Chapter 4
Learning Concepts and Theories, and Their Application to Educational Practice
Stephen A. May
Royal Veterinary College, UK
Introduction
Stephen Hawking spent most of his career attempting to unify the laws of physics, particularly as they relate to the four fundamental forces, into a single, overarching theory and equation. As scientists we like order, creating periodic tables and taxonomies and preferring to regard complex systems as merely complicated, ultimately capable of reduction to their constituent elements. Therefore, it is natural, when academics start to examine the evidence base for their teaching practice, that those with a scientific background look for some sort of classification of educational theories, or even a “theory of theories” that holds the secret to excellent teaching.
Readers who have already embarked on such a search will know that it is fruitless. The process of learning, including teaching and the teacher effect, is complex. We know that the most important factor in effective learning is what the student does (Shuell, 1986), but a crucial factor in this is the teacher (Rowe, 2002). However, what we choose to call excellent teaching – beautifully crafted presentations and handouts, which have all the key points highlighted and are scored highly by students – does not always stimulate the highest quality of learning. The teachers who are apparently less organized and greater risk-takers (McAlpine et al., 1999; Fryer-Edwards et al., 2006), who “teach on the edge of chaos” (Tosey, 2002), may ultimately support the development of better learners than the authorities in their field, who deliver lectures that contain the last word in their subject area (Entwistle and Entwistle, 1992).
The aim of this chapter is to bring some sense to this apparent paradox. In accepting that learning and teaching form a complex system, we need to start to view learning theories as different and complementary “lenses” for understanding individual aspects of the system (Bordage, 2009). We will begin by looking at the nature of knowledge and its implications for learning. We will then look at the “big three theories” of learning that dominated discussions over the whole of the twentieth century. After moving on to consider learning capacity and then the complexity of learning in relation to its social and emotional dimensions, finally we will discuss the implications of all this for teaching and the teacher. My intentions in providing this framework are to highlight the relevance of learning theories to understanding our individual teaching experience, and to provide the foundations for, and act as a bridge into, other chapters that explore in greater detail each aspect of our practice. If, in the process, this chapter acts as a catalyst for further exploration by readers of learning theories themselves, this will be a bonus!
What Is Knowledge; What Is Learning?
Aristotle (384–322 BC) regarded knowledge as falling into three categories: “episteme,” corresponding to scientific and theoretical knowledge, sometimes referred to as propositional knowledge; “techne” (the root of our word technical), corresponding to the practice of skills and crafts; and “phronesis,” corresponding to practical wisdom that comes with experience, including ethics and reasoning. In the case of the latter, he was keen to distinguish this from the Greek concept of “sophia,” often translated as wisdom, which relates to reasoning concerning universal truths. “Phronesis” is concerned with thinking and reasoning that are directly related to praxis (Schwartz, 2006; Reeve, 2010).
As practitioners and scientists, we can recognize all three categories of knowledge in our daily work. We build on an evidence base, including current theories, to advance our disciplines, and we combine this evidence base with our experience to make judgments about our actions, and also to take action in the clinic, the laboratory, and all other aspects of our professional lives. Therefore, for veterinarians, like members of all the other professions, it is vital that our educational systems support the development of all types of knowledge, and the related professional skills, both technical and non-technical, as well as the ability of graduates to continue developing throughout their professional lives.
Crucially, learning has been defined as any change in an individual that expresses itself in a relatively stable form of behavior (Bower and Hilgard, 1981). As we shall see, it is the process whereby individuals “perceive the world and reciprocally respond to its affordances physically, psychologically and socially … the simple recall of that which was previously learned does not constitute learning per se” (Alexander, Schallert, and Reynolds, 2009, p. 186). Learning must be distinguished from “maturation, development and accidental changes in a person’s capacities” (Säljö, 2009, p. 202), although motivation plays an important part in learner engagement and thus learning itself, physical development has an important part to play in our capacity to embrace concepts related to how we understand the material world. Collectively, for us as veterinary educational leaders and teachers, the behavior change that we are assessing is the ability of our graduates to work and succeed as “Day One” skilled members of our profession. Since the demands of society and the expectations of new graduates have never been higher, the better we can understand how to support learner development, the better we should be able to ensure that our charges meet these expectations (May, 2008).
The Big Three Theories of Learning
The big theories that dominated discussions in learning over the course of the twentieth century are behaviorism, cognitivism, and social constructivism. Much has been written on these elsewhere (e.g., Ertmer and Newby, 2013; Amirault and Branson, 2006), so the detail will not be repeated here. Nevertheless, it is helpful to highlight the aspects of each that are relevant to our teaching practice and the relatively recent coalescence of our understanding of the interaction of the learner’s mind with their environment.
