Student Retention Prediction
“Which students are at risk of dropping out?”
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A real-world example
Which students are at risk of dropping out?
US colleges lose $16.5B annually to student attrition. Each dropout costs the institution $25K-$50K in lost tuition and reduces completion rates that affect rankings and funding. Early warning systems based on GPA alone miss 40% of at-risk students because dropout is driven by a combination of academic struggle, financial stress, social isolation, and disengagement. For a university with 20,000 students and 15% annual attrition, preventing 200 dropouts saves $5-10M per year.
How KumoRFM solves this
Graph-powered intelligence for education
Kumo connects students, enrollments, grades, attendance, and financial aid into a student success graph. The GNN learns compound risk patterns: students whose peer group is disengaging, whose financial aid gap is widening, and whose course-specific struggle patterns match historical dropout trajectories. PQL predicts dropout risk per student per semester, giving advisors enough lead time to intervene with targeted support.
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 | first_gen |
|---|---|---|---|---|
| STU001 | Computer Science | Sophomore | 3.2 | No |
| STU002 | Biology | Freshman | 2.4 | Yes |
| STU003 | Business | Junior | 2.8 | No |
ENROLLMENTS
| enrollment_id | student_id | course_id | semester | status |
|---|---|---|---|---|
| ENR101 | STU001 | CS201 | Spring-2025 | Active |
| ENR102 | STU002 | BIO101 | Spring-2025 | Active |
| ENR103 | STU003 | BUS301 | Spring-2025 | Active |
GRADES
| student_id | course_id | midterm_grade | assignment_avg |
|---|---|---|---|
| STU001 | CS201 | B+ | 88% |
| STU002 | BIO101 | D | 52% |
| STU003 | BUS301 | C+ | 74% |
ATTENDANCE
| student_id | course_id | attendance_rate | trend |
|---|---|---|---|
| STU001 | CS201 | 92% | Stable |
| STU002 | BIO101 | 61% | Declining |
| STU003 | BUS301 | 78% | Stable |
FINANCIAL_AID
| student_id | aid_amount | unmet_need | work_study_hours |
|---|---|---|---|
| STU001 | $18,000 | $2,400 | 0 |
| STU002 | $12,000 | $14,500 | 20 |
| STU003 | $22,000 | $1,800 | 10 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ENROLLMENTS.status = 'Withdrawn', 0, 120, days) FOR EACH STUDENTS.student_id WHERE ENROLLMENTS.status = 'Active'
Prediction output
Every entity gets a score, updated continuously
| STUDENT_ID | MAJOR | DROPOUT_PROB | RISK_TIER |
|---|---|---|---|
| STU001 | Computer Science | 0.06 | Low |
| STU002 | Biology | 0.74 | Critical |
| STU003 | Business | 0.22 | Medium |
Understand why
Every prediction includes feature attributions — no black boxes
Student STU002 -- Biology Freshman, first-gen
Predicted: 74% dropout probability (Critical)
Top contributing features
Attendance rate decline (8-week trend)
-31%
29% attribution
Unmet financial need
$14,500
25% attribution
Midterm grade in gateway course
D in BIO101
21% attribution
Peer group engagement decline
3 of 5 peers flagged
15% attribution
First-generation status + work-study load
20 hrs/wk
10% 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 20,000 students saves $5-10M per year by preventing 200 dropouts through early intervention. Kumo's student graph detects compound risk patterns (financial stress + social isolation + academic struggle) that GPA-only early warning systems miss.
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.
