July 24, 2016

Online learning for beginners: 3. ‘Aren’t MOOCs online learning?’

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NZ MOOCs 2

What are MOOCs?

Just in case you don’t know what MOOCs are (massive, open online courses), they are usually courses that use video recordings of lectures from top professors from elite universities, such as Stanford, MIT and Harvard, and computer-marked assessments, sometimes combined with unmonitored online student discussions and peer review. MOOCs are made freely available to anyone who wants to sign up. The main platforms for MOOCs are Coursera, edX, Udacity and FutureLearn.

The first MOOCs attracted over 200,000 enrolments per course, although numbers in recent years are more in the 2,500 range. Nevertheless it is estimated that there are more than 34 million participants worldwide registering in MOOCs each year.

Since the first ones launched in 2008, MOOCs have been rapidly evolving.

MOOCs vs online credit courses

Given all the publicity and hype over MOOCs, you could be forgiven for thinking that MOOCs are all you need to know about online learning. However, you would be sadly mistaken.

Online learning existed as a serious part of education at least 15 years before MOOCs arrived on the scene. The following graph shows the increase in online courses for credit up to 2012 in the USA post-secondary education system, before the first MOOCs were launched:

Allen and Seaman, 2013

Allen and Seaman, 2013

By 2013 at least one in three students in post-secondary education was taking at least one online course as part of a degree program. At the moment according to the U.S. Department of Education somewhere between 8-15% of all university degree course enrolments are in fully online courses. Online course enrolments continue to grow at rate (10-20% per annum) much faster than enrolments for on-campus courses (2-3% per annum) (Allen and Seaman, 2016).

So what’s the difference?

  • MOOCs have much higher numbers of initial participants generally than online credit courses; MOOCs can have anywhere between 2,000 to 200,000 participants who sign up, whereas online courses for credit can have anywhere between 20 to 2,000 registered enrolments. Fully online courses for credit usually though have 100 enrolments per course or less;
  • MOOCs, with very few exceptions, do not provide credits towards degrees, although a certificate may be issued (for a price) for those that complete computer-based assessments. However, even the institutions offering MOOCs do not accept successful completion of their courses towards credit in their own institution;
  • MOOCs have very low successful completion rates (less than 10%, usually closer to 5%) whereas fully online courses for credit often have completion rates as high or just below those for equivalent face-to-face courses. For instance in Ontario in 2011, completion rates for all fully online courses for credit in the Ontario public post-secondary system were within 5% of completion rates for face-to-face classes in universities, and within 10% for two year colleges; in other words roughly 80% or more of students in fully online courses for credit will successfully complete;
  • MOOCs provide almost no personal learning support for learners from qualified instructors, whereas most successful fully online courses for credit have a strong instructor online presence;
  • MOOCs generally charge no fee to participate (although a fee may be charged for a certificate of completion); fully online courses for credit normally charge the same fee as, or slightly higher than, those for campus-based courses or programs.

In other words, MOOCs are just one, more recent, form of online learning. They are more like continuing education programs, except they are free. Think of them as a modern form of educational television.

MOOC participation Image: Phil Hill

MOOC participation rates Image: Phil Hill, 2013

The hype

Much has been made about MOOCs disrupting the higher education system (Christensen, 2010), being a solution to educational problems in developing countries (Friedman, 2013), and being a threat to the existence of universities. Leslie Wilson of the European University Association has commented that MOOCs have forced Vice Chancellors to focus on teaching and learning (which I find a somewhat sad comment: why weren’t they focusing on that before MOOCs came along)?

However, after all the initial publicity, MOOCs have settled down into an important but relatively small niche in post-secondary education, a form of continuing education that still struggles to find a successful business model that works for the universities that supply MOOCs.

Why then all the fuss?

Good question! There is a combination of factors that have resulted in the publicity and hype.

One of the most important is that the development of MOOCs was largely driven by faculty (and mainly computer-science faculty) from highly prestigious, elite universities such as Stanford, MIT and Harvard. This has resulted in a bandwagon effect of follow my leader from other universities. Whatever the faults or weaknesses of MOOCs, these elite universities have made online learning highly visible, whereas before, although online courses for credit had been slowly gaining ground, online learning was still seen as peripheral and slightly disreputable.

MOOCs also coincided with a time when states in the USA were making big cuts in higher education budgets due to the 2008 financial recession, leading to lack of tax revenues; many saw MOOCs as an alternative to high cost, campus-based universities. Over time, this argument has become less convincing, partly due to the lack of recognition for credit of successful MOOC completion, and partly due to the difficulties of developing the high level of skills needed outside the purely quantitative subject areas with so little learner support .

