Consider this: An organization’s capabilities—all the processes, technologies, and tools—are useless without the skills and abilities (which require learning) that people and their collaborative efforts produce. It is through learning that we become involved in everything that we do. This is the science of learning at work (pun intended).

Currently, there is a surge in interest in how we learn, which makes sense in light of today’s increasingly knowledge-heavy workplace. Learning truly is the essence of “human capital.” Trendy words and phrases aside, learning is what people do well. In other words, humans are learning machines.

What is the science of learning?

The science of learning (SOL) is an interdisciplinary field of study that examines how people learn, and how the L&D field can improve learning and instruction. Some of its subsets include:

• brain changes caused by learning (neuroscience of learning)

• effective use of technological tools in learning (educational technology)

• interdisciplinary scientific study of the mind and its processes (cognitive science)

• social interaction using a computer or through the internet (computer-supported collaborative learning)

• intelligence exhibited by machines or software (artificial intelligence). Recently, this includes machine learning—algorithms that learn from data and can make predictions and decisions.

Basically, L&D practitioners want to know how they can use the SOL to improve learning in their organizations.

Here’s the good news/bad news: The study of the science of learning covers a wide expanse, but it is almost too wide for anyone to keep up with. That’s where this community can help. Our goal is to focus and make sense of the foundations of the SOL—in language that is easy to understand so that those working in L&D can apply it to today’s learning problems.

Learning problems with knowledge work

The time when people had to deal with far less information on the job is long past. 

In the Information Age, technical workers (which define a rather large number of people, including many in our field) seem to benefit most from high-paying jobs. We expect nearly all technical workers to multitask (even though the ability to multitask is a myth), and work using multiple devices wherever they are (even at home). We also assume that the Internet will permeate people, places, and process. 

Many agree to work like this because no cultural and social norms have developed around working in this era. And recent economic problems have only intensified these pressures.

We’ve seen comparable changes in the organization. For example, many hiring and scheduling processes are less personal and have moved online. Likewise, there is a movement to self-service learning inside organizations—as traditional learning functions struggle with responding quickly to business needs or helping workers prepare for future job requirements.

If that doesn’t ping your radar, it should. Indeed, our field is changing just like other job functions throughout the organization.

Consider self-service learning. It puts workers in charge of their development. They can develop and maintain their own learning plans. They can potentially call on books, online courses, MOOCs, and others that the company has made arrangements with. But do workers really know what they need better than we do?

Here are some of the questions that the SOL needs to help us answer:

• How is self-service learning working among those who use it?

• What learning approaches work in this new way of working?

• What is the best way to help workers learn under these circumstances?

• How are technically-oriented workers handling the need to keep up with continually changing job needs?

• How are decreasing skills intersecting with increasing skill needs?

Next steps

The next blog post will start a series on the foundations of the SOL. For example, what does learning mean exactly? And is their a difference between learning and instruction—and does it matter? (Short answer: Yes, yes it does, but we’ll explore more this topic more later…)

In the meantime, please let me know which questions are of most interest to you. I WILL respond—and interest level will most certainly drive the direction of these posts. More important, feel free to disagree with anything I write. I’d love to have a dialogue.