Fordel StudiosAdaptive Learning Path Engine
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
One-size-fits-all course structure with 62% dropout rate after module 3. No personalization based on learner performance — all learners received the same content in the same sequence regardless of prior knowledge, pace, or demonstrated understanding.
Adaptive learning requires a curriculum structure that supports multiple valid paths through the same material — a constraint that existing content did not satisfy. The prior course was organized as a linear sequence, and adapting it required restructuring 180+ existing content objects into the concept-difficulty taxonomy and identifying prerequisite relationships between concepts. This content restructuring was a significant upfront investment that had to be completed before any engine development could begin. The performance signal model also required careful design: learner performance on a single quiz is noisy, and a system that reacts too aggressively to single data points produces erratic path changes that confuse learners. Smoothing the adaptation signal required calibrating the performance model on historical learner data, which was sparse for lower-frequency content nodes.
How we
built it
- 01
Analysed the dropout pattern by module and cohort to identify where the learning path was losing the most learners — the problem was not random attrition but predictable dropout at specific content transitions that assumed prior knowledge learners hadn't demonstrated.
- 02
Built a learner knowledge model that tracked demonstrated understanding per concept through quiz performance, time-on-task, and response patterns — not just module completion as a binary.
- 03
Designed the adaptive routing system to offer concept prerequisite paths when a learner's knowledge model indicated gaps, rather than forcing everyone through the same sequence regardless of demonstrated understanding.
- 04
Ran a controlled rollout with one cohort on the adaptive path and one on the fixed path, measuring dropout, completion rate, and post-course assessment scores across both groups before full deployment.
This engagement replaced a linear course structure with a dynamic curriculum graph where the learner's next content node is determined by their performance on each completed node. The system models each learner's demonstrated knowledge state across a curriculum ontology of 340 concepts, adjusts content difficulty within each concept using a three-level difficulty taxonomy, and routes learners to reinforcement or advancement paths after each assessment event. Content is served from a Sanity CMS that organizes learning objects by concept, difficulty level, and format (video, text, interactive exercise, quiz). The adaptive engine makes routing decisions in under 100ms, with no perceptible latency between assessment completion and next content load.
What we
delivered
Adaptive learning engine that adjusts content difficulty, pacing, and format based on real-time learner performance signals. The curriculum graph is reconfigured per-learner after each assessment, routing strong performers to accelerated paths and weaker performers to reinforcement content.
Measurable
outcomes
- Dropout rate reduced 41% — from 62% after module 3 to 37% across the full course — in the first cohort on the adaptive path.
- Module completion rate increased 2.3× as learners who would previously have abandoned after a knowledge gap were routed to prerequisite content and returned to the main path.
- Time-to-competency reduced 28% for learners who entered with relevant prior knowledge, as they were able to skip content they had already demonstrated mastery of.
“The dropout reduction was the KPI the business cared about most. Adapting the path to what learners actually know rather than what the course assumes they know turned out to be the core problem we needed to solve.”
— Chief Product Officer, Online Learning PlatformReady to build
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