Implications

  • Most faculty will need, at least in the short-term, to focus on online courses, blended or fully online, for credit, not MOOCs. These for credit online courses will need different approaches in terms of course design and learner support from MOOCs, if high completion rates are to be achieved and high level learning skills are to be developed in students;
  • For some ‘star’ faculty in subject areas where the university is particularly or uniquely strong, MOOCs will still be an attractive proposition, boosting both the star faculty member’s reach and reputation, and the brand of the university;
  • MOOC design will evolve, probably converging towards the designs used for successful for-credit online courses, but this will likely increase costs; at the same time, the design of for-credit courses may also benefit from some of the lessons in ‘scaling’ from successful MOOCs;
  • there are many other forms of online learning besides MOOCs, and within online courses for credit there are many different approaches; it is important to be aware of the strengths and weaknesses of each of these variations in online learning, so the appropriate choices can be made. This is the topic of my next post in this series.

Follow-up

If you want to know more about MOOCs, and their strengths and weaknesses, here is some suggested further homework (if you read/watch it all, possibly 2 hours of reading/watching):

Up next

‘What kinds of online learning are there?’ (to be posted early in the week 25-31 July, 2016)

Your turn

If you have comments, questions or plain disagree, please use the comment box below.

References

Allen, L. and Seaman, J. (2016) Online Report Card: Tracking Online Education in the United States Wellesley MA: Babson Survey Research Group

Christensen, C. (2010) Disrupting Class, Expanded Edition: How Disruptive Innovation Will Change the Way the World Learns New York: McGraw-Hill

Friedman, T. (2013) Revolution Hits the Universities New York Times, January 26

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

Low-cost online courses in film and media studies: do they work?

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Dustin Hoffman 2

Alexander, I. (2015) Over 30,000 students took these online film and media courses Film Industry Network, November 17

I haven’t been following Udemy, the former MOOC provider which has now moved to offering more vocational online courses, but I was interested in this article, which lists the 10 most popular online courses offered in film and media studies.  Udemy offers a number of these courses, but it is also facing strong competition from Masterclass, another provider of low cost, not-for-credit, online courses.

The average cost of a film and media studies program at a university in the USA is around $30,000. Udemy is offering courses in this domain from $300 downwards, some as little as $10 as promotional courses. However, this excludes the cost of the necessary equipment, which the article estimates at around $5,000.

Nevertheless there are huge savings to be made by going for these very low cost online courses. For instance, in the Masterclass series, you can learn acting from Dustin Hoffman, or tennis with Serena Williams, for as little as $90, through five hours of video lessons.

Most of the Udemy lessons on these courses are very short videos (less than six minutes each), although there are lots (50 in Udemy’s Facebook Marketing course, for example). Courses range in length of study, but are mainly in the range of two to ten hours each. Udemy offers a certificate for successful completion of these courses.

Comment

I have mixed feelings about these offerings. I can see that for people who want to dabble in the field or want to top up on their knowledge of a particular topic, such as Twitter marketing, or are interested in film or media production as a hobby, these courses are extremely good value. In particular, the Masterclass courses seem an excellent deal.

However, it’s hard to see how this would qualify anyone to work professionally in the field. There is no feedback from or interaction with the instructor, and no quality assessment of what has been learned.

So as always with MOOCs and their variations, there are large numbers of people who will get something they value from such courses. However, to pretend that such programs will enable people to get a well-paid, professional job in film and media on these qualifications alone is highly misleading. So it all depends on how they are marketed. Udemy walks quite close to the line on this.

However, I’d be interested to hear from anyone who has taken these courses. What was your motivation? Were you satisfied? What would you recommend to others thinking of taking such courses?

MIT introduces credit-based online learning

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MIT entrance

Bradt, S. (2015) Online courses + time on campus = a new path to an MIT master’s degree MIT News, October 7

MIT is famous for its non-credit MOOCs, but now, for the first time, it is offering a credit program at least partially online.

The one year Master in Supply Chain Management will consist of one semester taking online courses and one semester on campus, starting in February, 2016. This will run alongside the existing 10 month on-campus program. The online classes that make up the first semester will cost US$150, while the exam is $400 to $800. The second semester on campus will cost at least half what it costs for the yearlong program, which would mean about another $17,000. Students will still need to meet MIT’s academic standards for admission. It is expected to take about 30 to 40 students a year into the new program. The program will be offered using MIT’s own edX platform.

