Course Recommendations
“Which electives should this student take?”
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A real-world example
Which electives should this student take?
Poor course selection extends time-to-degree by 0.5-1.5 semesters, costing students $5K-$15K in additional tuition and the institution in reduced throughput. Academic advisors manage 300-500 students each and cannot deeply analyze each student's optimal path. For a university with 15,000 undergrads, reducing average time-to-degree by 0.3 semesters increases throughput worth $4M per year and saves students $6M collectively.
How KumoRFM solves this
Graph-powered intelligence for education
Kumo connects students, courses, grades, prerequisites, and career goals into an academic graph. The GNN learns which course sequences lead to the best outcomes for students with similar profiles: not just prerequisite satisfaction, but grade trajectory optimization, workload balancing, and career-relevant skill building. PQL ranks electives per student, optimizing for degree completion speed, GPA, and career alignment.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
STUDENTS
| student_id | major | year | gpa | career_goal |
|---|---|---|---|---|
| STU101 | Computer Science | Junior | 3.4 | ML Engineer |
| STU102 | Business | Sophomore | 3.1 | Product Manager |
| STU103 | Biology | Junior | 3.7 | Med School |
COURSES
| course_id | name | department | difficulty | career_relevance |
|---|---|---|---|---|
| CS301 | Machine Learning | CS | Hard | High for ML/AI |
| BUS250 | Product Strategy | Business | Medium | High for PM |
| BIO320 | Molecular Biology | Biology | Hard | High for Med |
GRADES
| student_id | course_id | grade | semester |
|---|---|---|---|
| STU101 | CS201 | A- | Fall-2024 |
| STU101 | MATH301 | B+ | Fall-2024 |
| STU102 | BUS101 | B | Fall-2024 |
PREREQUISITES
| course_id | prereq_id | min_grade |
|---|---|---|
| CS301 | CS201 | C |
| CS301 | MATH301 | C |
| BIO320 | BIO201 | C+ |
CAREER_GOALS
| career | key_courses | avg_starting_salary |
|---|---|---|
| ML Engineer | CS301, CS401, STAT302 | $135K |
| Product Manager | BUS250, CS101, BUS301 | $120K |
| Med School | BIO320, CHEM301, BIO350 | N/A |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(GRADES.grade_points, 0, 120, days) FOR EACH STUDENTS.student_id, COURSES.course_id RANK TOP 5
Prediction output
Every entity gets a score, updated continuously
| STUDENT_ID | COURSE_ID | COURSE_NAME | PREDICTED_GRADE | RANK |
|---|---|---|---|---|
| STU101 | CS301 | Machine Learning | A- | 1 |
| STU101 | STAT302 | Statistical Learning | B+ | 2 |
| STU101 | CS350 | Distributed Systems | A | 3 |
Understand why
Every prediction includes feature attributions — no black boxes
Student STU101 -- CS Junior, career goal: ML Engineer
Predicted: Top recommendation: CS301 Machine Learning (predicted A-)
Top contributing features
Career goal alignment
Core ML course
30% attribution
Prerequisite grades (CS201: A-, MATH301: B+)
Strong foundation
25% attribution
Similar students' success rate
82% got B+ or above
20% attribution
Workload balance with other courses
Manageable
14% attribution
Course timing (offered this semester)
Available
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A university with 15,000 undergrads saves students $6M collectively and gains $4M in institutional throughput by reducing average time-to-degree 0.3 semesters. Kumo's academic graph optimizes course sequences for GPA, career alignment, and completion speed.
Related use cases
Explore more education use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
