When it comes to learning evaluation, researching the successful and unsuccessful elements of a learning program is key to improving future efforts. Find out more, as we explain the last step in our "super method" for evaluating learning.
Dig into what training is working (and what's not)
Dig deeper into the success stories or lessons to be learnt by using more qualitative research methods—such as Brinkerhoff's Success Case Method, which suggests:
- identifying the program’s most significant successes and setbacks,
- researching the reasons for these outcomes, and then
- documenting the stories.
You may not want to dig into every learning program, but it’s worthwhile for the first few programs implementing this model as well as for larger programs.
Step 7 Objectives
- Document several of the program's most and least successful stories.
- Identify even better stories that promote the program's success.
- Gain a better understanding of how to improve future programs.
Making It Happen
- Identify particularly successful and unsuccessful cases.
- Interview those involved and document their stories.
- Promote and market the successes.
Watershed’s 7 Steps of Learning Evaluation
Evaluation of learning is a vital part of creating and maintaining successful learning programs, but it’s often overlooked.
Evaluation must be made at all stages of the journey—from learning resources to delivery and application in the workplace to the business goal, which must be aligned with your organization’s strategic objectives.
Remember, evaluation metrics will vary depending on your organization’s attitude to learning.
Your evaluation data should provide insights to improve your learning strategy, and you can get even deeper insights by further research into your programs' most significant successes and setbacks.
You don't have to limit yourself to one training evaluation model. That's why we created our own "super method" for learning evaluation to meet your organization's unique needs.
[Editor's Note: This blog post was originally posted on June 17, 2016, and has been updated for comprehensiveness.]
About the author
As a co-author of xAPI, Andrew has been instrumental in revolutionizing the way we approach data-driven learning design. With his extensive background in instructional design and development, he’s an expert in crafting engaging learning experiences and a master at building robust learning platforms in both corporate and academic environments. Andrew’s journey began with a simple belief: learning should be meaningful, measurable, and, most importantly, enjoyable. This belief has led him to work with some of the industry’s most innovative organizations and thought leaders, helping them unlock the true potential of their learning strategies. Andrew has also shared his insights at conferences and workshops across the globe, empowering others to harness the power of data in their own learning initiatives.
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