SMART-CARE
Intelligent Analytics
for Student Success
An Intelligent Analytics System for Identifying College Students at Risk of Academic Attrition Using Machine Learning
Academic attrition remains a persistent challenge in higher education. SMART-CARE leverages machine learning to detect early warning signs of dropout risk — enabling administrators, counselors, and faculty advisors at Western Leyte College of Ormoc to intervene at exactly the right moment.
SMART-CARE
Academic attrition remains a major challenge in higher education, largely due to the absence of effective systems for the early identification of at-risk college students. SMART-CARE was developed to address this gap.
By applying machine learning to institutional data, SMART-CARE provides administrators, counselors, and faculty advisers with actionable insights through risk classification, dashboards, and early warning alerts.
The system assists — but does not replace — human professional judgment. All student data is processed in compliance with RA 10173 (Data Privacy Act of 2012) and RA 8792 (Electronic Commerce Act of 2000).
Core System Features
Institutional tooling for proactive student success at Western Leyte College of Ormoc
Early Risk Prediction
ML models predict attrition likelihood from academic performance, attendance, and behavioral data.
Student Risk Profiling Dashboard
Visual insights into individual and cohort risk levels for counselors and faculty advisers.
Automated Alerts and Notifications
At-risk students are flagged automatically so stakeholders can act before problems escalate.
Data Integration and Analytics
Academic, attendance, and engagement data unified for institutional reporting and analysis.
Decision Support for Interventions
Advisors receive guidance on selecting the right support actions for each student profile.
Full Audit Trail
Every system action is logged automatically for accountability and compliance review.
How SMART-CARE Works
From institutional data to actionable intervention support
Academic records, attendance, and demographic data from WLC systems.
Clean, transform, and organize data for machine learning pipelines.
Train and validate attrition prediction models on historical cohorts.
Assign Low, Medium, High, or Critical risk levels per student.
Role-based dashboards and automated early warning notifications.
Measure accuracy, usability, and intervention outcomes each term.
Deliver actionable insights for timely counselor and faculty intervention.
Who Uses SMART-CARE
Role-based access for every stakeholder in the student success ecosystem
Students
View risk insights, academic standing, and wellness resources.
Faculty / Instructors
Monitor class rosters, at-risk learners, and submit intervention reports.
Counselors & Deans
Cohort analytics, alerts, and decision support for institutional action.
Administrators
User maintenance, subjects, audit logs, and system configuration.
Legal Basis
Built for compliance with Philippine data protection and e-commerce law
SMART-CARE incorporates controlled access, data confidentiality, and secure storage so personal and academic information is handled responsibly.
Digital reports, notifications, and analytics outputs align with lawful electronic records and transactions in educational institutions.