Courses—Designers build them, learners take them, and managers check them. But can executives leverage course results to drive their business forward? Yes, they can. eLearning courses can be a goldmine of actionable business intelligence. However, to do so, they need to be armed with the right mindset and analytics.
What’s the right mindset?
eLearning has often been perceived by executives as just a means to an end. For example:
- There are compliance regulations that must be adhered to: eLearning course
- We have new software that everyone must learn: eLearning course
- We need to train our managers on a new leadership program: eLearning course
In each of these scenarios, there is a business need … and an eLearning course is a cost-effective and efficient way of addressing it. Once the course is done, it has served its purpose.
However, if you’re an executive and you shift your mindset, there’s much more to the story. With a data-friendly mindset, you’ll find additional value in these courses hiding in plain sight. You’ll see that in addition to course completions, most courses also track learners’ progress and thereby generate a lot of data. There is data on individual learners, aggregated data on learners as a group, data on individual course items, and more. In order to have that data available, however, the course needs to be designed and hosted in a data-friendly way.
What is data-friendly course design?
To generate useful data, knowledge checks and/or interactive activities need to be incorporated throughout the course. These are questions, micro-games, sorting activities, basically, anything that learners interact with that demonstrates their understanding, application, or performance of the content.
For example, in a software simulation course, workers have an opportunity to try a new workflow after learning it with a guided demonstration. The results show that all of the workers completed the first two steps correctly, but in the third and seventh steps, accuracy dropped to 56% and 23%, respectively. Learners completed the rest of the steps with no problem. All of these are data points generated by the course.
Transforming data into analytics
It’s important to note that data is not the same as analytics. Data is the raw information. Analytics is the meaning derived from the analysis of the data. To continue the example, some analytical questions to explore within the data include:
- What was the content in the third and seventh steps?
- What were the roles and levels of experience for learners that missed that question?
- How many of those that missed the third step also missed the seventh step?
- How clear were the directions in the third and seventh steps?
In order to access this level of specificity within the data, the course needs to be hosted on a platform that captures and processes all of the data.
The data that an online course generates needs to be captured and managed by an online platform. There are different platform types including Learning Management Systems (LMS) and Learning Record Stores (LRS). These accept SCORM compliant course packages developed using authoring tools such as Storyline and Captivate. There are also proprietary online platforms where the courses are developed and accessed in an online platform such as Rise or Elucidat.
These platforms all track individual learner responses to specific questions as well as other activities within the course. However, platforms vary significantly in the level of analysis of the data they collect. Some platforms have real-time dashboards with heatmaps that point out where trouble spots are. Others just show completion rates. The better your hosting solution, the better your ability to analyze your data.
Turning analysis into actionable business intelligence
When you have a data-friendly designed and hosted course, you’ll be able to turn your analysis of the course data into actionable business intelligence. For example, let’s say the third step in the simulation involved entering a complex item description that needs to be validated. If accuracy is low in the simulation, it is likely that this type of error will occur in the regular workflow. Further, the cost to the business of this type of error can be calculated both in terms of work hours to resolve the error as well as the negative impact of the error itself.
But how does this analysis inform the business strategy? Here are some possible tactical actions:
- Assign follow-up three-minute MicroLearning activities that target steps three and seven specifically to those workers who missed those questions
- Modify the software or the workflow to provide just-in-time support at steps three and seven
- Implement a peer-review procedure for the workflow
Each of these actions addresses the problem identified by the course analysis in a different way. How the issue gets addressed is not as important as the fact that the issue became known in the first place. This is only possible because the course was designed and hosted in a data-friendly way.
The value of eLearning courses doesn’t end when the course ends- it’s actually just the beginning of a new chapter, one that’s tied to strategic business decisions and outcomes.