Learning Program Analytics: Maximizing Talent Development

Learning program analytics seek to understand how an overall learning program performs by gauging its effectiveness and impact on the business. And this post explains how you can apply learning analytics to better understand, optimize, and support your L&D programs.

Before we dive in, it's important to remember that learning analytics has two dimensions:

  1. Complexity—or the sophistication of the analytics that ranges from basic measurement and data evaluation to advanced evaluation and predictive and prescriptive analytics
  2. Category—which identifies the specific area or type of learning data that's being analyzed and covers learner analytics, learning program analytics, and learning experience analytics

What are learning program analytics and how can they help L&D?

Learning program analytics answers questions about L&D's strategic impact on the business. This includes topics such as:

  • Do learners behave differently after completing the training?
  • Has organizational performance improved because of learning?
  • Has this method of learning saved the company money?
  • Which learning methodology is most effective for improving organizational performance?

How to apply learning program analytics

Here's one way you can apply all four levels of complexity in the learning program analytics category. In this example, we'll focus on understanding a company's customer service onboarding program that wants to maximize customer satisfaction.

1) Learning program measurement

Track the assessment scores and question results from the final assessment in new hire training and the customer satisfaction scores each agent receives.

2) Learning program evaluation

Graph assessment scores across training cohorts. Is there an even distribution? If not, why? Are cohorts evenly distributed with more/less experienced candidates? Are some cohort instructors more effective than others?

3) Advanced learning program evaluation

Run each agent's assessment scores and average customer satisfaction scores through a correlation engine. Is there a strong positive correlation between assessment score and customer satisfaction score?

If not, is it possible that the assessment isn't effective at distinguishing more competent agents from less capable agents?

4) Predictive learning program analytics

Can we find any statistical evidence to indicate that individual question results are good predictors of strong or weak customer service agents? Can we use these predictions to fast-track the strong performers for promotion or schedule mentoring for the weak performers?

Up Next: What's a learning analytics platform and why do you need one?

Now that we've covered the basics of learning analytics, we'll explain a learning analytics platform and explore how it differs from a learning management system or BI tool.

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eGuide: 5 Steps to Getting Started with Learning Analytics

Now that you understand the basics of analyzing learning experiences, it's time to start applying them in your own learning program. And it's easier than you might think. In fact, there’s a lot you can do with simple metrics and the data you have right now. We've also created the following guide to help you get started right now!

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