January 23, 2017

Are you ready for blended learning?


I’ve just come back from visiting two universities in central Canada and I have also been getting feedback from pilot institutions on the questionnaire we are developing for a survey of online learning in Canada. Although I do not want to anticipate the results of the survey, some things are already becoming clear, especially about blended learning.


First of course there is the question of definition. What actually is blended learning? It clearly means different things to different people. I have tried to describe it as on a continuum of educational delivery (see graphic below):

From Chapter 9.1.2, Teaching in a Digital Age

From Chapter 9.1.2, Teaching in a Digital Age

Blended learning can be seen as

  • nothing more than Powerpoint slides in a classroom lecture,
  • extra homework online after a face-to-face class,
  • a ‘flipped’ classroom where the lecture is recorded and available online, and the class time is used for discussion and questions about the video
  • a totally re-designed course, where careful choices have been made about what is done online and what in class (hybrid).

When there are so many different meanings for the same phrase, it becomes somewhat meaningless. For this reason, one recommendation made to us most strongly was that in our survey blended should be counted only when there is a deliberate replacement of face-to-face time with online learning. At least that should be measurable. But what if, in a flipped class, the lecture time is merely replaced with a face-to-face seminar, with the lecture online? Same amount of face-to-face teaching but an increased workload for the student.

It’s not about quantity; it’s about quality

If we take the broad definition to include all or most of the points above, we can certainly make one fairly confident prediction. Nearly all post-secondary teaching, at least in North America, will be blended. In other words, almost all teaching will be either fully online, or a mix of classroom and online activities, if it is not already. Even in the most traditional lecture-based physics courses, for instance, students are likely to have online exercises to do associated with the course set book.

In fact we’ve been told in some of the feedback on the survey questionnaire that blended learning is already the norm in most Canadian post-secondary institutions. This may or may not be true – hopefully the survey will reject or confirm this assumption – but that seems to be the perception of many of those closest to the action. The issue then is not will blended learning become the norm, but how quickly, and my guess is that nearly all courses in Canadian post-secondary institutions will be online or blended within the next five years.

The key question then is not whether or not blended learning will be the norm, but will it be done well or badly? It is this question that keeps me awake at night, because there is no guarantee that classroom instructors drifting into blended learning know anything about the best practices for online teaching, or indeed whether these best practices will migrate successfully to the many different forms of blended learning that will emerge.

What do we do on campus when students can learn most things online?

One reason I lie awake at night is because we have no evidence-based research or theory that can guide instructors on this question. We certainly have a lot of opinions about what can best be taught online and what face-to-face, and we certainly have a lot of good research and theory, and best practice, about how to teach effectively fully online.

Indeed, it is the on-campus activities that are less well defined when students can study online. Or to put it more bluntly, what can we offer students on campus that makes it worth their time to get out of bed and on the bus on a cold and frosty morning that they can’t get by staying home and studying online? What is the added value of the campus or the classroom?

The answer to this question of course will vary from subject to subject. An experienced instructor will maybe intuitively work this out for herself, but there is a lot of scope for getting it wrong as well. I don’t want to under-rate instructor intuition, but theory and research on this question is desperately needed, at least to offset guessing and ‘I know best’ attitudes. Indeed, for far too long, many on-campus instructors have incorrectly assumed that certain teaching or learning activities can only be done well on campus when in fact we have found they can be done just as well or better online. In the future, if not at present, even laboratory work may be done as well online through the use of remote labs, online simulations and/or augmented reality.

So what guidelines or framework can we offer instructors in making these decisions? I have suggested in Chapter 9 of Teaching in a Digital Age four criteria and a simple process for making a decision about the mode of delivery but I am more aware than anybody how fragile and tentative this is without it being backed by theory and research. It is also one thing to decide to do a blended class rather than a face-to-face class, but quite another to decide what should best be done in each of the different modes of delivery.

Why get on the bus when you can study online?

Why get on the bus when you can study online?

Organizational issues

Another factor which unfortunately is often the first issue that institutions try to determine when moving to blended learning is the organizational structure for the learning support units, such as those housing instructional designers, web and media developers, and technical support for LMSs, etc. For many institutions, it is recognized that mainline, on-campus faculty will need substantial learning technology and instructional design support if they are to move to blended learning, but the problem is perceived as having the support in the wrong places.