Behaviorism
Behaviorism arose from Ivan Pavlov’s (1849–1936) classic work with conditioning in animals, and developed over the first half of the twentieth century through the work of John B. Watson and Edward Thorndike, and later B.F. Skinner, around 1950. Key to the theory is the way in which repetitive external stimuli and rewards can lead to physiological associations (salivation when a bell rings) and the establishment of certain behaviors (pushing a lever to obtain food). As researchers in the latter part of the twentieth century started to recognize the deficiencies of this perspective on learning, cognitivism came to prominence. However, behaviorism reminds us of the importance of external (and internal) rewards for certain types of learning, and the way in which repeated stimuli can lead to associations in our minds. Learners will quickly respond to our expectations of them, for both good and bad, if pleasing us as teachers is seen as a desirable course of action. They will also look for patterns in their experience and make associations that will affect their future decisions, some of which may be justified and some not (Gladwell, 2006). As scientists, we like to think of ourselves as objective and rational creatures, and to a certain extent we can cognitively overcome a tendency to act irrationally. However, it is sobering to discover, as research with trick problems and playing card arrangements demonstrates, that only about 20% of participants with the highest cognitive ability are able largely to inhibit matching bias (Evans, 2006).
Cognitivism
In contrast to behaviorism, with its emphasis on the environment, cognitivism, which emerged in the second half of the twentieth century, emphasized our nature as rational thinkers, using our mental processes to make sense of our world. However, once more this was seen as inadequate, and theorists then returned to the earlier work of Lev Vygotsky (1896–1934) in order to explore social constructivism and the relationship of those around the learner with the learner’s own thinking. Knowledge was viewed increasingly as collaboratively constructed, through learners collectively and individually making sense of the world, and through shared experiences and discussions, whether at the level of scientific disciplines or individual families and other social groups.
Social Constructivism
The social constructivist approach provides us with at least three important insights into learning and knowledge. Vygotsky recognized that for all of us there is a zone of proximal development that lies between what we can learn for ourselves and what, even with help, we are not currently able to comprehend (because we do not yet have sufficient foundational knowledge). This is a zone where, if our learning is “scaffolded” (a concept introduced by Jerome Bruner, building on Vygotsky’s work) through the support of a teacher, we can make progress (Wood, Bruner, and Ross, 1976). In relation to small-group teaching, this zone has been described as the “learning edge” (Fryer-Edwards et al., 2006) and it represents the best use of our most costly and precious resource, the teacher. Ideally, the learner’s zone of proximal development continues to move forward as new knowledge is integrated and forms more substantial foundations for more advanced concepts. This highlights the way in which our knowledge and skills need to progress sequentially, so that intermediate stages are successfully navigated in order to reach the forefront of any discipline or profession. If knowledge is presented haphazardly, particularly in large amounts, there is a risk of it being memorized but not integrated. This has been described as fragile knowledge (Perkins, 1995). It may be learned on a short-term basis for paper-based assessments, but is not easily applied to practical problems and is quickly forgotten. The final insight provided by social constructivism comes from a consideration of the work of Pierre Bourdieu and his concepts of the “habitus” and the “field.” Each of us negotiates a field, or various fields, the group or groups, social and professional, to which we belong, during the course of our lives. As individuals (the habitus), we both contribute to the field(s) and the field(s) affect us and the way in which we think. So, depending on our background, we will see ourselves in different ways. From some backgrounds, we form the view that we can achieve whatever we desire; from others, we may feel that certain paths in life are not open to us, so much so that these opportunities do not form part of our conscious thoughts (Hodkinson and Sparkes, 1997). Great scientists and inventors look at the same objects and events that others have viewed countless times and see them differently, making the previously unrecognized obvious to all. Our backgrounds mean that, as learners, we find some concepts and tests easy and others much harder, in completely different ways from others regarded as at the same stage of learning. All this has implications for widening participation and aspiration to access various educational tracks, as well as for teaching of more diverse groups of learners. It must be recognized that if the learning sequences we create do not work for some learners, they may never progress beyond the stage where the break occurred in that particular aspect of their thinking.
In contrast to behaviorism, where the animal gives relatively little thought to its response, social constructivism acknowledges the cognitive part that the learner plays. The social and collaborative aspects have been explored as “communities of practice” (Lave and Wenger, 1991) and iterative individual development conceptualized as a cyclical process, as in the case of Kolb’s learning cycle. When new situations are encountered, the effective learner not only observes but also reflects on the experience, including the part they played and their feelings about what happened (Mann, Gordon, and MacLeod, 2009). This allows new ideas to be generated and lessons to be learned, so that when required to act in similar (or even different) situations in the future, such actions can be that much better informed.
