CHAPTER 3

A Learning Science Strategy: Deepening the Impact of Talent Development

Jonathan Halls

If your doctor suggests a course of medication, they might discuss why you need it, how it helps, and its possible side effects. The reason most people trust their doctor’s prescriptions is because the doctor is a professional. Your doctor, meanwhile, is confident about their diagnosis because they have likely based it on science. So, here’s the question. Are you as confident offering learning solutions to your clients and stakeholders as your doctor is prescribing you medicine? And by confident, we mean are you assured they’ll work?

In this chapter, we look at what the emerging field of learning science is and how it can provide talent professionals with the confidence to offer learning solutions that help move the needle of performance. Given the size of this field, we’ll focus on an area within learning science to explore how learning happens, then consider some mindset changes that may deepen the impact of talent development in our organizations. While many conversations about learning science focus on the instructional event itself, such as how to make content easier to understand and remember, the field offers as many insights for executives and organizational stakeholders as it does for trainers and instructional designers. So, what is learning science, anyway?

Learning science is an interdisciplinary research-based field that works to further the understanding of learning, learning innovation, and instructional methodologies (ATD 2020). Drawing on neuroscience, cognitive science, instructional design, data analytics, anthropology, linguistics, computer science, psychology, and education, learning science explores things like genetics, environment, brain chemistry, and other influences that can foster or inhibit learning (Applied Learning Sciences Team 2017; Science of Learning Institute n.d.). Because it’s an interdisciplinary field, scientists are able to explore learning from multiple perspectives, which leads to a richer understanding of learning.

Science

The word science comes from the Latin word scientia, which literally means “knowledge.” Science is often used to describe bodies of knowledge, such as environmental science or biology. But it also describes the processes researchers follow to acquire that knowledge, which is represented by theories that provide a reasoned explanation of an observable phenomenon. A key aspect of scientific theories is that they are supported by overwhelming evidence (Olson 2004).

Strong evidence is important. Consider a new trainer who’s tasked with teaching a class. Over the years, he has found that he works best while listening to classical music. So, he decides to play Bach’s “Brandenburg” Concertos during class while learners complete their exercises. On the surface, it sounds like a nice idea. Creative, in fact. But how can he be sure it really helps learners do their best work? After all, he is drawing on his personal subjective experience. Does he know whether using classical music has worked for anyone else? Scientific theories require objective observation and measurement of ideas. And the ideas must be tested on enough people to be sure they apply to the broad population, and then be tested multiple times to ensure the findings can be replicated.

Learning science provides insight into how learning works, putting us in a better position to explain to learners and other stakeholders what we are doing and why. While learning science is a relatively new field, the notion of evidence-based learning in the world of training is not. An evidence-based approach has been championed by authors and learning authorities such as Ruth Colvin Clark, Patty Shank, Clark Quinn, and Elaine Biech, to name a few. However, our field has often been drawn to theories that lack rigorous inquiry, such as learning styles, left- and right-brain theory, and multitasking.

That said, not all science is foolproof. We need to be discerning with how we use it. Theories evolve as new evidence and new ways of measuring that evidence are found. And like any endeavor involving humans, it’s vulnerable to bias, fraud, and misunderstanding. Thirty years ago, scientists believed saturated fats caused heart disease, whereas today they believe sugar does (Cleveland Clinic 2017). Theories evolve. And as for bias, therapies like acupuncture, which successfully treated pain for thousands of years in China, were considered quack medicine by Western doctors until the 1990s because they hadn’t been studied empirically (Lu and Lu 2013). Fraud and sloppy research are also serious problems, with an alarming number of studies failing to pass replicability tests (Harvey 2020; Samarrai 2015). But overall, research gives us confidence that a theory or technique has been tested and is likely to work.

Learning

Philosophers and researchers have attempted to define learning since the days of Plato and Aristotle, led by the context in which learning takes place, its purpose, and their own philosophical frames of reference. Our frame of reference is adult learning in the workplace. Malcolm Knowles—who is probably the most influential thinker in adult learning—described learning as the process of gaining knowledge or expertise (Knowles, Holton, and Swanson 1972). But it doesn’t happen by accident. Theorist Robert Gagné (1970) believed that learning was an intentional process stretching beyond mere growth, involving the development of a skill that is retained over a period of time. So, we can classify learning as the intentional process of building workplace skills and consider learning science to be the collection of research-based theories and techniques from multiple disciplines that describes how learners can intentionally build workplace skills.

