What is learning? While there are multiple definitions, most center on a change in capacity:
- Learning is an enduring change in behavior, or in the capacity to behave in a given fashion, which results from practice or other forms of experience.
- Learning is the lifelong process of transforming information and experience into knowledge, skills, behaviors, and attitudes.
- Learning is a perpetual matter of making sense of our experiences.
Change in capacity comes about through learning, in two ways: explicitly and impicitly. Explicit learning “is learning that you have conscious awareness of, when you think about what you’re learning and you can articulate what you’ve learned, like memorizing a long passage in a book or learning the steps of a complex game like chess. Implicit learning is the opposite. You might call it motor skill learning or muscle memory, the kind of learning that you don’t have conscious access to, like learning to ride a bike or to juggle. By doing it you get better and better at it, but you can’t really articulate what you’re learning. Many tasks, like learning to play a new piece of music, require both kinds of learning" (in Ham, 2017).
Learning theories explain how this transformation or change in capacity occurs. To be useful, the theories must be able to predict the relationship between antecedent events (e.g., teaching methods) and observed consequences (e.g., learner outcomes). Physicists like Stephen Hawking (1998) are coming to the conclusion that there will be no single grand theory of the universe, but rather a number of overlapping theories that together explain the universe. And so it is with learning. Rather than a single theory of learning, we have a number of overlapping theories that together explain learning. Three dominant theories, plus a newer theory that helps explain learning in the digital age, are described in this section. Each theory can be applicable to learning design, depending on the goals of instruction. In fact, strategies for a single course will likely come from multiple theories. The key question for each learning task is, “What type and depth of processing is required for learning to occur?”
Mental models (schemas)
The concept of “mental models” or “schemas” is inherent in all models of learning. In fact, the theories themselves constitute mental models. While not physical in nature, “mental model” is a very useful construct for designers and instructors, as well as learners, in that it helps us understand how learning accumulates. “A mental model is an explanation of how something works. It is a concept, framework, or worldview that you carry around in your mind to help you interpret the world and understand the relationship between things. Mental models are deeply held beliefs about how the world works.” (James Clear). We each have within us thousands of mental models – ranging from our understanding of dogs to how our senses work, to how the universe is constructed.
|1. An incomplete mental model of an how a car works.|
In the beginning, our mental models are bare structures with little detail, and likely containing misconceptions. As our experience and understanding grow, they become more complex, refined, and accurate. We get by in life with most of our mental models remaining small and unadorned because our use for them remains rather rudimentary. However, the purpose of learning is to build and refine our models – and thus grow more adaptive and influential in these realms.
In a very real way, then, formal education and training are all about mental model building. We take learners with their existing schema and help them build and refine them into more complete and sophisticated models. Good mental models enable learners to discriminate new concepts, to solve problems, make predictions, interpret data from the environment, and eventually make original contributions (Merrill, 2013; Clark & Lyons, 2004).
Teaching mental models does not have to include visual representations; however, they can be tremendous learning aids.
|2. An instructor builds a visual schema for blood circulation.|
Dr. Petra Tschakert of Penn State University illustrates how farmers in a developing country understood and used four soil management strategies, but were entirely unaware of crucial soil processes occurring underneath the soil's surface. Thus, their crops were susceptible to insects, molds, and other underground phenomenon. Their mental models were incomplete.
|3. An incomplete mental model of soil management strategies.|
Theory – a theory is an organized set of declarations, based on research, observation, and reason, which allows us to explain, predict and control events. They are able to describe what happens and why, as well as prescribe courses of action to bring about desired outcomes.
Is learning theory really relevant to the modern practice of education and training? Discussing them, Zemke (2002) concludes, "within that often turgid mass of dueling ideas and obtuse experimentation are the golden nuggets of wisdom that underpin the rules of thumb, best practices and driving principles of the education and training field." Swanson (ibid.) adds, “the idea of ... learning is so important today, that knowing how to make it happen most effectively is critical. And with increased interest in training has come a great confusion between knowledge and expertise. [Education and] training are about creating expertise, not simply pouring knowledge into people. That difference is why learning and performance theories are so important.”
Four learning theories, behaviorism, cognitivism, constructivism and connectivism, are described in the following pages. While we do go into some level of detail, we emphasize the useful philosophies, evidence, and practical use of each.
Behaviorism - emphasizes the consequences of behavior; learners repeat behaviors that are rewarded
Cognitivism - focuses on mental processes, including how people perceive, think, remember, learn, solve problems, and direct their attention
Constructivism - argues that humans generate personal knowledge and meaning from an interaction between their experiences and their ideas
Connectivism - knowledge is distributed across a network of connections, and therefore learning consists of the ability to construct and traverse those networks
From Sharan Merriam of the University of Georgia (in Zemke, 2002): "It is doubtful that a phenomenon as complex as adult learning will ever be explained by a single theory, model or set of principles. Instead, we have a case of the proverbial elephant being described differently depending on who is talking and which part of the animal is examined. In the first half of the last century, psychologists took the lead in explaining learning behavior. From the 1960s onward, however, adult educators began formulating their own ideas about adult learning and, in particular, about how it might differ from learning in childhood. Both of these approaches are still operative. Where we are headed, it seems, is toward a multifaceted understanding of adult learning, reflecting the inherent richness and complexity of the phenomenon and derived from a multitude of theoretical bases."
While each theory has its own way of explaining how people learn, there is much overlap – a continuum of views and explanation instead of four discrete worlds. The older theories are not left behind but adapted, added to, and expanded upon. They are all appropriate for aspects of teaching and learning. Individual theorists may not agree on this point, but for practitioners of teaching and learning it is the most practical and effective approach.
Which theory best applies?
How do instructors and designers select the best match between learner, content, and strategy? First, consider how learners' knowledge changes as they become more familiar with a given content area. As they acquire more experience, they progress along a low-high continuum from (1) being able to recognize and apply standard rules, facts, and operations, to (2) thinking like a professional to extrapolate from general rules to particular problematic cases, and finally to (3) developing and testing new forms of understanding and actions when known ways of thinking fail (Schon, 1987). In other words, moving from (1) knowing what, to (2) knowing how, and (3) reflection in action. In general, then, we guide and move with learners as they develop from novice to expert using the best theory applications to match their current level of development: from behavioral to cognitive to constructivist to connectivist strategies.
Second, consider the requirements of the task or content to be learned, based on the level of cognitive processing required. This is an issue of efficiency. When tasks require a low level of processing, behavioral approaches are most efficient. Tasks requiring increased levels of processing such as classification and decision making probably required cognitive approaches. Tasks requiring new and undefined solutions, the highest level of complexity, require methods found in constructivist and connectivist approaches.
The best theory is the one that best matches the requirements of both the learner and the task. As learner and task requirements advance, so too do the appropriate methods derived from the appropriate theory.
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