Intervention Targeting
“Which at-risk students will respond to tutoring?”
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A real-world example
Which at-risk students will respond to tutoring?
Universities invest $5-15M annually in student support services (tutoring, advising, mental health) but allocate them broadly rather than targeting students most likely to benefit. Generic allocation means 40% of intervention spend goes to students who would have succeeded anyway, while students who would respond to support don't receive it. For a university spending $10M on interventions, targeting the 'persuadable' population improves retention outcomes by 35% with the same budget.
How KumoRFM solves this
Graph-powered intelligence for education
Kumo connects students, interventions, outcomes, grades, and engagement into a student success graph. The GNN learns uplift patterns: which student profiles show the largest outcome improvement from specific intervention types, based on their academic trajectory, engagement level, and peer group dynamics. PQL predicts the incremental impact of tutoring per student, enabling advisors to prioritize students where intervention makes the biggest difference.
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 | gpa | risk_tier | engagement_score |
|---|---|---|---|---|
| STU201 | Engineering | 2.3 | High | 45 |
| STU202 | English | 2.1 | Critical | 28 |
| STU203 | Chemistry | 2.5 | High | 62 |
INTERVENTIONS
| intervention_id | student_id | type | hours | semester |
|---|---|---|---|---|
| INT301 | STU201 | Peer Tutoring | 12 | Fall-2024 |
| INT302 | STU202 | Academic Coaching | 8 | Fall-2024 |
| INT303 | STU203 | Study Group | 15 | Fall-2024 |
OUTCOMES
| student_id | semester | gpa_change | retained | credits_completed |
|---|---|---|---|---|
| STU201 | Fall-2024 | +0.4 | Yes | 15 |
| STU202 | Fall-2024 | +0.1 | Yes | 12 |
| STU203 | Fall-2024 | +0.6 | Yes | 16 |
GRADES
| student_id | course_id | grade | attendance_pct |
|---|---|---|---|
| STU201 | ENGR201 | C | 72% |
| STU202 | ENG201 | D+ | 58% |
| STU203 | CHEM201 | C+ | 80% |
ENGAGEMENT
| student_id | lms_logins_week | office_hours | study_group |
|---|---|---|---|
| STU201 | 8 | 1 | No |
| STU202 | 3 | 0 | No |
| STU203 | 12 | 2 | Yes |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(OUTCOMES.gpa_change, 0, 120, days) FOR EACH STUDENTS.student_id WHERE STUDENTS.risk_tier IN ('High', 'Critical')
Prediction output
Every entity gets a score, updated continuously
| STUDENT_ID | RISK_TIER | PREDICTED_GPA_LIFT | INTERVENTION_TYPE | PRIORITY |
|---|---|---|---|---|
| STU203 | High | +0.55 | Study Group | 1 |
| STU201 | High | +0.35 | Peer Tutoring | 2 |
| STU202 | Critical | +0.10 | Academic Coaching | 3 |
Understand why
Every prediction includes feature attributions — no black boxes
Student STU203 -- Chemistry, High risk, engagement score 62
Predicted: Predicted GPA lift: +0.55 with Study Group (Priority #1)
Top contributing features
Baseline engagement level (receptive)
62/100
30% attribution
LMS activity trend (willing to engage)
12 logins/wk
24% attribution
Office hours attendance (seeks help)
2 visits
19% attribution
Similar students' response to Study Group
+0.5 avg GPA lift
16% attribution
Course difficulty vs current GPA gap
Closable
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 spending $10M on student interventions improves retention outcomes 35% by targeting students most likely to respond. Kumo's student graph identifies the 'persuadable' population where tutoring makes the measurable difference, rather than allocating support generically.
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.
