Data-driven Decision Making in Education
Generate causal insight and simulate interventions with AI
The Next Educational Design
One system. Many decisions.
See the long-term consequences.
Model educational pathways end-to-end from applicants to outcomes.
Attractiveness
Know what attracts students before you invest.
Programs, curricula, and access policies shape who applies and who enrolls. Test positioning and offering changes and see their impact on conversion across cohorts.
Reduce mismatch and drop-offs at enrollment with evidence-based scenarios.
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Regularity
See where progression flows and where it breaks.
Identify bottlenecks where courses, assessment, or teaching choices shape pass-rates, credits, and persistence.
Test interventions safely. Without experimenting on students.
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Employability
Connect education to skills not just graduation.
Visualize the causal relationship between your educational design and labor market needs.
Decrease skill gap and increase impact and satisfaction.
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Benchmark
Measurable Impact
Estimated annual economic value for a large European university (~15,000 new first-year enrolments)
Applicant → enrolment conversion
~€3.0M / pp
Incremental revenue
First → second year progression
~€1.0M / pp
Retained value
Graduation → employment rate
~€150k / pp
Incremental revenue
Our Process
How Didaflow works
Key capabilities that transform educational data into actionable insights.
Import your data
Unify career, exams, and surveys into one coherent model.
Generate causal insight
Understand what drives outcomes, not only what happened.
Simulate interventions
Test what-if scenarios and compare baseline vs simulated results.
Interactive Demo
Explore the Flow
Visualize the health of your program in terms of student progression. Identify bottlenecks, generate causal insights, and simulate the impact of targeted interventions on the flow.
Want to see your program's data? Schedule a personalized demo.
Ecosystem & Standards
Integrations & Compliance
Built for real university stacks — aligned with European quality standards.
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FAQ
Frequently Asked Questions
What data is required to get started?▼
Didaflow operates on existing administrative data.
Minimum required inputs typically include:
- Enrolments and student careers
- Exam attempts, credits, and outcomes
- Basic demographic and programme metadata
No surveys or additional data collection are required. Historical depth (3+ cohorts) improves predictive accuracy but is not mandatory.
How accurate are the predictions?▼
Performance varies by use case and data quality, but typical results include:
- Early dropout risk detection: actionable signals available within the first semester
- Cohort-level forecasts: stable trends with low variance across academic years
- Explainability: all predictions are accompanied by interpretable drivers
Models are validated on historical cohorts and calibrated with institutional benchmarks.
How does Didaflow support decision-making, not just analytics?▼
Didaflow is designed for operational and strategic use, not dashboards alone.
It supports:
- Early-warning lists for tutors and coordinators
- Scenario analysis (e.g. impact of +1 pp retention)
- KPI monitoring aligned with ANVUR / OECD indicators
- Evidence generation for accreditation, funding, and policy reporting
How do you handle privacy, security, and compliance?▼
Privacy and security are built in by design.
- Fully GDPR-compliant
- Data minimisation and purpose limitation
- Role-based access control
- Pseudonymisation and audit logging
- EU-based hosting or on-premise options available
No student-level decisions are automated.
What measurable impact can we expect?▼
Under conservative assumptions (+1 percentage point improvements):
- Applicant → enrolment: ~3M/year incremental value
- First-year retention: ~3M/year retained value
- Graduate employment outcomes: ~0.7M/year economic impact
(for a large institution ~15k new enrolments/year)
Actual impact depends on baseline performance and scale.
Conservative, evidence-aligned estimates based on OECD and Eurostat averages, assuming improvements enabled by AI-based guidance, early-warning analytics, and targeted interventions.
1 OECD baselines from Education at a Glance 2023 and Eurostat employment statistics (recent graduates, ages 20–34).
2 Economic values assume ~€20,000 PPP annual expenditure per tertiary student and GDP per employed person of ~€70,000.
3 Estimates represent incremental effects relative to baseline performance and exclude structural capacity expansion.
How much does Didaflow cost?▼
Pricing is institution-scaled, based on:
- Number of students and programmes
- Data complexity
- Deployment model (cloud / on-prem)
In all observed scenarios, the annual value retained or generated exceeds the cost of adoption by an order of magnitude.
Do you provide training and support?▼
Yes.
All deployments include:
- Initial onboarding workshops
- Training for governance, tutors, and technical teams
- Ongoing support and model recalibration
Turn educational data into decisions.
Request a demo and discover Didaflow with your own data.