July 26, 2016

Automation or empowerment: online learning at the crossroads

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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.

References

Bates, A. (1986) Computer assisted learning or communications: which way for information technology in distance education? Journal of Distance Education Vol. 1, No. 1

Bates, A. (2015) Teaching in a Digital Age Victoria BC: BCcampus

Harasim, L. (2012) Learning Theory and Online Technologies New York/London: Routledge

Mason, R. and Kaye, A (Eds).(1989)  Mindweave: communication, computers and distance education. Oxford: Pergamon

McGugan, I. (2015)Why the Zuckerberg donation is not a bundle of joy, Globe and Mail, December 2

Noble, D. (1998) Digital Diploma Mills, Monthly Review http://monthlyreview.org/product/digital_diploma_mills/

Papert, S. (1980) Mindstorms: Children, Computers and Powerful Ideas New York: Basic Books

Skinner, B. (1968)  The Technology of Teaching, 1968 New York: Appleton-Century-Crofts

Susskind, R. and Susskind, D. (2015) The Future of the Professions: How Technology will Change the Work of Human Experts Oxford UK: Oxford University Press

Watters, A. (2015) The Business of EdTech, Hack Edu, undated http://2015trends.hackeducation.com/business.html

Winters, M. (2015) Christmas Bonus! US Edtech Sets Record With $1.85 Billion Raised in 2015 EdSurge, December 21 https://www.edsurge.com/news/2015-12-21-christmas-bonus-us-edtech-sets-record-with-1-85-billion-raised-in-2015

That was the year, that was: main trends in 2015

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Image: http://goodbye2015welcome2016.com/

Image: http://goodbye2015welcome2016.com/

Well, here we are at the end of another year. Doesn’t time fly! So here is my look back on 2015. I’ll do this in three separate posts. This one focuses on what I saw as the main trends in online learning in 2015.

Gradual disengagement

It was April, 2014, when I decided to stop (nearly) all professional activities, in order to complete my book, Teaching in a Digital Age, which came out in April this year. A year and eight months later, though, I haven’t stopped completely, as you will see. However, most of my activities this year were related to the publication or follow-up from the book. As a result I have reduced considerably my professional activities and this reduction will continue into 2016. Because I was less engaged this year with other institutions, I don’t have a good grip on all the things that happened during 2015 in the world of online learning. For a thorough review, see Audrey Watters excellent Top Ed-Tech Trends of 2015.

Nevertheless I’m not dead yet, I have been doing some work with universities (see next post), and I have been following the literature and talking to colleagues, so here’s what I took away from 2015.

1. The move to hybrid learning

This is clearly the biggest and most significant development of 2015. More and more faculty are now almost routinely integrating online learning into their campus-based classes. The most common way this is being done (apart from using an LMS to support classroom teaching) still remains ‘flipped’ classrooms, where students watch a lecture online then come to class for discussion.

There are lots of problems with this approach, in particular the failure to make better pedagogical use of video and the failure of many students to view the lecture before coming to class, but for many faculty it is an obvious and important first step towards blended learning, and more importantly it has the potential for more active engagement from learners.

As instructors get more experience of this, though, they start looking at better ways to combine the video and classroom experiences. The big challenge then becomes how best to use the student time on campus, which is by no means always obvious. The predominant model of hybrid learning though is still the (recorded) lecture model, but adapted somewhat to allow for more discussion in large classes.

In most flipped classroom teaching, the initiative tends to come from the individual instructor, but some institutions, such as the University of British Columbia and the University of Ottawa, are putting in campus-wide initiatives to redesign completely the large lecture class, involving teams of faculty, teaching assistants and instructional and web designers. I believe this to be the ‘true’ hybrid approach, because it looks from scratch at the affordances of online and face-to-face teaching and designs around those, rather than picking a particular design such as a flipped lecture. I anticipate that university or at least program-wide initiatives for the redesign of large first and second year classes will grow even more in 2016.

UBC's flexible learning initiative focuses on re-design to integrate online and classroom learing

UBC’s flexible learning initiative focuses on re-design to integrate online and classroom learing

2. Fully online undergraduate courses

Until fairly recently, the only institutions offering whole undergraduate programs fully online were either the for-profit institutions such as the University of Phoenix, or specialist open universities, such as the U.K Open University or Athabasca University in Canada.

