Exemplar Collection
The exemplars present evidence-informed interventions implemented by higher education institutions in Ireland to support student retention and progression across the undergraduate lifecycle.
| HEI Name | Atlantic Technological University |
|---|---|
| Exemplar Title | REAL Analytics: Making Learning Trajectories Visible, Measurable and Responsive at Scale |
| Focus |
|
| Other focus | - Intelligent assessment design - Self-regulated learning and student agency - Disability and inclusion infrastructure (UDL) |
| Theme |
|
| Other theme | Integrates data analytics and self-regulated learning within intelligent assessment design |
| National/Institutional Context |
|
| Overview of Initiative | ATU serves Ireland’s only EU Transition Region. Identifying early signs of difficulty from the opening weeks matters: it allows support to be offered before students fall behind. Designed from the ground up by lecturers, for lecturer use, REAL Analytics was developed around a simple observation: student progression is shaped not only by academic performance, but also by how well students regulate their own learning during the semester. The system supports both. Assessments contribute a critical signal. STACK and Formulas questions form part of graded continuous assessment in first-year Science and Computing, not an optional add-on, capturing algebraic reasoning, computational workflows and laboratory data. Each student sees their own Moodle view: an On Track or Needs Attention indicator, a weekly self-regulated learning (SRL) reflection, and a confidence measure they update themselves. Nineteen per cent self-identify for support before the model flags them. By Weeks 5 to 7, the model identifies 89% of off-track trajectories. Module coordinators follow up through existing student support structures; disability and learning support is integrated, so accommodations are applied without repeated disclosure. |
| Aim and objectives of initiative | To make learning trajectories visible, measurable, and responsive at scale, through evidence that enables students to understand, shape, and regulate their own progression. Objective 1: Detect off-track learning trajectories through behavioural, engagement, and linguistic data, enabling intervention before academic failure becomes visible. Objective 2: Enable learners to understand, monitor, and regulate their own learning trajectories, while ensuring inclusive, equitable support through integrated, GDPR-aligned infrastructure. Objective 3: To strengthen the predictive and explanatory power of REAL Analytics by integrating advanced self-efficacy measurement, evaluating its contribution within the machine learning pipeline, and establishing causal evidence for trajectory-based interventions. |
| Monitoring & Oversight | At module level, the assessment system captures structured interaction data, with engagement visible to educators through Power BI dashboards. At student level, the Moodle view returns the On Track or Needs Attention indicator to the student alongside a confidence score and a weekly reflection; staff see the same indicator, and automated notifications go to named role owners when key thresholds are met. At institutional level, retention and pass rates report through School programme boards and the TSAF Work Package 5 steering group. |
| Data & Evidence of Impact | Three signals show the system is working.
Impact is evidenced through improved progression outcomes, early identification of off-track trajectories, learner-facing SRL engagement, 30+ peer-reviewed outputs (IEEE, ACM, Springer Nature), and international recognition (DELTA 2022; Dataiku 2022; LAK 2024). The 2026/27 TEF-funded matched-cohort study will extend the evidence base toward causal inference, with ongoing engagement on the implications of the EU AI Act for educational analytics. |
| Additional Information |
|
| Image |  |
| REAL-Analytics_Exemplar-visuals.pdf | |
| Licence | CC BY-NC-SA |
| Date Added | 20 May 2026 |
| Full Case Study PDF | Full-case-study-REAL_Analytics_Poster_combined.pdf |