September 21, 2017

2020 Vision: Outlook for online learning in 2014 and way beyond

 2020 visionTaking the long view

Doug Saunders in the Globe and Mail on  January 4 wrote an interesting piece on prediction, entitled: “Gadgets alone don’t make the future.” Having shown how amazingly accurate technologists in 1961 were in predicting what technologies would roll out in the future, he also showed how poorly they predicted how these gadgets would impact on our lives. In summary:

‘We are very good at guessing where our inventions might lead. We are very poor in understanding how humans might change their lives….the decision of what kind of life to live between the screens remains a political one, shaped not by our inventions but by our own decisions.’

Last year I spent some time discussing the value of predictions. One point I didn’t mention is the limitation of predicting just one year ahead, because you can’t identify the long term directions, and so often you’re driven by what happened in the very recent past, i.e. last year, because that’s the latest and often only data you have. More importantly, though, looking one year ahead assumes that there is no choice in what technologies we will use and how we will use them, because they are already entering our society. Also, this is likely to be the last year in which I make predictions for the future. I will be 75 in April, and I plan to stop all paid professional activities at that point (although I will keep my blog, but more as a journalist than as a practitioner).

So this seems to be a good point to look not just at 2014, but where we might be going five to ten years from now, and in doing this, I want to include choice or human decision-making as well as technological determinism. In other words, what kind of online learning do I expect in the future, given what I know so far?

The disappearance of online learning as a separate construct

In 2020, people won’t be talking about online learning as such. It will be so integrated with teaching and learning that it will be like talking today about whether we should use classrooms. In fact, we may be talking much more about classrooms or the campus experience in 2020, because of online learning, and how it is changing the whole way that students are learning. There is likely to be heated discussions about the role and purpose of campuses and school buildings, the design of classrooms, and who needs to be there (teachers and students) and more importantly what for, when students can do so much of their learning online – and generally prefer to, because of the flexibility, and of their control over their own learning. The big changes then are likely to be on-campus, rather than on-line.

Steelcase Node Classroom

Steelcase Node Classroom

Multi-mode delivery concentrated in fewer institutions – but more diversity

Quite a few public and smaller private post-secondary institutions will be gone or radically transformed by 2020. Particularly at risk are smaller, low status state or provincial universities and colleges or their campuses in metropolitan areas, where there is local and regional competition for students. They will have lost students to more prestigious universities and high status vocationally oriented institutions using online and flexible learning to boost their numbers. Government will be increasingly reluctant to build new campuses, looking to more flexible and more cost effective online delivery options to accommodate increasing demand. Nevertheless, politics will occasionally trump economics, with small new universities and colleges still being created in smaller towns away from the larger urban areas. Even these though will have much smaller campuses than today and probably as much as 50% of all course enrollments online, often in partnership with more established and prestigious universities through course sharing and credit transfer.

Those institutions that have survived will be offering students a range of choices of how they can access learning. Courses or programs will be deliberately designed to accommodate flexibility of access. Thus students will be able to decide whether to do all their studying on campus, all of it online, or a mix of both, although courses or programs are likely to have a common assessment strategy (see below). This will not be driven so much by academic or even political decisions, but by students voting with their feet (or mouses) to study at those institutions that provide such flexibility.

Multi-purpose, open delivery, with multiple levels of service and fees

Content will be multi-purposed, depending on a learner’s goals. Thus the same content can be part of a credit-based degree-level course, program or competency, part of a non-credit certificate or diploma, or available as open access. Learners will also be able to choose from a range of different course or program components, dependent on their needs and interests. Because most content will be open and modular, in the form of open textbooks, open multimedia resources, and open research, institutions will offer a variety of templates for courses and programs built around open content. For example, for a degree in physics, certain topics must be covered, with a strong recommendation for the sequence of study, but within those core levels of competency, there will be a variety of routes or electives towards a final degree, where broadly based learning outcomes are set, but multiple routes are offered for progress to these outcomes. Those content components can be accessed from a wide range of approved sources. It is the competency and academic performance of the learner that the institution will accredit.

Most institutions will have an open education portal, that contains not only a wide range of open educational resources, but also a range of open services, such as program templates or free academic guidance for specific target groups, as part of their enrollment strategy. Although such portals are likely to include materials from a wide range of sources from around the world, special emphasis will be given to open content developed by their own faculty, based on their latest research or scholarship, as a way of branding their institution. iTunesU, MIT’s Opencourseware, OpenLearn, and MOOCs are early prototypes, but content quality in the future will be greatly improved in terms of pedagogical and media design to accommodate online learners. Also states and provinces will also establish system-wide portals of open educational resources, particularly at the k-12 and two year college level (see eLearnPunjab and open.bccampus.ca as prototype models).