Most for-credit online programs in conventional universities were at the graduate level, and even then, apart from online MBAs, fully online master programs were relatively rare. At an undergraduate level, online courses were mainly offered in third or more likely the fourth year, and more on an individual rather than a program basis, enabling regular, on-campus students to take extra courses or catch up so they could finish their bachelor degree within four years.

However, this year I noticed some quite distinguished Canadian universities building up to full undergraduate degrees available fully online. For instance, McMaster University is offering an online B.Tech (mainly software engineering) in partnership with Mohawk College. Students can take a diploma program from Mohawk then take the third and fourth year fully online from McMaster. Similarly Queens University, in partnership with the Northern College Haileybury School of Mines, is developing a fully online B.Tech in Mining Engineering. Queens is also developing a fully online ePre-Health Honours Bachelor of Science, using competency-based learning.

Fully online undergraduate programs will not be appropriate for all students, particularly those coming straight from high school. But the programs from Queens and McMaster recognise the growing market for people with two-year college diplomas, who are often already working and want to go on to a full undergraduate degree without giving up their jobs.

3. The automation of learning

Another trend I have noticed growing particularly strong in 2015, and one that I don’t like, is the tendency, particularly but not exclusively in the USA, to move to the automation of learning through behaviourist applications of computer technology. This can be seen in the use of computer-marked assignments in xMOOCs, the use of learning analytics to identify learners ‘at risk’, and adaptive learning that controls the way learners can work through materials. There are some elements of competency-based learning that also fit this paradigm.

This is a big topic which I will discuss in more detail in the new year in my discussion of the future of learning, but it definitely increased during 2015.

4. The growing importance of open source social media in online learning design

I noticed more and more instructors and instructional designers are incorporating social media into the design of online learning in 2015. In particular, more instructors are moving away from learning management systems and using open source social media such as blogs, wikis, and mobile apps, to provide flexibility and more learner engagement.

One important reason for this is to move away from commercially owned software and services, partly to protect student (and instructor) privacy. In a sense, this also a reaction to the automation and commercialization of learning, reflecting a difference in fundamental philosophy as well as in technology. Again, the increased use of social media in online learning is discussed in much more detail by Audrey Watters (see Social Media, Campus Activism and Free Speech).

5. More open educational materials – but not enough use

For me, the leader in OER in 2015 was the BCcampus open textbook project, and not just because I published my own book this way. This is proving to be a very successful program, already saving post-secondary students over $1 million from a total post-secondary student population of under 250,000. The only surprise is that many BC instructors are still resisting the move to open textbooks and that more jurisdictions outside Western Canada are not moving aggressively into open textbooks.

The general adoption of OER indeed still seems to be struggling. I noticed that some institutions in Ontario are beginning to develop OER that can be shared across different courses within the same institution (e.g. statistics). However, it would be much more useful if provincial or state articulation committees came together and agreed on the production of core OER that could be used throughout the same system within a particular discipline (and also, of course, made available to anyone outside). This way instructors would know the resources have been peer validated. Other ways to encourage faculty to use OER – in particular, ensuring the OER are of high quality both academically and in production terms – need to be researched and applied. It doesn’t make sense for online learning to be a cottage industry with every instructor doing everything themselves.

Is that it?

Yup. As I said, mine is a much narrower view of online learning trends than I have done in the past. You will note that I have not included MOOCs in my key trends for 2015. They are still there and still growing, but a lot of the hype has died down, and they are gradually easing into a more specialist niche or role in the wider higher education market. My strategy with MOOCs is if you can’t beat them, ignore them. They will eventually go away.

Next

The next two posts will:

  1. provide a summary of my activities in 2015
  2. provide a statistical analysis of the most popular posts on my blog in 2015

In the new year I will write a more general post on the future of online learning. In the meantime, have a great holiday season and see you in 2016.

Research on ‘academic innovation centres’ supporting online learning

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One of the Academic Innovation Centres in the study

UT Austin Learning Sciences was one of the Academic Innovation Centres in the study

Bishop, M. and Keehn, A. (2015) Leading Academic Change: An Early market Scan of Leading-edge Postsecondary Academic Innovation Centers Adelphi ML: William E. Kirwan centre for Academic Innovation, University System of Maryland

What is this paper about?

