Success story
AI‑Driven Student Retention & B3 Outcomes
Challenge
QA Higher Education (QAHE) is a private UK higher education provider that partners with universities to widen access to high‑quality education for domestic and international students. It delivers over 100 foundation, undergraduate and postgraduate programmes across business, computing, and digital disciplines from city‑centre campuses across the UK.
A core part of QAHE’s mission is to support partner universities in meeting and exceeding Office for Students (OfS) B3 thresholds for student continuation, completion, and progression.
QAHE relied on a withdrawal analysis model built around static, point‑in‑time data and external consultancy support. Key limitations included: – Insights arriving weeks or months after critical student events – High cost for each new cohort or intake analysis – Limited accessibility of insights beyond specialist analysts-Fragmented risk and engagement data across campuses and programmers. This approach made early intervention difficult and restricted QAHE’s ability to proactively manage student outcomes at scale.
What TMotions did?
TMotions partnered with QAHE to design and implement an AI‑powered Insight Engine that replaces static reporting with a live, continuously updating view of student engagement and withdrawal risk.
Core components include a unified, automated data pipeline that ingests daily risk and engagement updates, along with data cleansing, standardisation, and anonymisation performed within Microsoft Fabric. Predictive machine learning models are used to identify heightened risk patterns, while Large Language Models generate clear, natural-language explanations of what is happening and why. Together, these elements created a reusable analytical capability rather than a one-off report.
Student engagement, attendance, and progression data are ingested daily and processed through Microsoft Fabric. Machine learning models identify emerging risk signals, while LLMs translate complex analytics into clear narratives that can be understood and acted upon by non‑technical users.
Value Delivered
By moving from outsourced, static analysis to an in‑house AI‑driven Insight Engine, QAHE achieved:
- A continuous early‑warning view of student withdrawal risk
- Earlier, more targeted interventions for at‑risk students
- Deeper understanding of the underlying drivers of attrition
- Lower marginal cost through reuse across cohorts and intakes
- Broader engagement with data across academic and support teams
- Continuous early‑warning signals for at‑risk students
- Faster, more confident intervention
- Reusable analytics capability replacing repeated external analysis
- Stronger support for OfS B3 outcomes
Why TMotions
TMotions combines deep analytics expertise with responsible AI design to help organisations build sovereign, reusable insight capabilities that directly support strategic and regulatory outcomes.
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