Figure 3.3. Adults learning in groups in a constructivist manner – and assisted by technology
Chapter 3 of my open textbook on ‘Teaching in a Digital Age‘ is about theory and practice in teaching for a digital age, which I am still in the process of writing. I have to admit that I approached writing about learning theories with some dread. In particular I was concerned (in order of dread) that:
- this will appear incredibly boring/lack originality, because it has been done so many times before by other, more qualified authors (but then those that already know this stuff can easily skip it)
- I’m not sure that theories of learning actually drive teaching (although surely an understanding of how students learn should do so)
- I would have to deal with connectivism somehow, and I am certainly not an expert on that topic – but maybe that might be an advantage in bringing it to the attention of people who have previously shown no interest in it, and how it differs from previous theories
- it could be argued that past learning theories are made irrelevant by digital technologies (and I certainly don’t agree with that point of view.)
In the end, I can’t see how a discussion of learning theories can be avoided. Unless readers of the book have this basic understanding of the different views of learning, they will not be in a good position to make choices, especially regarding the use of technology for teaching and learning. In particular, I see a danger of becoming dogmatic and blinkered by unchallenged assumptions about the nature of learning that results from not exploring alternative theories. But lastly, as Kurt Lewin said, there is nothing more practical than a good theory. A good theory helps us make informed decisions in areas of uncertainty. So, I am sharing here my first draft with you. Please note this is just part of the whole chapter, which also includes the following:
- Teaching and learning styles
- Deep vs surface learning.
- Learner-centered teaching, learner engagement.
- What we know about skills development
- Competency based learning.
- Learning design models
- learner characteristics: digital natives and digital literacy
- are we right to fear the use of computers for teaching?
- Summary of research on teaching.
Also, Chapter 2 discusses the nature of knowledge, and in particular different epistemologies that underpin different theories of learning. However, theories of learning are more than enough to chew on for the moment.
Theories of learning
“…there is an impressive body of evidence on how teaching methods and curriculum design affect deep, autonomous, and reflective learning. Yet most faculty are largely ignorant of this scholarship, and instructional practices and curriculum planning are dominated by tradition rather than research evidence. As a result, teaching remains largely didactic, assessment of student work is often trivial, and curricula are more likely to emphasize content coverage than acquisition of lifelong and life-wide learning skills.”
Knapper, 2010, p. 229
“There is nothing so practical as a good theory.” Kurt Lewin, 1951, p. 169
Why an understanding of theories of learning is important
Most teachers in the k-12 sector will be familiar with the main theories of learning, but because instructors in post-secondary education are hired primarily for their subject experience, or research or vocational skills, it is essential to introduce and discuss, if only briefly, these main theories. In practice, even without formal training or knowledge of different theories of learning, all teachers and instructors will approach teaching within one of these main theoretical approaches, whether or not they are aware of the educational jargon surrounding these approaches. Also, as online learning, technology-based teaching, and informal digital networks of learners have evolved, new theories of learning are emerging.
With a knowledge of alternative theoretical approaches, teachers and instructors are in a better position to make choices about how to approach their teaching in ways that will best fit the perceived needs of their students, within the very many different learning contexts that teachers and instructors face. This is particularly important when addressing many of the requirements of learners in a digital age. Furthermore, the choice of or preference for one particular theoretical approach will have major implications for the way that technology is used to support teaching.
In fact, there is a huge amount of literature on theories of learning, and I am aware that the treatment here is cursory, to say the least. Those who would prefer a more detailed introduction to theories of learning could, for an obscene price, purchase Schunk (2011), or for a more reasonable price Harasim (2012). The aim of my book though is not to be comprehensive in terms of in-depth coverage of all learning theories, but to provide a basis on which to suggest and evaluate different ways of teaching to meet the diverse needs of learners in a digital age.
Although initially developed in the 1920s, behaviourism still dominates approaches to teaching and learning in many places, particularly in the USA.
