August 1, 2014

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

Why lectures are dead (or soon will be)

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Artist: Laurentius de Voltolina; Liber ethicorum des Henricus de Alemannia; Kupferstichkabinett SMPK, Berlin/Staatliche Museen Preussiischer Kulturbesitz, Min. 1233

Artist: Laurentius de Voltolina;
Liber ethicorum des Henricus de Alemannia; Kupferstichkabinett SMPK, Berlin/Staatliche Museen Preussiischer Kulturbesitz, Min. 1233

As part of my open textbook on Teaching in a Digital Age, I am working my way through theories of learning and methods of teaching. I will post shortly my initial draft on theories of learning and their relevance for a digital age. In this post I want to discuss the lecture and its relevance for a digital age. Comments as always are more than welcome.

Definition:

‘[Lectures] are more or less continuous expositions by a speaker who wants the audience to learn something.’

Bligh, 2000

History

Lectures go back as far as ancient Greece and Roman times, and certainly from at least the start of the European university, in the 13th century. The term ‘lecture’ comes from the Latin to read. This was because in the 13th century, most books were extremely rare. They were painstakingly handcrafted and illustrated by monks, often from fragments or collections of earlier and exceedingly rare and valuable scrolls remaining from more than 1,000 years earlier from ancient Greek or Roman times, or were translated from Arabic sources, as much documentation was destroyed in Europe during the Dark Ages following the fall of the Roman empire. As a result, a university would often have only one copy of a book, and it may have been the only copy available in the world. The library and its collection therefore became critical to the reputation of a university, and professors had to borrow the only text from the library and literally read from it to the students, who dutifully wrote down their own version of the lecture.

The illustration at the head of this post from a thirteenth-century manuscript shows Henry of Germany delivering a lecture to university students in Bologna, Italy, in 1233. What is striking is how similar the whole context is to lectures today, with students taking notes, some talking at the back, and one clearly asleep. Certainly, if Rip Van Winkle awoke in a modern lecture theatre from his 800 years of sleeping, he would know exactly where he was and what was happening.

Lectures themselves belong to an even longer oral tradition of learning, where knowledge is passed on by word of mouth from one generation to the next. In such contexts, accuracy and authority (or power in controlling access to knowledge) are critical for ‘accepted’ knowledge to be successfully transmitted. Thus accurate memory, repetition and a reference to authoritative sources become exceedingly important in terms of validating the information transmitted. The great sagas of the ancient Greeks and much later, of the Vikings, and even today,the oral myths and legends of many indigenous communities, are examples of the power of the oral transmission of knowledge.

Nevertheless, the lecture format has been questioned for many years. Samuel Johnson (1709-1784) long ago produced his own straightforward critique of lectures:

People have nowadays…got a strange opinion that everything should be taught by lectures. Now, I cannot see that lectures can do as much good as reading the books from which the lectures are taken…Lectures were once useful, but now, when all can read, and books are so numerous, lectures are unnecessary.’

What is remarkable is that even after the invention of the printing press, radio, television, and the Internet, the lecture, characterised by the authoritative instructor talking to a group of students, still remains the dominant methodology for teaching in many institutions, even in a digital age, where information is available at a click of a button.

It could be argued that anything that has lasted this long must have something going for it. On the other hand, we need to question whether the lecture is still the most appropriate means of teaching, given all the changes that have taken place in recent years, and in particular given the kinds of knowledge and skills needed in a digital age.

What does research tell us about the effectiveness of lectures?

Whatever you may think of Samuel Johnson’s opinion, there has indeed been a great deal of research into the effectiveness of lectures, going back to the 1960s, and continued through until today. The most authoritative analysis of the research on the effectiveness of lectures remains Bligh’s (2000). He summarized a wide range of meta-analyses and studies of the effectiveness of lectures compared with other teaching methods and found consistent results:

  1. The lecture is as effective as other methods for transmitting information (the corollary of course is that other methods – such as video, reading, independent study - are just as effective as lecturing for transmitting information)
  2. Most lectures are not as effective as discussion for promoting thought
  3. Lectures are generally ineffective for changing attitudes or values or for inspiring interest in a subject
  4. Lectures are relatively ineffective for teaching behavioural skills.

It should be noted that are are also many studies that suggest that it makes little difference to the learning effectiveness of a lecture if it is live (with the lecturer and the audience together at the same place and time), if it is transmitted in real time across distance (such as via a webcast or video-conference) or is viewed once on a recording as a continuous event. Thus merely by transmitting a MOOC in the form of a video lecture makes it no more or less effective in terms of an individual’s learning than if it was delivered in a classroom (although of course the MOOC will reach a lot more learners). Thus the medium of transmission makes no difference to an individual’s learning if the form of the lecture remains the same.

However, my research colleagues and I at the U.K. Open University, as early as 1984, established that making a lecture available in a recorded format (either on video or audio) increased the learning effectiveness, because it increased students’ time on task, by enabling them to review and repeat the material. We also found that recorded video or audio was even more effective than a recorded lecture if the program was re-designed to break the transmission of information into small chunks, and if the stop-start facility of recordings was used to build in student activities and feedback following each chunk of information. Proponents of Coursera-style MOOCs are just beginning to rediscover this thirty years later.