One discipline that has guided adult learning since the 1950s is cognitive science, which was developed when researchers started exploring attentional and decisional processes with advanced mathematical modeling (Malmberg, Raaijmakers, and Shiffrin 2019). At a high level, cognitive science deals with memory and cognition.

One of the most influential models for memory research is the Multi-Store Model of Memory, developed by Richard Atkinson and Richard Shiffrin in 1968 (Malmberg, Raaijmakers, and Shiffrin 2019). The model divides memory into three structural components: sensory register, short-term store, and long-term store (Table v). Many theorists use this framework to describe memory functions, although most in education refer to the short-term store as the working memory, based on subsequent work by Alan Baddeley and Graham Hitch in 1972.

The sensory register acts like a gatekeeper for information entering the brain. Sensory information can be anything we see, hear, taste, smell, or feel, although most educational research focuses on visual and auditory information. The working memory acts like the RAM on your computer and processes this information to make sense of the world and perform day-to-day tasks. It has an extremely limited capacity, holding only three to five new chunks of information at any one time (Cowan 2010). The long-term memory acts like a filing cabinet for all the memories we need to keep. It is said to have an indefinite capacity, and no actual limit to its capacity has been found (Ericsson and Pool 2016).

Memory isn’t important just in cognitive science. Malcolm Knowles’s andragogy prioritizes adults’ experiences, while John Dewey (1938), in Experience and Education sees memories as crucial building blocks of knowledge. An influential cognitive theory suggests that memories are mental representations called schemata. Think of schemata (or schema in the singular) as schematic diagrams we hold in our mind of events, ways of performing a task, or details about a concept. First suggested by German philosopher Immanuel Kant, schema theory was proposed by Cambridge psychologist Frederick Bartlett in 1934, popularized by Swiss psychologist Jean Piaget, and more recently researched by educational psychologist David Rumelhart. Schemata are also referred to as mental models.

Schema theory suggests that as a matter of survival, our brains organize and categorize our memories so they are easy to access when we need them. Rumelhart (1980), who called memories the building blocks of cognition, described them as “data structures representing generic concepts stored in memory.” They are like computer programs our brain uses to think and perform tasks. On one level, it’s easy to think that learning professionals are in the business of delivering learning content. But from a cognitive perspective, we’re really in the business of helping learners build schemata that they can then use to perform their jobs.

Making Sense of Learning

To make sense of how these things work together, let’s break learning into three separate steps:

•  Understanding how to perform a task

•  Remembering how to perform a task

•  Mastering a task so it is performed on the job efficiently, creatively, and with few mistakes

Understanding a Task: Connection

In most cases, you need to understand a task before you can perform it. At first blush, it may seem like a one-way process. Learners listen to a presentation or watch a demonstration and voilà—they understand. However, what they’re doing is cognitively connecting new information, such as what they hear in a presentation, with something they already know, which, as we’ve seen, is a schema held in the long-term memory. As such, classroom presentations are not the only ways for learners to understand a skill or task. Experimentation, field trips, critical reflection, and conversations are among many ways people make sense of new ideas and tasks. The important thing is connecting new information from new experiences to existing memories and then manipulating it to create a new memory.

If you attend a class about workplace tension that focuses on how the lack of role clarity can increase stress, you might recall a personal experience you had where role clarity made your work life very stressful. As the trainer talks about it, you will use your own experiences to analyze and make sense of what the trainer is saying (Figure 3-1).

This takes considerable effort, and the capacity required to do this processing in the working memory is called cognitive load (Lovell 2020). When learning a new task for which we have little prior knowledge or experience, the cognitive load is considerably higher, which can cause learners to be overwhelmed and confused. This is the crux of cognitive load theory (CLT), which was developed in the 1970s by educational psychologist John Sweller. Hugely popular in K–12 education, this theory is backed by a plethora of studies and offers a detailed understanding of how to make it easier for learners to build their understanding.

