The key to meeting any and all moments of learning need is using data analytics in L&D (learning and development). With data gathered about learners, about employees’ job performance, and about activity and success (or failure) in eLearning and in application of skills, L&D teams have a wealth of clues as to what’s needed: The data offer insights into what skills people lack, where they need to update, and where they excel.

A discerning data scientist can discover what a person knows, what she doesn’t know, and the best way to engage her. Data analytics can sometimes even help L&D teams predict who will succeed and who will need additional support or review.

Data analytics is a broad, general term that is bandied about in, well, nearly every context: business, marketing, consumer research, education—and eLearning. The term, as Ellen Wagner comments in The eLearning Guild’s research report, Putting Data to Work, “covers a variety statistical techniques used by researchers and data scientists to extract meaning from all the numbers and to present any number of stories that the data may hold.” It’s possible to refine that definition and apply it more narrowly to an eLearning and performance support context.

Using data in L&D

Like most data presented in spreadsheets or tables, the data that an LMS or LRS collect may not be terribly useful on their own. In their raw form, most data are just a vast sea of numbers. It’s difficult to pick out salient numbers or information from all those lines and columns of numbers. But data analysis and data visualization—mapping the abstract data onto charts, graphs, and other visual forms—allows L&D teams, managers, even learners themselves to understand what the data are communicating.

In an eLearning context, that could mean:

  • Comparing data on what training an employee has completed (and when) with data on her job results can indicate whether the training has been effective.
  • Graphing the performance or results of employees within a division or of comparable teams can enable managers to see who’s doing well and who needs training or performance support.
  • Studying a visualization on engagement with various aspects of training—how many learners start watching a video vs. how many complete it or whether more learners complete a game-based review or listen to a podcast presenting the same information—can offer insight into how best to engage learners.
  • Data on performance and quiz scores can reveal whether learners are retaining and applying skills and knowledge, long after training has ended.

Types of data analysis relevant to eLearning

Data can be very loosely classified as quantitative or qualitative. Quantitative data include things you can count; things you can put in order or compare; and things you can group or categorize. Qualitative data pertain to people’s perceptions and experiences; one way to visualize this subjective data is to chart responses to surveys.

Quantitative data examples in an eLearning context might be:

  • How many learners completed a course
  • Number of hours of training employees in each division completed
  • How many new hires were still employed after one year
  • Percentage of learners who watched a video vs. listened to a podcast
  • Employee’s or team’s sales revenue before and after training
  • Whether top-performing sales personnel are in the group that got performance support after training, the group that received only training, or the control group that received neither
  • Whether job applicants have associate, bachelor’s, or master’s degrees
  • How many applicants with master’s degrees also have five or more years of project management experience

Qualitative data examples in an eLearning context might be:

  • Percentage of learners who indicate that the course they’ve just completed was relevant to their job duties
  • Whether customer satisfaction ratings are higher for retail employees who have completed training vs. those who have not

These are just a few examples. The eLearning Guild’s Data & Analytics Summit, August 22 & 23, is a great place to learn more about what data to collect from eLearning and how to explore and learn from those data!