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Personalizing Virtual Training

Personalizing Virtual Training

Personalizing Virtual Training

If you’re an aspiring chef, striving for the next level, you wouldn’t take a class that was too basic or repeat material that you’ve already mastered. At Aptima, we don’t train chefs—although we could use them at our occasional cook-out or code-a-thon—but we take this concept of not-too-easy and not-too-hard seriously. And while we’re not using it in our breakroom kitchen (yet), we are using it to personalize training across other domains.

Simulated and computer-based training has been a boon, scaling the preparation of large groups of trainees, such as in the military. But it’s also been limited by a one-size-fits-all approach to learning. New advances in learning management, however, are addressing these shortcomings, using insights garnered from big data to adapt and better tailor the learning experience to the individual trainee, while preparing them for more sophisticated tasks.

Consider the challenges imposed by the current state of part-task training. Used by many Government and commercial organizations, part-task training breaks down a job into components, teaching each serially, and often without integration among those skills and activities. Not only can this approach to training take more time, it doesn’t necessarily support or replicate the demands found in real-life missions, like those of an Unmanned Aerial System (UAS) operator, who must fly a UAV, view video feeds, engage in radio communications, and perform other tasks—all at the same time.

Delivering the right learning experiences at the right time to support continuous learning

Advanced Learning Management, as the name implies, advances or evolves training by synthesizing many different elements or dimensions of a job into the training environment. The training system collects the different measures of the trainee’s performance—from simple responses to more complex metrics, like eye gaze and other measures for a pilot. Our algorithms then determine the most appropriate content to offer up, selecting and sequencing it to form the building blocks or scaffolding for the next level of skills. Because the curriculum is not fixed or rigid throughout, it can adapt to the trainee’s performance.

This forms a tighter, more responsive loop with the trainee, much like GPS, where the system knows the starting point and destination and can re-plan the optimal path based on the driver’s location even if they make a wrong turn. Similarly, Advanced Learning Management continually assesses and re-assesses the trainee, tailoring the content and learning path until they’ve mastered the requisite skills.

 On the instructor side, Advanced Learning Management offers more insight on the status of trainees, what they’re more or less proficient at, and their skill retention.

Studies have shown that Advanced Learning Management facilitates faster, more efficient training, which can save time and money. Our framework allows for better measurement and better planning. And because it can account for multiple skills simultaneously, versus sequential, time-consuming part-task approaches, it’s well suited to preparing trainees for inherently more complex tasks that require a blending of skills.

To learn how we’re putting Advanced Learning Management to work, check out the related post at http://www.aptima.com/aptima-awarded-12m-contract-by-us-air-force-to-develop-virtual-training-system-for-distributed-common-ground-system/.

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Alan Carlin is a Senior Research Engineer at Aptima, Inc. As the Lead for Advanced Learning Management, Aptima’s model-based approach to tailoring training and feedback to the individual learner, he develops adaptive algorithms that predict performance, sequence content, evaluate training solutions, and engineer the learning context to deliver the right learning experiences at the right time to support continuous learning.

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