How to Design Effective Training for Learning Evaluation & Analytics

    

We talk a lot about learning evaluation and measuring the effectiveness of training on the overall business. But how can we design effective training in the first place? And what do we even mean by effective training? Inspired by our annual Measuring the Business Impact of Learning survey, this blog series is aimed at instructional designers, their managers, and anyone else interested in designing a learning program, experience, or resource that’s easy to measure and evaluate.

First Stop: Designing a Delicious Course

Design for learning evaluation

Just like cooking a meal, without a recipe and intended goal in mind, your end result probably isn't going to turn out too great. In fact, it's probably going to wind up in the trash bin.

And the same principle applies to designing effective training. So, we'll start our series by digging into this challenge while also examining the question: Why is learning evaluation so hard?

We’ll also explore three fictional scenarios of learning programs that have been designed and implemented, but lack the ability to show if they were successful—or even what “success” looks like in each scenario.

Popular Instructional Design Models

From there, we’ll discuss four common instructional models and approaches:

  1. ADDIE
  2. Action Mapping
  3. Training Needs Analytics
  4. Chain of Evidence

Then, we’ll pick out the best bits from each model and bring these concepts together to present our own model—which we’ve named BALDDIE for short. (There will be more about each step later in the series.) Having set this theoretical foundation, we’ll get practical and show you how to execute this model. We’ll also include worksheets to help walk you through the different steps.

And then we’ll wrap up with some tips to counter any potential naysayers when it comes to our instructional design approach and discuss how this series relates to learning evaluation.

Key Ingredients: Measurable Learning Programs

Design measurable learning programs

During this series, we’ll argue the key to effective instructional design is having good performance goals that inform good learning goals. And because we believe in modeling good practice at Watershed, here’s your performance goal for this series:

Design a learning program, experience, or resource with a clear plan for impact on business performance.

In order to reach this performance goal, your learning objectives for the series are:

  • Outline the problems of designing learning without a clear plan for impact on business performance.

  • Explain common models of instructional design and how they relate to designing effective learning.

  • Write a clear, measurable business goal for a learning program.

  • Write clear, measurable performance goals and explain how they will lead to success in meeting the business goal.

  • Write clear, measurable learning goals and explain how they will lead to success in meeting particular performance goals.

  • Design learning experiences to address learning goals.

  • Produce a complete chain of evidence design for a learning program.

  • Identify measurable metrics for each stage of your evidence chain.

Up Next: Why is learning evaluation so hard?

In our next post, we explore the problem of trying to evaluate training effectiveness when the learning hasn’t been designed effectively. Be sure to subscribe to Watershed Insights so you don’t miss out!


See how you measure up!

Our fourth annual survey on measuring the business impact of learning is now open through Friday, Dec. 13. This short survey captures L&D practitioner's insights, challenges, and progress when it comes to measuring learning's impact on the organization. And as soon as you submit your survey, you'll see the live results!  

Take the 2-minute survey

 

Tim Dickinson

About The Author

As Watershed’s Director of Learning Analytics Strategy, Tim Dickinson is skilled in leading organizations through strategic changes, getting positive results through learning analytics, and translating complex ideas and trends into easy-to-understand explanations.