Scrap Learning widget

The Scrap Learning metric indicates what percentage of the material presented during the training will not be applied back on the job. Scrap learning could also be thought of as the inverse of how much a learner thinks they will apply back on the job.

The term scrap was inspired by the manufacturing industry. Imagine a steel factory. As the is being shaped and shaved into sheets of steel. As the steel is processed and shaped, slivers of steel will fall to the floor and be wasted while the majority is utilized. The more scrap metal that falls to the floor is money wasted.

The same concept can be applied to learning. The more learning that is not applied back on the job, is wasted learning, which in turn is wasted money in terms of development, delivery, and time away from the job.

For the Scrap Learning report to populate, your organization must be collecting data on the scrap learning questions (tied to the "Job Impact" question category):

Post Event Survey SmartSheets

• Pre-2017 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]
• 2017 version: "I will use ___ % of this content on the job." [Question ID: 518230]

Follow-up Survey SmartSheets

• Pre-2017 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]
• 2017 version: "I have used ___ % of this content on the job." [Question ID: 521462]

Tools for identifying and investigating Scrap Learning in MTM:

Scrap Learning Widget

1. The Scrap Learning dashboard widget provides a summary view of the current scrap rate for a defined dataset, compared to a benchmark of your choice. The widget also provides insight into the question categories or courses which are driving the scrap rate, based on proprietary algorithms built into the MTM tool.
2. Use the Scrap Learning widget to easily monitor scrap overall, for a specific course, etc.
3. Drill-down into the details: The Key Metrics Report
   1. This report allows you to compare the Scrap Learning % by a variety of elements, such as Course, Location, Class, Instructor, Portfolio, and more. You can also filter the dataset to only include specific data. This is the best choice for drilling down to identify specific drivers of high scrap.
4. Examine Scrap Learning alongside other key metrics: the Report Card
   1. Report Card report: The Scrap Learning metric is displayed at the top of the report in the summary section, and is also included within the body of the report (question level)