Predictive Learning Analytics helps you analyze whether learners are applying training on the job
Training often doesn't translate to the workplace for numerous reasons. Sometimes learners haven't acquired necessary new information, or the organizational culture doesn't lend itself to learners using new skills. At other times, learners don't have managerial support to use what they have learned.
In "Evaluate Learning With Predictive Learning Analytics," Ken Phillips describes a methodology for reviewing a training program to more fully understand whether individuals are applying learning on the job and how to remedy it when they are not. The methodology begins with collecting data and analyzing it, implementing appropriate solutions, and reporting results to the C-suite.
The model involves choosing a training program that has a high profile and is worth your time and effort, estimating the lost time and money from learning that is not applied, and identifying a group of learners to survey to pinpoint the underlying causes of scrap learning.
Next, predict which learners are least likely to apply or at risk of not applying learning, as well as predict which managers are least likely to provide active support to their direct report learners.
If learners aren't likely to apply new knowledge, email reminders about covered content may be useful. Another option is distributing a job aid shortly after the training program. And a job aid or coaching on how to help direct reports with their training could help a manager who is providing less-than-active support.
These tips were adapted from the October 2020 issue of TD at Work. Learn more at td.org/TDatWork.