Turning daily activity into timely intervention

Every day, students generate signals across the systems institutions rely on — course logins, assignment submissions, discussion participation, grades, attendance, advising notes. None of these signals alone tells the full story, but together they reveal patterns that can indicate when a student is beginning to disengage.

The challenge isn’t collecting this data. Institutions already have it. The challenge is turning it into something timely, trustworthy, and actionable.

Many advising teams still rely on midterm reports, manual extracts, or delayed dashboards assembled from multiple systems. By the time trends are visible, the window for early intervention may have already narrowed.

Mage helps education teams unify LMS activity, SIS data, and engagement signals into reliable daily workflows that surface meaningful changes early — giving advisors and student success teams the context they need to act sooner.

What this enables

• combine LMS activity, attendance, and grading signals

• flag meaningful drops in engagement or performance

• deliver daily dashboards for advisors and success teams

• trigger alerts or downstream workflows when risk thresholds are met

• provide consistent data to analytics tools and retention models

Instead of waiting for periodic reports, teams gain a current view of student engagement patterns.

How a workflow runs

Mage orchestrates the data movement and transformation required to make early signals reliable:

1. Ingest activity & academic data
   Pull LMS activity, assignment status, grades, and SIS enrollment records.

2. Normalize & model engagement signals
   Standardize timestamps, participation metrics, and course structures.

3. Detect meaningful changes
   Identify patterns such as sudden inactivity, missed assignments, or declining performance.

4. Deliver insights where they’re needed

   • dashboards for advisors & administrators

   • alerts or notifications for follow-up

   • data feeds for analytics & retention models

5. Monitor reliability & data quality
   Ensure workflows run consistently and surface issues before they impact reporting.

What changes for advising & success teams

Instead of waiting weeks:

✔ advisors see disengagement signals within 24 hours
✔ outreach happens earlier, when support can still make a difference
✔ conversations begin with context, not guesswork
✔ teams operate from a shared, trusted view of student engagement

Why Mage works for this workflow

Early signal detection depends on reliability. If pipelines fail, data arrives late, or definitions change unexpectedly, trust erodes quickly.

Mage provides:

• Reliable orchestration across LMS, SIS, and operational data sources

• Version-controlled transformations to maintain consistent engagement metrics

• Observability & alerts to detect pipeline or data quality issues

• Flexible deployment within existing institutional infrastructure

• Reusable workflows that support dashboards, analytics, and retention modeling

This ensures advisors and administrators can rely on the data — not question it.

A note on privacy & governance

Student data requires careful stewardship. Mage workflows can be deployed within secure environments and designed to align with institutional data governance policies, ensuring sensitive data remains protected while still enabling timely insights.

From signals to support

Early awareness doesn’t replace human judgment — it strengthens it. By surfacing meaningful changes sooner, institutions give advisors more time to engage students, understand challenges, and connect them with support resources.

When engagement signals become timely and reliable, intervention becomes proactive instead of reactive.