May 22, 2015

Learning theories and online learning

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Figure 3.3. Adults learning in groups in a constructivist manner - and assisted by technology

Figure 3.3. Adults learning in groups in a constructivist manner – and assisted by technology

Introduction

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.

Behaviourism

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.

Skinner and his machine 2

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.

Cognitivism

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.

© Agile Development Blog, 2013

© Agile Development Blog, 2013

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.

Constructivism

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

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 discussio
  • interactivity: ‘massive’ communication between learners and co-operative learning, resulting in emergent knowledge
Figure 2.1: A map of connectivism, © Stephen Downes, 2011 (accessed via pkab.wordpress.com)

Figure 2.1: A map of connectivism, © Stephen Downes, 2011 (accessed via pkab.wordpress.com)

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.

Conclusion

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?

References

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

The nature of knowledge and the implications for teaching

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© LifeSun, 2013

© LifeSun, 2013

Teaching in a Digital Age

I’ve now just published Chapter 2 of my open textbook, Teaching in a Digital Age.

Chapter 1 looks at the fundamental changes that are happening in our digital age, and the broad implications these changes have for teaching and learning.

The book examines the underlying principles that guide effective teaching in an age when everyone,and in particular the students we are teaching, are using technology.

The Preface spells out in more detail the reasons why I decided to publish the book, and the reasons for choosing an open format.

Chapter 2: The nature of knowledge and the implications for teaching

This chapter discusses the relationship between our views on the nature of knowledge and the way we decide to teach. It’s about epistemology, but don’t be frightened off by the term: its basically about what makes us believe something is ‘true.’ This has fundamental implications for how we decide to teach. The chapter covers the following:

1. A dinner party scenario showing a clash of fundamental beliefs about the nature of knowledge between an engineer and a writer.

2. Art, theory, research and best practices in teaching: what guides (or should guide) the way we teach.

3. A brief introduction to epistemology and why it’s important. In particular it very briefly describes three currently popular epistemological positions in education, objectivism, constructivism and connectivism, and their implications for teaching and learning.

4. Academic knowledge. I make the distinction between academic knowledge and everyday knowledge, and then discuss whether new digital technologies change the nature of knowledge, ending with a justification for academic knowledge in a digital age, while also arguing that other forms of knowledge can be equally important, depending on the circumstances.

The key takeaways from the chapter are as follows:

1. Teaching is a highly complex occupation, which needs to adapt to a great deal of variety in context, subject matter and learners. It does not lend itself to broad generalizations. Nevertheless it is possible to provide guidelines or principles based on best practices, theory and research, that must then be adapted or modified to local conditions.

2. Our underlying beliefs and values, usually shared by other experts in a subject domain, shape our approach to teaching. These underlying beliefs and values are often implicit and are often not directly shared with our students, even though they are seen as essential components of becoming an ‘expert’ in a particular subject domain.

3. It is argued that academic knowledge is different from other forms of knowledge, and is even more relevant today in a digital age.

4. However, academic knowledge is not the only kind of knowledge that is important in today’s society, and as teachers we have to be aware of other forms of knowledge and their potential importance to our students, and make sure that we are providing the full range of contents and skills needed for students in a digital age.

Comments and criticisms are welcome, either as comments to this blog post, or as comments directly to the chapter (but see below).

Technical challenges with open publishing

As I reported in an earlier post, I’m trying to push the boundaries with open publishing. I want to make the book as interactive as possible but to date the open publishing technology ironically is very restraining. I’m getting tremendous help from the open textbook team at BCcampus, but the platform, PressBooks, is still very much designed in the mode of a traditional book.

So far, BCcampus has been able to add functions for learning objectives, tables, activities, and key takeaways, which have been very helpful. A moderated comment  function has just been added for the end of each chapter (I’m still trying to work out how to moderate this – I’m bloody useless with the technology!)

