Learning Science for Instructional Designers: From Cognition to Application

CHAPTER 1

Introduction to Learning Science

•  What is learning science?

•  Why we need learning science

•  How learning science is conducted

•  How to find learning science resources

plunger (plŭn′jr)

The plunger in the pump was broken. A plunger is a:

(a) dolphin

(b) pump part

(c) brown car

—A found example of online learning

What possible learning purpose does this example serve? The question comes right after the content. It asks a question where the answer is implied by the immediately preceding material, and the alternatives are nonsensical or silly.

This example is emblematic of why we need learning science. Because when we design learning experiences, we want to achieve an outcome. And, if we don’t do it according to learning science, we could waste our stakeholders’ resources and our learners’ time.

To address the need in this book, we’ll go through basic cognitive architecture, and then the learning phenomena (cognitive artifacts like mental models) of reasoning that arise from this architecture. We’ll look beyond cognitive to emotional aspects, and we’ll point out the implications for learning experiences and the design of specific elements.

First, however, we should establish more about the science we’re investigating.

What Is This Learning Science?

Learning science is, not surprisingly, the scientific study of learning. It means looking at how learning works, and also what facilitates and hinders learning. It provides a strong basis for designing instruction. It is relatively new, however.

Our brains are the core organs of learning. We perceive the world, act, observe the outcomes, and reflect. Consequently, studying learning comes from studying the mind. The ancient Greeks philosophized about how our brains work, but scientific exploration of learning really only began with Hermann Ebbinghaus’s studies of memory in the 1800s (Figure 1-1).

Figure 1-1. Ebbinghaus’s Memory Study

The field of psychology has subsequently gone through several movements, including behaviorist, cognitive, and constructivist. Each of these added insights have furthered our understanding.

The behaviorist school started out by saying that we can’t talk about what’s going on inside the brain; we only can connect inputs with outputs. This was the era of Pavlovian conditioning and stimulus-response approaches. Robust findings include the value of different reinforcement schedules (think gambling; Figure 1-2) and the Yerkes-Dodson performance-arousal curve.

The cognitive revolution said that we can hypothesize what brain structures must exist. Research showed various facets of our information processing that have stood the test of time. There was a vision that we were formal, logical reasoners.

Revelations that we’re not the formal-thinking beings we thought prompted the move to a more situated, or constructivist, view of cognition. Here, we realize that our thoughts are an interaction between context and previous experience. We may use concepts differently depending on context, and certain types of reasoning are problematic.

Figure 1-2. Reinforcement Schedules

Importantly, psychology isn’t the only field that talks about how our minds work. Insight still comes from fields such as philosophy, neuroscience, linguistics, anthropology, and sociology. The field of cognitive science was created to be an umbrella under which these differing elements could be integrated. And it’s provided a solid foundation for developments including communication and collaboration practices, interface design, and artificial intelligence.

Learning science is similarly interdisciplinary. Research insights have come from psychological investigations, educational studies, ethnographic approaches, and sociological work. (A side effect is that results from one discipline may not take into account results from another, related discipline.) A growing awareness of this relatedness led to the establishment of the discipline in the 1990s.

Learning science is also global. I was a graduate student in the United States during the establishment of the discipline, and then a post-doctoral fellow. There is so much research done in the US, it was easy for me be focused nationally instead of internationally.

Later, I had the good fortune to take a faculty position in Australia, and quickly (and shamefacedly) learned my awareness of research was blinkered. I was also able to visit global conferences and get exposed not only to the field’s interdisciplinary nature, but also to its global cohort of researchers. And it’s important to realize, and recognize, that the results and implications properly span cultures and nationalities.

Why Should We Care?

Another plausible question is why learning science? Why should we understand the underlying cognitive mechanisms, the artifacts and limitations of our mental architecture, and the associated elements? Can’t we just follow the resulting precepts of instructional design? I’ll suggest that the answer is a resounding no.

My short answer is that it’s a professional imperative. Instructional design is applied learning science. How can we claim to be scrutable in our approaches if we don’t track the underlying research, and can’t articulate how our designs reflect what is known? Just as you expect your doctor and financial adviser to be applying the latest outcomes, so too should you feel such an obligation when designing learning experiences. We don’t want to be guilty of design malpractice, after all.

The longer answer starts with the realization that instructional design is a dynamic field. Even David Merrill, one of the founders of and forces in instructional design (and a truly nice person to boot), has had phases of change. His Component Display Theory, for instance, progressed to ID2, and now he’s on about a pebble in a pond. New understandings in learning science drive the need to revise our approaches.

