A resource for safe and effective troubleshooting from the leaders in simulation training.
Welcome back, Troubleshooters. It’s that day of the week again, and time for TST. Last week we began a series on Adaptive Learning (AL) with a general overview. Today in Part 2, we’re going into a bit more depth—welcome to Personalized Learning 101!
Let’s Get Personal
On the face of it, probably nothing seems less personal than big data. And yet, big data is a big part of adaptive (or personalized) learning.
As an educational approach, personalized learning is all about catering to the distinct learning needs, skills, strengths, and interests of individual learners. The goal is to facilitate the success of each individual learner by customizing content and content delivery to best suit them. It involves tailoring accommodations, supports, and accessible learning strategies to each student.
In schools, where most work has been done in this field, it can mean restructuring the entire school into smaller learning communities such as teams or programs; replacing academic tracks with differentiated learning (delivering flexibly designed course content to groups of students with similar learning styles and needs); offering a variety of learning pathways, creating personal learning plans for each student, and so on. There is no one method set in stone for personalization of learning.
Technological advances have now made it possible to deliver personalized learning on a mass scale. Instead of one tutor or mentor customizing the curriculum for one student, adaptive learning tools are making it possible to use software powered by complex algorithms to optimize learning paths for individual learners on a mass scale. This ambitious goal requires a lot of preparation up front in terms of detailed curriculum mapping and content development (e.g., all student responses must be anticipated in advance and clear, unambiguous feedback prepared for each eventuality.) It also requires big data to fuel and refine the algorithms. The more data the algorithms have to work with, the better they approximate the instincts of a human instructor.
Personalized Learning Will Impact Your Training
More and more, organizations are recognizing that the same concepts can be applied to employee training, for the purpose of getting similarly improved training results that are scalable and efficient. For employee training, organizations are increasingly turning to online training, often with a widely diverse employee base, often with different cultural experiences, geographic locations, proficiency levels and multiple generations (ages).
Learners themselves are asking for content they can pull when they need it, rather than having it pushed to them when they don’t
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