Here’s what I’m still trying for at the moment:

1. A comment facility that an author can add to each section, as well as the whole chapter.

2. To find a neat way for me as author to provide feedback on readers’ responses to the activities.

3. To find a good, robust, reliable, secure open source, free threaded discussion forum that will allow me to manage discussion forums on different topics covered by the book – or another way to integrate an asynchronous discussion function within the book. (Yes, I AM a social constructivist!)

Any suggestions welcome – we are actively exploring options at the moment. There are probably good solutions already out there. As I said, I’m not primarily a technologist but an educator, so help is definitely needed.

Next chapter: Theory and practice in teaching for a digital age: 

  • Summary of current learning theories and teaching approaches
  • Teaching and learning styles
  • Deep vs surface learning.
  • Learner-centered teaching, learner engagement, motivation.
  • What we know about skills development
  • Competency based learning
  • Learning design models (ADDIE, communities of practice, flexible design models, personalized learning environments).
  • Digital natives and digital literacy
  • Summary of research on teaching.

I still have more work to do on this outline: suggestions welcome.

Your homework

In the meantime, please take a look at Chapter 2 and send me your comments. In particular:

1. Is it too theoretical or abstract?

2. Have I accurately represented objectivism, constructivism and connectivism?

3. Do you agree that academic knowledge is different from everyday knowledge, and that it is an important distinction?

4. Does the scenario work for you?

5. Would you recommend this chapter to your teaching colleagues as worthwhile reading?

Hey – it IS an open textbook, and there’s no more World Cup football after Sunday.

Are universities teaching the skills needed in a knowledge-based economy?

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Knowledge worker 2

I’ve been on holiday the last two and a half weeks, but also doing some writing for my open textbook on teaching in a digital age.

Are universities teaching the skills needed in a knowledge-based economy?

This is one of the questions I have been asking myself, and there of course a couple of ways to respond to this:

1. Of course – we teach critical thinking, problem solving, research skills, and encourage original thinking: just the skills needed in today’s work force.

2. That’s not our job. Our job is the pure exploration of new knowledge and ideas and to pass that love of knowledge on to the next generation. If some of that rubs off in the commercial world, well and good, but that’s not our purpose.

I have a little bit of sympathy for the second answer. Universities provide society with a safe way of gambling on the future, by encouraging innovative research and development that may have no immediate apparent short-term benefits, or may lead to nowhere, without incurring major commercial or social loss. Another critical role is the ability to challenge the assumptions or positions of powerful agencies outside the university, such as government or industry, when these seem to be in conflict with evidence or ethical principles or the general good of society. There is a real danger in tying university and college programs too closely to immediate labour market needs. Labour market demand can shift very rapidly, and in particular, in a knowledge-based society, it is impossible to judge what kinds of work, business or trades will emerge in the future.

However the rapid expansion in higher education and the very large sums invested in higher education is largely driven by government, employers and parents wanting a work-force that is employable, competitive and if possible affluent. Indeed, this has always been one role for universities, which started as preparation and training for the church, law and much later, government administration.

So it’s the first response I want to examine more closely. Are the skills that universities claim to be developing (a) actually being done and (b) if they are being done, are they really the skills needed in a knowledge-based economy.

The characteristics of knowledge-based workers

To answer that question let’s attempt to identify the characteristics of knowledge-based workers. Here’s my view on this (somewhat supported by bodies such as the Canadian Chamber of Commerce and the OECD – I’m searching for the actual references.)

  • they usually work in small companies (less than 10 people)
  • they sometimes own their own business, or are their own boss; sometimes they have created their own job, which didn’t exist until they worked out there was a need and they could meet that need
  • they often work on contract, so they move around from one job to another fairly frequently
  • the nature of their work tends to change over time, in response to market and technological developments and thus the knowledge base of their work tends to change rapidly
  • they are digitally smart or at least competent digitally; digital technology is often a key component of their work
  • because they often work for themselves or in small companies, they play many roles: marketer, designer, salesperson, accountant/business manager, technical support, for example
  • they depend heavily on informal social networks to bring in business and to keep up to date with current trends in their area of work
  • they need to keep on learning to stay on top in their work, and they need to manage that learning for themselves
  • above all, they need to be flexible, to adapt to rapidly changing conditions around them.