Behaviourist psychology is an attempt to model the study of human behaviour on the methods of the physical sciences, and therefore concentrates attention on those aspects of behaviour that are capable of direct observation and measurement. At the heart of behaviourism is the idea that certain behavioural responses become associated in a mechanistic and invariant way with specific stimuli. Thus a certain stimulus will evoke a particular response. At its simplest, it may be a purely physiological reflex action, like the contraction of an iris in the eye when stimulated by bright light.
However, most human behaviour is more complex. Nevertheless behaviourists have demonstrated in labs that it is possible to reinforce through reward or punishment the association between any particular stimulus or event and a particular behavioural response. The bond formed between a stimulus and response will depend on the existence of an appropriate means of reinforcement at the time of association between stimulus and response. This depends on random behaviour (trial and error) being appropriately reinforced as it occurs.
This is essentially the concept of operant conditioning, a principle most clearly developed by Skinner (1968). He showed that pigeons could be trained in quite complex behaviour by rewarding particular, desired responses that might initially occur at random, with appropriate stimuli, such as the provision of food pellets. He also found that a chain of responses could be developed, without the need for intervening stimuli to be present, thus linking an initially remote stimulus with a more complex behaviour. Furthermore, inappropriate or previously learned behaviour could be extinguished by withdrawing reinforcement. Reinforcement in humans can be quite simple, such as immediate feedback for an activity or getting a correct answer to a multiple-choice test.
Figure 3.1 YouTube video/film of B.F. Skinner demonstrating his teaching machine, 1954
You can see a fascinating five minute film of B.F. Skinner describing his teaching machine in a 1954 YouTube video, either by clicking on the picture above or at: http://www.youtube.com/watch?v=jTH3ob1IRFo
Underlying a behaviourist approach to teaching is the belief that learning is governed by invariant principles, and these principles are independent of conscious control on the part of the learner. Behaviourists attempt to maintain a high degree of objectivity in the way they view human activity, and they generally reject reference to unmeasurable states, such as feelings, attitudes, and consciousness. Human behaviour is above all seen as predictable and controllable. Behaviourism thus stems from a strongly objectivist epistemological position.
Skinner’s theory of learning provides the underlying theoretical basis for the development of teaching machines, measurable learning objectives, computer-assisted instruction, and multiple choice tests. Behaviourism’s influence is still strong in corporate and military training, and in some areas of science, engineering, and medical training. It can be of particular value for rote learning of facts or standard procedures such as multiplication tables, for dealing with children or adults with limited cognitive ability due to brain disorders, or for compliance with industrial or business standards or processes that are invariant and do not require individual judgement.
Finally, it should be noted that behaviourism, with its emphasis on rewards and punishment as drivers of learning, and on pre-defined and measurable outcomes, is the basis of populist conceptions of learning among many parents, politicians, and, it should be noted, computer scientists interested in automating learning. It is not surprising then that there has also been a tendency until recently to see technology, and in particular computer-aided instruction, as being closely associated with behaviourist approaches to learning, although we shall see that this does not necessarily follow.
An obvious criticism of behaviourism is that it treats humans as a black box, where inputs into the black box, and outputs from the black box, are known and measurable, but what goes on inside the black box is ignored or not considered of interest. However, humans have the ability for conscious thought, decision-making, emotions, and the ability to express ideas through social discourse, all of which may be highly significant for learning. Thus we will likely get a better understanding of learning if we try to find out what goes on inside the black box. Cognitivists therefore have focused on identifying mental processes – internal and conscious representations of the world – that they consider are essential for human learning. Fontana (1981) summarises the cognitive approach to learning as follows:
‘The cognitive approach … holds that if we are to understand learning we cannot confine ourselves to observable behaviour, but must also concern ourselves with the learner’s ability mentally to re-organize his psychological field (i.e. his inner world of concepts, memories, etc.) in response to experience. This latter approach therefore lays stress not only on the environment, but upon the way in which the individual interprets and tries to make sense of the environment. It sees the individual not as the somewhat mechanical product of his environment, but as an active agent in the learning process, deliberately trying to process and categorize the stream of information fed into him by the external world.’ (p. 148)
Thus the search for rules, principles or relationships in processing new information, and the search for meaning and consistency in reconciling new information with previous knowledge, are key concepts in cognitive psychology. Cognitive psychology is concerned with identifying and describing mental processes that affect learning, thinking and behaviour, and the conditions that influence those mental processes.