Bligh also examined research on student attention, on memorizing, and on motivation, and concluded (p.56):

We see evidence… once again to suppose that lectures should not be longer than twenty to thirty minutes – at least without techniques to vary stimulation.’

These research studies have shown that in order to understand, analyze, apply, and commit information to long-term memory, the learner must actively engage with the material. In order for a lecture to be effective, it must include activities that compel the student to mentally manipulate the information. Many lecturers of course do this, by stopping and asking for comments or questions throughout the lecture – but many do not.

Again, although these findings have been available for a long time, and You Tube videos now last approximately eight minutes and TED talks 20 minutes at a maximum, teaching in many educational institutions is still organized around a standard 50 minute lecture session, with, if students are lucky, a few minutes at the end for questions or discussion. Indeed in some institutions it is not uncommon to find even longer lecture sessions.

There are two important conclusions from the research:

1. Even for the sole purpose for which lectures may be effective – the transmission of information – the 50 minute lecture needs to be well organized, with frequent opportunities for student questions and discussion. (Bligh provides excellent suggestions on how to do this in his book.)

2. For all other important learning activities, such as developing critical thinking, deep understanding, and application of knowledge – the kind of skills needed in a digital age – lectures are ineffective. Other forms of teaching and learning – such as opportunities for discussion and student activities – are necessary.

Does new technology make lectures more relevant?

Over the years, institutions have made massive investments in ‘technologising’ the lecture. Powerpoint presentations, multiple projectors and screens, clickers for recording student responses, even ‘back-chat’ channels on Twitter, enabling students to comment on a lecture – or more often, the lecturer – in real time (surely the worse form of torture), have all been tried. Students have been asked to bring tablets or lap-tops to class, and universities in particular have invested millions of dollars in state of the art lecture theatres.

Nevertheless, all this is just lipstick on a pig. The essence of a lecture remains the transmission of information, all of which is now readily and, in most cases, freely available in other media and in more learner-friendly formats.

I worked in a college where in one program all students had to bring laptops to class. At least in these classes, there were some activities to do related to the lecture that required the students to use the laptops during class time. However, in most classes this took less than 25 per cent of the lesson time. Most of the other time, students were talked at, and as a result used their laptops for other, mainly non-academic activities, especially playing online poker.

Faculty often complain about students use of technology such as mobile phones or tablets, for ‘non-relevant’ multitasking in class, but this misses the point. If most students have mobile phones or laptops, why are they still having physically to come to a lecture hall? Why can’t they get a podcast of the lecture? Second, if they are coming, why are the lecturers not requiring them to use their mobile phones, tablets, or laptops for study? Why not break them into small groups and get them to do some online research then come back with group answers to share with the rest of the class? If lectures are to be offered, the aim should be to make the lecture engaging in its own right, so the students are not distracted by their online activity. If lecturers can’t do this, perhaps they should give up lecturing and find more interactive ways of engaging students.

Is there then no role for lectures in a digital age?

I do believe that lectures have their uses. As an example, I have attended an inaugural lecture for a newly appointed research professor. In this lecture, he summarised all the research he and his team had done, resulting in treatments for several cancers and other diseases. This was a public lecture, so he had to satisfy not only other leading researchers in the area, but also a lay public with often no science background. He did this by using excellent visuals and analogies. The lecture was followed by a small wine and cheese reception for the audience.

The lecture worked for several reasons:

  • first of all, it was a celebratory occasion bring together family, colleagues and friends.
  • second, it was an opportunity to pull together nearly 20 years of research into a single, coherent narrative or story.
  • third, the lecture was well supported by an appropriate use of graphics and video.
  • lastly, he put a great deal of work into preparing this lecture and thinking about who would be in the audience – much more preparation than would be the case if this was just one of many lectures in a course.

More importantly, though, that lecture is now publicly available via You Tube for anyone to view.

McKeachie and Svinicki (2006, p. 58) believe that lecturing is best used for:

  • providing up-to-date material that can’t be found in one source
  • summarizing material found in a variety of sources.
  • adapting material to the interests of a particular group.
  • initially helping students discover key concepts, principles or ideas
  • modelling expert thinking.

The last point is important. Faculty often argue that the real value of a lecture is to model for students how the faculty member, as an expert, approaches a topic or problem. Thus the important point of the lecture is not the transmission of content (facts, principles, ideas), which the students could get from just reading, but an expert way of thinking about the topic. The trouble with this argument for lectures is three-fold:

  • students are rarely aware that this is the purpose of the lecture, and therefore focus on memorizing the content, rather than the ‘modelling’ of expert thinking
  • faculty themselves are not explicit about how they are doing the modelling (or fail to offer other ways in which modelling could be used, so students can compare and contrast)
  • students get no practice themselves in modelling this skill, even if they are aware of the modelling.

So, yes, there are a few occasions when lectures work very well. But they should not be the default model for regular teaching. There are much better ways to teach that will result in better learning over the length of a course or program, and that lectures, whether live, or on MOOCs, YouTube videos or TED talks, are a poor way to prepare learners for a digital age.

Why are lectures still the main form of educational delivery?