This is a paper about the development of ‘academic innovation centers’ in the USA. These go by a variety of names, such as ‘the Centre for Teaching and Learning’ or ‘the Centre for Learning Sciences’, but they are basically integrating faculty development, instructional design and a range of other services for faculty (and in some cases also directly for students) to provide a locus for innovation and change in teaching and learning.

Methodology

Information was collected in three ways:

  • a Leading Academic Change summit, to which 60 academic innovation leaders were invited to engage in discussions around how academic transformation efforts are unfolding in their campuses
  • interviews with 17 ‘particularly  innovative academic transformation leaders’, to talk about the evolution of teaching and learning centres at their institutions
  • a ‘national’ survey of campus centres for teaching and learning; 163 replied to the survey (there are over 4,000 colleges and universities in the USA).

Main results and conclusions

The paper should be read carefully and in full, as there are some interesting data and findings, but here are the main points I was interested in:

  • the information collected in this study ‘seems to point to the  emergence of new, interdisciplinary innovation infrastructures within higher education administration.’
  • this includes new senior administrative positions, such as Vice Provost for Innovation in Learning and Student Success, or Associate Provost for Learning Initiatives
  • the new centres bring together previously separate support departments into a single integrated centre, thus breaking down some of the previous silos around teaching and learning
  • their focus is on online, blended and hybrid course design or re-design, improving faculty engagement with students, and leveraging instructional/learning platforms  for  instruction.
  • some of the centres are going beyond faculty development and are focusing on ensuring new initiatives lead to student success;
  • the leaders of these new centres are usually respected academics (rather than instructional designers, for instance) who may lack experience or knowledge in negotiating institutional cultures or change management

Comment

Despite the methodological issues with such a study, which the authors themselves recognise, the evidence of the development of these ‘academic innovation centres’ fits with my recent experience in visiting Canadian universities over the last two years or so, although I suspect this study focuses more on the ‘outliers’ with regard to innovation and change in USA universities and colleges.

What I find particularly interesting are the following:

  • the desire to ensure that faculty become the leaders of such centres, even though they may lack experience in bringing about institutional change, and in addition may not have a strong background in learning technologies. Perhaps they should read the book I co-wrote with Albert Sangra, ‘Managing Technology in Higher Education‘, which directly addresses these issues;
  • the study found that neither technology nor even faculty success was the leading focus of these centres, but rather student success. This is a much needed if subtle change of direction, although the report did not suggest how the link between innovation in teaching and student success might be identified or measured. I suspect that this will be a difficult challenge.
  • where does the move to integrated centres leave Continuing Studies departments, which often have the instructional design and online learning expertise (at least in many Canadian universities)? The actual location of such staff is not so important as the intent to work collaboratively across institutional boundaries, but for that to happen there has to be a strongly supported common vision for the future development of teaching and learning shared across all the relevant organizational divisions. Organisational re-alignment can’t operate successfully in a policy vacuum.

Nevertheless if what is reported here is representative of what is happening in at least some of the leading U.S. universities, it is encouraging, although I would like to see a more rigorous and comprehensive study of the issue before I throw my hat into the air.

Using MOOCs to help refugees

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Refugees applying to the University of Magdeburg in Germany

Refugees applying to the University of Magdeburg in Germany

Yohannes, M. and Bhatti, J. (2015) Migrants get help through German online university, USA Today, October 29

This article reports on Kiron University, a non-profit university set up exclusively to support refugees awaiting official asylum status while remaining in their host countries. Currently it is supporting approximately 1,000 students from 60 different countries.

The Kiron web site states:

Kiron is an international university for refugees, headquartered in Germany, providing refugees with higher education and the opportunity to graduate at a university free of charge. Because the first two years of the degree programs are online, Kiron’s students can study flexibly from all over the world and according to their own schedule. The special circumstances refugees have to face are carefully considered by offering additional services like preparation courses for university, language courses, psychological counselling, life coaching, hardware, internet access and facilities such as Kiron’s campus in Berlin. All of this is also free of charge.

For the first two years, Kiron’s students can choose courses out of the whole universe of MOOCs. Kiron takes these courses, modifies them, and designs study programs with real-life working sessions, projects in teamwork, mentoring, student support and modern ways of learning and testing. All of this is done with the careful supervision of their partner universities as well as experienced professors, experts in education and established educational institutions. For the third year, Kiron’s students go to a classic university attending regular courses. They can choose out of a variety of well established institutions like RWTH Aachen, the Applied University Heilbronn or the Open University of West Africa.