Figure 3.2: Some of the areas covered by cognitivism, based on Bloom’s taxonomy (1956). Note that this becomes a reductionist exercise, as psychologists delve deeper into each of these cognitive activities to understand the underlying mental processes.
Cognitive approaches to learning cover a very wide range. At one end, the objectivist end, cognitivists consider basic mental processes to be genetic or hard-wired, but can be programmed or modified by external factors, such as new experiences. Early cognitivists in particular were interested in the concept of mind as computer, and more recently brain research has led to a search for linking learning to the development and reinforcement of neural networks in the brain. In terms of practice this concept of mind as computer has led to several technology-based developments in teaching, including:
- intelligent tutoring systems, a more refined version of teaching machines, based on analysing student responses to questions and redirecting them to the appropriate next steps in learning. Adaptive learning is the latest extension of such developments;
- artificial intelligence, which seeks to represent in computer software the mental processes used in human learning (which of course if successful would result in computers replacing many human activities – such as teaching, if learning is considered in an objectivist framework.)
- pre-determined learning outcomes, based on an analysis and development of different kinds of cognitive activities, such as comprehension, analysis, synthesis, and evaluation
- certain instructional design approaches that attempt to manage the design of teaching to ensure successful achievement of pre-determined learning outcomes or objectives.
On the other hand, many other cognitivists, coming from a more constructivist epistemological perspective, would argue that mental states or even processes are not fixed but constantly evolving as new information is integrated with prior knowledge, and new strategies for seeking meaning are developed by the individual. Thus teachers who place a strong emphasis on learners developing personal meaning through reflection, analysis and construction of knowledge through conscious mental processing would represent much more of a constructivist epistemological position. It is here that the boundaries between cognitivist and constructivist learning begin to break down.
Cognitive approaches to learning, with a focus on comprehension, abstraction, analysis, synthesis, generalization, evaluation, decision-making and creative thinking, seem to fit much better with higher education than behaviourism, but even in k-12 education, a cognitivist approach would mean for instance focusing on teaching learners how to learn, on developing stronger or new mental processes for future learning, and on developing deeper and constantly changing understanding of concepts and ideas.
Put simply, brains have more plasticity, adaptability and complexity than current computer software programs, and other factors, such as emotion, motivation, self-determination, values, and a wider range of senses, make human learning very different from the way computers operate, at least at the moment. Education would be much better served if computer scientists tried to make software to support learning more reflective of the way human learning operates, rather than trying to fit human learning into the current restrictions of behaviourist computer programming.
Nevertheless, cognitivists have increased our understanding of how humans process and make sense of new information, how we access, interpret, integrate, process, organize and manage knowledge, and have given us a better understanding of the conditions that affect learners’ mental states.
Both behaviourist and some elements of cognitive theories of learning are deterministic, in the sense that behaviour and learning are believed to be rule-based and operate under predictable and constant conditions over which the individual learner has no or little control. However, constructivists emphasise the importance of consciousness, free will and social influences on learning. Carl Rogers (1969) stated that: ‘every individual exists in a continually changing world of experience in which he is the center.’ The external world is interpreted within the context of that private world. The belief that humans are essentially active, free and strive for meaning in personal terms has been around for a long time.
