Most colleges find out a student is leaving when they submit the withdrawal form. By then, it's already too late. CampusAlly's Early Warning System spots at-risk students in Week 4 of the semester — and automatically triggers the right support: counsellor alerts, parent notifications, tutoring referrals.
What is a student dropout prediction system?
It's an AI-powered Early Warning System (EWS) that tracks attendance patterns,
internal assessment scores, and LMS engagement to calculate a risk score for each student.
When a student's behaviour signals they may disengage or leave, the system automatically
alerts the right people — advisor, counsellor, or parent — weeks before a dropout happens.
CampusAlly's EWS shifts your retention strategy from reactive (acting after
the exam failure) to proactive (intervening before the semester is lost).
No separate integration needed. CampusAlly connects data already flowing through your college ERP — attendance, marks, LMS — and turns it into actionable early warnings.
Attendance (biometric + mobile), Continuous Internal Assessment marks, and LMS login activity are pulled from the CampusAlly modules you already use — no manual data entry.
The model analyses patterns — not just absences, but which classes are missed, submission delays, and drops in LMS engagement — to assign each student a Low / Medium / High risk score, updated daily.
When a student crosses a risk threshold, the system auto-assigns a counsellor, sends an email to the student with support resources, and (if configured) sends a parent SMS — all without admin intervention.
Advisors log meeting notes, referrals, and outcomes in the Case Management module. Reports show whether interventions actually improved the student's risk score over time.
Goes far beyond who was absent today. The model combines three independent data streams to build a comprehensive risk profile for each student.
A prioritised, colour-coded view that tells advisors exactly who needs attention — and why — without digging through spreadsheets.
Identifying risk is only half the job. CampusAlly automatically triggers the right response based on risk level — no admin chasing required.
The EWS doesn't end at the alert. Advisors have a full case management workspace to log, track, and measure every interaction.
Generate ready-to-submit Student Support & Progression reports for NAAC Criterion V without last-minute data scrambling.
The AI is a tool, not a judge. Faculty advisors always have the final word.
Traditional ERPs tell you what happened. CampusAlly tells you what's about to happen — in time to stop it.
| Situation | Traditional ERP Response | CampusAlly Early Warning System |
|---|---|---|
| Trigger point | Failed final exam | 3 missed assignments in Week 4 |
| First action | Academic probation notice | Auto-email with tutoring links |
| Next step | Withdrawal form | Counsellor session scheduled |
| Financial outcome | Lost tuition revenue | Retained student, retained revenue |
| NAAC impact | Poor Criterion V data | Documented Student Success evidence |
From autonomous colleges to deemed universities, CampusAlly's EWS adapts to your institution's structure, semester patterns, and accreditation needs.
Flag students struggling with core engineering subjects before end-semester exams. Particularly useful for managing AICTE compliance on student pass rates.
Track attendance against MCI-mandated 75% thresholds. Auto-notify parents and generate eligibility risk reports before exam hall ticket generation.
Identify first-year students adjusting to college life who may disengage early. Trigger peer mentoring and counselling workflows in the first 6 weeks.
Generate NAAC Criterion V Student Progression data automatically. Maintain intervention records that demonstrate institutional commitment to student success.
Monitor placement readiness indicators alongside dropout signals. Flag students who may be disengaging due to job search stress during final year.
Track practical session attendance separately from theory. Identify students at risk of failing skill assessments and trigger workshop-specific support resources.
A student dropout prediction system (also called an Early Warning System or EWS) uses machine learning to analyse behavioural data — attendance records, internal assessment marks, and LMS engagement — and calculate a risk score for each student. When that score crosses a threshold, the system alerts advisors and counsellors automatically so they can intervene before the student disengages or leaves the institution. CampusAlly's EWS is designed specifically for Indian higher education, and factors in semester patterns, CIA-based assessment, and attendance compliance requirements.
CampusAlly typically flags at-risk behaviour by the 4th week of the semester — up to 8–10 weeks before mid-term exams. This gives faculty advisors and counsellors enough time to schedule a meeting, refer the student to tutoring resources, or coordinate with parents before the situation becomes critical. The earlier the intervention, the higher the chance of course correction without academic penalty.
The AI analyses three types of behavioural data: (1) Attendance patterns — not just total absences, but which subjects are missed, whether absences are clustered, and late arrival trends; (2) Academic performance — CIA marks, quiz failure rates, and how late assignments are submitted; and (3) LMS engagement — login frequency, resource access, and quiz attempt rates. The model does not use demographic variables like caste, gender, religion, or socioeconomic background, ensuring the risk score is behavioural and fair.
CampusAlly's Attendance Management module tells you who is absent today and tracks compliance with the 75% minimum. The Dropout Prediction system analyses patterns across time and across multiple data types to tell you who is likely to leave the institution — weeks in advance. Attendance data feeds into the dropout model, but the two modules serve distinct purposes: one is operational, the other is predictive and strategic.
Yes. The system generates Student Success and Student Progression reports that serve as supporting evidence for NAAC Criterion V (Student Support and Progression). Specifically, you can submit: department-wise retention rates, at-risk identification timelines, intervention logs, and outcome tracking showing whether flagged students improved after counselling. This documentation is typically time-consuming to compile manually — CampusAlly generates it from live data.
Yes, always. Faculty advisors can manually mark any student as "Safe" and add a reason note — for example, if a student was absent due to a medical emergency with prior permission, or is dealing with a family situation the advisor is already managing. Override actions are logged with a timestamp and the advisor's name, creating an audit trail that protects both the student and the institution. The AI is designed to surface information, not replace human judgement.
Parent notifications are configurable. For attendance compliance (most colleges require notifying parents when attendance falls below 75%), automatic SMS alerts can be set up. For academic risk flags, notification preferences can be configured by department — some colleges prefer to notify only the advisor first, while others prefer immediate parent outreach. All notification rules are set by your institution's administration, not by the AI.
See how CampusAlly's Early Warning System identifies at-risk students before it's too late — in a 30-minute demo, using your institution's own structure as the example.