Given all of the above, some explanation needs to be offered for the persistence of the lecture into the 21st century. Here are my suggestions

  1. in fact, in many areas of education, the lecture has been replaced, particularly in many elementary or primary schools (although parents often are unhappy about this, because a lecture represents their understanding of what teaching is all about); online learning (usually avoiding recorded lectures) and open education is also increasing more rapidly than classroom based learning
  2. a false assumption that the lecture is economically efficient. It is true it is efficient in the use of an instructor’s time, in that increasing student numbers in a lecture is no more work for the instructor for each student added to a class. However, efficiency needs to take into account output. If the output is to transfer information both less effectively per individual learner and in terms of providing the knowledge and skills in demand, then large lecture classes are a false efficiency;
  3. architectural inertia: a huge investment has been made by institutions in facilities that support the lecture model. What is to happen to all that real estate if it is not used?
  4. the Carnegie unit of teaching, which is based on a notion of one hour per week of classroom time per credit over a 13 week period. It is easy then to divide a three credit course into 39 one hour lectures over which the curriculum for the course must be covered. It is on this basis that teaching load and resources are decided.
  5. faculty in post-secondary education have no other model for teaching. This is the model they are used to, and because appointment is based on training in research or work experience, and not on qualifications in teaching, they have no knowledge of how students learn or confidence or experience in other methods of teaching.
  6. many experts prefer the oral tradition of teaching and learning, because it enhances their status as an expert and source of knowledge: being allowed an hour of other people’s time to hear your ideas without major interruption is very satisfying on a personal level (at least for me).

Is there a future for lectures in a digital age?

That depends on how far into the future one wants to look. Given the inertia in the system, I suspect that lectures will still predominate for another ten years, but after that, in most institutions, courses based on three lectures a week over 13 weeks will have disappeared. There are several reasons for this.

  • the first is that all content can be easily digitalized and made available on demand at very low cost.
  • second, institutions will be making greater use of dynamic video (not talking heads) for demonstration, simulations, animations, etc. Thus most content modules will be multi-media.
  • third, open textbooks incorporating multi media components and student activities will provide the content, organization and interpretation that are the rationale for most lectures.
  • lastly, and most significantly, the priority for teaching will have changed from information transmission and organization to knowledge management, where students have the responsibility for finding, analyzing, evaluating, sharing and applying knowledge, under the direction of a skilled subject expert. Project-based learning, collaborative learning and situated or experiential learning will become much more widely prevalent. Also many instructors will prefer to use the time they would have spent on a series of  lectures in providing more direct, individual and group learner support, thus bringing them into closer contact with learners.

This does not mean that lectures will disappear altogether, but they will be special events, and probably multi-media, synchronously and asynchronously delivered. Special events might include a professor’s summary of his latest research, the introduction to a course, a point mid-way through a course for taking stock and dealing with common difficulties, or the wrap-up to a course. A lecture will provide a chance for an instructor to makes themselves known, to impart their interests and enthusiasm, and to motivate learners, but this will be just one, relatively small, but important component of a much broader learning experience for students.

 In the meantime, institutions should be looking at their building plans, and deciding if the money they are thinking of in terms of classrooms and lecture theatres may not be better spent on digitizing the curriculum and making it openly available.

References

Bates, A. (1985) Broadcasting in Education: An Evaluation. London: Constable

Bligh, D. (2000) What’s the Use of Lectures? San Francisco: Jossey-Bass

Boswell, James (1986), Hibbert, Christopher, ed., The Life of Samuel Johnson, New York: Penguin Classics, ISBN 0-14-043116-0.

McKeachie, W. and Svinicki, M. (2006) McKeachie’s Teaching Tips: Strategies, Research, and Theory for College and University Teachers, 13th Edition Independence KY: Cengage

Guest blog: MOOCs: Disruptor or Indicator of Something Deeper?

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Guest blogger: Nicole Christen

Guest blogger: Nicole Christen

Introduction

I don’t usually do guest blogs, and when I do it’s always because I know they will be of the highest quality – and I NEVER accept unrequested guest blogs from people I don’t know.

However, I was a participant in a study on MOOCs by Nicole Christen for a paper as part of her Master in Educational Technology program at the University of British Columbia. She kindly sent me a copy of her final paper. I was so struck by the quality of this paper and its significance that I immediately asked her if she would be willing to provide a summary in the form of a blog post. Here is the summary of her paper. I found no need to change it. I strongly recommend though that you read the paper in full, which is available here.

Nicole Christen

MOOCs: Disruptor or Indicator of Something Deeper?

Why have massive open online courses, known as MOOCs, established a stronghold in the educational marketplace? Are they responsible for disrupting the traditional system of higher education? And, how can post-secondary institutions survive the changes taking place?

In the summer of 2013, amidst the early hype surrounding MOOCs, I conducted a qualitative research project. My objective was to explore the motivations driving institutions to launch MOOCs and join MOOC consortiums. MOOCs have been labeled as a disruptive force to the traditional system of post-secondary education; however, my research argues otherwise. MOOCs, themselves, are not the source of disruption. Deeper forces are at work.