Because academic content is almost all open, free and easily accessible over the Internet, students will not pay tuition fees for content delivery, but for services such as academic guidance and learning support, and these fees will vary depending on the level of service required. Thus students who want a traditional course that covers guidance on and access to content, tutorial help, access to campus facilities, feedback and assessment will pay full fee (some of which may still be government subsidized in the public system). Students who want just open access will pay nothing, but will get few if any support services, and if they need a formal assessment, they will need to pay for this (although again this may be subsidized in a public system). Other students may want feedback and some form of continuous assessment, but will not want to pay for full tutorial support.

There are several consequences of this increased flexibility. Some institutions will specialize in small-class, on-campus education at high cost. Others will focus on high quality delivery through a variety of delivery modes, with a particular emphasis on course design and learner support. Some institutions will focus on low cost, competency-based open access programs, supported by businesses requiring specific skilled labour, and a few institutions will be specialists in fully online distance delivery operating on a national or international basis, at a lower cost but equally high quality as campus-based institutions. The majority of institutions though will become multi-purpose, multiple delivery institutions because of the economies of scale and scope possible.

Goodbye to the lecture-based course

In most institutions, courses based on three lectures a week over 13 weeks will have disappeared. There are several reasons for this. The first is that all content can be easily digitalized and made available on demand at very low cost. Second, institutions will be making greater use of dynamic video (not talking heads) for demonstration, simulations, animations, etc. Thus most content modules will be multi-media. Third, open textbooks incorporating multi media components and student activities will provide the content, organization and interpretation that are the rationale for most lectures. Lastly, and most significantly, the priority for teaching will have changed from information transmission and organization to knowledge management, where students have the responsibility for finding, analyzing, evaluating, sharing and applying knowledge, under the direction of a skilled subject expert. Project-based learning, collaborative learning and situated or experiential learning will become much more widely prevalent. Also many instructors will prefer to use the time they would have spent on a series of  lectures in providing more direct, individual and group learner support, thus bringing them into closer contact with learners.

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

61730023

Goodbye to the written exam – and welcome to the final implementation of lifelong learning

For most post-secondary qualifications, written exams will have been replaced by assessment through multimedia portfolios of student work. These will show not only students’ current knowledge and competencies, but also their progression over time, and a range of equally important skills, such as their ability to work collaboratively, self-management of learning, and general communication skills. Assessment will be mainly on a continuous, on-going basis.

As well as change in the method of assessing learning there will be greater variety in the range of accredited qualifications. Degrees, certificates and diplomas will still be important, but these will be complemented with a wide range of assessments of informal or non-formal learning, such as badges, some offered by post-secondary institutions, others offered by employers’ organizations or co-operatives of professionals. University and college diplomas and degrees will increasingly be seen as milestones on the journey to lifelong learning, and for demographic and economic reasons, the lifelong learning market will become a much larger market than the high school leaver market.

This means academic departments will need to develop programs and courses that range from introductory or foundational through undergraduate degrees to professional masters to lifelong learning, again using similar content modules adapted to different markets, as well as creating or adapting new content, based on the latest research in a field, for these newer markets. Much of the lifelong market will lend itself to online and hybrid learning, but in different structures (short modules, for instance) than the undergraduate and higher degree market. Universities and colleges will increasingly compete with the corporate training industry for these post-postgraduate learners, who will be able and willing to afford top dollar for top-level lifelong learning opportunities, based on the latest research coming out of universities, government and businesses.

However, a large part of the lifelong learning market will become occupied by communities of practice and self-learning, through collaborative learning, sharing of knowledge and experience, and crowd-sourcing new ideas and development, particularly assisted by an evolution of what are now known as cMOOCs. Such informal learning provision will be particularly valuable for non-governmental or charitable organizations, such as the Red Cross, Greenpeace or UNICEF, or local government, looking for ways to engage communities in their areas of operation. These communities of learners will be open and free, and hence will provide a competitive alternative to the high priced lifelong learning programs being offered by research universities. This will put pressure on universities and colleges to provide more flexible arrangements for recognition of informal learning, in order to hold on to their current monopoly of post-secondary accreditation.

Image: © Etienne Wenger, 2010

Image: © Etienne Wenger, 2010

New financial models

Because most content will be freely accessible, and because students will pay incrementally for a wide variety of services, new financial models will need to be developed, to support the flexibility and range of services that students will increasingly demand and require. The biggest move is likely to be away from block funding or enrollment-driven funding by government towards pay-for-service through student fees for teaching. There will be further separation of the funding for research and teaching (this has already happened in some countries, such as in England and Wales.) As a result government financing may well change, so that students are given a post-secondary grant at the age of 17, and have the right to decide how to spend that grant on post-secondary education, rather than funding institutions directly for teaching.

