Boost Training Transfer Using Predictive Learning Analytics
What is arguably the number one issue facing L&D professionals today? The answer: “Scrap Learning.” Scrap learning is a term that describes the gap between training that is delivered and what is actually applied back on the job. It’s also a critical business issue because learning that is delivered but not applied is a waste of precious organization resources.
Join Ken in this session and learn about a revolutionary new measurement and evaluation methodology that provides a means to systematically measure and manage scrap learning. And most amazing of all, see how it allows you to predict, at the conclusion of a learning program, which learners are most and least likely to apply what they learned back on the job; which managers of the learners are likely to do a good and poor job of supporting the training they directed their employees to attend; and what obstacles are preventing participants from applying what they learned.
In this session, you will learn:
- The meaning of scrap learning and its impact on wasted organizational resources and lost credibility with business executive stakeholders
- How to build an algorithm that predicts which learners are most and least likely to apply what they learned back on the job and which managers of the learners are likely to do a good and poor job of supporting the training they directed their employees to attend
- How the 3-phase, 11-step Predictive Learning Analytics methodology was used by an organization to measure and manage the amount of scrap learning associated with a learning program