About My Research Project

In order to understand the reasons behind the rapid implementation of MOOCs by post-secondary institutions, I interviewed educational technology thought leaders from around the world whose areas of expertise included distance learning and open learning at the post-secondary level. During each 30 minute interview, I asked a series of questions designed to help me identify common underlying themes surrounding MOOCs and the overall concept of open learning. The themes extrapolated from my interview data provide a solid overview of fundamental shifts that have occurred as a result of the technological revolution and remain relevant regardless of any changes to MOOCs that have taken place since this research was conducted.

Forces Driving the MOOC Movement

Media hype that portrays MOOCs as an all-powerful disruptive force overlooks the underlying factors behind the adoption of MOOCs. In particular, the post-secondary marketplace is becoming increasingly driven by learner desires. Self-directed, distance education at the post-secondary level has existed for decades; however, the relative ease with which people around the world can now access the Internet, has created a tipping point. In many cases, learners are no longer as limited by geographical boundaries or technological limitations. Open learning initiatives, such as MOOCs, remove financial barriers as well. Instead, learners can (and do) go where their needs will best be met. The educational marketplace is becoming learner-driven.

Interpretations and Implications

Why, then, are MOOCs significant? Because MOOCs are a clear indicator that the realm of post-secondary education is changing as a result of advances in technology. The shift from a top-down, institution driven marketplace to one where a learner can use technology to create a  personalized, piecemeal learning experience from multiple institutions requires institutions to ask themselves what they offer learners that is unique. If one institution meets a unique need, and can fulfill this need on a mass scale for learners better than any other institution, then other institutions need to find a different competitive edge.

Furthermore, if MOOCs become a viable educational option (viable in the sense that employers begin to value emerging credentialing systems created by MOOC providers), then there is a real risk that MOOCs will encroach upon the territory of undergraduate education. Post-secondary institutions rely on heavy enrollment of first and second year students to fund their operations and programs. Losing first and second year students to MOOCs will be detrimental to any institutions.

With that said, according to many of the people I interviewed, there will always a be place for research universities and Ivy league schools. These research-based schools fulfill a market need for an element of prestige attached to credentials, networking opportunities with leaders in the field of study, and the opportunity to conduct innovative research. The institutions most at risk of losing students to online and open learning initiatives are those that simply disseminate information generated elsewhere (typically from prestigious research-based institutions).

Given the potential impact of MOOCs, they can certainly be classified as disruptive; however, they are not a disruptor. The shift toward a learner-directed marketplace, widespread access to high-speed Internet, and the ever-increasing global network of information are the true disruptive forces. If MOOCs had not emerged, then some other form of open learning would have emerged to meet the need for low-cost access to educational resources.

Additionally, MOOCs may not be a lasting phenomenon, especially because a sustainable model for operation has yet to be proven; however, if their popularity fades, another innovative open learning opportunity will arise. Things will not go back to the way they were. The demand for open learning will not disappear.

How can institutions survive the disruption taking place in post-secondary education?

My hope is that my research can provide a starting point for institutions to explore the ways in which they can withstand the changes taking place within post-secondary learning by exploring new niches to fill and discovering which specific learner needs they are best equipped to meet. For example, open learning programs (such as MOOCs) often provide information in a way that can be considered akin to a free, interactive textbook. Certain institutions can build on MOOCs by providing classes that help students understand the material being presented to them. In essence, the institutional programs would complement MOOCs.  The most important take-away from my research is that the conditions which have lead to the rise of MOOCs have also created new gaps in the educational marketplace, opening the door for many other innovative approaches to adult education.

My formal research report is titled Open Online Learning: This Changes Everything and can be found at http://nicolechristen.com/wp-content/uploads/2014/03/Open-Online-Learning.pdf

Bio: Nicole Christen is a digital media strategist and a recent graduate from the Master of Educational Technology program at UBC. Read more about Nicole’s professional background and areas of interest at www.nicolechristen.com/portfolio.

Submitting a doctoral thesis on online learning? Some things to keep in mind

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© Relativity Media, 2011

© Relativity Media, 2011

Old people often complain that the world is going to hell in a hand-basket, that standards are falling, and it used to be better in our day. Having examined over 40 doctoral students over the last 45 years, often as the external examiner, it would be easy for me to fall into that trap. On the contrary, though, I am impressed with the quality of theses I have been examining recently, partly because of the quality of the students, partly because of the quality of the supervision, and partly because online learning and educational technology in general have matured as a field of study.

However, one advantage of being old is that you begin to see patterns or themes that either come round every 10 years or so or never go away, and that certainly applies to Ph.D. theses in this field. So I thought I might offer some advice to students as to what examiners tend to look for in theses in this field, although technically it should be the supervisors doing this, not me.

Who’s being examined: student or supervisor?

When I have failed a student (which is rare but has happened) it’s ALWAYS been because the standard of supervision was so poor that the student never stood a chance. Somewhat more frequently (although still fairly uncommon), the examiners’ recommendation was pass with substantial revision, or ‘adequate’ in some European countries. Both these classifications carry a significant message to the academic department that the supervisor(s) weren’t doing their job properly. (Although to be fair, in at least one case the thesis was submitted almost in desperation by the department, because the student had exhausted all his many different supervisors, and was running out of the very generous time allowed to submit.)