There are two principal types of cognitive load in CLT. Intrinsic load refers to how complex the topic is itself. The actual level of complexity is affected by the learner’s prior knowledge or experience. Extraneous load refers to how information is conveyed by the trainer, as well as the instructional materials and other factors like distractions in the learning environment. We can make the process of understanding new information easier by reducing extraneous load and optimizing intrinsic load (Lovell 2020).

CLT offers research-based techniques to actively reduce extraneous load, including managing redundancy, split attention, transient information, and dual coding. Strategies that optimize intrinsic load include sequencing and matching instructional techniques to appropriate skill levels. Matching skill level to the instruction is based on a theory called the expertise reversal effect, which confirms the suspicion many trainers have that mixing different levels of experience in the same class is not always productive (Kalyuga 2003).

Remembering a Task: Practice

To perform a new task back on the job, a learner needs to not just understand it but remember how to perform it. Intentionally building memory happens through practice.

Humans aren’t good at remembering stuff; it doesn’t matter whether it’s the name of someone you just met or a telephone number. Hermann Ebbinghaus famously demonstrated this in a series of experiments published in 1885 in which he introduced the concept of the forgetting curve. Recently replicated, Ebbinghaus’s experiments showed that despite our tendency to forget, memory decays can be arrested by regular repetitions spaced out over time (Murre and Dros 2015; Ebbinghaus 1885).

The more times we bring a memory from long-term memory into our working memory, the stronger it becomes (Figure 3-2). This is what practice is all about, which probably isn’t surprising, especially if you’ve ever performed in a school play or learned a foreign language. You likely learned your lines by practicing them over and over, and used flash cards to learn a new language. But mindlessly repeating something over and over a process called massed practice, doesn’t lead to longer-term memory. It may work for short-term retention, such as cramming for an exam, but those memories are shown to quickly decay. Ebbinghaus found that creating a space between practices is more effective and leads to greater long-term retention.

We can use techniques such as spaced practice to help learners remember a task. Spaced practice involves practicing a task or skill multiple times with space—perhaps a day or two or even a few hours—between each practice. Interleaving, which during instruction can feel messy, could also be called “mixing it up.” For example, let’s say you’re learning three tasks. Rather than practicing task A for an hour, then task B for an hour, and then task C for an hour, you’d practice all three tasks in that three-hour window, switching back and forth between each. Retrieval practice involves pulling a schema from long-term memory into working memory and processing it.

How many corporate training experiences involve practice time? A lot of energy is put into crafting presentations, but how much time do we put into activities for learners to reflect and refine their performance? The science tells us that just presenting or sharing information is not enough. We need to plan time for practice.

Mastering a Task

Understanding how to perform a task requires us to connect new information to a schema and manipulate it. Remembering that task requires us to practice it. But what is necessary for doing it well? Intentional practice.

The science of expertise and expert performance offers insight into what it takes to be really good at something. One of the leading proponents of expertise, Anders Ericsson, suggested that top performers are superb at what they do because of the amount of time they put into practice and the very specific way they do it. He calls this deliberate practice. Thousands of hours of practice—sometimes 15,000 to 25,000 hours—are necessary to be good enough at a skill to be considered an expert (Ericsson, Prietula, and Cokely 2007). Deliberate practice goes beyond interleaving or retrieval practice because it zeroes in on weaker areas of performance, using goals to stretch learners out of their comfort zone and expert feedback that is direct and often harsh to keep them on track. The bottom line—becoming an expert at anything is generally hard work and time consuming.

If talent development is about equipping people to be not just performers but high performers, there’s no escaping the fact that they need to invest lots of time. It doesn’t matter if that’s in or out of the classroom. And while achieving high performance may not quite require the number of hours needed to become an expert, a two-day learning event, which accounts for 10 to 14 hours, will likely fail to provide the necessary time. Unfortunately, this is not what many supervisors want to hear—many like to send staff to a training program to gain skills like people send a car through the car wash to clean it up.

Deepening the Impact of Talent Development

What does all this mean? While we’ve explored only a tiny area within the broader field of learning science, we can still glean insights that may help us deepen the impact of talent development. Many involve evolving our mindset about training. Here are some initial thoughts.