Learning Theories and Their Relationships
Learning Theories Related to Learner Maturity
Our consideration of Bourdieu has introduced us to the idea of learner maturity, both in terms of a learner’s integrated knowledge base and also the way in which their mind works around what they consider possible and not possible. Jean Piaget (1896–1980) demonstrated that at an early point in life, our physical stage of development has a marked effect on our ability to recognize volume as opposed to linear measurements, and this applies to us all. However, when we start to be able to cope with hypothetical and counterfactual thinking at the age of 11, our learning experiences themselves become increasingly important in our educational maturity and how we view our abilities (Dweck, 2003) (see Box 4.2). In early adolescence, based on the nature of the feedback that parents and teachers deliver, the learner is likely to have formed a view either that they are clever or that they are not: either that they are capable of accomplishing certain tasks or that they are not (fixed mindset), or that they are capable of a lot provided that they work hard (growth mindset). This view of our abilities, which is fundamental to all our learning and will profoundly affect our motivation and engagement with learning opportunities, is dramatically related to the nature of the praise that we receive throughout our childhood. If, on successful completion of a task, the learner is told that their achievement is because they are a clever person, they will quickly develop a fixed mindset. If on successful completion they are told that their achievement is because they have worked hard, they will increasingly believe that with hard work and practice they can complete whatever tasks they are set: the growth mindset. For those with a fixed mindset, undertaking tests is all about performance and avoidance of being exposed as incompetent in any area. In contrast, those with a growth mindset are much more interested in mastery. They know that this will allow them to understand advanced concepts and undertake more complicated tasks. Avoidance of exposure and mere performance in a test will not help them to develop, so they will frequently seek challenges in areas in which they know they might fail (safely) in order to learn.
A significant development in learner maturity comes with the acknowledgment that they themselves have a significant part to play in their own understanding. In the 1970s, William Perry recognized several distinct phases through which learners go in their journey from “basic dualism,” with the teacher having the right answers that just need to be learned, through more relativistic positions, leading to an understanding of good and less good explanations of phenomena, to a stage of “evolving commitments,” where the responsibility for recognizing and establishing current best evidence lies with the individual. This is where early educational preparation is important. Those who are well advanced in this kind of thinking at high school may progress to Perry’s more advanced positions by the end of their formal education. Others may still cling to dualist perspectives, or have progressed to the position of multiplicity but no further, believing that “everyone has a right to his own opinion” and one person’s is just as valid as that of another (Thoma, 1993; Dale, Sullivan, and May, 2008).
This maturity of thinking about the nature of knowledge is likely to be paralleled by a change in preference for the way it is delivered. In our youth, we accept pedagogical approaches where the teacher is the authority figure transmitting their knowledge to us, and this is appropriate both to our needs and to our educational maturity. However, as adults we are much more likely to embrace an andragogical approach, where we have a hand in directing our own learning, based on our learning preferences and our analysis of our current learning needs (Dale, Sullivan, and May, 2008). Rather than being subject oriented, the problems we are currently tackling motivate us to seek knowledge and, where necessary, individual expert support to help us solve these new challenges.
Learning Theories Related to Learner Capacity
So far, I have explored cognitive development in a social context, with only learner maturity as the limiting factor once we have progressed beyond the stages where physical development plays a significant role. However, it is clear that our brain is a limiting factor in the amount of information that we can process and integrate at any one time, and this relates to our working memory capacity. Our working memory allows us simultaneously to process new information and solve problems, by retrieving from our long-term memory prior knowledge and solutions to aid with this processing. Subsequently, it supports the transfer of new knowledge and solutions to long-term memory for future use. Working memory has two components: a phonological loop that processes sound and a visuospatial sketchpad that deals with images (Baddeley, 2003). These can work in tandem to increase our processing capacity – hence the learning advantage of well-used audiovisual aids (Mayer, 2010) – but it is well established that our working memory can only handle a small number of units of information simultaneously. Classically, the number of units available has been regarded as 7 ± 2, with some individuals having a more extensive working memory and others being more restricted in terms of working memory capacity. One key to understanding working memory is that these units can either be single pieces of information or “chunks” of related information at various levels of aggregation. So in memory games, such as remembering telephone numbers, an average individual can remember a number containing seven digits with relative ease. However, to remember longer numbers individuals have to engage in various associative strategies, involving groups of digits, so the units of working memory contain several digits as a chunk.
So far we have viewed knowledge as developing into sophisticated concepts in our long-term memory through the sequential integration of new knowledge. The recognition that working memory can handle increasing aggregates of information, retrieved from long-term memory for the purposes of problem-solving, helps us to understand how, with rehearsal and chunking, the learner is capable of solving more and more complex and sophisticated problems (van Merriënboer and Sluijsmans, 2009).
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