So the good news, students, is that, despite what might appear to be the opposite, by the time it comes to submitting your thesis for exam, the university is (or should be) 100 per cent behind you in wanting to get you through. (In recent years, this pressure from the university on examiners to pass students sometimes appears to be almost desperate, because a successful Ph.D. may carry a very significant weight towards the performance indicators for the university.)

Criteria for success

So at the risk of over-simplification, here is my advice for students, in particular, on what I, as an examiner, tend to look for in a thesis, starting with the most important. My comments apply mainly, but not exclusively, to traditional, research-based theses.

Level 1.

I have three main criteria which MUST be met for a pass:

  • is it original?
  • does it demonstrate that the student is capable of conducting independent research?
  • does the evidence support the conclusions drawn in the thesis?

Originality

The minimum a doctoral thesis must do is tell me something that was not already known in the field. Now this can still be what students often see as a negative outcome: their main hypothesis is found to be false. That’s fine, if it is a commonly held hypothesis in the field. (Example: digital natives are different from digital immigrants: no evidence was found for this in the study.) If it disproves or questions current wisdom, that’s good, even if the result was not what you were expecting. In fact, that’s really good, because the ‘null hypothesis’ – I’m trying to prove my hypothesis is false - is a more rigorous test than trying to find evidence to support something you actually thought to be true before you started the research (see Karl Popper (1934) on this).

Competence in research

For students, there are three good reasons for doing a Ph.D.:

  • because you want an academic position in a university or college
  • because you want to work as a full-time researcher outside the university
  • because you have a burning question to answer (e,.g.: what’s best done face-to-face, and what online, when teaching quantum physics?)

However, the main purpose of a Ph.D. (as distinct from other post-graduate qualifications) from a professional or institutional perspective is to enable students to conduct independent research. Thus the thesis must demonstrate this competency. In a sense, it is a trust issue: if this person does research, we should be able to trust him or her to do it within the norms and values of the subject discipline. (This is why it is stupid to even think of cheating by falsifying data or plagiarism: if found out, you will never get an academic job in a university, never mind the Ph.D.)

Evidence-based conclusions

My emphasis here is on ensuring that appropriate conclusions are drawn from whatever evidence is used (which should include the literature review as well as the actual data collected). If for instance the results are contrary to what might be expected from the literature review, some explanation or discussion is needed about why there is this difference. It may have to be speculative, but such contradictions need to be addressed and not ignored.

Level 2

Normally (although there will be exceptions) a good thesis will also meet the following criteria:

  • there is a clear narrative and structure to the thesis
  • there is a clear data audit trail, and all the raw/original data is accessible to examiners and the general public, subject to normal privacy/ethical requirements
  • the results must be meaningfully significant

Narrative and structure

Even in an applied thesis, this is a necessary component of a good thesis. The reader must be able to follow the plot – and the plot must be clear. The usual structure for a thesis in our field is:

  • identification of an issue or problem
  • review of relevant previous research/studies
  • identification of a research question or set of questions
  • methodology
  • results
  • conclusions and discussion.

However, other structures are possible. In an applied degree, the structure will or should be different, but even so, the reader in the main body of thesis should be able to follow clearly the rationale for the study, how it was conducted, the results, and the conclusions.

Data audit

Most – but not all – theses in the educational technology field have an empirical component. Data is collected, analysed and interpreted. All these steps have to be competently conducted, whether the data is mainly quantitative, qualitative or both. This usually means ensuring that there is a clear trail linking raw data through analysis into conclusions that can be followed and checked easily by a diligent reader (in this case, the examiners). This is especially important with qualitative data, because it is easy to cherry-pick comments that support your prior prejudices or assumptions while ignoring those that don’t fit. As an examiner, I do want access to raw data, even if it’s in an appendix or an online database.

However, I am also willing to accept a thesis that is pure argument. Nevertheless, this is a very risky option because this means offering something that is quite original and which can be adequately defended against the whole collective wisdom of the field. In the field of educational technology, it is hard to see how this can be done without resorting to some form of empirical evidence – but perhaps not impossible.

Significance of the research question and results

This is often the best test of how much the thesis is mainly the work of the supervisor and how much the student. A good supervisor can more or less frogmarch a student through the various procedural steps in doing a doctoral thesis, but what the supervisor cannot – or should not – provide is the original spark of a good research question, and the ability to see the significance of the study for the field as a whole. This is why orals are so important – this is the place to say why your study matters, but it also helps if you address this at the beginning and end of your written thesis as well.

Too often I have seen students who have asked questions that inevitably produce results that are trivial, already known, or are completely off-base. Even more tragic is when the student has an unexpected but important, well-founded set of data, but is unable to see the significance of the data for the field in general.

The problem is that supervisors quite rightly drill it into students that they must chose a research question that is manageable by an individual working mainly alone, and that their conclusions must be based on the data collected, but this does not mean that the research question needs to be trivial or that once the conclusions have been properly drawn, there should be no further discussion of their significance for the field as a whole. This is the real test of a student’s academic ability.

