Describing vs Prescribing: Using your learner data more intelligently

Anyone can go to the store and buy all the ingredients to make a delicious meal. But having ingredients isn’t the same as having a meal ready on the table to eat, nor does it mean you’ll have food ready for hundreds of people to eat every day of the week! The struggle with using learner data more intelligently can often be similar…

Once we analyze and gather all the data we can, we have the tendency to think we’re data-driven enough. Or, by analyzing once a month or a quarter, we might feel we’re regularly keeping up with our data potential.

If you don’t yet have systems in place to analyze learner data, make predictions, or automatically prescribe recommendations from it, you haven’t yet reached learner analytics maturity. At Learning Pool we measure this with the Learning Analytics Maturity Model

Cutting-edge organizations use their learner data to predict and make prescriptions for their learners automatically and often. On the other hand, other organizations are just starting out. The greater your capabilities of learning data maturity, the greater you assert L&D as a strategic business unit, which ultimately demonstrates ROI to the rest of the business.

 

Start Out With the “Why” for Your Learner Data

Why do I want to utilize our learner data in the first place?

Some organizations are still figuring out why learning analytics is important to their organization. This first milestone of the Learning Analytics Maturity Model is an admirable goal. At this point, there are a few achievements to pursue, including:

 

  • Deciding which goals to achieve via collecting learner data
  • Getting strategic buy-in from other stakeholders
  • Choosing the right people to manage learner data
  • Finding the right technology to accelerate the process

 

It’s up to you to identify your L&D department’s goals and understand the level of buy-in you’ll need within your organization. But finding the right type of technology can accelerate the conversations in these earliest stages. 

 

Describe Your Learners’ Data

What does the learning data tell us about what things are happening?

Most organizations can answer this question very well. In fact, it is probably the most common utilization of data in an L&D department. In this stage of describing data, you find answers to questions like:

 

  • How many learners have completed onboarding?
  • How long on average are they taking to complete it?
  • How many comments for feedback are there on specific courses?
  • What is the average quiz score for the first time someone takes a certain compliance exam?

 

Some haven’t yet considered how helpful it could be to ask these questions about their learner data. And it represents the first step in being able to make simple changes and data-driven decisions. Having a system to tell what happens when learning takes place is the essence of describing learner data.

 

Analyze Your Learners’ Data

What does the learning data tell us about why things are happening?

Discovering insights for why things are happening is the next level of the Learning Analytics Maturity Model. Among a few common insights, here are a few examples that play off the questions from the section above:

 

  • Quite a few don’t complete onboarding, which seems to be an indicator for leaving the company.
  • Learners seem to take a while on one of the onboarding sections. Perhaps some ‘fluff’ could be removed.
  • There aren’t any comments on any courses. Could it be that learners aren’t aware of the feature?
  • The first time people take the compliance exam, they don’t do very well. Maybe we need to do better at preparing learners beforehand.

 

It’s the inevitable next step that comes after you describe what’s happening with your learning experiences. Unfortunately, it’s also where most organizations stop.

There are lots of tools that can help you gather data, clean it, and visualize it through reports. But few tools can take your learners’ incoming data and give it some sense. This is where an analytics tool like Stream Analytics becomes uniquely valuable to an organization. 

It identifies data anomalies for you, as well as tells you when someone needs help or a ‘congratulations’. Even further, we believe it’s the only way for an organization to take learner data and make both predictions and prescriptions via machine learning. You can set up a way to consistently make predictions and prescriptions with your learner data, that make up the last two levels of learning analytics maturity.

 

Predict Trends for Your Learners’ Data

What is likely to happen from what the learning data tell us?

Predicting can take place with the help of models. Stream Analytics can help you take advantage of these models, including those that are powered by machine learning. One such feature in Stream Analytics is called Forecast. This predicts how the future will look based on existing data.

These two levels of learning analytics maturity (predicting and prescribing) can be difficult to tell apart, but here’s an easy way to think of the two: What Describing does to Analyzing, Predicting does to Prescribing. 

 

  1. When you describe your data, you determine what is happening in the past and present.
  2. When you analyze your data, you determine insights of why those things you described are happening and can make data-driven decisions based on those insights.
  3. When you predict using your data, you begin to describe and anticipate what will happen in the future.
  4. When you prescribe based on your data, your predictions are utilized to make data-driven recommendations based on your predictions.

 

Prescribe Based on Your Learners’ Data

What should happen from what the learning data is telling us?

When your organization predicts and makes prescriptive decisions based on learner data, you’ve reached full maturity of learner analytics. This is one of the reasons Stream Analytics proves to be a huge differentiator. Its capabilities include:

 

  • The ability to see trends and forecasts on how your learners will perform in the future
  • Explanations that analyze your data and provide insight into any changes or fluctuations in your analytics
  • Pulse notifications that will notify you of any significant changes based on your own configuration

 

Now, remember that the ideal isn’t to go in and do all of this yourself over and over for each learner, every week. The key is consistency. Systemize all of this at scale, so that prescriptions can be made early and often. 

 

Conclusion

Conversations around data can seem intimidating. But when stakeholders say they want data, what they really want is to feel more empowered in their decision-making. They want the confidence that comes from being backed by data. 

As you’ve learned about learning analytics maturity and Stream Analytics, you’ve hopefully noticed action items to take back to your organization. Whether it’s evaluating the current expectations of your learner data, or pursuing the enterprise-caliber data analysis and predicting of Stream Analytics, you’ll be better equipped to bring this confidence to your organization.

 

Next Steps

Using your learner data in a more intelligent way means running through each layer of the learning analytics maturity model. If you’d like to discover more about our research on this model, download our eBook today.

We’ve also seen many fast-track their way to learning analytics maturity through Stream Analytics, the Reporting & Analytics capabilities of Stream Learning Suite. 

 

Depending on the specific challenges that face your organization, solutions engineers and learning experience consultants at Learning Pool pride themselves on being helpful in an award-winning way. If you would like more information, a quotation or you’d like to invite us to tender, click here. Our team always strives to be in touch within 24 hours.

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