There’s a cliché saying that knowledge is power. And if that’s true, are your learning and development efforts empowering people with knowledge that impacts behavior and performance?
When learning and development is at its best, it builds knowledge that helps people execute with performance that achieves business goals.
Through the many methods for learning and development, the question is whether knowledge transfer actually takes place. Does a simulation that shows how to diagnose an issue with cable television reception result in a customer service rep’s ability to resolve the issue or dispatch a technician? Will the training program that demonstrates how to address conflict result in managers’ ability to neutralize anger? Does the virtual classroom program that compares products and services help salespeople explain the difference between features and benefits? These are questions where the answers give insight into the effectiveness of learning that leads to knowledge transfer and ultimately, performance.
Indicators for Effectiveness
In the chain of evidence that shows the link between knowledge and performance, there are powerful indicators for the effectiveness of learning. When you measure knowledge, there’s:
Predictive capability for impact on behavior.
Prescriptive capability for addressing gaps.
And planning capability for removing barriers.
The data you get from measuring transfer of knowledge through learning tells the story of how training effectively changes thought, behavior or skill.
Data for performance shows occurrence of behavior after knowledge transfer, for example, producing client deliverables free of errors after learning how to use a new data integration tool, 100% compliance for cloud-based transactions after learning how to use mobile tablets and decrease in product defects after learning how to use robot-assisted assembly. These examples show the result of learning that leads to knowledge transfer. In the opposite direction, knowledge transfer failures show opportunities for diagnosing and correcting performance.
What If My Data Tells a Bad Story?
Data that tells a “bad story” about people not applying what they learned is just as valuable as data that tells a “good story.” A disconnect between what people know and what they do is an indication of barriers that need to be removed or addressed. An example of a knowledge transfer barrier is an employee who learned how to process payment through a mobile credit card reader but has a manager that believes the “old way” of manual credit card processing is the best way. Another knowledge transfer barrier is an employee who learned how to sell a new product but doesn’t because it belongs to another unit who does not share revenue recognition. Until it does, she’s not motivated to sell. These are examples of knowledge transfer failures but there’s also examples of knowledge transfer successes.
Riptide clients have been able to show increased knowledge retention and competency through the adoption of strategic eLearning. With the use of Storepoints LRS gathering all of the interaction and behavioral data from digital learning, instructors can effectively map certain areas of training, to certain assessment questions. This can help prove the competency levels of each trainee, and instructors also have the ability to view competency levels of all trainee’s to analyze and react to patterns and changes.
Example: Knowledge-Transfer in Onboarding
An example of this is a company who needs to provide software training as part of their onboarding process. This company implements Waypoints in order to guide all of their learners through the process and features of using a business-critical software. At the end, there is an assessment to test the users on the software. This works through Waypoints producing an activity stream of the learner actually using the software. During this process, Waypoints is certifying the user as they complete each step or task. By guiding the user first, and then having them prove they can use the software, this proves the transfer of knowledge to competency. This same certification process, or a more difficult process, can be delivered later on in the employee’s time at the company. Not only does this support a continuous onboarding approach, but it also helps to reinforce the competency.
In this stage, it is important to be data-driven. Rather than simply receiving a score or completion for an assessment that tests knowledge, it is much more insightful for the educator to get all of the learning activity data for learning. This allows the educator to take control of their data and wield it in a powerful way. Evaluating competency has been difficult without data to support it. Now, educators are using top performer statistics and behavioral patterns to baseline how the rest of their learners can reach the same performance and competencies.
Stay tuned for one more step we think you should follow to sum-up your data-driven journey: Here’s How To Build The Best Surveys To Predict Learning’s Impact On Performance.
About the Authors:
Kevin M. Yates
Kevin M. Yates is a data detective for training, learning and development answering the question, “Did training work?” with facts. He is also creator of The COURAGE Model©. Connect with Kevin on his website, LinkedIn, Facebook, YouTube and Twitter.
Elements makes eLearning easier through enabling, behavior-focused learning technology that provides insightful analytics (Storepoints Learning Record Store), walk-through software training (Waypoints), and adaptive eLearning courseware (Learnpoints).