It can be seen then that it is difficult to predict with any accuracy what many graduates will actually be doing ten or so years after graduation, except in very broad terms. Even in areas where there are clear professional tracks, such as medicine, nursing or engineering, the knowledge base and even the working conditions are likely to undergo rapid change and transformation over that period of time. However, we shall see that it is possible to predict the skills they will need to survive and prosper in such an environment.

Content and skills

Knowledge involves two strongly inter-linked but different components: content and skills. Content includes facts, ideas, principles, evidence, and descriptions of processes or procedures.

The skills required in a knowledge society include the following (Conference Board of Canada, 1992):

  • communications skills: as well as the traditional communication skills of reading, speaking and writing coherently and clearly, we need to add social media communication skills. These might include the ability to create a short YouTube video to capture the demonstration of a process or to make a sales pitch, the ability to reach out through the Internet to a wide community of people with one’s ideas, to receive and incorporate feedback, to share information appropriately, and to identify trends and ideas from elsewhere;
  • the ability to learn independently: this means taking responsibility for working out what you need to know, and where to find that knowledge. This is an ongoing process in knowledge-based work, because the knowledge base is constantly changing. Incidentally I am not talking here necessarily of academic knowledge, although that too is changing; it could be learning about new equipment, new ways of doing things, or learning who are the people you need to know to get the job done;
  • ethics and responsibility: this is required to build trust (particularly important in informal social networks), but also because generally it is good business in a world where there are many different players, and a greater degree of reliance on others to accomplish one’s own goals;
  • teamwork and flexibility: although many knowledge workers work independently or in very small companies, they depend heavily on collaboration and the sharing of knowledge with others in related but independent organizations. In small companies, it is essential that all employees work closely together, share the same vision for a company and help each other out. The ‘pooling’ of collective knowledge, problem-solving and implementation requires good teamwork and flexibility in taking on tasks or solving problems that may be outside a narrow job definition but necessary for success;
  • thinking skills (critical thinking, problem-solving, creativity, originality, strategizing): of all the skills needed in a knowledge-based society, these are some of  the most important. Businesses increasingly depend on the creation of new products, new services and new processes to keep down costs and increase competitiveness. Universities in particular have always prided themselves on teaching such intellectual skills, but we have seen that the increased move to larger classes and more information transmission, especially at the undergraduate level, challenges this assumption. Also, it is not just in the higher management positions that these skills are required. Trades people in particular are increasingly having to be problem-solvers rather than following standard processes, which tend to become automated. Anyone dealing with the public needs to be able to identify needs and find appropriate solutions;
  • digital skills: most knowledge-based activities depend heavily on the use of technology. However the key issue is that these skills need to be embedded within the knowledge domain in which the activity takes place. This means for instance real estate agents knowing how to use geographical information systems to identify sales trends and prices in different geographical locations, welders knowing how to use computers to control robots examining and repairing pipes, radiologists knowing how to use new technologies that ‘read’ and analyze MRI scans. Thus the use of digital technology needs to be integrated with and evaluated through the knowledge-base of the subject area;
  • knowledge management: this is perhaps the most over-arching of all the skills. Knowledge is not only rapidly changing with new research, new developments, and rapid dissemination of ideas and practices over the Internet, but the sources of information are increasing, with a great deal of variability in the reliability or validity of the information. Thus the knowledge that an engineer learns at university can quickly become obsolete. There is so much information now in the health area that it is impossible for a medical student to master all drug treatments, medical procedures and emerging science such a genetic engineering, even within an eight year program. The key skill in a knowledge-based society is knowledge management: how to find, evaluate, analyze, apply and disseminate information, within a particular context. This is a skill that graduates will need to employ long after graduation.