Kiron has a campus in Berlin which provides a housing option for more than 500 students and the opportunity to offer 20 seminar rooms and 10 lecture rooms, to support the online curriculum via tutorials and on-campus class experiences.

Kiron is funded currently by a German foundation but is also using crowdfunding to provide scholarships for refugees (see https://kiron.university/). The cost to Kiron for one student for an academic year is approximately 400 euros (US$450), although a full scholarship costs Kiron 1,200 euros (US$1,400). As well as funding, Kiron is looking for volunteers to help with its programs.

Kiron has asked selected scholars across different disciplines such as philosophy and computer science to join their evaluation board and help them understand better who refugees are and how they can help them. Kiron would like to financially support academic field studies as well as the publication of academic research in the field of (forced) migration and e-learning. Several research projects are already in preparation and will be presented to the public at a conference in Berlin.

Comment

Although I would like to know more about Kiron, this seems a splendid idea. Less than 1% of refugees globally have access to higher education, according to the United Nations High Commissioner for Refugees. Of the estimated 60 million refugees globally, around half are under 18 — a record high — meaning many young people have little opportunity to train for future jobs. I believe the Arab Open University is working with refugees in Jordan. (I would be happy to publicise any such efforts in this blog).

All this makes me wonder though whether some of the existing open universities in the U.K., Netherlands, Spain and Canada could not partner with Kiron or establish their own programs to extend both the range of courses and support the learning of refugees, given the millions still in refugee camps.

For instance, the new Canadian government has pledged to accept 25,000 Syrian refugees by Christmas this year. However, that will still leave many thousands more waiting to be processed. “People have to wait for a year to have an interview to begin the asylum process, which means that in this time they can’t even do a language course,” said Kiron co-founder Markus Kressler, a graduate student who runs the online university with 80 other volunteers. Could not Athabasca University for instance work with UNHCR and Kiron to identify those waiting processing for Canada, and provide them with appropriate courses and programs before they arrive? I’m sure there are many obstacles to this, but having refugees arriving with qualifications from your own country must certainly benefit both the refugees and the host country.

In the meantime I hope you will join me in supporting Kiron, in one way or another.

Thinking about theory and practice in online learning

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Taking the float plane to Victoria: always a wonderful experience

Taking the float plane to Victoria: always a wonderful experience

I ran a short face-to-face workshop yesterday on ‘Thinking about Theory and Practice’ for about a dozen students taking the Masters of Arts in Learning and Technology at Royal Roads University  My online open textbook, Teaching in a Digital Age, is being used in this program and the instructors asked me to run a workshop on this topic, as students struggle with the relationship between epistemology, theories of learning, and methods of teaching.

The exercise

I’m not surprised that students struggle with this, as the relationships are by no means clear. I started by asking them to define different epistemologies. I then asked them what the connection was between different epistemologies and different learning theories. Then I asked them to choose from about 18 different methods or approaches to teaching (all covered in my book) and try to place them in relationship to theories of learning, as in Figure 1 below.

Figure 1: Thinking about theory and practice

Figure 1: Thinking about theory and practice

I also raised questions about whether constructivism and connectivism are epistemologies, or theories of learning, or both.

This was meant as a heuristic exercise, to get students arguing about and discussing the relationship between epistemology, theory, and practice, and why it is important to think about this in terms of learning design.

I ended my session with the following questions:

  • Constructivism and connectivism: are they epistemologies or learning theories?
  • Is there a direct relationship between epistemology, theory and practice?
  • How well do different teaching methods ‘fit’ with a specific learning theory?
  • Does technology change the nature of knowledge? If so, is connectivism an ‘adequate’ epistemology for a digital age?

Following my workshop, in the afternoon the students were divided into two teams to formally debate the motion (chosen by the instructors):

Connectivism should be adopted as the learning theory for educating students in our digital culture.

Both the workshop and the debate resulted in very thoughtful and forceful, sometimes impassioned, discussion.

Outcomes

It is impossible to capture the richness of the discussions in a short blog (I am hoping that the MALAT team will make an edited recording of the sessions available online). Different participants will have come away from the two sessions with different conclusions. Although I am fairly confident about discussing theories of learning and methods of teaching, I am not a trained or qualified philosopher, so I hesitate to tell students what the truth is in this area (OK, so I’m a relative constructivist).

