject prefixes (e.g. "ENGL,CRWT,WRIT")
--attribute	-a	Class attribute code (e.g. "DCDA")
--out	-o	Output file path (omit for stdout)
--max-level	-l	Max course number to include (e.g. "49999" for undergrad only)
--all-sections		Keep all individual sections (don't collapse multi-section courses)
--csv		Output CSV instead of JSON
--dry-run		Print output to stdout, don't write file

Examples

English wizard (collapsed, undergrad only):

node scrape.mjs -t "Spring 2026" -s ENGL,CRWT,WRIT -l 49999 \
  -o ../english-advising-wizard/src/data/offerings-sp26.json

DCDA wizard (all sections, using DCDA attribute filter):

node scrape.mjs -t "Spring 2026" -a DCDA --all-sections \
  -o ../dcda-advising-wizard/data/offerings-sp26.json

Dry run with CSV output:

node scrape.mjs -t "Fall 2026" -s ENGL --csv --dry-run

Output Format (JSON)

{
  "term": "Spring 2026",
  "updated": "2026-02-22",
  "offeredCodes": ["ENGL 10103", "ENGL 10803", ...],
  "sections": [
    {
      "code": "ENGL 10103",
      "title": "Introductory Writing Seminar",
      "modality": "In Person",
      "status": "Open",
      "sectionCount": 12
    }
  ]
}

With --all-sections, each section is its own entry with section, schedule, and enrollment fields instead of sectionCount.

How It Works

1. GET classes.tcu.edu/psc/... to obtain ASP.NET-style ViewState tokens and term dropdown values
2. POST with form fields (term code, subject/attribute filters) to retrieve the results table
3. Parse the HTML results table with Cheerio, extracting course code, title, section, schedule, modality, status, enrollment, and core codes
4. Normalize statuses: "Dept Permit" and "Instr Permit" become "Closed" (manifest schema only allows Open/Closed/Waitlist)
5. Collapse multi-section courses into a single entry with sectionCount (default) or keep all sections (--all-sections)

Project Structure

scrape.mjs           # CLI entry point
lib/
  term-codes.mjs     # Term name ↔ code conversion (e.g. "Spring 2026" → "4263")
  fetcher.mjs        # HTTP GET/POST to classes.tcu.edu
  parser.mjs         # Cheerio HTML table parser

Term Code Formula

TCU term codes: base = (academicYear - 1599), digit = {fall: 7, spring: 3, summer: 5, wintersession: 1}. Academic year = fall's calendar year (Spring/Summer/Wintersession 2026 → academic year 2025). Examples:

• Spring 2026 → academic year 2025 → (2025 - 1599) = 426 → 4263
• Fall 2025 → academic year 2025 → (2025 - 1599) = 426 → 4257
• Fall 2026 (draft) → use --term 4267 --term-hint "26-fall-4267" for unpublished semesters

See lib/term-codes.mjs.

Generating Frequency Data

To build a course frequency JSON from multiple offerings files:

node -e "
const fs = require('fs');
const files = [
  { path: '../english-advising-wizard/src/data/offerings-fa25.json', term: 'Fall 2025' },
  { path: '../english-advising-wizard/src/data/offerings-sp26.json', term: 'Spring 2026' },
  { path: '../english-advising-wizard/src/data/offerings-fa26.json', term: 'Fall 2026' },
];
const terms = [], courses = {};
for (const { path, term } of files) {
  const data = JSON.parse(fs.readFileSync(path, 'utf8'));
  terms.push(term);
  for (const code of data.offeredCodes) {
    if (!courses[code]) courses[code] = [];
    courses[code].push(term);
  }
}
const sorted = Object.fromEntries(Object.entries(courses).sort(([a],[b]) => a.localeCompare(b)));
fs.writeFileSync('../english-advising-wizard/src/data/course-frequency.json', JSON.stringify({ terms, courses: sorted }, null, 2));
"

This produces the format expected by the admin Frequency Tracker: { terms: string[], courses: Record<string, string[]> }.