In many North American universities, this support is often concentrated in Continuing Studies, because, historically, this is the unit that has supported distance and fully online learning. Now that support is needed for on-campus activities. However, the units supporting fully online courses and programs are usually themselves over-stretched, just managing the fully online courses.

Although it is important eventually to align support to where it is most needed, the problem should not be seen as an organizational issue but as a resource issue: there is just not enough existing resources going into academic support to cope with an expansion into blended learning.

The scaling issue

This is the main reason for my lying awake at night. Institutions are already spending a good deal to support just the fully online courses or programs. We have good models here based on instructional designers and media specialists working in a team with instructors in developing fully online courses. This way, the special design requirements for students studying off campus can be met.

However, at the moment, fully online courses constitute somewhere around 10-15% of all the credit-based teaching in North American universities. What happens when we go to 85% or more of the teaching being blended? The current learning technology support model just won’t be able to handle this expansion, certainly not at the rate that it is being predicted. However, without a design strategy for blended learning, and adequate support for faculty and instructors, it is almost certain that the quality will be poor, and it is certain that all the potential benefits of blended learning for transforming the quality of teaching will not be achieved.

Trying to extend the support system from fully online to blended courses and programs will ultimately be unsustainable. Although support units will be essential to get blended learning successfully started, teaching activities must be economically sustainable, which means faculty and instructors will eventually need to become able to design and manage blended learning effectively without continuous and ongoing support from instructional designers and media producers. This will require a huge training and retraining effort for instructors.

Possible solutions

As always, identifying a challenge is much easier than resolving it. But here are some suggestions (please suggest others):

  • Develop an institutional strategy for teaching and learning. Give priority in terms of resources and support to those academic areas ready and wanting to move into innovative teaching, in whatever mode it takes.
  • Identify additional resources for a move to innovative teaching, in the form of extra instructional designers, media producers and release time for faculty for initial course design and development. (This is a good indicator of just how serious the institution is about changing teaching). This will provide a core of support to get things going in an effective manner.
  • Give priority to supporting innovative blended learning designs, where the course is re-designed with a clear rationale for what is being done online and what face-to-face.
  • In particular give priority to supporting academic programs that have a clear strategy for blended and online learning and how it will be delivered across the program
  • Encourage innovation in blended learning design, but ensure that it is properly evaluated and that there is a strategy, if the innovation is successful, for ensuring the design is more widely applied.
  • Don’t mess with successfully operating support units that already exist. If they were needed before for what they do, they are still needed for that. Set up new units to support the move to blended learning and locate them close to the academic departments where they will be needed. Build an institutional community of practice so that the different support units can learn from each other.
  • The most important suggestion of all: overhaul completely your faculty development and training. Start with an online or blended course on how to teach online or in a blended format. Make it mandatory for instructors getting institutional support for blended or online learning. Provide a teaching track for appointments, promotion and tenure to reward innovative teaching. Redesign the post-graduate experience to ensure that teaching methods and pedagogy are also covered as well as research expertise, and ensure a direct link between such courses and teaching appointments. Provide badges, certificates or post-graduate diplomas or degrees for instructors who can demonstrate they have taken courses on teaching in post-secondary education.
  • Give research into blended learning a high priority in the SSHRC; this is going to be the norm and we need to know what works and what doesn’t. In particular we need some good theory on the pedagogical differences between online and classroom teaching – not comparative research about which is best, but what each is uniquely suitable for within a particular subject discipline and teaching context.

Then you will be ready for blended learning.

Over to you

Do you share my concerns or am I just a nervous Nellie? Should we just leave everyone to work it out for themselves?

Alternatively, what do you think needs to be done to ensure that blended learning is introduced sustainably and with high quality?

Does your institution have a plan for dealing with the move to blended learning? Is it a good plan?


EDEN Research Workshop, October, 2016

The city of Olenburg Image: © Marcus Thielen, 2015

The city of Oldenburg
Image: © Marcus Thielen, 2015

What: Forging New Pathways of research and innovation in open and distance learning: reaching from the roots

The Ninth EDEN Research Workshop in Oldenburg, Germany, will bring together researchers from all walks of life and provide a platform for engaging in discussion and debate, exchanging research ideas, and presenting new developments in ODL, with the goal of creating dialogues and forming opportunities for research collaboration.

