Learning data provide an objective way to measure learning outcomes. When we correlate learning data with business goals, metrics tell us whether the learning was relevant and effective. Yet, we should examine learning data cautiously, as several factors impact what we interpret from the numbers.
- No two brains are the same: If I compare two different interventions’ impacts on business outcomes, does the group outcome tell me which intervention is worth using?
Group averages give us a population statistic and an average impact factor. However, there is no way to apply these metrics to any individual outside of the original sample.
For example, 80% of participants reported one learning solution was more effective than another intervention. However – only knowing eight of 10 people benefitted from one intervention over an alternative – you cannot predict which solution will be more effective for learners outside of the original population.
Moreover, the population changes every time you experience turnover. Old datasets no longer apply. The new hire could be part of the 80% or part of the 20%. Individuals and brains differ.
Solution: Continuously measure learning’s impact on business outcomes in various teams and at different points throughout the year. Additionally, pay less attention to double-blind, controlled trials and more to prospective data with multiple datapoints. Eventually, you must interpret data for individual learning. Artificial intelligence can help you fine-tune this process. Moreover, statisticians should work side-by-side with social psychologists, brain scientists and business leaders.
- Context matters: Can the same intervention have a different impact at a different time?
Yes. Learning is dependent on context. A tense work environment can impact how people learn. The brain is very sensitive to context, and if the environment changes, the learning outcome changes alongside it.
Some people may learn well under stress while others don’t. Even if the intervention is the same, the next cohort will learn differently and in the context of their unique environment.
Solution: Test the same learning under different contexts. Also, cultivate a learning-friendly environment, and pay attention to changes.
- Soft skills matter: Can soft skills be correlated to business outcomes?
Anxiety can help or hurt learning, and uncertainty can do the same. Knowing how to manage anxiety and uncertainty can improve learning. Therefore, it’s important to build a learning platform for soft skills.
Additionally, team learning can occur collectively. Collective intelligence depends on social sensitivity, turn-taking and valuing diverse perspectives among the group. Collective intelligence differs from the average or highest intelligence in a group. In a group setting, our brains learn differently, and soft skills can support that learning.
Even for the simplest processes, soft skills matter. Learning how to use a program or how to execute a process can be a simple and straightforward process. But consider how much easier it is to learn when learners’ minds are at ease and free of distraction.
Solution: The way people feel changes how they learn and can impact business outcomes. Invest in quality soft skills training; do not skimp on your soft skills learning solutions. I always tell people, “Think of soft skills as the new hard skills.” For example, children in impoverished communities don’t have the same access to education and opportunities as children of greater economic means, but instilling a growth mindset early on in life can begin to level the playing field.
Data is neither fact nor fallacy. A cross-sectional datapoint is a measure of learning at a specific point in time. Informed examination of data can help you make meaning of analytics as you guide your organization toward data fluency. Superficial learning data are far less meaningful.
When integrated into the unique social context of an organization, brain-based learning can help training professionals leverage accurate metrics that drive business and learning outcomes.