Most faculty, at least in universities, are well trained in content and have a deep understanding of the subject areas in which they are teaching. Expertise in skills development though is another matter. The issue here is not so much that faculty do not help students develop skills – they do – but whether these intellectual skills match the needs of knowledge-based workers, and whether enough emphasis is given to skills development within the curriculum.

Embedding skills in the curriculum

We know a lot from research about skills and skill development (again, references to come):

  • skills development is relatively context-specific. In other words, these skills need to be embedded within a knowledge domain. For example, problem solving in medicine is different from problem-solving in business. Different processes and approaches are used to solve problems in these domains (for instance, medicine tends to be more deductive, business more intuitive; medicine is more risk averse, business is more likely to accept a solution that will contain a higher element of risk or uncertainty);
  • learners need practice – often a good deal of practice – to reach mastery and consistency in a particular skill;
  • skills are often best learned in relatively small steps, with steps increasing as mastery is approached;
  • learners need feedback on a regular basis to learn skills quickly and effectively; immediate feedback is usually better than late feedback;
  • although skills can be learned by trial and error without the intervention of a teacher, coach, or technology, skills development can be greatly enhanced with appropriate interventions, which means adopting appropriate teaching methods and technologies  for skills development.
  • although content can be transmitted equally effectively through a wide range of media, skills development is much more tied to specific teaching approaches and technologies.

What should we do?

So here are some questions to discuss at the next departmental meeting discussing curriculum:

  • what are the skills we are trying to develop in this program? Are they explicitly stated and communicated to students?
  • how well do they match the skills required by knowledge-based workers? Do we need to add or adapt  existing skills to make them more relevant? If so, would this have a negative or a positive effect on the academic integrity of the program and particularly on the choice of content?
  • what teaching methods are most likely to lead the development of such skills?
  • what opportunities should we provide for practice and feedback on the development of the skills we have chosen?
  • how do we assess such skills?

Your feedback requested

1. Have I covered the main skills needed in a knowledge-based society? What have I missed?

2. Do you agree that these are important skills? If so, should universities explicitly try to develop them?

3. What are you or your university doing (if anything) to ensure such skills are taught, and taught well?

4. What roles if any do you think technology, and in particular online learning, can play in helping to develop such skills?

5. Any other comments on this topic

Time to retire from online learning?

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Working in my study

Working in my study

Forgive me for being personal in this post (well, it is a blog), but I also have a few important things to say professionally.

The context

I was 75 yesterday and as I’ve tried to do each birthday for the last 25 years, I spent the day skiing at Whistler. (A wonderful day: sunshine and still tons of snow, and a lot of terrain to cover). How to spend yesterday was an easy decision. The hard one is how to spend the rest of my life (yeah, welcome to the club).

In particular, I have decided to stop (nearly) all professional activities from now onwards. I want to go through the reasons for this, because the reasons are as much professional as personal. Also this change has implications for my blog in particular.

What I’m not going to do

In general, I’m not going to accept any invitations to do paid consultancy work nor to accept invitations to be a keynote speaker or a participant at conferences from now on. I will not be taking on any more thesis supervision or examinations, nor reviewing articles or books for publication, unless they are directly relevant to my own writing (see below). I say in general, because it’s stupid to be inflexible, but there will not be many exceptions.

Why stop now?

First, if 75 is good enough for judges in Canada to retire, it’s sure good enough for me, and after 45 years continuously working in online and distance education, I’ve certainly earned the right to stop. However, many people just don’t believe me (including my wife), because online learning and open and distance education are my passion and my life, and that’s not going to go away. As the day spent skiing illustrates, I’m really fortunate to be healthy and fit, so health is not the reason. But there are good reasons for me to stop now, and I want to share these with you.