Workshop Themes:

  • emerging distance education systems and theories
  • management and organizational models and approaches
  • evolving practices in technology-enhanced learning and teaching


  • Olaf Zawacki-Richter, Carl von Ossietzki University, Oldenburg
  • Inge de Waard, The Open University, UK
  • Adnan Qayyum, Penn State university, USA
  • Som Naidu, Monash University, Australia
  • Paul Prinsloo, University of South Africa
  • George Veletsianos, Royal Roads University, Canada
  • Isa Jahnke, University of Missouri, USA

Types of sessions:

  • paper presentations
  • hands-on workshops
  • posters
  • demonstrations
  • ‘synergy’ sessions (to share and discuss EU projects)
  • training sessions

Where: Carl von Ossietzki University, Oldenburg, Germany. Oldenburg is a charming city in north east Germany between Bremen and Groningen.

When: 4-7 October, 2016

Who: The European Distance and e-Learning Network and the Centre for Distance Education, Carl von Ossietzki University. The university is a partner with the University of Maryland University College in offering a fully online Master in Distance Education and e-Learning, which has been running for many years. The Centre for Distance Education has published 15 books on distance education and e-learning in its ASF series.

How: Registration opens mid-August. For more details on registration, fees and accommodation go to the conference web site

Comment: EDEN Research Workshops are one of my favourite professional development activities. They bring together online learning researchers from all over Europe, and it is a remarkably efficient way to keep up to date not only with the latest research but also the technology trends in open and distance education that are getting serious attention. The conference is usually small (about 100-200 participants) and very well focused on practical aspects of research and practice in online learning and distance education.


Online learning for beginners: 2. Isn’t online learning worse than face-to-face teaching?

Distance education: anyone sitting more than 10 rows from the front

Distance learning: anyone sitting more than 10 rows from the front

The short answer to this question is: no, online learning is neither inherently worse – nor better – than face-to-face teaching; it all depends on the circumstances.

The research evidence

There have been thousands of studies comparing face-to-face teaching to teaching with a wide range of different technologies, such as televised lectures, computer-based learning, and online learning, or comparing face-to-face teaching with distance education.

With regard to online learning there have been several meta-studies. A meta-study combines the results of many ‘well-conducted scientific’ studies, usually studies that use the matched comparisons or quasi-experimental method (Means et al., 2011; Barnard et al., 2014). Nearly all such ‘well-conducted’ meta-studies find no or little significant difference in the modes of delivery, in terms of the effect on student learning or performance. For instance, Means et al. (2011), in a major meta-analysis of research on blended and online learning for the U.S. Department of Education, reported:

In recent experimental and quasi-experimental studies contrasting blends of online and face-to-face instruction with conventional face-to-face classes, blended instruction has been more effective, providing a rationale for the effort required to design and implement blended approaches. When used by itself, online learning appears to be as effective as conventional classroom instruction, but not more so.

However, the ‘no significant difference’ finding is often misinterpreted. If there is no difference, then why do online learning? I’m comfortable teaching face-to-face, so why should I change?

This is a misinterpretation of the findings, because there may indeed within any particular study be large differences between conditions (face-to-face vs online), but they cancel each other out over a wide range of studies, or because with matched comparisons you are looking at only very specific, strictly comparable conditions, that never exist in a real teaching context.

For instance the ‘base’ variable chosen is nearly always the traditional classroom. In order to make a ‘scientific’ comparison, the same learning objectives and same treatment (teaching) is applied to the comparative condition (online learning). This means using exactly the same kind of students, for instance, in both conditions. But what if (as is the case) online learning better suits non-traditional students, or will achieve better learning outcomes if the teaching is designed differently to suit the context of online learning?

Asking the right questions

Indeed, it is the variables or conditions for success that we should be examining, not just the technological delivery. In other words, we should be asking a question first posed by Wilbur Schramm as long ago as 1977:

What kinds of learning can different media best facilitate, and under what conditions?

In terms of making decisions then about mode of delivery, we should be asking, not which is the best method overall, but:

What are the most appropriate conditions for using face-to-face, blended or fully online learning respectively? 

So what are the conditions that best suit online learning?

