Attempting to explain how learning and thinking work is not easy. After doing this for a living, I am still uncertain what is the best approach. We all know we know things and we all know we learn, but just how we did and do this is often not obvious. To help understand, I would suggest we make use of abstractions that simplify, but still have utility. To have value, a model or abstraction must be consistent with data (observations of how a process works) and be useful in suggesting testable practices.
I could use the jargon of my profession and theoretical perspective on this profession, but I propose that the concepts of thinking and model both meet the conditions I have identified. Thinking implies learning requires mental work (at least most kinds of academic learning). Easy to understand attributes of thinking are helpful. The learner is the one who thinks (or not) so learning ultimately depends on the activity of the learner no matter the circumstances external to the learner. However, others people and experiences outside of ourselves can encourage us to think. We think about things with which we are already more familiar more easily hence what we already know is important in how easily and how successfully we can think. The thinking we do that uses existing knowledge and skills can result in the modification and improvement of what we already know and this is what most mean by learning. These are the basics.
What we accomplish by learning means several different things. We know things after learning that we did not know before. We can do things after learning that we did not do before. We acquire understanding and capabilities. We also acquire information which may or may not be part of understanding.
The idea of a model is helpful in thinking about both understanding and capabilities. The construction of models of understanding and models as actions is a natural process. By natural I mean we as individuals construct models continuously whether to deal with mysteries of daily life or because of formal education. To help students understand and think about the commitment we continuously make to models, I like to ask students what they mean by the concept of a theory. A theory is one type of model. Students often use the word theory in a condescending way perhaps dismissing the information (models) acquired in a course as theoretical implying that theories are not useful. To the contrary, I claim, theories are how we think and if we do not acquire theories we apply from formal instruction, we make them up based on personal experience. A theory, personal or formal, is the abstraction we use to understand, to predict, and decide on action when we encounter a unique life experience – pretty much any new input from the world. What is kind of cool (interesting) is that individuals are quite capable of storing contradictory or at least inconsistent personal and formal theories about the same phenomena. In other words, we may develop one interpretation of a certain kind of situation based on our life experiences that is different than an interpretation we are taught. How can this happen and what can be done about it? This can happen because an external experience activates one internal model or the other. This is one of the frustrations of education. We can help learners acquire a model of the way something works in the world. They can understand this model and use it effectively within the classroom setting, but they can still revert to their own primitive model of how something works when encountering circumstances in their daily life to which the more formal theory should have been applied.
This was a very long introduction to get to the core issue. What are the models educators use to guide their work in helping students learn? This could be a question of whether or not educators activate formal models or personal models to guide their practice. Given what I have said about formal models and naive models (this is the term applied and the intent is to describe models built from field experience without the use of formal guidance), this could be a great topic to consider. I will have to save a discussion of this distinction for another time. Here, I want to share my personal bias when it comes to the utility of several formal theories.
Models of learning
Somewhere in the preparation of educators, most are exposed to multiple models of learning. At the least, they have probably been told about behaviorism, cognition, and constructivism. Recently, some preparation experiences may include some biology – brain structure and function. Certainly, biology and biochemistry have the potential to describe learning most accurately. I think an important issue is whether a more accurate description advances education or not. My personal opinion at this point in time is that our understanding of how the brain functions in learning is rather crude and I am aware of very little that improves on what other models describe and explain. I have an undergraduate major in biology but that was a long time ago and what I know now I describe as the content included about brain biology in your average Introduction to Psychology textbook. The one exception I can think of to my claim that there is nothing unique about a biological perspective is neural plasticity – the finding that long term experiences of a type can restructure the brain to predispose individuals to different patterns of mental behavior. I believe this idea is helpful. However, the interpretation of this phenomenon within education has also been generalized in ways that I think are inaccurate and certainly not a basis for significant changes in practice.
Here is my short list of models (actually categories of models) of learning and a very brief comment on what I see as the core mechanism of each.
- Behaviorism – emphasis on external events and consequences that increase and decrease the frequency of behaviors.
- Cognitive – constructivist (macro) and information processing (micro) – mental activity under the control of the learner. Thinking develops internal models.
- Biological – chemical and biological action and storage (internal). Learning results in changes in the brain (vague) and must be accomplished by a combination of chemical and electrochemical actions taking place in physical structure some of which are specialized to accomplish certain things.
I find the concept of fidelity useful in understanding learning. Fidelity could be defined as the exactness of fit between a model and the actual thing/process. One might think that the more exact the fit, the better. We have learned from research on the use of simulations in learning that this is not the case. With simulations, in the early stages of learning, too much realism (match) can overload, confuse, and in some cases produce unproductive emotions. For example, the training of pilots does not begin by putting a novice in the pilots seat and letting him/her explore. The experience would be overwhelming and certainly terrifying. Typically, training makes use of experiences in simulators that simplify the experience to a limited number of actions and possible reactions. Using various techniques and equipment, more and more realism and experiences are added until the more experienced individual can deal with the emotions and complexity of full application.
I see a similarity in the usefulness of models of learning. Behaviorism offers little insight into mental behavior (I think supporters leave that to the biological researchers) and is really more a model of instruction (manipulation of external events). I regard behavioral models as useful for understanding and investigating incentives. I see biological models as eventually having the potential for high fidelity, but I see these models at present as mostly descriptive. At best the future might provide a level of understanding that encourages practices through a process of find out how to produce a given combination of chemical and anatomical conditions. I see the cognitive models as most useful, but differing in level of fidelity with information processing models offering a more detailed level of process clarity. Constructivism offers a broad perspective, but may or may not be sufficient to propose useful interventions. Especially when what seems like a productive process is not, analysis based on information processing models is often usefuL
Models of learning, models of instruction
Comparisons of approaches generated from models of how learning happens are important. Approaches may differ in the external events created, but any event allows “thinking” by learners. The relative effectiveness of different approaches is important. Putting down books, lectures, worksheets, life experiences, or task of one format or another all offer some type of input that learners will attempt to process. The capacity to point to idiosyncratic cases of students who learned from this or that experience is not really justification for much of anything. It is the relative productivity of one approach in contrast to another within defined requirements for a common set of learning circumstances (group size, time allowed, variations in past experiences, etc.) that provide the basis for application.
Arguing that one model of instruction based on this model of learning is superior seems pointless without data allowing those who must evaluate these claims. Models offer different ways to think about learning. These can be helpful in the design of learning experiences, but ultimately, it is the response of learners exposed to these experiences that matter.
[Image included purchased through the Noun Project]
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