Tips for success

There are thousands of possible tips one could give to help Ph.D. students, but I will focus on just a few issues that seem to come up a lot in theses in this area:

1. Do a masters degree on online learning first

This will give you a good overview of the issues involved in online learning and should provide some essentially preparatory skills, such as an introduction to research methods and extensive writing.

Do this prior to starting a Ph.D. See: Recommended graduate programs in e-learning for a list of appropriate programs.

Do it online if possible so you know what its’s like to be an online student.

At a minimum, take a course on research methods in the social sciences/online learning.

2. Get a good supervisor

The trick is to find a supervisor willing to accept your proposed area of research. Try to find someone in the local Faculty of Education with an interest in online learning and try to negotiate a research topic of mutual interest. This is really the hardest and most important part. Getting the right supervisor is absolutely essential. However, there are many more potential students than education faculty interested in research in online learning.

If you find a willing and sympathetic local faculty member with an interest in online learning but worried they don’t have the right expertise to supervise your particular interest, suggest a committee with an external supervisor (anywhere in the world) who really has the expertise and who may be willing to share the supervision with your local supervisor. Again, though, your chances of getting either an internal or external supervisor is much higher if that person already knows you or is aware of your work. Doing an online masters might help here, since some of the instructors on the course may be interested in supervising you for a Ph.D., especially if they know your work through the masters. But again, good professors with expertise in online learning are already likely to have a full supervision load, so it is not easy. (And don’t ask me – I’m retired!)

This means that even before applying for a Ph.D., you need to do some homework. Identify a topic with some degree of flexibility, have in mind an internal and an external supervisor, and show that you have done the necessary courses such as research methods, educational theory, etc., that will prepare you for a Ph.D. (or are willing to do them first).

3. Develop a good research question

See above. Ideally, it should meet the following requirements:

a. The research is likely to add something new to our knowledge in the field

b. The results of the research (positive, negative or descriptive) are likely to be significant/important for instructors, students or an institution

c. You can do the research to answer the question on your own, within a year or so of starting to collect data.

d. It can be done within the ethical requirements of research

It is even better if you can collect data as part of your everyday work, for example by researching your own online teaching.

4. Get a good understanding of sampling and the level of statistics that your study requires

Even if you are doing a qualitative study, you really need to understand sampling – choosing subjects to participate in the study. The two issues you need to watch out for are:

1. Bias in the initial choice of subjects, especially choosing subjects that are likely to support any hypotheses or assumptions you may already have. (Hence the danger of researching your own teaching – but you can turn this to advantage by taking care to identify your prior assumptions in advance and being careful not to be unduly influenced by them in the design of the research).

2. Focusing too much on the number of respondents and not on the response rate, especially in quantitative studies. Most studies with response rates of 40 per cent or less are usually worthless, because the responders are unlikely to be representative of the the whole group (which is why student evaluation data is really dangerous, as the response rate is usually biased towards successful students, who are more likely to complete the questionnaires than unsuccessful students.) When choosing a sample, try to find independent data that can help you identify the extent of the likely bias due to non-responders. For instance, if looking at digital natives, check the age distribution of your responders with the age distribution of the total of the group from which you drew the sample, if that is available. If you had a cohort of 100 students, and 20 responded, how does the average age of the responders compare with the average age of the whole 200? If the average age of responders is much lower than non-responders, what significance does this have for your study?

Understanding statistics is a whole other matter. If you intend to do anything more complicated quantitatively than adding up quantitative data, make sure you understand the necessary statistics, especially what statistically different means. For instance, if you have a very large sample, even small differences are likely to be statistically significant, but they may not be meaningfully significant. Small samples increase the difficulty of getting statistically significant results, so drawing conclusions even when differences look large can be very dangerous from small samples.

5. Avoid tautological research design or quantitative designs with no independent variables

Basically, this means asking a question, stating a hypothesis, or designing research in such a way that the question or  hypothesis itself provides the answer. To elaborate, research question” “What is quality in online learning?’ ‘Answer: “It is defined by what educators say makes for quality in online courses and my research shows that these are clear learning objectives, accessibility, learner engagement, etc..” There is no independent variable here to validate the statements made by educators. (An independent variable might be exam results, participation rates of disabled people, etc.). Education is full of such self-justifications that have no clear, independent variables against which such statements have been tested. Merely re-iterating what people currently think is not original research.

For this reason, I am very skeptical of Delphi studies, which merely re-iterate already established views and opinions. I always ask: ‘Would a thorough literature review have provided the same results?’ The answer is usually: ‘No, you get a far more comprehensive and reliable overview of the topic from the literature review.’

6. Write well

Easily said, but not  easily done. However, writing that is clear, well-structured, evidence-based, grammatically correct and well argued makes a huge difference when it comes to the examination of the thesis. I have seen really weak research studies get through from the sheer quality of the writing. I have seen other really good research studies sent back for major revision because they were so badly written.

Writing is a skill, so it gets better with practice. This usually means writing the same chapter several times until you get it right. Write the first draft, put it away and come back to it several days later. Re-read it and then clarify or improve what you’ve written. Do it again, and again, until you are satisfied that someone who knows nothing about the subject beforehand can understand it. (Don’t assume that all the examiners will be expert in your particular topic.) If you can, get someone such as a spouse who knows nothing about the subject to read through a chapter and ask them just to put question marks alongside sentences or paragraphs they don’t understand. Then re-write them until they do.

