PR-01Active
Data Analyst Agent
Reads LMS and SIS data, calculates engagement and academic metrics, predicts dropout risk
SHEET 01Use CaseStudent Performance & Retention Analytics
Predict student dropout risk and enable targeted retention interventions
Identify at-risk students early by correlating engagement, grades, attendance, and financial-aid data. Agents surface actionable insights for advisors and generate intervention recommendations.
18%improvement in student graduation rate
42%of at-risk students successfully retained with intervention
60 daysaverage early warning lead time before dropout
$3.2Mannual tuition revenue retained (per 5K student cohort)
SHEET 02The ChallengePROBLEM
The student retention analytics challenge
Universities and online schools lose 25-40% of students before completion. Dropout risk signals (poor grades, low engagement, financial stress) are identified too late for intervention. Student success teams lack proactive insights into who’s at risk. Intervention resources are allocated reactively, not strategically. Early warning systems exist but are hard to use; actionable insight is limited. Retention efforts are generic; interventions aren’t personalized.
SHEET 03The SolutionASSEMBLY
How assistents automates student retention analytics
assistents student success analyst connects to your LMS, SIS, and student engagement platforms to predict dropout risk. The agent identifies at-risk students by analyzing engagement (login frequency, assignment submission), academic performance (GPA trend, course pass rates), and financial status. High-risk students are flagged for targeted intervention: tutoring referral, financial counseling, mentor matching. The agent recommends intervention type based on student risk profile. Follow-up engagement is tracked to measure intervention effectiveness.
PR-02Active
Conversational Agent
Reaches out to at-risk students, offers support, directs to resources
PR-03Active
Workflow Agent
Triggers intervention alerts, matches students to mentors/tutors, tracks engagement post-intervention
SHEET 04How It WorksPIPELINE
How student retention analytics agents work
- STEP 01Connect student data
LMS, SIS, and student engagement platforms are integrated. Agent accesses student data continuously.
- STEP 02Calculate risk scores
Agent analyzes GPA trends, assignment submission, login frequency, and financial status.
- STEP 03Predict at-risk students
ML model predicts dropout probability. Students with >40% risk are flagged for intervention.
- STEP 04Reach out & offer help
Agent contacts student, offers support (tutoring, financial help, mentoring). Schedules follow-up.
- STEP 05Track & improve
Agent tracks student re-engagement post-intervention. Measures which interventions work best.
5 steps deploy to production in weeks
SHEET 05Measurable OutcomesMEASURED
Measurable student retention analytics outcomes
18%improvement in student graduation rate
42%of at-risk students successfully retained with intervention
60 daysaverage early warning lead time before dropout
$3.2Mannual tuition revenue retained (per 5K student cohort)
SHEET 08Sign-offREADY
Ready to see this in action?
Schedule a personalized demo to see how assistents’s AI agents can solve this challenge for your organization.
- Stage
- Discovery to production · 4 weeks
- Deployment
- Cloud · On-premise · Hybrid
- Governance
- Audit trail on every action
- Sheet
- 8 of 8 · Use Case