A Learner-Centered Mindset

Many people approach training and talent development with the mindset that it is about delivering content. That’s why so much energy is focused on what the trainer does, which then leads to low retention rates and a lack of opportunity for learners to improve their performance. Learning is about connecting new information with existing schemata, practicing it, and then going deeper with deliberate practice. This doesn’t diminish our training role, which still includes sharing information in presentations; however, we will have a greater impact if we help learners navigate this process rather than simply presenting it to them. The notion of shifting from “sage on the stage” to “guide on the side” is a helpful metaphor, as is seeing the trainer not as a mini keynote speaker, but as a professional whose work more mirrors a physiotherapist’s (Halls 2019).

A Research Mindset

Many trainers are not given the resources they need to develop an evidence-based practice, so they end up using techniques they’ve seen others use or have heard about at conferences. We need to take time to reflect on the instructional techniques we use through the lens of research—not just through cognitive science but the other disciplines within learning science as well. For example, it’s amazing how many trainers still use learning styles, which have been conclusively proven to have no impact on learning (Colvin Clark 2015). This means purging practices that we cannot reliably argue are supported by research and adopting new approaches that lead with evidence, such as those proposed in cognitive load theory. We also need to look at every aspect of talent development from a research perspective because it can inform the strategy as much as the tactics.

A Talent Continuum Mindset

Training is often seen as what might be described as a retail experience. A supervisor looks at a catalog, sends one of their direct reports to a class, and expects them to return to the job with new skills. Sure, they may return with some basic proficiency, but it won’t be enough to be a high performer. Rather than approaching learning as an instructional event—a one-day class or one-hour online module—we need to see learning as a continuum that lasts months, years, or even a whole career. Trainers don’t have to be an intricate part of every moment within that continuum, but they should be key influencers. It’s a daunting thought; while it’s easy to plan and feel in control of a two-day instructional event, a learning continuum mindset includes many unpredictable dynamics. As a profession, we need to explore how to make this work in the spirit of a learning organization, with the understanding that research shows the learner needs lots of time and intentional methodologies that stretch beyond those two days to develop high performance.

A Learning Ecosystem Mindset

Another common mindset in today’s profession is that learning happens in a classroom or an online module. However, the truth is that learning happens anywhere or with anything that causes learners to connect, practice, and refine their skills. We don’t need a classroom, workshop, or laboratory for every learning situation. In fact, the workplace itself offers more productive resources that support learning. Maybe it’s time to adopt a learning ecosystem mindset in which we see the whole organization as the classroom, the people in it as the teachers, and the equipment as the instructional resources (Theodotou 2020). How can we tap into this? How can we use Peter Senge’s learning organization approach to support this?

A Professional Learner Mindset

As we discussed earlier, science is never settled. New data and methods of reading that data constantly expand our perspectives. The same goes for learning; even the most seasoned learning professionals will say they can’t possibly know all there is to know about learning. Therefore, it’s incumbent on learning professionals to also be professional learners seeking to find out more about how to help other people be better at what they do. Scientists are never satisfied with a theory and will constantly evaluate it and seek to either disprove or affirm it. We too should approach learning and talent in such a way.

Nothing New Under the Sun

To be fair, these aren’t all new suggestions. Adult learning theory has always talked about emphasizing active learner participation, drawing on experiences and problem solving over a formal presentational approach. And while the idea of a learning ecosystem has been discussed extensively over the past decade, notions of the learning organization have been with us for 30 years. Learning science, however, gives us greater clarity, more insights, and a framework to deepen our practice. In tension with a learning philosophy, it offers incredible opportunities for us to reimagine our practice.

Challenges for Talent Professionals

Learning science doesn’t speak just to people who craft individual learning experiences, but to anyone involved in an organization who is interested in improving performance—from the learning and talent executives to the people doing the learning. And it speaks to strategy as much as it does to tactics. Here are some questions for different people in the talent world to consider about their roles.

Learning and Talent Executives

Learning executives can usually influence substantive change, and as such bear much of the responsibility in evangelizing a learning science approach. They should ask:

•  How can I help trainers and instructional designers review their practice based on research?

