Understanding Core Metrics: Scrap learning

Overview

Scrap Learning provides insight into the amount of learning that is delivered but is not applied back on the job.

Scrap learning can come from several sources:

Content that is not relevant to the jobs of the learners
Learners who already know the information being presented
Content that isn't sufficiently practical
Content that's delivered at the wrong time
Inadequate support outside class for applying the training

The Scrap Learning metric allows you to diagnose areas within your learning programs with the most waste and gives you the ability to analyze scrap for your courses, locations, learning methodologies, learning event types (formal learning, overall conference, and conference sessions), instructors, and more (via the filtering options). Additionally, this metric enables you to compare scrap from the Post Event survey to the Follow Up survey and to compare your scrap learning percentage against a relevant benchmark.

How is it calculated?

Scrap Learning is calculated by taking the inverse of the question " I will use X% of this content on the job".

So, if a learner would respond that they will use 40% of the content on the job, that individual's Scrap Learning would be 60%. The response to all scrap questions is averaged together to create the scrap score.

Scrap Learning is measured using the following questions:

Post Event:

"I will use ___ % of this content on the job." [Question ID: 518230]
Legacy version: "What percentage of new knowledge and skills learned from this training do you estimate you will directly apply to your job? Check only one." [Question ID: 2788]

Follow Up:

"I have used ___ % of this content on the job." [Question ID: 521462]
Legacy version: "What percent of new knowledge and skills learned from this training did you directly apply to your job? Check only one." [Question ID: 2818]

Manager

My employee has used _% of the content on the job. [Question ID: 521746]

How is it used?

The Scrap Learning metric is specifically designed to give you insights into the amount of training that is never implemented back on the job. L&D specialists at your organization can determine if the training you're providing is effective and has the desired level of on-the-job application. Scrap Learning can be displayed by various elements of your learning programs ( ex: courses, instructors, learning method, etc.), and by applying filters, you can easily identify where there may be performance gaps with training application. This metric will also enable you to compare your scrap learning against a benchmark, providing insight into the effectiveness of your learning compared to other organizations.

Additionally, comparing your scrap learning percentages from the Post Event to the Follow Up survey will help you to understand if there was a significant difference between the predictive on-the-job application vs. the actual on-the-job application. If you note a significant discrepancy from the Post Event to the Follow Up survey, you will most likely want to re-visit both the training content and the on-the-job support to ensure better learning application.