There are a number of possible answers:

  • learners:
    • fully online learning best suits more mature, adult, lifelong learners who already have good independent learning skills and for work and family reasons don’t want to come on campus
    • blended learning or a mix of classroom and fully online courses best suits full time undergraduate students who are also working part-time to keep their debt down, and need the flexibility to do part of their studies online
    • ‘dependent’ learners who lack self-discipline or who don’t know how to manage their own learning probably will do better with face-to-face teaching; however independent learning is a skill that can be taught, so blended learning is a safe way to gradually introduce such students to more independent study methods
  • learning outcomes:
    • embedding technology within the teaching may better enable the development of certain ’21st century skills’, such as independent learning, confidence in using information technologies within a specific subject domain, and knowledge management
    • online learning may provide more time on task to enable more practice of skills, such as problem-solving in math
    • redesign of very large lecture classes, so that lectures are recorded and students come to class for discussion and questions, making the classes more interactive and hence improving learning outcomes

Even this is really putting the question round the wrong way. A better question is:

What are the challenges I am facing as an instructor (or my learners are facing as students) that could be better addressed through online learning? And what form of online learning will work best for my students?


However, the most important condition influencing the effectiveness of both face-to-face and online teaching is how well it is done. A badly designed and delivered face-to-face class will have worse learning outcomes than a well designed online course – and vice versa. Ensuring quality in online learning will be the topic of the last few blogs in this series.


  1. Don’t worry about the effectiveness of online learning. Under the right conditions, it works well.
  2. Start with the challenges you face. Keep an open mind when thinking about whether online learning might be a better solution than continuing in the same old way.
  3. If you think it might be a solution for some of your problems, start thinking about the necessary conditions for success. The next few blog posts should help you with this.

Follow up

Here is some suggested further reading on the effectiveness of online learning:

Up next

‘Aren’t MOOCs online learning?’ (to be posted later in the week July 18-22, 2016)

Comparing modes: horses for courses

Comparing modes: horses for courses

MIT aims to expand its research into learning

Diffusion tension imaging Satrajit Ghosh, MIT

Diffusion tension imaging Satrajit Ghosh, MIT

Chandler, D. (2016) New initiatives accelerate learning research and its applications MIT News, February 2

The President of MIT has announced a significant expansion of the Institute’s programs in learning research and online and digital education, through the creation of the MIT Integrated Learning Initiative (MITili).

The integrated science of learning — now emerging as a significant field of research — will be the core of MITili (to be pronounced “mightily”), a cross-disciplinary, Institute-wide initiative to foster rigorous quantitative and qualitative research on how people learn.

MITili will combine research in cognitive psychology, neuroscience, economics, engineering, public policy, and other fields to investigate what methods and approaches to education work best for different people and subjects. The effort will also examine how to improve the educational experience within MIT and in the world at large, at all levels of teaching.

The findings that spin out of MITili will then be applied to improve teaching on campus and online.


First, I very much welcome this initiative by a prestigious research university seriously to research what MIT calls the ‘science of learning’. Research into learning has generally been relatively poorly funded compared with research into science, engineering and computing.

However, I hope that MIT will approach this in the right way and avoid the hubris they displayed when moving into MOOCs, where they ignored all previous research into online learning.

It is critical that those working in MITili do not assume that there is nothing already known about learning. Although exploring the contribution that the physical sciences, such as biological research into the relationship between brain functionality and learning, can make to our understanding of learning is welcome, as much attention needs to be paid to the environmental conditions that support or inhibit learning, to what kind of teaching approaches encourage different kinds of learning, and to the previous, well-grounded research into the psychology of learning.

In other words, not only a multi-disciplinary, but also a multi-epistemological approach will be needed, drawing as much from educational research and the social sciences as from the natural sciences. Is MIT willing and able to do this? After all, learning is a human, not a mechanical activity, when all is said and done.

Automation or empowerment: online learning at the crossroads

Image: Applift

Image: AppLift, 2015

You are probably, like me, getting tired of the different predictions for 2016. So I’m not going to do my usual look forward for the year for individual developments in online learning. Instead, I want to raise a fundamental question about which direction online learning should be heading in the future, because the next year could turn out to be very significant in determining the future of online learning.

The key question we face is whether online learning should aim to replace teachers and instructors through automation, or whether technology should be used to empower not only teachers but also learners. Of course, the answer will always be a mix of both, but getting the balance right is critical.

