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Data & AI for education
An educational institution knows a great deal about its students, but often only sees what is happening when it is too late.
Applications, grades, attendance, timetables and financial records sit in different systems. As a result, it may not become clear until the end of a term which students are falling behind or which programmes are attracting fewer applicants. Data and AI bring these sources together and show what is happening now.
Challenges,
Enrolment may be falling or growing, students may drop out in their first year, teachers are in short supply, and timetables have to be pieced together again every term. At the same time, student and pupil data must be handled carefully. That calls for reliable insights and clear agreements about who can see what.
How we support you,
We bring student administration, the learning environment, timetables and financial data together in a single data foundation. On that foundation, we build reports and models for student progress, enrolment and planning. We also help staff find the right information more quickly in policies and documents. These are just some examples of what is possible. The best solution always depends on the challenge and the organisation.
DSL in numbers,
40
Data experts
10+
Years of experience
200+
Projects completed
8,7
Client satisfaction
Get in touch with our experts,
Want to know what we can do for your organisation?
Younes Seghrouchni
Managing Consultant GenAI & Data StrategyE-mail info@datasciencelab.nl
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Where we do that,
Latest blog posts,
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The organisations that do this,
Frequently asked questions,
AI can combine data on student progress, attendance and previous cohorts to identify students at risk sooner. This allows mentors and academic advisers to start a conversation in time and gives programmes better insight into enrolment and planning.
Start with a question that comes up every year, such as first-year dropout rates or expected enrolment by programme. Data from previous cohorts can quickly show whether a model can identify something that currently only becomes clear after the fact.
Applications and enrolments, grades and credits, attendance, data from the learning environment, timetables and financial records. The main work lies in connecting these sources and making clear agreements about privacy and access.
We use only the data needed to answer the question, manage access by role and agree in advance with the data protection officer on how the data will be used. A model provides a signal; the mentor or academic adviser decides what to do with it.
By displaying the signal in the systems mentors and timetable planners already use, and agreeing who will maintain the model when programmes, assessments or rules change. That way, it remains a tool that fits how the institution works.