Learning science is a relatively new field. It emerged in the late ’80s and was formalized in the ’90s with a society, journal, and conference. An integrative field, it tries to provide an umbrella that includes educational psychology, cognitive science, and more. And, rightly so, instructional design should be grounded in what learning science research tells us.

Instructional design preceded learning science, having emerged as a discipline after World War Two. It was then based on the prevailing model of how we learn—behaviorism. As the predominant paradigm shifted to cognitive approaches then connectionist, so too did instructional design.

That dynamism still continues. As such, it’s important to reflect on what trends are emerging. This is a personal account, as much aspirational as evidentiary, but based upon observing the field for all too long.

1. Increasing Interest

The first trend to talk about is that interest in learning science appears to be increasing. A variety of forms of evidence bolster this view. For one, the Association of Talent Development created a community for learning science in 2014.

A growing number of books on the topic have been published. Aside from Ruth C. Clark and Richard E. Mayer’s e-Learning and the Science of Instruction in 2003, there wasn’t much else until Julie Dirksen’s Design for How People Learn in 2012. Recently, however, that’s been changing. In 2016, Elaine Biech released The Art and Science of Training, and in 2020 Mirjam Neelen and Paul Arthur Kirschner’s Evidence-Informed Learning Design was joined by Clark’s third edition of Evidence-Based Training Methods.

Recent trade books Make It Stick by Peter C. Brown, Henry L. Roediger III, and Mark A. McDaniel join K. Anders Ericsson’s Peak as further evidence of growing interest beyond just our field. Another indication is the creation of a society for practitioners specifically focused on evidence-based methods. The Learning Development Accelerator grew out of a conference last summer that generated considerable interest.

And this is all to the good. Learning science underpins our work, and a growing interest means a growing quality of work. I’ll also suggest that 2014’s the Serious eLearning Manifesto grew out of a frustration with the lack of learning science awareness. That it was supported by major organizations and top individuals in our field is a good indicator of the importance of learning science.

2. The Importance of Context

A second trend is a growing awareness of the importance of context. The rise of connectionism arose from a recognition that we’re not the formal logical reasoners that the cognitive model suggested. So-called situated cognition posits that our reasoning is a construct of our current context and our previous experience, creating an emergent interpretation. Thus, context is a critical consideration in reliable performance.

For example, when we think of a dog after petting a friendly one versus having been barked at by a vicious one, our representation is colored. We might, therefore, behave differently. This is important for our design solutions because we need consistent responses.

The importance is to accurately promote transfer. Transfer asks where any learning can be applied. Near transfer is transfer to situations very like ones that were seen in the learning experience. Far transfer asks us to apply principles in situations that aren’t like the ones experience. Different learnings have different transfer needs. We may learn how to operate a specific piece of equipment, or we may need to use our customer service skills on a wide variety of different emotional experiences. Yet we want transfer to all appropriate situations (and no inappropriate ones).

The awareness of context means we have a better understanding and can better prepare to ensure that we get the necessary performance. We can design our examples and practices to provide sufficient coverage to support the necessary transfer.

3. The Importance of Emotion

A third trend is a growing awareness of the importance of emotion in learning. A focus on engagement is a start, but there’s a growing recognition that there’s more on tap than just fun. And this is an important recognition because getting it right leads to better learning outcomes.

When learners are motivated and their anxiety kept in check, learning outcomes are improved. As Nick Shackleton-Jones documented in his book How People Learn, emotion makes learning more memorable. While his theoretical background was off, the importance of caring about what you’re learning is valid.

We also know that anxiety can interfere with learning. While a small amount of pressure can promote learning, too much interferes. And learning itself can induce anxiety. Making learning safe is an important component in ensuring learners are willing to take the chances that lead to learning.

The early approach to engagement was trivialized. Scores, quiz-show templates, and click to see more approaches were supposed to make the experience more compelling. Yet while these approaches may have a temporary effect, they don’t persist and may even impede the desired outcomes.

Instead, we are recognizing that tapping into intrinsic motivation is of a longer-term benefit. Moreover, we’re seeing more guidance on how to reliably tap into meaningful motivation. Edward L. Deci and Richard Ryan’s self-determination theory gives us some distinctions that empower us to figure out how to unpack the real reasons to learn.

4. Learning to Learn

I’ll pick up one more trend that I think is an important direction. Meta-learning, or learning to learn, has been too-long neglected, but that’s changing. We are recognizing that we can’t assume that learners are effective at learning. Fortunately, we are also finding out that this isn’t immutable.

In the afore-mentioned book Evidence-Informed Learning Design, authors Neelen and Kirschner make the important distinction between self-directed and self-regulated learning. The former is where we choose our learning goals (as opposed to a curriculum). The latter is how we control our own learning. And there are skills here that can be improved, such as being clear about goals, making smart choices, and monitoring the process.

That element about smart choices is also worth unpacking. We are learning that certain types of learning activities are better than others. For instance, we’re finding out that just taking notes isn’t the issue but that how notes are taken matters. We’ve learned that recording the lecturer’s comments isn’t as valuable as reprocessing the material, say as paraphrasing, outlining, or mind-mapping. Similarly, rereading isn’t as valuable as testing yourself.

As we recognize this, we have the opportunity to develop learning as well as the learner’s ability to learn. This isn’t done in a vacuum. You need a topic to learn about. However, you can layer learning-to-learn on top of other learning. And should.

So?

These trends implicate for practice and our profession. For practice, they clearly let us know about how we should choose practice and example stories. We also know we have to engage the heart as well as the mind. And we can work to improve our learners as well as our learning.

To do this, however, entails some professional obligations. First, you need to understand the basics of learning science. It may sound good to just take the prescriptions from design, but there will still be gaps. Research can’t cover all situations, and you need to make inferences. You do that better if you know the cognitive architecture on which learning runs.

You also need to keep up. As these trends indicate, the field is dynamic. While tracking the literature written in the original academese is challenging, there are translators who write books, speak, and run workshops. You should find those who are recognized, and track them. You should also reward their efforts; they also consult, and you should hire them.

And there are myths that permeate the field, as discussed in my most recent tome, Millennials, Goldfish & Other Training Misconceptions. These are detrimental to your outcomes and your bottom line. One of the best ways to be myth-resistant is to have a basis of learning science to withstand implausible claims.

If, as I claim, the human brain is arguably the most complex thing in the known universe, learning science could be the equivalent of rocket science. However, you don’t need to be the scientist. There are good prescriptions to follow, and then you can move forward with the steps above. My next book, Learning Science for Instructional Designers, comes out in April 2021.