Project Overview
This project presents a framework for an adaptive, pretest-driven knowledge validation course designed to streamline learning time while maintaining knowledge retention. The structure outlines how learners can test out of redundant content, allowing for a more efficient and personalized training experience.
Rather than a fully developed course, this project focuses on the instructional design strategy, learning pathways, and data-driven analysis that would guide course implementation.
Key Features & Instructional Design Approach
Scenario-Based Learning Paths
* Designed three learner scenarios: full test-out, partial module completion, and full-course completion.
* Applied adaptive learning strategies to personalize each learner’s journey based on pretest performance.
AI & Data-Driven Course Optimization
* Created a cost and time-savings analysis formula to quantify the impact of the test-out system.
* Outlined SCORM data tracking to monitor completion paths, test performance, and engagement trends.
Real-World Impact & Cost Savings (Projected)
* Users who fully tested out would save 80 minutes of training time.
* Partial test-out users would save 20-40 minutes on average.
* Estimated cost savings of $61.99 per three users, scalable across the organization.
Implementation & Future Recommendations
* Refine the Framework into a Full Course: Develop interactive content and final assessments based on initial testing data.
* Enhance Time Tracking: Implement precise tracking of pretest, module, and test completion times for better accuracy.
* Optimize Pretest & Assessment Design: Adjust test difficulty and question variety based on learning outcome analysis.
* User Experience Considerations: Design an opt-in/opt-out system to encourage more pretest participation.
Key Takeaway
This project serves as a blueprint for an optimized eLearning experience. By leveraging instructional design best practices, scenario-based learning, and data-driven decision-making, it outlines a scalable and efficient approach to validating knowledge while reducing unnecessary training time.