This may have some unexpected benefits for academic departments. Under this model it makes much more sense to fund programs directly from fees for the program, than to pool grants and fees centrally then break out money for teaching and filter it down through the departments. Thus program fees or service fees  would come to academic departments (or more accurately the program areas) directly, then the programs would pay for university services such as registration and financial services on a direct cost basis, plus a percentage for general overheads. This is already happening in some public universities at post-graduate levels, where tuition fees for online professional masters more than cover all the costs, direct and indirect, of a program, including the cost of full-time research professors who teach on the program.

This model would also have two other benefits. It would put pressure on service departments, such as HR, financial services, the Registry, etc., to become more cost-efficient, because direct costs to programs become more transparent. Second, since online students do not need a range of campus services such as campus building maintenance, lighting, and heating, it would lead to the different costs of online vs campus-teaching becoming more transparent and comparable, with an economic incentive to move more towards the most cost-efficient delivery model.

There are also disadvantages. Some model would be needed to support more expensive programs to deliver, or programs that are specialized but important in a university community. However, a program-based financial model may help save small departments who are struggling for minimal enrolments from their local market. Online courses can open the market to regional or international students and offer the chance of collaboration and partnership with other institutions, through course and student sharing.

The disaggregation of institutional activities required for the flexible delivery of programs in a world where content is free offers opportunities for rethinking how teaching and learning is funded.

Systematic faculty development and training

Since content will be freely accessible, institutions’ reputation and branding will increasingly depend on the way they support learners. This will put much greater emphasis on instructors having good teaching skills as well as subject expertise. Thus most universities and colleges will require faculty to have assessed teaching skills before tenure or permanent appointment, and equal attention will be given to teaching expertise as research in promotion. This will mean incorporating teaching practice and methods within most post-graduate subject areas, college instructors having compulsory pre-service teacher training, and regular faculty having systematic ongoing professional development as new technologies and new teaching approaches develop over time. The immediate benefit of this will be better student retention rates and higher quality learning outcomes.

Devolved decision-making and organizational models

A move to program-based funding, the need for effective course designs to attract students, the differentiation of services, the increased professionalism in teaching, and freely available open content will result in a move to systematic program planning and team teaching. A typical team will consist of a senior research professor, several junior or adjunct professors, an instructional designer/project manager and a media/web designer. The senior faculty member, in collaboration with the other team members, will be responsible for decisions about curriculum content, methods of learner support, and assessment standards. The team will develop assessment criteria and rubrics, and where necessary hire additional instructors for learner support and marking of assessments , under the supervision of the senior faculty members.

One consequence will be the disappearance of central centres for teaching and technology, except in small institutions. Instructional design staff will be located in program areas and will be responsible with academic faculty for faculty development activities, as well as with overall course design input. There will be increased demand for media designers, while instructional designers will be in less demand in the future, but still necessary to support faculty, especially as new learning technologies develop.

Student privacy, data security and student online behaviour will become more difficult

Learning will increasingly be delivered through student-owned devices, and learners will increasingly integrate social life, work and study in a seamless manner. Services will increasingly be delivered through the cloud. Security agencies, Internet-based companies and knowledge-based companies will constantly be seeking access to student data, especially student learning performance and online behaviour, as this information will be increasingly valuable for state security and commercial reasons. As a result it will become increasingly difficult for institutions to protect student data and their privacy. This may turn out to be the biggest challenge for students, institutions, and government in the next 20 years and could seriously inhibit the development of online learning in the future, if students or faculty lose trust in the system.

The future is about choices

This is my view about where we could be going with online learning in the next five to ten years. However, I will not be making the decisions, as I am retiring in April. If you do not like this vision, then you are in a position to influence a different kind of vision. Although as McLuhan says, we are shaped by our devices, we also shape the world around these devices. The worst thing we could do is to leave it to computer scientists to decide our future.

The value such a vision lies not in its detail, but in identifying some of the key choices or decisions that will need to be made. So here are the decisions that are thrown up by this vision for the future, for students, faculty, institutions and government (and some of these, such as those about campus facilities, should be being made right now):

Students and learners

  • at this point in my life, what are my learning goals? What is the best way to meet these? Where can I get advice for this?
  • do I need a qualification and if so, what kind?
  • what is the best way for me to access this learning? On-campus; online; or a mix of both?
  • what kind of learning support do I need?
  • how much do I want to – or must I – pay for these services?
  • what institution or other method of delivery will provide what I want? Where can I get independent advice on this?
  • how can I protect my privacy when I am online studying?