An old but increasingly important question

This question, automation or human empowerment, is not new. It was raised by B.F. Skinner (1968) when he developed teaching machines in the early 1960s. He thought teaching machines would eventually replace teachers. On the other hand, Seymour Papert (1980) wanted computing to empower learners, not to teach them directly. In the early 1980s Papert got children to write computer code to improve the way they think and to solve problems. Papert was strongly influenced by Jean Piaget’s theory of cognitive development, and in particular that children constructed rather than absorbed knowledge.

In the 1980s, as personal computers became more common, computer-assisted learning (CAL or CAD) became popular, using computer-marked tests and early forms of adaptive learning. Also in the 1980s the first developments in artificial intelligence were applied, in the form of intelligent math tutoring. Great predictions were made then, as now, about the potential of AI to replace teachers.

Then along came the Internet. Following my first introduction to the Internet in a friend’s basement in Vancouver, I published an article in the first edition of the Journal of Distance Education, entitled ‘Computer-assisted learning or communications: which way for IT in distance education?’ (1986). In this paper I argued that the real value of the Internet and computing was to enable asynchronous interaction and communication between teacher and learners, and between learners themselves, rather than as teaching machines. This push towards a more constructivist approach to the use of computing in education was encapsulated in Mason and Kaye’s book, Mindweave (1989). Linda Harasim has since argued that online collaborative learning is an important theory of learning in its own right (Harasim, 2012).

In the 1990s, David Noble of York University attacked online learning in particular for turning universities into ‘Digital Diploma Mills’:

‘universities are not only undergoing a technological transformation. Beneath that change, and camouflaged by it, lies another: the commercialization of higher education.’

Noble (1998) argued that

‘high technology, at these universities, is often used not to ……improve teaching and research, but to replace the visions and voices of less-prestigious faculty with the second-hand and reified product of academic “superstars”.

However, contrary to Noble’s warnings, for fifteen years most university online courses followed more the route of interaction and communication between teachers and students than computer-assisted learning or video lectures, and Noble’s arguments were easily dismissed or forgotten.

Then along came lecture capture and with it, in 2011, Massive Open Online Courses (xMOOCs) from Coursera, Udacity and edX, driven by elite, highly selective universities, with their claims of making the best professors in the world available to everyone for free. Noble’s nightmare suddenly became very real. At the same time, these MOOCs have resulted in much more interest in big data, learning analytics, a revival of adaptive learning, and claims that artificial intelligence will revolutionize education, since automation is essential for managing such massive courses.

Thus we are now seeing a big swing back to the automation of learning, driven by powerful computing developments, Silicon Valley start-up thinking, and a sustained political push from those that want to commercialize education (more on this later). Underlying these developments is a fundamental conflict of philosophies and pedagogies, with automation being driven by an objectivist/behaviourist view of the world, compared with the constructivist approaches of online collaborative learning.

In other words, there are increasingly stark choices to be made about the future of online learning. Indeed, it is almost too late – I fear the forces of automation are winning – which is why 2016 will be such a pivotal year in this debate.

Automation and the commercialization of education

These developments in technology are being accompanied by a big push in the United States, China, India and other countries towards the commercialization of online learning. In other words, education is being seen increasingly as a commodity that can be bought and sold. This is not through the previous and largely discredited digital diploma mills of the for-profit online universities such as the University of Phoenix that David Noble feared, but rather through the encouragement and support of commercial computer companies moving into the education field, companies such as Coursera, Lynda.com and Udacity.

Audrey Watters and EdSurge both produced lists of EdTech ‘deals’ in 2015 totalling between $1-$2 billion. Yes, that’s right, that’s $1-$2 billion in investment in private ed tech companies in the USA (and China) in one year alone. At the same time, entrepreneurs are struggling to develop sustainable business models for ed tech investment, because with education funded publicly, a ‘true’ market is restricted. Politicians, entrepreneurs and policy makers on the right in the USA increasingly see a move to automation as a way of reducing government expenditure on education, and one means by which to ‘free up the market’.

