How an Azure Machine Learning model integrated into Power BI gave academic advisors an early-warning system that identified at-risk students by week 4 — enabling proactive intervention weeks before grades failed.
Academic advisors only became aware of struggling students after mid-term grades were posted — usually 10–12 weeks into the semester. By that point, many students had already disengaged and withdrawal was often the only remaining option. The university had rich historical data but no proactive system to act on it.
Built a weekly feature dataset in Azure Synapse combining attendance, assignment completion, grade trends, financial aid status, and prior GPA to feed the predictive model.
Trained a classification model in Azure Machine Learning on 5 years of historical data. The model predicts the probability of student withdrawal or academic failure by week 4 with 81% accuracy.
Published the Azure ML model as a real-time endpoint and embedded weekly risk scores directly into the Power BI advisor dashboard, along with key contributing factors.
Created a Power BI dashboard that sorts each advisor’s caseload by risk score and uses Copilot to generate personalized student summaries with recommended interventions based on historical success patterns.
Configured Power Automate to send immediate Teams notifications to advisors when any student crosses a risk threshold, including a direct link to the student’s profile and suggested outreach talking points.