However, here are some of my conclusions:

  • the most important is that I believe that connectivism is more of an epistemology than a theory of learning. Indeed it is an epistemology that relies on other theories of learning to explain how learning occurs in networks, although it has established conditions that make for ‘effective’ networks (see, for instance, Downes, 2007). In this sense it can be seen as an overall belief system about the importance of networks for sustaining and creating knowledge, but the mechanisms by which learning occurs in networks still need to be identified or worked out, or explained in terms of existing theories, such as constructivism.  This does not mean that over time, particular ways of learning and creating new knowledge through networking will not be identified, but more importantly, it would seem to make sense that we should be making use of networks and social media in education, since we are all becoming increasingly immersed in a connectivist world, and learning how to adapt and thrive in such a world probably requires using connections and networks for teaching and learning;
  • similarly, I am uncomfortable with defining constructivism as an epistemology. It is a strong theory in terms of explaining how learning occurs, but it takes its philosophical roots from other more general epistemologies. I would need to be a philosopher to define accurately what these would be, but constructivism is strongly influenced by philosophers such as John Stuart Mill (free will), Jean Jacques Rousseau (the Natural Human), and Jean Piaget (‘genetic’ epistemology);
  • although there is some relationship between epistemologies and theories of learning, they are not isomorphic, in the sense that a single theory of learning derives solely from one epistemological position. For instance, cognitive theories of learning draw heavily on both objectivist approaches (e.g. brain research) and more subjective or reflective approaches, such as constructivism;
  • there is even less isomorphism between theories of learning and methods of teaching, because methods of teaching are driven primarily by context. For instance, in a digital age, trades apprentices increasingly need both manual and cognitive learning. The learning of manual or mechanical skills through an apprenticeship model may be behaviourist in approach, but cognitive apprenticeship may draw much more heavily on a constructivist approach. Nevertheless some teaching methods, such as lectures or xMOOCs, are generally more towards the objectivist spectrum, while cMOOCs are more towards the connectivist spectrum (even though in practice they may include other approaches, such as more objectivist webinars, and support from teachers or experts through constructivist forms of discussion);
  • different subject areas tend to favour different epistemological positions, such as science favouring more objectivist approaches to teaching, and arts more subjective and interpretive approaches. However, it is still possible to teach science in a constructivist way – for instance through problem or inquiry-based learning – and arts in a more objectivist way (for instance, Mrs. Thatcher wanted British school children to learn the facts about British history, rather than discuss imperialism or racism and their legacies), although purists will argue that students will not become ‘true’ scientists or historians if the teaching does not reflect the ‘core’ epistemological nature of the subject area.

However, I’m a ‘relativist’ on all these points and open to be persuaded.

Does it matter?

Isn’t this all terribly abstract and philosophical? Nothing seems clear and definite, so how does thinking about these things help to teach better?

Well, if you are going to be an instructional designer, you will come across instructors and subject experts who may have a fundamentally different epistemological position from you. It will really help if you understand their position and how to take this into account when designing courses.

Second, there is nothing more practical than a good theory. If you have a theory that is convincing to you in terms of explaining how learners best learn, this should drive your teaching practice. It may not tell you exactly what to do as a teacher, but it should enable you to work out for yourself what to do – and more importantly, what learners need to do. But this theory needs to fit with your overall epistemological position about the nature of knowledge in your subject area.

Third, teaching is a pragmatic profession. It may take several different approaches, depending on the context and above all on the learner. In some contexts, such as safety compliance, employers don’t want workers questioning the process; they need to learn exactly what to do in a particular circumstance (behaviourism rules). In others, where problem-solving is essential, rote learning is not going to help dealing with a new or unanticipated danger.  Having a range of options in terms of teaching approaches for a range of different kinds of learners and contexts is more likely to produce results than slavishly following one particular method.

Lastly, all this uncertainty and choice illustrates why teaching and learning are not well defined activities that can be easily mechanised. Humans are better than machines at dealing with uncertainty and fuzzy or ambiguous circumstances, but only if they have a deep understanding of the options available to them and the circumstances in which each option is likely to succeed. This means thinking carefully about epistemology and theories of learning as well as various methods of teaching.

Galiano Island, on the way to Victoria

Galiano Island, on the way to Victoria. Vancouver Island is in the background.