The foundations we build our design processes on have shifted. Instructional design emerged as an artifact of World War II, when behaviorism was in force. As we’ve gone through the cognitive era and into a post-cognitive constructivist awareness, our design bases similarly adapted. Each of those transitions has had implications for what we think learning is, and consequently what makes sense as pedagogy.

Recent understandings continue to drive our approaches. To be able to react to new approaches means grasping some fundamental underpinnings. Separating them from other explanations is a critical component of being a successful practitioner. (I once was presented with a hydraulic model of learning, an engineering metaphor misapplied to understanding our thinking!)

This implies a second reason to understand the basics: Folks will continue to propose new approaches. They will come to these approaches sometimes from pure conviction, rightly or wrongly, but also for commercial reasons. Practitioners need to be careful about evaluating new claims. With an understanding of the basic mechanisms, you’re better prepared to avoid the learning myths that plague our field (as I documented in my last tome, Millennials, Goldfish & Other Training Misconceptions).

Also, instructional design has great recommendations, such as we see in the movement to evidence-based methods as discussed in recent books by Ruth Clark and by Mirjam Neelen and Paul Kirschner. Thus, a third reason is that there are still gaps that prescriptions won’t fill. Making good choices in lieu of guidance depends on understanding the mechanisms as suggested by theories.

How Does One Learning Science?

Learning science is the result of the usual processes of science. While there are many different methods, the basis we should be using is the result of experimentally tested and statistically validated approaches.

A major distinction is between quantitative and qualitative studies. In quantitative studies, you have clear metrics that are objectively obtained, such as scores on tests or observationally clear performance while completing tasks. Here we typically have some subjects working in one way (such as under the experimental treatment), and a control group in another, typically pre-existing, way, and we then look at the outcomes.

Statistics are used to determine with some degree of certainty whether the outcome is due to chance (Figure 1-3). It is a probabilistic game, because even significant tests have a chance of being false 5 percent of the time. This is one of the reasons it is preferable if the results are replicated. Reputable reports, such as those in a peer-reviewed journal or book chapter, will detail the study sufficiently so that someone can conduct the same study. And this happens.

Figure 1-3. Statistical Significance

Note that despite their empirical nature, experiments tend to be driven by theory. While there are some purely experimental studies (What will we find?), most times someone creates a hypothesis, and then tests it. For instance, someone may say, Hmm, one prediction of cognitive load theory might be that we need to integrate labels into diagrams to keep people from generating more mental overhead by looking back and forth between them, trying to integrate the element and its meaning. They run a test, and find out if it’s true (spoiler: it is).

Qualitative studies similarly use the scientific method, but they take data that’s more complex than just numbers (verbal protocols, interviews) and code it, and then look for patterns. Typically, you need some controls on the interpretation to support the resulting analysis, such as someone independently coding a subset of the data and looking for the agreement. For my PhD thesis, for instance, I coded transcripts of subjects’ verbal efforts in problem solving. As a control, I also hired a student to code a subset of the data using my rubric, and checked to see that the coding was reliably consistent. Finding (and reporting) that the degree of agreement was sufficiently high meant that we could then report that the data was reliably coded.

Importantly, your data should get reported in a journal. What this means is that you write it up in unambiguous language and peers review it, and it must pass scrutiny. Along with the results, you situate your work in others’, via a literature review, and you make clear what the unique contribution is. The peer scrutiny can be a problem, particularly if the work is upending established protocol. There’s a whole field of science about science, and concepts like Thomas Kuhn’s paradigm shift are used to characterize the bigger changes. Yet overall the process works.

Be aware that the language used for journals is an obscure dialect known as academese. This is typically based on English, but uses an esoteric and almost deliberately impenetrable vocabulary. It takes training to be able to comprehend it. Learning to read academese is a valuable skill, but probably not for most folks.

At its core, however, the systematic process of experimentation and theory advancement, as well as theory revision and replacement, is all part of science. And its results are the best basis upon which to determine our approaches.

On the Lookout for Learning Science

As suggested, the best way to track science is to read journals. And, again, not everyone should be expected to read them. The nuances of appropriate methodologies are subtle, and not necessarily of use to all. What to do instead?

Fortunately, there is a cohort of folks who are reliable translators. In addition to the scientists—those who can reliably communicate to laypeople, and that’s not all