Faculty and instructors

  • why do students need to come to campus? What am I offering on-campus that they couldn’t get online? Have I looked up the research on this?
  • what teaching methods will lead to the kind of learning outcomes that students will need in life?
  • what should be my role if content is freely available online?
  • what kind of teaching spaces do I need for what I want to offer on campus?
  • how should I best use my time in teaching? In what kind of teaching activities can I really make a difference for students?
  • if I create new or original content for my teaching, should I make it openly available to anyone to use?
  • what methods of assessment should I use in a digital age? How do I assess prior or informal learning?
  • what kind of courses or programs should we be offering for lifelong learners?
  • what do I need to know about student data, and the protection of student privacy?
  • what training or professional development do I need to ensure that I can meet the learning needs of my students?

Institutions

  • what kind of campus will we need in 10 years time?
  • what proportion of course enrollments are likely to be accessed off-campus?
  • what will be the best way to accommodate more students – online learning or more buildings?
  • what kind and number of teaching spaces will we need?
  • what partnerships or strategies should we adopt to protect our enrollment base?
  • what are our strategies and policies regarding open educational resources?
  • what is our strategy for lifelong learning?
  • what financial models should we put in place to encourage innovation in teaching and to attract students?
  • how do we ensure that faculty have the skills necessary for teaching in a digital age?
  • how can we best reward innovation and high quality teaching?
  • what kind of organization and staff do we need to support faculty in their teaching?
  • how do we best protect student data and privacy (as well as our staff’s) in a digital age?

Government

  • what kind of post-secondary system, in terms of institutional differentiation, program delivery and innovations in teaching, do we need in a digital age?
  • how many, and what kind of, campuses do we need when students are also studying online? What is the best way to accommodate expansion in the system?
  • how can we best support system-wide open education, to reduce costs and increase quality?
  • how should we fund post-secondary education in a digital age? How much and what should ‘first-time’ students pay for themselves? What should lifelong learners who have already been through the system pay? What funding models would encourage innovation in teaching and help improve quality?
  • how can online learning help to increase the productivity of the post-secondary educational system? What can we do to encourage this?
  • what does government need to do to protect student data and student privacy?

What’s YOUR vision?

I won’t be around to make or influence these decisions, but most of you will. Are there decisions I’ve missed? What decisions would you make? What’s your vision for the future?

If you are willing to share just one response to any of these questions or decisions, this will be very much appreciated. Because the future will be increasingly about sharing knowledge.

Why predicting online learning developments is risky but necessary

A probabilistic approach to prediction is wise

Before drawing up my outlook for 2013, I want to discuss the important topic of prediction in online learning, in particular how predictions are made, and what value they may have. Nate Silver’s excellent book (references are at the end of this article) looks at prediction in a number of fields: weather forecasting (excellent up to three days, useless after eight days), economic forecasting (hopeless by both media pundits and professional economists), baseball players’ performance (pretty good and improving), earthquakes (bad for major quakes, but promising for lesser quakes), poker and a number of other areas. He also has some interesting reflections on big data as well. Unfortunately though he doesn’t discuss prediction in online learning, so I’ll try and help out with this!

Factors associated with reliable predictions

Silver’s book is valuable because he sets out some of the factors associated with good prediction (or forecasting):

  1. Well understood and empirically supported theory about what drives the field under inquiry (excellent in weather forecasting and earthquakes; poor in politics and economics)
  2. Large, reliable sets of relevant data and the ability to crunch large data sets
  3. Relatively stable movement within the data (i.e. not too much ‘noise’ or randomness)
  4. Elimination of or accounting for as far as possible the unknown
  5. Above all, a probabilistic approach to prediction that takes account of uncertainty.

Factors associated with online learning

The problem for online learning is that few of these factors exist. In terms of theory, we do have some some empirically supported theories about what makes for effective online learning (e.g. Linda Harasim’s Learning Theory and Online Technologies) and some standards for best practices. However, these are often not practiced, or are ignored, in the field of online learning, and more importantly we lack good, empirically based theories of organizational decision-making in post-secondary education. This makes the application of what theory we have to understanding data and looking for the signal in the noise particularly hazardous for online learning.

The situation is even worse with regards to data. Weather forecasting data is detailed, localized and goes back over 60 years. Online learning is itself barely 20 years old (at least as we now know it), and is continually changing (as is the weather of course, but at least meteorologists know why the weather changes).

