Making Employees Clean Their Plates: How to cut down on Scrap Learning
How much job training equates to time wasted: about 20%, according to one LinkedIn study. That’s the percentage of learners who never apply new training to their job. That same study says 67% of learners apply the lessons learned, but in the end, revert to previous habits.
For HR professionals designing or monitoring the Return on Investment of training programs and in a world where we all have to do more with shrinking budgets and resources, those are disturbing statistics. Not only does it equate to a loss of productivity, but also a financial loss.
The problem stems from a variety of causes including, but certainly not limited to:
- Low learner motivation
- The content is not directly relevant
- Inadequate support materials
- Insufficient practice
- Delivered at the wrong time
- Examples don’t connect
So, how do you mitigate or address the issue?
“The way that people learn is evolving and I think that the way we think about learning is really changing too,” said Christopher Leady, the Director of Talent Management at the Campbell Soup Company. “I think a great learning experience is one that not only helps people prepare for what they need to do today, but is also going to give them what they need to take on tomorrow. It really helps build capabilities and skills not only for today, but for the future as well. Otherwise, it is like running on a treadmill where you are constantly running but not getting anywhere.”
What Leady is alluding to is the reality that everyone learns differently. That in itself is a challenge. Introducing learners to information with the same methodology, for instance a classroom setting, you are inevitably going to teach some learners information they will use and others information that will not be used.
It’s called scrap learning. It’s learning that is delivered, but not applied on the job. To further explain the metaphor, it’s learning that is thrown out once it’s learned.
There are several ways reduce the scrap learning.
Predictive Learning Analytics
Predictive learning analytics or PLA can help HR professionals identify and mitigate the causes that make learning programs less effective. Those include:
- Content relevancy to learning goals
- Lack of opportunity to apply knowledge
- Learning goals and business goals are not aligned
- Lack of appropriate training
PLA can help learning professionals understand what is likely to happen. It also predicts learners’ success. PLA as a strategy focuses on the learner as an individual and not the program being used. This allows learning professionals to know who did and who did not learn the material, and who is most likely to apply the training they learned to their jobs.
PLA works best when it involves every stakeholder in the process. Those include:
Learners can be empowered by PLA in that it gives them a clear view of where they are in the process. It allows them to make adjustments in order to better learn and develop skills that will transfer into their daily work tasks.
Purdue University, for instance, uses a process called Course Signals. It uses symbolic traffic lights as feedback to let learners how they’re doing – read for at risk, green for on track.
Instructors using PLA can see which employees are at risk so that recommendations and interventions can be managed. It also allows for instructors to see how employees are performing, compare employee performance, and monitor status in terms of predicted success.
For managers, PLA shows them who is experiencing ineffective learning. It also allows for progress monitoring.
Successful use of PLA in a company requires thoughtful planning and preparation. That includes the support of executives and other stakeholders. Consideration of skills and tools will also be important.
In any case, scrap learning does not have to negatively impact productivity. Predictive learning analytics can ensure learners are successfully applying their training to their daily job tasks.
When it comes to corporate learning and eLearning, companies typically follow a specific curriculum. They test employees on this curriculum, and assign grades as a result. The problem with this strategy is that it doesn’t take the individual learner’s strength or weaknesses into account.
Adaptive learning, however, uses an algorithm to examine the employees individually, specifically what the employee does and doesn’t know. Furthermore, it asks questions designed to identify what information they know and understand and what information they struggle with.
When these areas are identified, learning can be designed with focus put on each employees individual needs. This means less scrap learning overall because the employee is only focused on the information they need to know and not something they already know or apply to their jobs daily.
In the end, the name of the game is making sure employees learn or enhance skills for the betterment of themselves and the company as a whole. The ability to predict this outcome can become a reality if learning programs and the content within are effective, the outcomes align with the business strategy, and learners are treated individually and their preferred method of learning acknowledged.