The main reason for stopping now is that I want to stop when I am still at my best. I’ve been really on form over the last 12 months, as far as one can be objective about these things. But I have seen far too many great people who continued long after they should have stopped – and unfortunately it’s the later years that people often remember. Much of my expertise comes from having done things: teaching online, managing a department. But it’s over 10 years since I taught a full course, and a similar amount of time since I was responsible for a department. Given the pace of change, it is dangerous for a consultant to become adrift from the reality of teaching and management. It’s time to hang up my boots before I get really hurt (or more importantly, really hurt others).

Related to this is the difficulty in keeping up in this area of knowledge. It’s a full-time job just to keep abreast of new developments in online and distance learning, and this constant change is not going to go away. It’s tempting to say that it’s only the technology that changes; the important things – teaching and learning – don’t change much, but I don’t believe that to be true, either. Teaching in higher education is about to go through as major a revolution as one can imagine. This is not going to be easy; indeed it could get brutal.

Even the processes of learning, which used to be relatively stable, given how much is biological, are also undergoing change. Technology is not neutral; it does change the way we think and behave. Furthermore, I foresee major developments in the science of learning that will have major implications for teaching and learning – but it will also have major false directions and mistakes (be very careful with artificial intelligence in particular). So this is a field that needs full-time, professional application, and very hard work, and I just don’t have the energy any more to work at that level. To put it simply, this is not a profession where you can be half in and half out. Dabbling in online learning is very dangerous (politicians please note).

And then there’s MOOCs. I can’t express adequately just how pissed off I am about MOOCs – not the concept, but all the hubris and nonsense that’s been talked and written about them. At a personal level, it was as if 45 years of work was for nothing. All the research and study I and many others had done on what makes for successful learning online were totally ignored, with truly disastrous consequences in terms of effective learning for the vast majority of participants who took MOOCs from the Ivy League universities. Having ignored online learning for nearly 20 years, Stanford, MIT and Harvard had to re-invent online learning in their own image to maintain their perceived superiority in all things higher educational. And the media fell for it, hook, line and sinker. This is a battle I no longer want to fight – but it needs fighting. But my reaction did make me wonder, am I just an old man resisting the future? And that has definitely left a mark.

Lastly, I am concerned that the computer scientists seem to be taking over online education. Ivy League MOOCs are being driven mainly by computer scientists, not educators. Politicians are looking to computer science to automate learning in order to save money. Computer scientists have much to offer, but they need more humility and a greater willingness to work with other professionals, such as psychologists and teachers, who understand better how learning operates. This is a battle that has always existed in educational technology, but it’s one I fear the educators are losing. The result could be disastrous, but that’s a theme for a whole set of blog posts.

So yes, time to go, and to leave the good fight to the next generation.

What I will continue to do

I will continue to write. In particular, I have already started writing an open textbook on ‘Teaching in a Digital Age’, and when that is done, I will write a semi-autobiographical novel (only the names will be changed to protect the innocent). I will also complete any existing professional commitments.

I will also continue this blog focused on online learning, but it will be more journalistic and less based on my immediate and recent experiences in online learning. So I hope it will continue to be of interest and value.

And yes, plenty of golf, and more time with family.

Last words

This post has ended up being a bit too personal. But it’s been an incredible, wonderful 45 years. Open and distance education are honourable fields of endeavour, aimed at widening access. Online learning is an exciting field, constantly under development, and has huge potential for both increasing the quality of teaching and the productivity of higher education. Above all, though, the journey has brought me many marvellous and true friends and colleagues. It has been an honour and a privilege to work with such great people. Thank you all.

Teaching assistants, adjunct faculty and online learning

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Lecture to hybridI am struggling these days with the issue of who should teach online courses, in terms of qualifications and status, and in particular, the issue of how to scale up credit-based online courses while maintaining or improving quality.

These questions are coming to the forefront because, through blended learning, practices that are common in face-to-face teaching come head to head with quite different practices in online learning.

