Student Dropout Prediction and Early Intervention Dashboard for Indian EdTech Platforms

Objective: To develop a predictive dashboard that identifies at-risk students and recommends targeted interventions to reduce dropout rates on Indian EdTech platforms.

Context: Student dropout is a persistent challenge for Indian EdTech companies, impacting learning outcomes and platform revenues. Causes include low engagement, digital divide, language barriers, and lack of timely support, especially in Tier 2/3 cities and rural areas.

Approach: The team will collect and analyze historical engagement data from an Indian EdTech provider (or public datasets like NPTEL/SWAYAM), apply predictive analytics to flag at-risk students, and design a dashboard for educators to monitor risk and suggest evidence-based interventions.

Deliverables: The project includes a data pipeline, machine learning model for dropout risk, an interactive dashboard (Power BI/Tableau), and a report analyzing top risk factors, model performance, and recommended interventions tailored to Indian learners.

Business Impact: The solution enables EdTech managers to proactively address student dropouts, optimize intervention resources, improve retention, and inform future product enhancements—directly supporting business sustainability and learner success in the Indian context.