The more practice and feedback you can get on your writing, the better, and this is best done long before you get to a final draft.

Is the Ph.D. process broken?

A general comment about the whole Ph.D. process: while not completely broken, it is probably the most costly and inefficient academic process in the whole university, riddled with bureaucracy, lack of clarity for students, and certainly in the non-quantitative areas, open to all kinds of challenges regarding the process and standards.

This is further complicated by a move in recent years to applied rather than research theses. In an applied thesis, the aim is to come up with something useful that can be applied in the field, such as the design of an e-portfolio template that can be used for an end of course assessment, rather than the traditional research thesis. I believe this to be a step in the right direction. Unfortunately though education departments often struggle to provide clear guidance to both students and examiners about the criteria for assessing such new degrees, which makes it even more of a shot in the dark in deciding whether a thesis is ready for submission.

Other suggestions or criticisms

These are (as usual) very personal comments. I’m sure students would like to hear from other examiners in this field, particularly if there is disagreement with my criteria and advice. And I’d like to hear from doctoral students themselves. Suggestions for further readings on the Ph.D. process would also be welcome.

I would also like to hear from those who question the whole Ph.D. process. I must admit to mixed feelings. We do need to develop good quality researchers in the field, and I think a research thesis is one way of doing this. I do feel though that the whole process could be made more efficient than it is at the moment.

In the meantime, good luck to all of you who are struggling with your doctoral studies in this field – we need you to succeed!

Reference

Popper, K. (1959) The Logic of Scientific Discovery London: Routlege

Conference: 8th EDEN Research Workshop on research in online learning and distance education

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Oxford Spires Four Pillars Hotel

Oxford Spires Four Pillars Hotel

What: Challenges for research into Open & Distance Learning: Doing Things Better: Doing Better Things

The focus of the event is on quality research discussed in unusual workshop setting with informal and intimate surroundings. The session formats will promote collaboration opportunities, including: parallel ‘research-speed-dating’ papers, team symposia sessions, workshops and demonstrations.

When: 26-28 October, 2014

Where: Oxford Spires Four Pillars Hotel, Oxford, England

Who: The Open University (UK) is the host institution in collaboration with the European Distance and E-Learning Network. Main speakers include:

  • Sian Bayne, Digital Education, University of Edinburgh, UK
  • Cristobal Cobo, Research Fellow, Oxford Internet Institute, University of Oxford, UK
  • Pierre Dillenbourg, CHILI Lab, EPFL Center for Digital Education, Swiss Federal Institute of Technology, Lausanne, Switzerland
  • Allison Littlejohn, Director, Caledonian Academy, Glasgow Caledonian University, Chair in Learning Technology, UK
  • Philipp Schmidt, Executive Director, Peer 2 Peer University / MIT Media Lab fellow, USA
  • Willem van Valkenburg, Coordinator Delft Open Education Team, Delft University of Technology,
    The Netherlands

How: Submission of papers, workshop themes, posters and demonstrations are due by September 1: see: http://www.eden-online.org/2014_oxford/call.html

 

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.

WCET’s analysis of U.S. statistics on distance education

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IPEDS 2

U.S.Department of Education (2014) Web Tables: Enrollment in Distance Education Courses, by State: Fall 2012 Washington DC: U.S.Department of Education National Center for Education Statistics

Hill, P. and Poulin, R. (2014) A response to new NCES report on distance education e-Literate, June 11

The U.S. Department of Education’s Institute of Education Sciences operates a National Center for Education Statistics which in turn runs the Integrated Postsecondary Education Data System (IPEDS). IPEDS is:

a system of interrelated surveys conducted annually by the U.S. Department’s National Center for Education Statistics (NCES). IPEDS gathers information from every college, university, and technical and vocational institution that participates in the federal student financial aid programs. The Higher Education Act of 1965, as amended, requires that institutions that participate in federal student aid programs report data on enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid. These data are made available to students and parents through the College Navigator college search Web site and to researchers and others through the IPEDS Data Center

Recently IPEDS released “Web Tables” containing results from their Fall Enrollment 2012 survey. This was the first survey in over a decade to include institutional enrollment counts for distance education students. In the article above, Phil Hill of e-Literate and Russell Poulin of WCET have co-written a short analysis of the Web Tables released by IPEDS.

The Hill and Poulin analysis

The main points they make are as follows:

  • overall the publication of the web tables in the form of a pdf is most welcome, in particular by providing a breakdown of IPEDS data by different variables such as state jurisdiction, control of institution, sector and student level
  • according to the IPEDS report there were just over 5.4 million students enrolled in distance education courses in the fall semester 2012 (NOTE: this number refers to students, NOT course enrollments).
  • roughly a quarter of all post-secondary students in the USA are enrolled in a distance education course.
  • the bulk of students in the USA taking distance education courses are in publicly funded institutions (85% of those taking at least some DE courses), although about one third of those taking all their classes at a distance are in private, for-profit institutions (e.g. University of Phoenix)
  • these figures do NOT include MOOC enrollments
  • as previously identified by Phil Hill in e-Literate, there is major discrepancy in the number of students taking at least one online course between the IPEDS study and the regular annual surveys conducted by Allen and Seaman at Babson College – 7.1 million for Babson and 5.5 million for IPEDS. Jeff Seaman, one of the two Babson authors, is also quoted in e-Literate on his interpretation of the differences. Hill and Poulin comment that the NCES report would have done well to at least refer to the significant differences.
  • Hill and Poulin claim that there has been confusion over which students get counted in IPEDS reporting and which do not. They suspect that there is undercounting in the hundreds of thousands, independent of distance education status.

