Demand never reaches the people building the schedule
Degree audits, waitlists, and admits live apart from the schedule.
Academic Operations
Scheduling agent
Biology 210 needs another section
64 students can’t get a seat. Room 204 and Dr. Lee are free Monday and Wednesday afternoons.
Add sectionReview
Section added, 38 seats filled in a day
Why academic operations are hard to run today
Full here, half empty there
Sections fill unevenly, so waitlists and empty seats sit side by side.
Bottlenecks are predictable months out
Prerequisite chains and degree plans show them a term ahead.
The academic operations model
We bring your systems, outside data, and policies together, model the domain, and compute what it runs on.
What the model enables
Forecast
Next term’s demand
Seats needed in every course, from what each student still needs.
Optimization
Bottlenecks to unblock first
Unmet demand weighted by the courses each one unlocks.
Prediction
Census fill
Enrollment expected at census for every section.
Rule
Sections safe to merge
Under-filled sections no enrolled student needs to progress.
Optimization
Teaching load
Sections needed against faculty load targets, by department.
Rule
Prerequisite chains
The longest chain in each program, where students lose terms.
What we deploy
Applications, agents, and workflows on the model. Each one reads the model and changes it only through governed actions.
App
An app where the provost’s office, deans, and chairs plan next term
Next term’s demand against seats, bottleneck courses, sections at risk, and teaching load by department.
Reads from the model
- Course
- Section
- Department
- Instructor
Agent
An agent that drafts schedule changes
It adds sections where demand exceeds seats and merges sections that won’t fill. Chairs and deans approve every change.
Reads from the model
- Course
- Section
- Room
- Term
Workflow
A workflow that carries each approved change through
It books the room, assigns the instructor, and sends notices to the students affected.
Reads from the model
- Section
- Room
- Instructor
- Term
What the academic operations model lets you do
How we measure
We report what a change should add, and what it actually added.
Projected value
What the model expects a change to add, set when it goes live.
Holdout
Courses that don’t get the change, compared with those that do.
Matched group
Similar courses from the same term, when a holdout isn’t possible.
Prior cycle
The same term last year, for changes that reach every course.
Start with Academic Operations
We scope the first domain with your team and agree on the outcomes to measure.
How we deploy