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, realtime 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 AIbased counseling and multilingual interfaces in the future will further make it more accessible and available to a greater number of people.