A recent article explores how learning outcomes can be improved by understanding cognitive science behind learning. One learning practice in particular is critical to achieving performance outcomes, creating meaningful practice.
Meaningful practice involves tasks in the training experience that require retrieval and application of appropriate knowledge and skills in contexts that resemble the performance situation. In short, practice that resembles the learner’s real work.
By including realistic practice in training, we not only build capability to perform, but also improve the transition from training into the real work – resulting in reduced ramp up time and lessening the potential for “job shock,” both of which are important metrics to the business leader and the learner. Our goal when designing training is to identify the appropriate amount of instruction and practice necessary to successfully perform in the real world. Achieving this goal is not always easy.
Let’s explore this by using an example of a bank teller cashing a check for a customer. In order to perform this task, the teller needs knowledge or skill in a variety of areas:
Talking to customers with proper tone and manner
Products or services
Policies for holds, approvals, exceptions, etc.
Each individual knowledge or skill is comparatively simple on its own. Each one provides the learner with a slice relevant information and, for the learning professional, assessments of each provide insights into the employee’s grasp of the content.
Things get considerably more interesting – and often more complex – when we focus on ways to mimic real work by combining and integrating the enabling knowledge and skills. As the graphic below suggests, there is potential to create an unwieldy number of practice scenarios.
Let’s assume the training experience starts with developing the foundational knowledge and skills of process flow, system basics and policies and procedures. The training experience must beyond that to integrate the foundational knowledge and skills to closely resemble real world performance and give the learner the opportunity practice.
How do we engineer the right practice to cover the widest possible set of real world scenarios . . .?
We’ve developed an approach for engineering the right variety and amount of practice to include in training. Here are some highlights of the approach:
Work with SMEs to understand the real work performance and typical situations employees face. Typical questions for the SME may include:
What does real world performance look like? What is the trigger to start work and when does it end?
What are the typical situations an employee will face when performing in the real world? Which situations are most common? Which are most difficult and why? Which situations have high risk?
How many different situations would a learner need to experience in training to prepare them to perform in the real world?
Be laser focused on your goals to ensure practice resembles the learner’s real work and to identify the minimum number of practice scenarios needed to create the expected performance.
Clearly document the variety and amount of practice to be included in the training. A simple table, like the one shown below, can be used to convey information about the attributes of each practice scenario.
Review and refine the plan for the practice scenarios with SMEs to ensure they represent the real world context.
As you set out to design practice for your training, keep in mind:
There will be trade-offs to make during the design process to balance the right variety and amount of practice given constraints around training seat time, development timelines, technology, capabilities, and/or budget to support developing realistic training scenarios.
Not all scenarios are of equal value, so work to identify the scenarios that will give the most “bang for the buck”.
This approach can be used to design just a few scenarios or scaled to engineer practice at a larger scale. For one client, we designed more than 100 different scenarios using this approach. Designing at a large scale increases the complexity, so additional design steps are needed to manage the requirements that emerge at that level.
If you’d like to learn more about this approach, or you’d like some help, please contact us at firstname.lastname@example.org or call us at (800) 592-2080.