Constructivists argue that individuals consciously strive for meaning to make sense of their environment in terms of past experience and their present state. It is an attempt to create order in their minds out of disorder, to resolve incongruities, and to reconcile external realities with prior experience. The means by which this is done are complex and multi-faceted, from personal reflection, seeking new information, to testing ideas through social contact with others. Problems are resolved, and incongruities sorted out, through strategies such as seeking relationships between what was known and what is new, identifying similarities and differences, and testing hypotheses or assumptions. Reality is always tentative and dynamic.
For many educators, the social context of learning is critical. Ideas are tested not just on the teacher, but with fellow students, friends and colleagues. Furthermore, knowledge is mainly acquired through social processes or institutions that are socially constructed: schools, universities, and increasingly these days, online communities. Thus what is taken to be ‘valued’ knowledge is also socially constructed. Thus knowledge is not just about content, but also values. One set of values are those around the concept of a liberal education. According to this ideology, one of the principal aims of education is that it should develop a critical awareness of the values and ideologies that shape the form of received knowledge. This then suggests a constant probing and criticism of received knowledge.
One consequence of constructivist theory is that each individual is unique, because the interaction of their different experiences, and their search for personal meaning, results in each person being different from anyone else. Thus behaviour is not predictable or deterministic, at least not at the individual level. The key point here is that learning is seen as essentially a social process, requiring communication between learner, teacher and others. This social process cannot effectively be replaced by technology, although technology may facilitate it.
It can be seen that although constructivist approaches can be and have been applied to all fields of knowledge, it is more commonly found in approaches to teaching in the humanities, social sciences, education, and other less quantitative subject areas.
Online collaborative learning
The concurrence of both constructivist approaches to learning and the development of the Internet has led to the development of a particular form of constructivist teaching, originally called computer-mediated communication (CMC), but which has developed into what Harasim (2012) now calls online collaborative learning theory (OCL). She describes OCL as follows (p. 90):
‘OCL theory provides a model of learning in which students are encouraged and supported to work together to create knowledge: to invent, to explore ways to innovate, and, by so doing, to seek the conceptual knowledge needed to solve problems rather than recite what they think is the right answer. While OCL theory does encourage the learner to be active and engaged, this is not considered to be sufficient for learning or knowledge construction……In the OCL theory, the teacher plays a key role not as a fellow-learner, but as the link to the knowledge community, or state of the art in that discipline. Learning is defined as conceptual change and is key to building knowledge. Learning activity needs to be informed and guided by the norms of the discipline and a discourse process that emphasises conceptual learning and builds knowledge.‘
This approach to the use of technology for teaching is very different from the more objectivist approaches found in computer-assisted learning, teaching machines, and artificial intelligence applications to education, which primarily aim to use computing to replace at least some of the activities traditionally done by human teachers. With online collaborative learning, the aim is not to replace the teacher, but to use the technology primarily to increase and improve communication between teacher and learners, with a particular approach to the development of learning based on knowledge construction assisted and developed through social discourse. This social discourse furthermore is not random in OCL, but managed in such a way as to ‘scaffold’ learning, by assisting with the construction of knowledge in ways that are guided by the instructor, that reflect the norms or values of the discipline, and that also respect or take into consideration the prior knowledge within the discipline.
