How is the neuroscience-based learning model, 'GEAR', applied?

In this article we explain the basics of our in-house learning model and how this is applied as a template during AI journey generation.

Why GEAR?

As we learn, our brains and bodies change through a process of neuroplasticity. It's an energy consuming process so our brains try to resist change and cling to old well-trodden pathways.

Our model, helps build motivation, and creates a space for active learning, including contextual knowledge application and repetition to make knowledge stick.

The model was ideated by Stella Collins, author of the book Neuroscience for learning and development (currently in its 3rd edition). It is based on the writer's knowledge of neuroscience and acquired experience, with more than 25 years in learning consultancy and over 10,000 people trained across various organisations and diverse countries.

What is 'GEAR'?

GEAR is an acronym for the 4 main module activity types that are applied within Stellar Labs:

1. Guide: Absorb crucial information at the learner's own pace.
2. Experiment: Explore new knowledge and practice new skills in a safe environment.
3. Apply: Build habits through targeted practice, feedback & coaching on the job.
4. Retain: Beat the forgetting curve & build long term memories.

You can get an overview in the following video:

How is it integrated within the platform?

As soon as a new learning journey is generated, each module follows the GEAR structure. This means it's composed of its 4 main activities.

1. Guide

When you go to a new city a good guide shows you the highlights to stop you feeling overwhelmed and prevent you wasting time in dull or dangerous areas. With good guidance you can always return later to explore further at your own pace.

Relevant information is presented in the beginning of each module to provide an introduction to each topic.

We support different formats: practical tips, videos, ​infographics, websites, etc. ​

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2. Experiment

Once your learners have the building blocks they need, they can experiment and test how new ideas or behaviours link to, or challenge, their current practices, experiences, or worldviews.

There are different ways to do this:

• By default experiment steps include learning scenarios that take learners to a specific and relevant cases, asking how they would act and giving feedback based on their choices. 

• You can also set up live, practical workshops with colleagues and/ or a facilitator (we call them Live Labs). You can do this with a content step linking to your preferred communication channel.

3. Apply

Learning is a continuous, iterative process. Therefore, we include specific Work-Based Actions (WBAs) to link knowledge with practice and foster a commitment to change.

Learners can adapt their WBAs to their own situations - each may be different. They commit to a deadline to apply new knowledge and skills to the tasks and then report the outcome within a specific time frame.

Mentors are responsible for reviewing the action and providing another point of view on the outcome.

There are different statuses for actions:

• Open / available: the action is in the journey waiting to be scheduled.
• Submitted / incomplete: learner has entered the due date. 
• Completed /executed: learner has given feedback on the action.
• Reviewed: Mentor has checked the result and given input.

Retain

Brains are built to forget or else we'd be completely overwhelmed with the bombardment of sensory information we receive every day. If an experience isn't tagged as 'important' for the future we tend to forget it. So how can you make information memorable?

We use spaced repetition questions to prompt people to recall key information, multiple times at specific moments to strengthen long term memory.

Enabling Spaced Repetition in Stellar Labs:

• Quiz Questions: Quiz questions can be enabled for spaced repetition within a journey. 
• Initial Answer: Once these questions are answered by the learner during the journey, they are scheduled for repeated review. 
  

Leitner Model 

The Leitner Model, a proven method for spaced repetition, is used to determine the frequency of question reviews. Here’s a more detailed explanation of the model: 

• Buckets System: Questions are sorted into different "buckets" based on how well the learner answers them. Each bucket represents a different review frequency. 
• First Bucket: Questions start here and are reviewed frequently. 
• Subsequent Buckets: Correctly answered questions move to the next bucket, where the review interval increases. Incorrectly answered questions move back to the first bucket. 
• Review Frequency: The review frequency decreases as questions move to higher buckets, reinforcing learning at optimal intervals 
• Penultimate Bucket: Learners have the option to move a question to this bucket, where it will be reviewed again in two weeks as a final test. 
• Final Test: This final review helps ensure the information is retained long-term. 

The use of the Leitner Model ensures that learners focus more on challenging questions, reinforcing their knowledge effectively over time. 

Expanding the journey

Designers and collaborators can build on the steps that are initially generated through AI suggestions, incorporating follow up steps, questions, pictures, videos, and links more efficiently. You can learn more on the article "How can I edit an existing journey using AI?"