Learner Engagement Analytics and Personalised Nudge System for Indian EdTech Platforms

Objective: To design and implement a data-driven system that analyses learner engagement and delivers personalised nudges to improve course completion rates on Indian EdTech platforms.

Indian EdTech platforms face significant challenges with low learner engagement and high dropout rates, especially in self-paced online courses. Many learners lose motivation without timely interventions, leading to poor outcomes and reduced platform credibility.

The project will involve studying engagement patterns using anonymised learner activity data from an Indian EdTech platform, designing a personalised nudge mechanism (such as push notifications, emails, or in-app prompts), and developing analytics dashboards to track impact.

Deliverables include: (1) engagement metric definitions, (2) data analysis and segmentation, (3) a rule-based or ML-driven nudge engine prototype, (4) dashboard for educators/administrators, and (5) pre/post intervention analysis report. The team will use real or simulated datasets (such as SWAYAM or DIKSHA), and validate solutions via A/B testing or retrospective analysis.

This project helps EdTech managers make data-driven decisions to improve learner retention and satisfaction, thereby enhancing course outcomes and platform reputation in the competitive Indian market.