Since many other universities have been offering a mix of online and campus-based programs for many years, perhaps of more interest is MIT’s announcement of a new qualification, a MicroMaster, for those that successfully complete just the online portion of the program. MIT states that those that do well on the MicroMaster will ‘significantly enhance their chances of being accepted to the full master’s program‘.

Comment

First, congratulations to MIT for finally getting into credit-based online learning. This is a small but significant step.

It will be interesting to see how much the Master’s online courses differ in design from MOOCs. Will there be more interaction with the MIT faculty in the Master’s program? Will MIT use existing best practice in the design of credit-based online learning, or will they use a different model closer to MOOCs? If so, how will that affect the institution’s willingness to accept credit for MOOCs? All interesting questions.

Answering questions about teaching online: assessment and evaluation

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How to assess students online: remote exam proctoring

How to assess students online: remote exam proctoring (see Chapter 4.5 on competency-based learning)

Following on from my Contact North webinar on the first five chapters of my book, Teaching in a Digital Age, and my blog post on this yesterday, there were four follow-up questions from the seminar to which I posted written answers. Here they are:

Unanswered Questions

Q: ­If multiple choice is not great for applied learning assessment – could you please give us some tips for more effective assessment in the virtual environment?­

Big question! There are several ways to assess applied learning, and their appropriateness will depend on the subject area and the learning goals (Look particularly at Appendix A, Section 8). Here are some examples:

  • via project work, where the outcome of the project is assessed. (This could be either an individual or group project). Marking a project that may take several weeks work on the part of students helps keep the marking workload down, although this may be offset to some extent by the help that may need to be given to learners during the project.
  • through e-portfolios, where students are asked to apply what they are learning to practical real-life contexts. The e-portfolio is then used to assess what students have learned by the end of the course.
  • use of online discussion forums, where students are assessed on their contributions, in particular on their ability to apply knowledge to specific real world situations (e.g. in contemporary international politics)
  • using simulations where students have to input data to solve problems, and make decisions. The simulation collects the data and allows for qualitative assessment by the instructor. (This depends on there being suitable simulations available or the ability to create one.)

Q: I am finding in my post-graduate online courses the professor is interacting less and less in the online weekly forums, while I know there are competing theories as to how much they should interact with students, do you have an opinion on whether or not professors should or should not interact weekly? Personally, I enjoy their interaction I find it furthers my learning.

This is another big issue. In general, the research is pretty much consistent: in online learning, instructor ‘presence’ is critical to the success of many students. Look particularly at Chapter 4, Section 4 and Chapter 11, Section 10. However presence alone is not sufficient. The online discussion must be designed properly to lead to academic learning and the instructor’s intervention should be to raise the level of thinking in the discussion (see 4.4.2 in the book). Above all, the discussion topics must be relevant and from a student’s perspective clearly contribute to answering assessment questions better. The instructors should in my view be checking daily their online discussion forums and should respond or intervene at least weekly. Again though this is a design issue; the better the course design, the less they should need to log in daily.

Q: Can you give an example of how a MOOC can supplement a face-to-face or fully online course?

I think the best way is to consider a MOOC as an open educational resource (OER). There is a whole chapter (Chapter 10) in the book on OERs. Thus MOOCs (or more likely parts of MOOCs) might be used in a flipped classroom context, where students study the MOOC then come to class to do work around it. But be careful. Many MOOCs are not OER. They are protected by copyright and cannot be used without permission. They may be available only for a limited period. If it is your own MOOC, on the other hand, that’s different. My question is though: is the MOOC material the best OER material available or are there other sources that would fit the class requirement better, such as an open textbook? Or even better, should you look at designing the course completely differently, to increase student interaction, self-learning and the development of higher order thinking skills, by using one of the other teaching methods in the book?

Q: Would better learning analytics reports help teachers have a more relevant role in MOOCS?

Learning analytics can be helpful but usually they are not sufficient. Analytics provide only quantitative or measurable data, such as time on task, demographics about successful or unsuccessful students, analysis of choices in multiple-choice tests, etc. This is always useful information but will not necessarily tell you why students are struggling to understand or are not continuing. Compare this with a good online discussion forum where students can raise questions and the instructor can respond. Students’ comments, questions and discussion can provide a lot of valuable feedback about the design of the course, but require in most cases some form of qualitative analysis by the instructor. This is difficult in massive online courses and learning analytics alone will not resolve this, although they can help, for instance, in focusing down on those parts of the MOOC where students are having difficulties.

Any more questions?

I’m more than happy to post regular responses to any questions you may have about online teaching, either related to the book or quite independent of it. Just send them to me at tony.bates@ubc.ca