Realistic Practice is Essential

Realistic practice is essential for training that is both engaging and effective. The authenticity improves when an analysis captures and training practice reflects work accountabilities, expectations, contexts, scenarios, and outputs. Various mental models or taxonomies help the designer properly formulate an approach then organize and make sense of analyses and data. Similarly, mental models and taxonomies inform design decisions. 

One useful taxonomy, the Taxonomy of Performance (created by Dr. Richard A. Swanson), organizes work into actions that either maintain or change it (performance is a methodprocessdevice, or system).

Maintaining performance includes:

  • Understanding – comprehend

  • Operating – run or control

  • Troubleshooting – locate and eliminate

Changing performance includes:

  • Improving – advance

  • Inventing – new

Dr. Swanson observed that organizations often deliver “support and resources at one level and expect performance at another . . . without realizing the built-in discrepancy between their intervention and expectations.” (1) This same discrepancy arises in training. When the business expectation is an improvement in some skill, but the training develops only awareness or knowledge, training fails to meet expectations. 

From a training perspective, it’s possible to define and categorize essential facets to organize work contexts, scenarios, routines, and practices in a manner that builds from understanding to troubleshooting and beyond. Analysts and designers use such a model while collaborating with representatives who know about or are currently in the role. Subject matter experts name and describe a complete library of scenarios and work outputs of that level – and how to determine when an employee meets the expectations of that level. 

For example, call center agents that help customers make benefit plan decisions may need to practice dozens of different scenarios to meet expectations. The graphic below is from a client project and indicates the number of practice scenarios involved. 

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How much practice is enough? And what practice?

You probably won’t be surprised, but there isn’t a one-size-fits-all answer to either question. Many factors are in play: business goals, stakeholder expectations, the complexity or importance of the work, what the learning ecosystem can support, where the employee is in their development (what they already know or can do), does the situation involve a customer, etc. In short, every situation is unique.

During the scenario selection and engineering process, we often use a matrix – it’s simple, gives everyone a common framework, and is an easy visual to understand. A matrix can be part of the work analysis or the design process. 

Step 1: Determine two factors that drive the difficulty and complexity of the work (business stakeholders or top performers will be able to tell you). When a role works with customers (a bank teller or call center agent, for example), interacting with the system and interacting with the customer are options that have worked well for us.

Step 2: Describe what Low, Medium, and High difficulty or complexity mean for each factor. For example, for the customer, the levels might be 

  • Low = happy/agreeable – easy to work with

  • Medium = neutral/hesitant

  • High = unhappy/resistant – difficult to work with

Step 3: Draw the matrix; here’s an example:

ConvertKit_Scenario Practice Matrix_empty.png

Step 4: Work with the stakeholders to determine what constitutes “ready” or “enough” – at what point will stakeholders be confident that the learner “is ready?”

Step 5: Discuss and determine the combination of practice scenarios necessary to achieve “ready” and mark the matrix (add details to each mark as needed). Also, sequence the practice, building from low difficulty and complexity to high. 

ConvertKit_Scenario Practice Matrix.png

These steps are just part of the design process. From here, the conversation evolves topics like formative and summative learning objectives, modality, content flow, treatment, appropriate spacing, blocking and interleaving, spacing, setting aside time for reflection, feedback, support back at the job, etc.

Committing to realistic practice reshapes thinking and conversations. It infuses all aspects of analysis and design processes, resulting in different questions, creating different discussions with stakeholders and SMEs, and exploring the nature of job performance and associated knowledge and skill requirements.

Building your Best Employee: Learning Strategies that Drive Results

(1) Analysis for Improving Performance: Tools for Diagnosing Organizations & Documenting Workplace Expertise, by Richard A. Swanson, 1994, pg. 57