An online learning provider serving 12,000 students was running on a five-year-old LMS that loaded slowly on mobile, had no personalization, and generated constant complaint tickets. Student weekly active usage had been declining 8% per quarter. The engineering team had no capacity to rebuild it alongside product maintenance.
We embedded a six-person team alongside the client's two in-house engineers for a 20-week build. We began with user research — student and faculty interviews — to understand what drove disengagement, then designed a mobile-first experience with personalized learning paths, progress tracking, and proactive nudges for at-risk students.
The new platform is built on Next.js with a headless CMS, real-time notifications, offline support for mobile, and a recommendation engine that surfaces the next most relevant content based on learning history. An early-warning dashboard for advisors uses ML to flag at-risk students based on engagement and performance signals.
Weekly active usage increased 2.4× within 8 weeks of launch. Course completion rates improved 38% in the first semester. Average platform rating rose from 2.9 to 4.9 stars. Advisor early-warning system flagged 340 at-risk students, 280 of whom were successfully supported to course completion.
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