What has made this an issue for me

Recently I’ve been involved in assessing proposals for funding for large-enrollment online credit courses. Most of the proposals have focused on using several/many teaching assistants working under a professor to provide the learning support. I’m also finding this model being increasingly used where institutions are moving to a hybrid model, combining both online and face-to-face components, especially where a former very large lecture-based course is being redesigned for hybrid learning. Even including the TAs, the instructor/student ratio is often 1:100 or higher for these large enrollment courses (in other words, the same ratio more or less as when the course was delivered solely through large lectures.) In the proposals, and in the reports I am receiving, there is usually no additional training for TAs about how to teach online, although in many – but by no means all – cases, they do get some kind of training in teaching face-to-face.

This is a problem for me, because I have always worked with a model for online courses where the instructor: student ratio has been under 40 for undergraduate courses, and under 30 for graduate courses. Scaling up has been handled by hiring on contract additional part-time adjunct or associate professors, either with a doctoral degree in the subject area, or with strongly related work experience. The adjuncts would be paid to take a short online briefing course on teaching online which sets out the expectations for online teaching. This was an affordable model because the additional student tuition fees would more than cover the cost of hiring additional contract instructors, once the course was developed.

However, this has been possible because most of the online courses I have been responsible for have been aimed mainly at higher level undergraduate students or graduate students. With both blended and online courses now being targeted at large first and second year classes, new models are being developed that I fear will not have the same level of quality as the ‘best practice’ online courses I have been working with.

Why this is not an easy issue for me

This is a particularly difficult issue for me to discuss for several reasons:

  • most of my experience is with fully online courses; when I have taught face-to-face, it’s usually been me on my own, and generally with relatively small groups of between 25 to 200 maximum
  • practices both for dealing with large face-to-face classes and with online classes vary considerably within each form of delivery, and from one institution to another, so making generalizations is fraught with danger
  • decisions about whether to use teaching assistants or part-time, contract instructors, are driven more by financial considerations than by best pedagogical practice, although institutions do their best to make it as effective educationally as possible once a model for TAs and/or adjuncts has been decided on
  • there are other factors at work besides money and pedagogy in the use of teaching assistants and adjunct faculty, such as the desire to provide financial support to international and graduate students, the idea of apprenticeship in teaching, and the supply and demand effects on the employment of doctoral graduates seeking a career in university teaching and research
  • there is no golden mean for instructor/student ratios in either blended or online learning. In the mainly quantitative/STEM subjects, much higher ratios are sustainable without the loss of quality, through the use of automated marking and feedback
  • MOOCs (rightly or wrongly) are giving the impression that it is possible to scale up even credit-based online learning at lower cost.

What follows then is tentative, and I’m ready to change my views especially on the evidence of others who have grappled with this issue.

My concern

My real concern is that the over-reliance on teaching assistants for online and blended courses will have three negative consequences for both students and online learning in general:

  • As with the large face-to-face classes, the pedagogy for online or blended courses will resort more to information transmission.
  • however, for the online or hybrid courses, student drop-out and dissatisfaction will increase because, especially in first and second year teaching, they will not get the learning support they need when studying online.  As a result, faculty and students will claim that online learning is inferior to classroom-based instruction
  • faculty will see online learning and blended learning being used by administrations to cut costs and over time to reduce the employment of tenured faculty, and will therefore try to block its implementation.

Why can’t TAs provide the support needed online if they can do this for face-to-face classes? First, I’m not sure they do provide adequate support for students in large first year classes, but I’m not in a position to judge. But in online courses in subject domains where discussion is important, where qualitative judgements and decisions have to be made by students and instructors, where knowledge needs to be developed and structured, in other words in any field where the learning requires more than the transmission and repetition of information, then students need to be able to interact with an instructor that has a deep understanding of the subject area. For this reason, I am more than happy to hire adjunct faculty to teach online, but not TAs in general (although there will always be exceptions). Furthermore this kind of teaching and learning (‘the learning that matters most’) is very difficult to do with a very large instructor/student ratio, although with good design and faculty training, we could possibly push numbers higher than 1/40.

