Education · AWS
University system identifies at-risk students 22% sooner with a Student 360
22% earlier at-risk identification · 6 months from vision to production
At a glance
Key metrics
Challenge
The situation
Student data lived in SIS, LMS, advising, and residential-life systems. Early-alert flags fired too late — often after the student had already disengaged. Advisors didn't trust the dashboards.
Approach
How we delivered
- 01 Deployed the Apptad Student 360 accelerator on AWS, pre-configured for common SIS/LMS sources.
- 02 Worked with advisors to define an early-warning signal they could act on, not just a score.
- 03 Integrated outreach workflows into the advisor's day-to-day tool so insight to action took seconds.
Architecture
Solution architecture
AWS Lake Formation governs the student data lake. Glue jobs ingest SIS (Banner), LMS (Canvas), advising, and residential-life data. Amazon SageMaker trains the early-warning model. Results surfaced via QuickSight + pushed to Salesforce Education Cloud for advisor outreach.
Outcomes
Measured results
- 22% improvement in correctly identifying at-risk students within the first 6 weeks of the term.
- Advisor NPS on the early-warning tool rose from 31 to 72.
- Accelerator now deployed at 30+ institutions with partial federal/state match funding.
Technology
Tech stack
Platform
- AWS Lake Formation
- AWS Glue
- Amazon S3
- Amazon SageMaker
Analytics & CRM
- Amazon QuickSight
- Salesforce Education Cloud
Apptad IP
- Student 360 accelerator
“Our advisors now get a signal they believe in — and the time to act on it.”
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