Data-driven Decision Making in Education
Generate actionable insights and simulate interventions
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 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 to outcomes.
Understand the connection between your educational design and labor market needs.
Decrease skill gap and increase impact and satisfaction.
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Explore the Flow
Visualize the health of your program in terms of student progression. Identify bottlenecks, generate actionable insights, and simulate the impact of targeted interventions on the flow.
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Measurable Impact
Didaflow increases enrolment, retention, and employability, translating educational quality into measurable economic value.
Enrolment conversion rate
€3.0M / pp
Incremental revenue
Second year progression
€1.0M / pp
Retained value
Employment rate
€150k / pp
Incremental revenue
Estimates based on conservative benchmarks from a large European public university (~150,000 students), assuming an average annual cost/revenue of €20,000 per student. Full methodology available on request.
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 actionable insights
Understand what drives outcomes, not only what happened.
Simulate interventions
Test what-if scenarios and compare baseline vs simulated results.
Integrations & Compliance
Built for real university stacks — aligned with European quality standards.
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):
- Enrolment conversion: €3.0M/year incremental revenue
- Second year progression: €1.0M/year retained value
- Employment rate: €150k/year incremental revenue
(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 2025 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.