Another development that threatens the public education model is the move by very rich entrepreneurs such as the Gates, the Hewletts and the Zuckerbergs to move their massive personal wealth into ‘charitable’ foundations or corporations and use this money for their pet ‘educational’ initiatives that also have indirect benefits for their businesses. Ian McGugan (2015) in the Globe and Mail newspaper estimates that the Chan Zuckerberg Initiative is worth potentially $45 billion, and one of its purposes is to promote the personalization of learning (another name hi-jacked by computer scientists; it’s a more human way of describing adaptive learning). Since one way Facebook makes its money is by selling personal data, forgive my suspicions that the Zuckerberg initiative is a not-so-obvious way of collecting data on future high earners. At the same time, the Chang Zuckerberg initiative enables the Zuckerberg’s to avoid paying tax on their profits from Facebook. Instead then of paying taxes that could be used to support public education, these immensely rich foundations enable a few entrepreneurs to set the agenda for how computing will be used in education.

Why not?

Technology is disrupting nearly every other business and profession, so why not education? Higher education in particular requires a huge amount of money, mostly raised through taxes and tuition fees, and it is difficult to tie results directly to investment. Surely we should be looking at ways in which technology can change higher education so that it is more accessible, more affordable and more effective in developing the knowledge and skills required in today’s and tomorrow’s society?

Absolutely. It is not so much the need for change that I am challenging, but the means by which this change is being promoted. In essence, a move to automated learning, while saving costs, will not improve the learning that matters, and particularly the outcomes needed in a digital age, namely, the high level intellectual skills of critical thinking, innovation, entrepreneurship, problem-solving , high-level multimedia communication, and above all, effective knowledge management.

To understand why automated approaches to learning are inappropriate to the needs of the 21st century we need to look particularly at the tools and methods being proposed.

The problems with automating learning

The main challenge for computer-directed learning such as information transmission and management through Internet-distributed video lectures, computer-marked assessments, adaptive learning, learning analytics, and artificial intelligence is that they are based on a model of learning that has limited applications. Behaviourism works well in assisting rote memory and basic levels of comprehension, but does not enable or facilitate deep learning, critical thinking and the other skills that are essential for learners in a digital age.

R. and D. Susskind (2015) in particular argue that there is a new age in artificial intelligence and adaptive learning driven primarily by what they call the brute force of more powerful computing. Why AI failed so dramatically in the 1980s, they argue, was because computer scientists tried to mimic the way that humans think, and computers then did not have the capacity to handle information in the way they do now. When however we use the power of today’s computing, it can solve previously intractable problems through analysis of massive amounts of data in ways that humans had not considered.

There are several problems with this argument. The first is that the Susskinds are correct in that computers operate differently from humans. Computers are mechanical and work basically on a binary operating system. Humans are biological and operate in a far more sophisticated way, capable of language creation as well as language interpretation, and use intuition as well as deductive thinking. Emotion as well as memory drives human behaviour, including learning. Furthermore humans are social animals, and depend heavily on social contact with other humans for learning. In essence humans learn differently from the way machine automation operates.

Unfortunately, computer scientists frequently ignore or are unaware of the research into human learning. In particular they are unaware that learning is largely developmental and constructed, and instead impose an old and less appropriate method of teaching based on behaviourism and an objectivist epistemology. If though we want to develop the skills and knowledge needed in a digital age, we need a more constructivist approach to learning.

Supporters of automation also make another mistake in over-estimating or misunderstanding how AI and learning analytics operate in education. These tools reflect a highly objectivist approach to teaching, where procedures can be analysed and systematised in advance. However, although we know a great deal about learning in general, we still know very little about how thinking and decision-making operate biologically in individual cases. At the same time, although brain research is promising to unlock some of these secrets, most brain scientists argue that while we are beginning to understand the relationship between brain activity and very specific forms of behaviour, there is a huge distance to travel before we can explain how these mechanisms affect learning in general or how an individual learns in particular. There are too many variables (such as emotion, memory, perception, communication, as well as neural activity) at play to find an isomorphic fit between the firing of neurons and computer ‘intelligence’.

The danger then with automation is that we drive humans to learn in ways that best suit how machines operate, and thus deny humans the potential of developing the higher levels of thinking that make humans different from machines. For instance, humans are better than machines at dealing with volatile, uncertain, complex and ambiguous situations, which is where we find ourselves in today’s society.