Comment

There are lies, damned lies and statistics. Nevertheless, although the IPEDS data may not be perfect, it does a pretty good job of collecting data on distance education students across the whole of the USA. However, it does not distinguish between mode of delivery of distance education (are there still mainly print-based courses around)?

So we now have two totally independent analyses of distance education students in the USA, with a minimum number of 5.5 million and a maximum number of 7.1 million, i.e. between roughly a quarter and a third of all post-secondary students. From the Allen and Seaman longitudinal studies, we can also reasonably safely assume that online enrollments have been increasing between 10-20% per annum over the last 10 years, compared with overall enrollments of 2-5% per annum.

By contrast, in Canada we have no national data on either online or distance education students. It’s hard to see how Canadian governments or institutions can take evidence-based policy decisions about online or distance education without such basic information.

Lastly, thank you, Phil and Russ, for a very helpful analysis of the IPEDs report.

Update

For a more detailed analysis, see also:

Haynie, D. (2014) New Government Data Sheds Light on Online Learners US News, June 13

 

Opening up: chapter one of Teaching in a Digital Age

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The view when I was writing Chapter 1, from the Island of Braç, Croatia

The view when I was writing Chapter 1, from the Island of Braç, Croatia

I’ve not been blogging much recently, because (a) I’ve been on holiday for a month in the Mediterranean and (b) I’ve been writing my book.

Teaching in a Digital World

As you are probably aware, I’m doing this as an open textbook, which means learning to adapt to a new publishing environment. As well as writing a darned good book for instructors on teaching in in a digital age, my aim is to push the boundaries a little with open publishing, to move it out of the traditional publishing mode into a a truly open textbook, with the help of the good folks at BCcampus who are running their open textbook project.

You will see that there’s still a long way to go before we can really exploit all the virtues of openness in publishing, and I’m hoping you can help me – and BCcampus- along the way with this.

What I’d like you to do

What I’m hoping you will do is find the time to browse the content list and preface (which is not yet finalized) and read more carefully Chapter 1, Fundamental Change in Higher Education, then give me some feedback. To do this, just go to: http://opentextbc.ca/teachinginadigitalage/

The first thing you will realise is that there is nowhere to comment on the published version. (Ideally I would like to have a comment section after every section of each chapter.) I will be publishing another post about some of the technical features I feel are still needed within PressBooks, but in the meantime, please use the comment page on this post (in which case your comment will be public), or use the e-mail facility  at the bottom of the chapter or preface (in which case your comment will be private). Send to tony.bates@ubc.ca .

What kind of feedback?

At this stage, I’m looking more for comments on the substance of the book, rather than the openness (my next post will deal with the technical issues). To help you with feedback, here are some of the questions I’m looking for answers to:

  1. Market: from what you’ve read so far, does there appear to be a need for this type of book? Are there other books that already do what I’m trying to do?
  2. Structure: does Chapter 1 have the right structure? Does it flow and is it logically organized?How could it be improved?
  3. Content: is there anything missing, dubious or just plain wrong? References that I have missed that support (or challenge) the content would also be useful.
  4. Do the activities work for you? Are there more interesting activities you can think of? How best to provide feedback? (e.g. does the use of ‘Parts’ work for this?)
  5. Presentation: are there other media/better images I could use? Is the balance between text and media right?

What’s in it for you?

First, I hope the content will be useful. Chapter 1 is probably the least useful of all the chapters to come for readers of this blog, because it’s aimed at instructors who are not comfortable with using technology, but if the material is useful to you, you are free to use it in whatever way you wish, within the constraints of a Creative Commons license.

Second, the whole point of open education is to share and collaborate. I’m opening up my book and the process; in return can I get some help and advice? In anticipation and with a degree of nervousness I look forward to your comments.

Washington State Community College System plans online competency-based associate degree

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Whatcom Community College, Bellingham, WA

Whatcom Community College, Bellingham, WA

The Olympian (2014) Competency-based learning makes college credit more accessible for all The Bellingham Herald, June 3

The State of Washington is introducing a new online associate degree program in business studies. Some of the features::

  • competency-based, recognizing the value of what students already know and can do
  • state-wide
  • transferable to four year state universities
  • students could advance by demonstrating a command of the subject matter through a test or writing assignment rather than taking a whole course

The program offers great promise to older students who may draw on work experiences to complete their community college credits. It is also well-suited to students who are place-bound and unable to attend classes in person due to family and job commitments.

Washington State Community Colleges already have extensive online programs, but this will be the first competency-based program that they are offering.

 

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