•  Should I offer them further training or time to stay up to date or learn new techniques?

•  How can I help trainers and designers, especially subject matter experts, get comfortable with presenting information and shift from a keynote to physiotherapist mindset?

•  How can I educate key stakeholders across the broader organization to see learning as a talent continuum that they also have significant influence over?

•  How can I equip people throughout the learning ecosystem with skills that ensure their influence is constructive?

•  Can I redefine the role of trainer or instructional designer to clearly encompass the talent continuum and learning ecosystem?

•  How can I model a learning science approach in my decisions and the practical support and feedback I give to my teams?

Trainers and Instructional Designers

In some organizations, trainers and designers are different roles; in others, they are integrated into the same role. These are the people who have direct influence over the development of workplace performance. Questions they should ask include:

•  How can I increase my knowledge of current research around learning? Not just in the cognitive field but in the broader fields, such as biology, anthropology, or mathematics?

•  How can I consistently manage cognitive load in the classroom through planning, preparing content such as learning aids, and my facilitation techniques?

•  How can I shift from being a content deliverer to a performance improver by crafting experiences that help learners access their memories to practice and refine performance?

•  How can I extend constructive learning beyond the classroom?

•  How can I better network across the organization to ensure all resources are being tapped as part of talent development?

Organizations

Many innovations start with enthusiasm and die at the feet of organizational disinterest. What should we think about organizationally to take talent development from being a catalog of classes to a strategy that improves performance with a long-term view? Organizations should ask:

•  Do we need to change policies around appraisals and independent development plans? Does that involve changing the way they are done or equipping incumbents to better facilitate them?

•  Do we need a leadership-driven cultural change initiative that emphasizes the importance of learners taking control of their learning?

•  Should we review the reward structures associated with performance development?

•  What policies, resources, infrastructure, or other aspects of an organization that might prevent growth in this new direction need to be changed or challenged?

There are two self-assessments on the handbook’s website at ATDHandbook3.org. One is for trainers and instructional designers and the other is for talent executives.

Final Thoughts

So where does this leave us with learning science? Can it deepen the impact of talent development for you? Many organizations throughout the world have a good record on talent development; others are struggling. Some already implement solutions they are confident will move the needle of performance because they know their methods are based on research; some have already seen great results. Learning science is more than just a bunch of theories to write in your notes at a conference or plug into the trainer’s toolkit. It’s a mindset that gives us greater confidence that our work will have long-lasting impact. And it increases our credibility because we’ll be able to better explain what we do, and clients and stakeholders will see real results. Just as we feel confident taking prescriptions from our doctors.

About the Author

Jonathan Halls helps learning and talent leaders reinvigorate their training and talent departments with a focus on boosting credibility with stakeholders. He specializes in supporting learning science and digital media content. A former BBC learning executive, Halls has worked with clients in 25 countries over 30 years and was a member of the advisory panel for ATD’s new Talent Development Capability Model. He has both a master’s and bachelor’s in adult education and is an adjunct professor at George Washington University. He runs professional development workshops for learning and talent professionals and has written a number of books, including Confessions of a Corporate Trainer. Learn more about his work at JonathanHalls.com.

References

Applied Learning Sciences Team. 2017. “What Is Learning Science?” McGraw Hill, February 28. medium.com/inspired-ideas-prek-12/what-is-learning-science-a1dc07ec4ce.

ATD (Association for Talent Development). 2020. “Talent Development Body of Knowledge.” Alexandria, VA: ATD Press.

Atkinson, R.C., and R.M. Shiffrin. 1968. “Human Memory: A Proposed System and Its Control Processes.” In The Psychology of Learning and Motivation, vol. 2, edited by K.W. Spence and J.T. Spence. New York: Academic Press, 89–195.

Baddeley, A.D., and G. Hitch. 1974. “Working Memory.” Psychology of Learning and Motivation 8:47–89. doi.org/10.1016/S0079-7421(08)60452-1.

Cleveland Clinic. 2017. “Why a Sweet Tooth Spells Trouble for Your Heart.” Cleveland Clinic, Health Essentials, April 6. health.clevelandclinic.org/sweet-tooth-spells-trouble-heart.

