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Documentation Index

Fetch the complete documentation index at: https://cors-lau.vercel.app/docs/llms.txt

Use this file to discover all available pages before exploring further.

This guide walks you through the full CORS workflow from first login to an exported semester schedule. You’ll upload your institutional data, run a machine learning analysis, review the recommendations, build a timetable, and export the final result — all in one session.
Before you begin, make sure you have your three datasets ready: a course catalog (JSON), historical offering records (CSV or XLSX), and faculty records (JSON, CSV, or XLSX). Your CORS administrator can provide these if you don’t have them on hand.
1

Log in to CORS

Navigate to your institution’s CORS URL in a browser. On the login page, enter your username and password, then click Secure Login.CORS uses two-factor authentication. After submitting your credentials, the system sends a 6-digit verification code to your registered academic email address. Enter that code on the next screen and click Complete Sign In to access your dashboard.
The 6-digit code is single-use and expires shortly after it is sent. If you don’t receive it within a few minutes, check your spam folder or contact your CORS administrator.
2

Upload your datasets

In the left navigation, go to Data Management. This is where you provide the three datasets that power every analysis.Upload them in any order using the file upload controls on the Data Management page:
DatasetAccepted formatsWhat it contains
Course catalogJSONAll courses in the current academic year registry, including codes, names, credit hours, and prerequisites
Historical offeringsCSV, XLSXPast semester enrollment records across all departments
Faculty recordsJSON, CSV, XLSXFaculty roster including names, departments, and teaching constraints
Make sure your course catalog is uploaded before running an analysis — it defines the universe of courses the ML model will score.
3

Configure the analysis

Navigate to the Recommendation Engine page from the sidebar.Before running the analysis, configure two inputs:1. Select your campusChoose either Beirut or Byblos from the campus selector. Each campus has its own ML model trained on that campus’s enrollment history. Selecting the correct campus ensures recommendations are calibrated for your context.2. Enter expected new enrollmentEnter the number of incoming students you expect next semester, broken down by level:
  • Freshman (new first-year students)
  • Sophomores (continuing or transferring students at sophomore level)
  • Masters (incoming graduate students)
These numbers allow the model to project section demand proportionally.
4

Execute the analysis

Click Execute Analysis. CORS sends your data to the campus-specific ML model, which scores every course in your catalog.Depending on the size of your dataset, the analysis may take a few moments to complete. A loading indicator will display while the model runs.
5

Review the recommendations

Once the analysis completes, a results table appears on the Recommendation Engine page. Each row represents a course and includes:
  • Course code and name
  • Recommendation status — either Recommended or De-prioritized
  • Suggested section count — the number of sections the model projects are needed
  • Supporting metrics — including historical failure rate and projected demand
Use this table to decide which courses to include in the upcoming semester schedule.
Courses marked Recommended have strong historical demand and a favorable failure-rate profile. Courses marked De-prioritized have low projected demand or high historical failure rates that may indicate a need for curriculum review.
6

Build the timetable

Navigate to the Timetable Scheduler from the sidebar. The scheduler imports your recommended courses automatically.For each course section, assign:
  • A time slot (day of week and time block)
  • A room from the available room inventory
  • A faculty member from your uploaded faculty records
CORS detects and flags conflicts — such as a faculty member assigned to two sections at the same time, or a room double-booked — in real time. Resolve all flagged conflicts before exporting.
7

Export the final schedule

Once your timetable is complete and conflict-free, click Finalize & Export at the top of the scheduler page.CORS generates and downloads an Excel file containing the complete semester schedule, including course codes, section numbers, time slots, rooms, and assigned faculty. This file is ready to share with departments or import into your institution’s academic information system.
The export only includes sections that have been fully assigned — courses without a time slot, room, or faculty member will not appear in the exported file.

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What’s next

Now that you’ve completed your first analysis, explore the rest of the platform:

Recommendation engine

Understand how the ML model scores courses and what the confidence metrics mean.

Prerequisite graph

Visualize course dependencies and identify curriculum bottlenecks.

Data formats reference

See the exact schema required for course catalog, historical offerings, and faculty record uploads.

Interpreting results

Learn how to read recommendation scores and decide which courses to prioritize.