A Smart Solution to Reduce Student Dropouts at various Educational Stages

Abstract

Student dropouts at the group level in the education sector are a matter of serious concern for the development of an individual as well as for the development of the education sector itself. A smart data-driven solution has been proposed in this paper that tries to avoid such dropouts by proactively keeping the dropout rate in check with the aid of predictive analytics, real-time monitoring, and intervention. The platform tracks and evaluates such educational, socio-economic, and behavioral data to mark most-at-risk students who are likely to drop out. Dashboards are designed cloud-based and machine learning models are integrated to trigger timely reminders and actionable recommendations to the instructors. Student-driven recommendations effectively respond to the particular requirements of the students. The solution also facilitates cross-functional coordination among teachers, parents, and guidance counselors and offers an extensive support plan for students. Pilot programs have recorded historic enrollment and retention of students. The system is scalable and adaptable at various levels of education and across institutions. By aligning with global education objectives, it is making education accessible and equitable. Adding AI-based counseling and multilingual interfaces in the future will further make it more accessible and available to a greater number of people.

Introduction

Problem & Need:
Student disengagement and dropout are pressing issues in education, affecting individual learners and overall institutional growth. Traditional methods like counseling and mentorship often fail to identify and support at-risk students in time. A smarter, data-driven, and technology-integrated approach is needed to address this.

Proposed Solution:

A web-based Student Dropout Management System (SDMS) is proposed to proactively identify and support at-risk students through real-time data collection, intelligent analytics, and targeted interventions.

System Features:

• Technology Stack:

  • Backend: Laravel (PHP)

  • Frontend: Vue.js or React (with Vite)

  • Database: MySQL

  • Visualization: Chart.js or Apex Charts

  • Google Meet API for mentorship scheduling

• Key Functionalities:

  • Attendance, assignment submissions, and issue reporting by students

  • Alerts for poor academic behavior or performance

  • Predictive analytics using machine learning to assign dropout risk scores

  • Role-based dashboards for students, mentors, and administrators

  • Auto-reminders, mentorship scheduling, and improvement tracking

Literature Review Highlights:

1. Traditional Interventions: Counseling, mentorship, and community engagement help but lack timeliness and personalization.

2. Predictive Models: Data mining and ML models have improved risk prediction.

3. Deep Learning: Longitudinal analysis helps identify early warning signs.

4. Challenges: Implementation and scalability remain issues.

5. Online Learning: AI and ML integration have proven effective, even in low-resource environments.

6. Integrated Frameworks: Modern systems combine LMS, alerts, and real-time analytics for better outcomes.

Methodology:

• Data Collected:

  • Academic: Grades, assignments, exams

  • Behavioral: Attendance, discipline, engagement

  • Socio-economic: Parental occupation, income, commute

• Machine Learning Integration:

  • Predicts dropout risk using historical and live data

  • Flags students exceeding risk thresholds

• Intervention Strategy:

  • Auto-reminders to students and mentors

  • Schedule counseling via integrated Google Meet

  • Dashboards to monitor student progress and actions taken

System Modules:

• Student Dashboard: Tracks attendance, performance, submits tasks, and reports issues

• Mentor Dashboard: Monitors mentees, risk alerts, meeting schedules

• Admin Dashboard: View full-system data, manage users, configure settings

• Notification System: Real-time alerts by email or in-app

UI Highlights:

• Clean, minimal design with mobile-friendly interfaces

• Role-based login for students, mentors, and admins

• Visual insights via charts for attendance, grades, and risk levels

Deployment & Testing:

• Deployment Platforms: Local (XAMPP/WAMP) and scalable LAMP servers

• Testing: Laravel API unit tests, browser testing, and E2E user flow validation

• Security: Authentication via Laravel Sanctum/Passport

• Feedback System: Allows students to report issues for continuous improvement

Future Enhancements:

• AI-based counseling bots

• Multilingual support for global accessibility

• Integration with other LMS platforms

Conclusion

The proposed smart system properly addresses the most critical issue of student dropouts through predictive analytics and real-time monitoring tools. Early identification of students at risk and immediate interventions allow institutions to take proactive steps to retain students. Applying machine learning, mentorship counseling via Google Meet, and adaptive dashboards provides a balanced and scalable solution. The system provides an opportunity for teachers, parents, and school counselors to work together to build a strong support system. Its flexibility with regard to education levels makes it a prime candidate for mass implementation. Pilot results have shown tremendous enhancement in student persistence and engagement. Coupled with features like AI-based counseling and multilingual compatibility, the system has vast potential to revolutionize student success mechanisms.

References

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Copyright

Copyright © 2025 R. Raja, Bheemavaram Mokshagna Chary, Konduru Chandra , Nallapaneni Venkata Hema, Thatiboina Anusha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.