Project Overview

I developed an adaptive, pretest-driven eLearning framework aimed at optimizing learning efficiency by allowing learners to bypass content they have already mastered. This approach personalizes the training experience, reduces redundancy, and maintains high levels of knowledge retention.

Challenges

• Traditional eLearning courses often require all learners to complete the same content, regardless of their existing knowledge, leading to inefficiencies and disengagement.​
• There was a need to quantify the impact of adaptive learning strategies on time savings and cost-effectiveness.​

Solution

• Scenario-Based Learning Paths: Designed three learner scenarios—full test-out, partial module completion, and full-course completion—to tailor the learning journey based on pretest performance.​
• AI & Data-Driven Optimization: Developed a formula to analyze cost and time savings, and outlined SCORM data tracking to monitor learner progress and engagement.​

Results

• Time Savings: Learners who fully tested out saved approximately 80 minutes, while those with partial test-outs saved between 20-40 minutes.​
• Cost Efficiency: Projected savings of $61.99 per three users, demonstrating significant scalability and organizational impact.​

Future Recommendations

• Develop the framework into a full course with interactive content and assessments.​
• Enhance time tracking mechanisms for more accurate data collection.​
• Refine pretest and assessment designs based on ongoing analysis to ensure effectiveness.​
• Incorporate user experience enhancements, such as opt-in/opt-out systems, to encourage pretest participation.​

This project serves as a blueprint for creating efficient and personalized eLearning experiences that validate knowledge while reducing unnecessary training time. You can also experience the course by using this link. https://app.cloud.scorm.com/sc/InvitationConfirmEmail?publicInvitationId=240c9dc4-c096-41d9-83e0-dca856f12881