One-size-fits-all training programs can waste time and resources when all students have to follow the same learning path. Our Optimizing Scheduler increases training program effectiveness by accelerating student throughput, saving classroom or simulation time, and reducing training costs—all while maintaining training quality.
The Optimizing Scheduler ingests and analyzes training data to provide evidence-based recommendations about the best sequence of training events. Our scheduler algorithm simplifies complexity; it enables schedules to be created in seconds and allows for fine-tuning of operations as needed.
It also offers a proficiency advancement feature that assesses a student’s skill levels across classes and training events to estimate the student’s ability to advance within the program based on proficiency rather than the current syllabus’s fixed timeline. With this feature, more proficient students can advance rapidly while others spend more time solidifying required skills.