School Dropout-Risk Prediction and Counsellor Alert Platform

Objective: Build a digital system to predict dropout risk among school students and alert counsellors for timely intervention in Indian government schools.

School dropout rates remain a significant challenge in India, especially in government schools across rural and urban areas. Early identification of at-risk students is crucial for timely intervention, supporting the Ministry of Education’s goals under SDG 4 (Quality Education). Students facing socio-economic difficulties, poor attendance, academic struggles, or family issues are particularly vulnerable.

The proposed solution is a digital platform that leverages data analytics and machine learning to predict dropout risk using available school records and community datasets. The system generates alerts for school counsellors, enabling proactive engagement with at-risk students and their families. It empowers educators to target resources more effectively, improving retention.

Key features include secure integration with school management systems, customizable risk indicators, explainable risk scores, counsellor dashboards, automated alerting, and intervention tracking. The working prototype will demonstrate end-to-end data flow, prediction models, and user interfaces for counsellors and school administrators.

The measurable impact will be increased school retention and reduced dropout rates in pilot schools. The solution can scale across states by adapting to local data sources and languages, supporting government skilling and education initiatives.