Online Class Engagement Analytics for Educators

Added Jun 2025 3 design docs

In online classes, disengagement is invisible: cameras are off, silence reads as attention, and by the time grades reveal a problem the term is nearly over. Educators need early, evidence-based signals about which students are drifting away and what interventions might actually bring them back. The intern builds a web platform where agentic AI analyzes participation data from online classes, including attendance, chat activity, and assignment interactions, and turns it into actionable feedback for teachers. The AI detects engagement patterns, flags students whose participation is declining before it becomes a crisis, and suggests concrete interventions, while trend visualizations show how a whole class responds over the term. A Node.js backend manages class rosters and data ingestion with MongoDB as the store, React powers the educator dashboard, and authentication with class-level permissions keeps student data appropriately scoped. Reports export cleanly for department reviews and parent conversations. The project teaches applied analytics with real stakes: designing metrics that are fair proxies for engagement, building AI suggestions teachers can trust, and shipping a complete web product for education.

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