Lastly, both AI and adaptive learning depend on algorithms that predict or direct human behaviour. These algorithms though are not transparent to the end users. To give an example, learning analytics are being used to identify students at high risk of failure, based on correlations of previous behaviour online by previous students. However, for an individual, should a software program be making the decision as to whether that person is suitable for higher education or a particular course? If so, should that person know the grounds on which they are considered unsuitable and be able to challenge the algorithm or at least the principles on which that algorithm is based? Who makes the decision about these algorithms – a computer scientist using correlated data, or an educator concerned with equitable access? The more we try to automate learning, the greater the danger of unintended consequences, and the more need for educators rather than computer scientists to control the decision-making.

The way forward

In the past, I used to think of computer scientists as colleagues and friends in designing and delivering online learning. I am now increasingly seeing at least some of them as the enemy. This is largely to do with the hubris of Silicon Valley, which believes that computer scientists can solve any problem without knowing anything about the problem itself. MOOCs based on recorded lectures are a perfect example of this, being developed primarily by a few computer scientists from Stanford (and unfortunately blindly copied by many people in universities who should have known better.)

We need to start with the problem, which is how do we prepare learners for the knowledge and skills they will need in today’s society. I have argued (Bates, 2015) that we need to develop, in very large numbers of people, high level intellectual and practical skills that require the construction and development of knowledge, and that enable learners to find, analyse, evaluate and apply knowledge appropriately.

This requires a constructivist approach to learning which cannot be appropriately automated, as it depends on high quality interaction between knowledge experts and learners. There are many ways to accomplish this, and technology can play a leading role, by enabling easy access to knowledge, providing opportunities for practice in experientially-based learning environments, linking communities of scholars and learners together, providing open access to unlimited learning resources, and above all by enabling students to use technology to access, organise and demonstrate their knowledge appropriately.

These activities and approaches do not easily lend themselves to massive economies of scale through automation, although they do enable more effective outcomes and possibly some smaller economies of scale. Automation can be helpful in developing some of the foundations of learning, such as basic comprehension or language acquisition. But at the heart of developing the knowledge and skills needed in today’s society, the role of a human teacher, instructor or guide will remain absolutely essential. Certainly, the roles of teachers and instructors will need to change quite dramatically, teacher training and faculty development will be critical for success, and we need to use technology to enable students to take more responsibility for their own learning, but it is a dangerous illusion to believe that automation is the solution to learning in the 21st century.

Protecting the future

There are several practical steps that need to be taken to prevent the automation of teaching.

  1. Educators – and in particular university presidents and senior civil servants with responsibility for education – need to speak out clearly about the dangers of automation, and the technology alternatives available that still exploit its potential and will lead to greater cost-effectiveness. This is not an argument against the use of technology in education, but the need to use it wisely so we get the kind of educated population we need in the 21st century.
  2. Computer scientists need to show more respect to educators and be less arrogant. This means working collaboratively with educators, and treating them as equals.
  3. We – teachers and educational technologists – need to apply in our own work and disseminate better to those outside education what we already know about effective learning and teaching.
  4. Faculty and teachers need to develop compelling technology alternatives to automation that focus on the skills and knowledge needed in a digital age, such as:
    • experiential learning through virtual reality (e.g. Loyalist College’s training of border service agents)
    • networking learners online with working professionals, to solve real world problems (e.g. by developing a program similar to McMaster’s integrated science program for online/blended delivery)
    • building strong communities of practice through connectivist MOOCs (e.g. on climate change or mental health) to solve global problems
    • empowering students to use social media to research and demonstrate their knowledge through multimedia e-portfolios (e.g. UBC’s ETEC 522)
    • designing openly accessible high quality, student-activated simulations and games but designed and monitored by experts in the subject area.
  5. Governments need to put as much money into research into learning and educational technology as they do into innovation in industry. Without better and more defensible theories of learning suitable for a digital age, we are open to any quack or opportunist who believes he or she has the best snake oil. More importantly, with better theory and knowledge of learning disseminated and applied appropriately, we can have a much more competitive workforce and a more just society.
  6. We need to educate our politicians about the dangers of commercialization in education through the automation of learning and fight for a more equal society where the financial returns on technology applications are more equally shared.
  7. Become edupunks and take back the web from powerful commercial interests by using open source, low cost, easy to use tools in education that protect our privacy and enable learners and teachers to control how they are used.

That should keep you busy in 2016.

Your views are of course welcome – unless you are a bot.


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