We have very little data on what is actually happening in online learning, and over-reporting in some areas (e.g. MOOCs) and under-reporting in others (for-credit programs). We are almost entirely dependent on the Sloan/Babson annual surveys for online learning enrollments and the Kenneth Green survey for IT developments on campuses, both covering just the USA. These surveys are invaluable, especially because they use a consistent methodology from year to year, enabling comparisons to be made, but they depend on the voluntary participation of selected staff within institutions, which tends to provide a bias to over-reporting online activities. In Canada, we have nothing, except a 2010 survey in Ontario which is unlikely to be repeated. So the statistical basis for reliable prediction in online learning just isn’t there.

With regard to ‘unknowns’ in online learning, they are everywhere but of course not visible until they hit you. MOOCs are a good example of something suddenly jumping out of the bush at you. But we have had other scares as well, such as for-profit universities. And some of the scares or unknowns quickly become very real in online learning, while others disappear almost as quickly as they came.

Fairy stories are perhaps a better basis for understanding online learning than scientific prediction. This is how I see MOOCs

The last factor though, a more probabilistic approach, is one we can apply to online learning. Silver makes the distinction between hedgehogs – pundits who have a strong view on everything, a ‘biased’ or strong ideological position, and who tend to make statements with a high degree of certainty, but who are frequently and routinely completely wrong- and foxes, who tend to be more cautious in their statements, are more equivocal in their predictions, but in the long haul have a better track record of accurate predictions. Foxes take a more probabilistic approach, recognizing degrees of uncertainty in their predictions (not necessarily in mathematical terms).

Timing as a factor in online learning predictions

A particular problem with prediction in online learning is the timing. The Horizon reports deal with this by having one, three and five year projections which is a more probabilistic approach, but I would argue it is more of a hedgehog than a fox because it focuses mainly on technology and not on pedagogy, and usually does not hedge its bets. Jon Baggaley, in a forthcoming analysis of the Horizon reports, also shows how unreliable their predictions have been.

In online learning, technology moves faster than people, and people move faster than organizations. So where you see changes in individuals, it may be another 10 years before that filters through to true organizational adoption. Also when does a prediction become true? Let’s take hybrid learning. Does 100 instructors moving to hybrid learning constitute a ‘trend’? However, if 100 institutions move in that direction within a year, that would be more significant. So as well as timing, the level of analysis matters too.

Why prediction in online learning is still necessary

Audrey Watters, who is my favourite blogger on online learning and educational technology, has also read Nate Silver’s book, and is aware of many of the problems I have just laid out. For these reasons, she has decided not to make any predictions for 2013. She is no doubt wiser than me, but I think it’s a pity she’s opting out. She is in a much better position than most of us to make predictions about online learning because she has a very broad overview, a full picture, of what is going on, even if the details are not always clear.

The fact is, we have to make predictions, every day in our lives – is it going to rain (so I take an umbrella), will the stock market go down (so I won’t invest $5,000), will my house still have enough equity when I need to go into an old folk’s home (yes, so I’ll go on holiday this year), will my bosses want to do MOOCs (yes, so I’d better be prepared)? And we always have to make these predictions with biases, less than perfect data, and lots of nasty unknowns lurking in the garden.

Silver’s book in fact does not argue against predictions, but doing them as well as possible. You do the best you can, and take a probabilistic approach. (If I don’t use the umbrella, and it doesn’t rain, no big deal, I’ll wait two months before reconsidering my investment, I’ll chose a budget holiday, I’ll suggest a way to do MOOCs that enhances their quality). We will all have to make some predictions, some intelligent guesses, as to what’s going to happen in online learning this year, so we can at least be prepared.

Go for it, baby

I will be making some predictions, not because I have all the data I’d like (you never do, even in meteorology). I also have my biases and prejudices. However, I do have a lot of experience in online learning, which provides at least some sort of theoretical framework for analysis, I do get to see what’s happening in about 10-15 universities and colleges a year (not enough but more than many), I do read a lot of the research literature on online learning, and I cover a huge amount of news and developments for my blog. So you decide whether or not my predictions are likely to be better than yours. At least you can make a comparison. (Silver points out that the average of multiple sources of predictions is usually more accurate than single sources of prediction, so let’s all share).

So, yes, you will get an outlook for 2013 for online learning from me. I will make some firm predictions, but I will use a one to five year horizon, and there will be caveats, and the unknowns will still jump out at you during the year – but at least you’ll have an umbrella to fend them off, and you can then blame me if it all goes wrong.

Silver, N. (2012) The Signal and the Noise: Why So Many Predictions Fail – but Some Don’t New York: The Penguin Press

Watters, A. (2013) Why I’m not making Ed-Tech predictions for 2013 Hack Education, January 1

Baggaley, J. (in press) Shifting Horizons, Distance Education