Connectivism is a relatively new theory of learning or epistemology (there’s not even agreement about which it is), it is still being refined and developed, and it is currently highly controversial, with many critics. Siemens, Downes and Cormier constructed the first massive open online course (MOOC), Connectivism and Connective Knowledge 2011, partly to explain and partly to model a connectivist approach to learning. More recently, Downes (2014) has spelled out, in a presentation called The MOOC of One, some of the relationships between individual learning, the contribution of individuals to knowledge and its flow, and networks of learners, within a broad interpretation of connectivist theory. In this presentation Downes sets out some design principles for connectivist ‘courses’ or cMOOCs, such as:
- learner autonomy, in terms of choice of content and how they choose to learn
- openness, in terms of access to the course, content, activities and methods of assessment
- diversity: varied content, individual perspectives and multiple tools, especially for networking learners and creating opportunities for dialogue and discussion
- interactivity: ‘massive’ communication between learners and co-operative learning, resulting in emergent knowledge
Connectivists such as Siemens and Downes tend to be somewhat vague about the role of teachers or instructors, as the focus of connectivism is more on individual participants, networks and the flow of information and the new forms of knowledge that result.. The main purpose of a teacher appears to be to provide the initial learning environment and context that brings learners together, and to help learners construct their own personal learning environments to enable them to connect to ‘successful’ networks, with the assumption that learning will automatically occur as a result, through exposure to the flow of information and the individual’s autonomous reflection on its meaning. There is no need for formal institutions to support this kind of learning, especially since such learning often depends heavily on social media readily available to all participants.
There are numerous criticisms of the connectivist approach to teaching and learning, which include:
- there is no control on the quality of content, or on contributions from participants;
- assessment strategies, such as peer assessment, are primitive and unreliable, thus making reliable or valid recognition of achievement more difficult;
- the kinds of learning that take place in connectivist MOOCs or courses are not necessarily academic, in the sense of meeting the requirements for academic knowledge, as defined in Chapter 2;
- many participants struggle with the lack of structure and are overwhelmed by the volume of content generated by other learners;
- most students need a high level of explicit support in learning from an ‘expert’ teacher and this is lacking in connectivist courses
- this kind of learning requires learners already to have at least some level of more formal or traditional education before they participate if they are to fully benefit from this kind of learning experience (and there is substantial evidence that MOOC participants tend to have an already high level of post-secondary education).
- thus this kind of learning is more appropriate for non-formal learning or communities of practice than for formal education.
Some of these criticisms may be overcome as practice improves, as new tools for assessment, and for organizing co-operative and collaborative work with massive numbers, are developed, and as more experience is gained. More importantly, connectivism is really the first theoretical attempt to radically re-examine the implications for learning of the Internet and the explosion of new communications technologies.
Different theories of learning reflect different positions on the nature of knowledge. With the possible exception of connectivism, there is some form of empirical evidence to support each of the theories of learning outlined here.
However, while the theories suggest different ways in which all people learn, they do not automatically tell teachers or instructors how to teach. Indeed, theories of behaviourism, cognitivism and constructivism were all developed outside of education, in experimental labs, psychology , neuroscience, and psychotherapy respectively. Educators have had to work out how to move from the theoretical position to the practical one of applying these theories within an educational experience. In other words, they have had to develop teaching methods that build on such learning theories. The next section of the book examines a range of teaching methods that have been developed, their epistemological roots, and their implications for teaching in a digital age.
Over to you
Your feedback on this will be invaluable. In particular:
- are theories of learning still relevant in a digital age? Is it important to discuss these?
- is the description of the various theories accurate and useful; if not, what should be changed?
- are there important theories or theoretical positions that have been missed?
Bloom, B., Englehart, M., Furst, E., Hill, W. and Krathwohl, D. (1956) Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain, Longmans Green, New York, 1956
Downes, S. (2014) The MOOC of One, Stephen’s Web, March 10
Fontana, D. (1981) Psychology for Teachers London: Macmillan/British Psychological Society
Harasim, L. (2012) Learning Theory and Online Technologies New York/London: Routledge
Knapper, C. (2010) ‘Changing Teaching Practice: Barriers and Strategies’ in Christensen Hughes, J. and Mighty, J. eds. Taking Stock: Research on Teaching and Learning in Higher Education Toronto ON: McGill-Queen’s University Press
Lewin, K. (1951) Field theory in social science; selected theoretical papers. D. Cartwright (ed.). New York: Harper & Row.
Rogers, C. (1969) Freedom to Learn Columbus, OH: Charles E. Merrill Publishing Co.
Schunk, D. (2011) Learning Theories: An Educational Perspective (6th edition) New York: Pearson