Over the last year, all eyes have been on digital learning as organizations quickly transitioned their learning and training programs online due to the pandemic. Now, nearly a full year later, program leaders are looking back at the efficacy and results of their eLearning programs to better understand what worked vs. what didn’t.
To tell if your learning program was a hit or a miss you need two essential components: data and learning science. Data will tell you where your learners get stuck and where they need to improve confidence in your material. Then, you can leverage learning science strategies to help improve the behaviors you’re seeing within your learning program to increase your bottom line.
In a recent webinar, BenchPrep’s Director of Business Strategy & Operations Nish Ravichandran and Solutions Content Manager Abby Spoerl laid up 7 steps to help you use data and learning science principles for a slam-dunk learning program. Here are the key highlights:
“If you don’t know where you’re going, any road will get you there.” - Alice in Wonderland
The fundamental truth behind why so many data analysis adventures fall short is because analyzing data can’t provide meaningful answers unless we know what the destination is in the first place.
For any learning program, the northstar that we are looking for should be the learning objectives. How do you determine your learning objectives?
The best way is to start with the end goal in mind. If you were sharing a program update with your leadership committee, how would you show the program is successful for learners?
Thinking through potential answers to this question will help you effectively state the 1 or 2 most important learning objectives for your programs.
Here are some quick tips for defining learning objectives:
Once you have identified the learning objectives, the next step is to define the learner behaviors. Behaviors are the actions, or set of actions, that data measures - just described in plain English.
The reason behaviors are important is because they enable focus to save you from common data analysis pitfalls. A simple way to think about behaviors is to bucket them into two categories.
The first bucket of behaviors is called “in-learning behavior”, which are actions that take place when the end-user is actively learning. An exercise to implement this type of behavior in your learning platform is:
The second bucket of behaviors is called “out-of-learning behavior”, which are actions that occur before and after the learner participates in the course. Some behaviors to consider are:
💡 Tip: The behaviors you identify should clearly tie back to your learning objectives!
When designing your content, make sure your content is in a format that caters to the needs of the modern learner.
Related Content: Learn more about the modern learners’ needs, check out our blog, 5 Things You Need to Know About the Modern Learner.
Here are some tips for designing your content for the modern learner:
In addition to designing your content with the modern learner in mind, you should also designing your content using learning science principles which can help the modern learner learn more efficiently and effectively. Learning science is the foundation for understanding strategies and contexts that promote learning.
Source: McGraw Hill Education - What is learning science?
We’ve covered 3 steps for a slam-dunk learning program, but we need 4 more to win the game. To discover more about how data and learning science can positively impact your learning program, check out this on-demand webinar, 7 Steps to Use Data and Learning Science for a Slam Dunk Learning Program