Colvin Clark, R. 2015. Evidence-Based Training Methods. Alexandria, VA: ATD Press.

Cowan, N. 2010. “The Magical Mystery Four: How Is Working Memory Capacity Limited, and Why?” Current Directions in Psychological Science 19(1): 51–57.

Dewey, J. 1938. Experience and Education, 1997 reprint. New York: Free Press.

Ebbinghaus, H. 1885. Memory: A Contribution to Experimental Psychology. Translated ed. 1913. Teachers College, Columbia University.

Ericsson, K.A., M.J. Prietula, and T.T. Cokely. 2007. “The Making of an Expert.” Harvard Business Review, July-August.

Erricsson, K.A., and R. Pool. 2016. Peak: Secrets From the New Science of Expertise. New York: Mariner Books.

Gagné, R. 1970. The Conditions of Learning, 2nd ed. New York: Holt, Rinehart & Winston.

Halls, J. 2019. “Trainers Aren’t Keynote Speakers.” ATD blog, May 1. td.org/insights/trainers-arent-keynote-speakers.

Harvey, L. 2020 “Research Fraud: A Long-Term Problem Exacerbated By the Clamour for Research Grants.” Quality in Higher Education 26(3): 243-261. DOI: 10.1080/13538322.2020.1820126.

Kalyuga, S. 2003. “The Expertise Reversal Effect.” Educational Psychologist 38(1): 23–31.

Knowles, M.S., E.F. Holton, and R.A. Swanson. 2005. The Adult Learner: The Definitive Classic in Adult Education and Human Resource Development. New York: Elsevier.

Lovell, O. 2020. Sweller’s Cognitive Load Theory in Action. Woodbridge, AU: John Catt Educational.

Lu, D., and G.P. Lu. 2013. “An Historical Review and Perspective on the Impact of Acupuncture on U.S. Medicine and Society.” Medical Acupuncture 25(5): 311–316.

Malmberg, K.J., J.G.W. Raaijmakers, and R.M. Shiffrin. 2019. “50 Years of Research Sparked by Atkinson and Shiffrin (1968).” Mem Cogn 47:561–574. doi.org/10.3758/s13421-019-00896-7.

Murre, J.M., and J. Dros. 2015. “Replication and Analysis of Ebbinghaus’ Forgetting Curve.” PLoS One 10(7): e0120644. doi: 10.1371/journal.pone.0120644.

Olson, S. 2004. “Science Produces Explanations That Can Be Tested Using Empirical Evidence.” In Evolution in Hawaii: A Supplement to Teaching about Evolution and the Nature of Science. Washington, DC: National Academies Press.

Rumelhart, D. 1980. “Schemata: The Building Blocks of Cognition.” Chap. 2 in Theoretical Issues in Reading Comprehension, edited by R.J. Spiro, B.C. Bruce, and W.F. Brewer. New York: Routledge.

Samarrai, F. 2015. “Massive Study Reports Challenges in Reproducing Published Psychology Findings.” UVA Today, August 27. news.virginia.edu/content/massive-study-reports-challenges-reproducing-published-psychology-findings.

Science of Learning Institute. n.d. “About Us.” John Hopkins University. scienceoflearning.jhu.edu/about-us.

Senge, P. 1990. The Fifth Discipline: The Art & Practice of the Learning Organization. New York: Doubleday.

Theodotou, M. 2020. “Learning Ecosystem: Why You Need One, How to Build It.” ATD Blog, December 14. td.org/insights/learning-ecosystem-why-you-need-one-now-and-how-to-build-it.

Recommended Resources

Biech, E. 2016. The Art and Science of Training. Alexandria, VA: ATD Press.

Erricsson, K.A., and R. Pool. 2016. Peak: Secrets From the New Science of Expertise. New York: Mariner Books.

Kirschner, P.A., and C. Hendrick. 2020. How Learning Happens: Seminal Works in Psychology and What they Mean in Practice. New York: Routledge.

Quinn, C. 2018. Millennials, Goldfish & Other Training Misconceptions: Debunking Learning Myths and Superstitions. Alexandria, VA: ATD Press.