One possible solution

I’m not sure there is an easy solution to this problem. Whether online or face-to-face, large numbers of students per instructor limits what is possible pedagogically.

Furthermore, in my view online learning works better for some kinds of students than others. Students in their first year of university or college are not the best target group. They are often young, have little experience of independent learning, lack confidence or discipline in their study habits, and indeed expect to be in a face-to-face teaching environment and want the social and cultural milieu that a campus provides. What we should be doing though in their first and second year is gradually introducing them to online components so that they slowly develop the discipline and skills required for successful online learning. This still doesn’t resolve the issue though of very large classes.

So here’s my suggestion for these large introductory courses of 1,000 students or more (this is not new – see the National Center for Academic Transformation‘s course redesign):

  • create a team to design, develop and deliver the course. The team will include a senior professor, several adjunct professors, and two or three TAs, plus an instructional designer and web/multimedia designer.
  • The senior professor acts as a teaching consultant, responsible for the overall design of the course, hiring and supervising the work of the adjuncts/TAs, and the assessment strategy/questions and rubrics. This though is done in consultation with the rest of the team.
  • Most content is provided online.
  • Students work in groups of 30, and each of the adjuncts is responsible for several student groups. Students do both individual and group work (e.g. projects, problem-solving),
  • Students participate in ongoing online discussion forums, under the moderation of an adjunct or TA
  • The senior professor meets for one hour a week three times face-to-face or synchronously with  a group of 30 students; this brings the professor in face-to-face contact with just over 1,000 students a semester; adjuncts where possible meet once a week with a group on campus or synchronously.
  • Adjuncts and TAs mark assignments, and the senior professor monitors/calibrates the marking between instructors
  • Now think of what could happen if this course was shared with other universities. Savings could be made on course development, but the delivery of the course would still need instructors at the other universities. So there would be some economies of scale from sharing, but not a very large saving, because the development cost is a small proportion of the overall cost. This does not mean that institutions shouldn’t co-operate and share resources, but this will not bring the large economies of scale that are often claimed for sharing online courses.

Whatever detailed design is done, these large courses should have a clear business model to work with, which basically provides an overall budget for the course, that includes the cost of tenure track and adjunct faculty and TAs, and takes account of the students numbers (more students, more budgeted money), but allowing the senior professor to build the team as best as possible within that budget.

The two elephants in the room

The above scenario works with the current system of allocating resources to different level of courses. But there are two factors that lead to the very large class sizes in first and second year that no-one really wants to talk about:

Elephant in room

  • the starvation of first and second year students of teaching resources; senior faculty concentrate more on upper level courses, and want to keep these class sizes smaller. As a consequence first and second year students suffer
  • teaching subsidizes research: too often tuition revenues get filtered off into supporting research activities. The most obvious case is that if teachers spent more time teaching and less doing research, there would be more faculty available for teaching. Teaching loads for experienced, tenured faculty are often quite light and as stated above, focused on small upper level classes.

Do a simple calculation: divide the total number of students by the number of tenure track instructors  in your institution, and that will give you an overall average instructor/student ratio for the university as a whole. So if you have 40,000 students and 2,000 full-time instructors , you have an overall instructor/student ratio of 1:20. However, then deduct 40% of their time for research, so that equals 1,200 full time equivalent, or a ratio of one instructor for 33.3 students. Then deduct another 20% of their time for administration and public service and that leaves 800 FTEs, or a ratio of one instructor for every 50 students. Even with this fairly generous allowance of 60% of their time for other activities, and WITHOUT adjuncts or TAs, in this large university there should be enough instructors to teach without having the absurdly large first and second year classes commonly found in such large universities. Add in adjuncts and TAs, and this ratio drops even further.

So don’t expect online learning to solve this problem on its own.

Your turn

I would particularly like to hear from the relatively rare instructors who have taught large classes both face-to-face and online. Do you share my concern about using TAs for distance or hybrid courses?

I’d also like to hear in general about experiences with TAs or adjunct/contract instructors as well on this topic.