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Ken Lerner is a technical programs attorney at Argonne and analyzes issues in emergency preparedness, governmental power and authority, environmental regulation, energy regulation, and infrastructure protection. He has supported CSEPP since 1991. Contact him at klerner@anl.gov.

Ken Lerner

Winter 2013 - The Public Manager

Using Integrated Process Teams for Interagency Coordination

October 2020 - TD Magazine

Predictive Learning Analytics helps you analyze whether learners are applying training on the job.

Calculate Learning Losses and Remedy Them

What is arguably the number one issue facing L&D professionals today? The answer: scrap learning. Scrap learning refers to the gap between training that is delivered and what is actually applied back on the job. It’s a critical business issue because learning that is delivered but not applied is a waste of an organization’s resources.
In this webcast, you will learn how to use predictive learning analytics to reduce the amount of scrap learning associated with a learning program. You will:

Discover the meaning of the term scrap learning and its impact on wasted organizational resources and lost credibility with business executive stakeholders.
Analyze how to build an algorithm that predicts which learners are most and least likely to apply what they learned in a training program back on the job.
Examine the three-phase, nine-step predictive learning analytics methodology using data from an actual implementation.


Boost Training Transfer Using Predictive Learning Analytics™ (PLA)

In this session, you will:

Examine the role scrap learning plays in wasting organization resources and damaging your credibility with business executive stakeholders.
Analyze how to build a predictive learning analytics model that identifies those individual learners who are most and least likely to apply what they learn back on the job.
Discover the four benefits of using predictive learning analytics in your organization and eight guidelines for getting started.


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