These changes turned a rigid progression into an adaptive learning environment—one that meets students where they are and moves with them, not ahead of them.

In response to the core challenges, I designed a dynamic progression system capable of adapting in real time to student performance. At the heart of this redesign was a weighted logic model—a system that interprets the type and frequency of student struggle to drive individualized game paths.

Key struggle indicators—such as specific error types, repeated incorrect attempts, and whether support scaffolds (like the correct answer) had already been shown—were each assigned different weights. These inputs shaped both the pacing and complexity of each student’s experience, allowing the game to respond with targeted adjustments rather than a one-size-fits-all sequence.

One major outcome of this system was a shift in how obscured labels were handled. Previously required for all students, these challenges are now treated as stretch goals, surfaced only when performance data indicates readiness. Similarly, we removed covered-label scenarios that mirrored lines students would later draw themselves, eliminating tasks that didn’t offer meaningful learning value.

In tandem with progression changes, I introduced a simplified interface path to reduce friction and cognitive load. This included button-based inputs in place of numeric entry and easier denominator sets (e.g., thirds over sixths) for students demonstrating early struggle—creating a smoother re-entry point without breaking learning continuity.

This adaptive redesign ensures that each learner can stay engaged, make progress, and encounter productive challenge—without being derailed by early friction or fragile understanding.