February 10, 2016

Why digital technology is not necessarily the answer to your problem

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The darker the shade, the higher the Internet population

The darker the shade, the higher the Internet population

World Bank (2016) World Development Report 2016: Digital Dividends. Washington, DC: World Bank.

What is the report about?

This 359 page report, partly funded by Canada’s Department of Foreign Affairs, Trade, and Development and the International Development Research Centre, involved the work of several hundred people and was based on consultation meetings in 27 different countries (see here for more information on these consultations).
The report notes that more households in developing countries own a mobile phone than have access to electricity or clean water, and nearly 70 percent of the bottom fifth of the population in developing countries own a mobile phone. The number of internet users has more than tripled in a decade—from 1 billion in 2005 to an estimated 3.2 billion at the end of 2015.

The key question addressed by this report then is as follows:

Have [these] massive investments in information and communication technologies (ICTs) generated faster growth, more jobs, and better services? Indeed, are countries reaping sizeable digital dividends?
I can only briefly summarise this lengthy report, and if this post peaks your interest, please read the full report.

Main conclusions

Despite this vast investment in digital technologies, the digital dividend in terms of greater productivity, less inequality, more democracy, and greater wealth for all has not been gained. For instance:

  • global productivity growth has slowed;
  • labor markets have become more polarized and inequality is rising—particularly in the wealthier countries, but increasingly in developing countries;
  • while the number of democracies is growing, the share of free and fair elections is falling.

Digital divide Africa 2

Furthermore:

  • nearly 60 percent of the world’s people are still offline and can’t participate in the digital economy in any meaningful way;
  • many advanced economies face increasingly polarized labor markets and rising inequality—in part because technology augments higher skills while replacing routine jobs, forcing many workers to compete for low-paying jobs;
  • public sector investments in digital technologies, in the absence of accountable institutions, amplify the voice of elites, which can result in policy capture and greater state control;
  • because the economics of the internet favour natural monopolies, the absence of a competitive business environment can result in more concentrated markets, benefiting incumbent firms;
  • not surprisingly, the better educated, well connected, and more capable have received most of the benefits, circumscribing the gains from the digital revolution.

The fundamentals (‘complements’) of development are:

  • good governance;
  • a robust and open business market;
  • accountability (i.e. lack of corruption);
  • strong human capital (i.e. a well educated work-force)

In countries where these fundamentals are weak, digital technologies have not boosted productivity or reduced inequality. These poor trends persist, not because of digital technologies, but in spite of them. Thus while digital technologies have been spreading, digital dividends have not. On the other hand, countries that complement technology investments with broader fundamental reforms reap digital dividends in the form of faster growth, more jobs, and better services.

Thus the report’s main conclusion is:

The full benefits of the information and communications transformation will not be realized unless countries continue to improve their business climate, invest in people’s education and health, and promote good governance.

Barriers to success

For digital technologies to improve productivity and reduce inequality, there are important ‘analogue’ factors that must accompany or support (‘complement’) the introduction of digital technology in order to get the benefits:
  • digital technologies can make routine, transaction-intensive tasks dramatically cheaper, faster, and more convenient. But most tasks also have an aspect that cannot be automated and that requires human judgment, intuition,and discretion; the better educated the workforce the higher the quality of such human decision-making;
  • when technology is applied to automate tasks without matching improvements in other, non-digital social, economic and political activities, such as governance, accountability and education, technology can fail to bring broad-based gains;
  • the digital revolution can give rise to new business models that would benefit consumers, but not when incumbents control market entry;
  • technology can make workers more productive, but not when they lack the know-how to use it;
  • digital technologies can help monitor teacher attendance and improve learning outcomes, but not when the education system lacks accountability.

What should be done?

  • make the internet universally accessible and affordable. The internet, in a broad sense, has grown quickly, but it is by no means universal. For every person connected to high-speed broadband, five are not. Worldwide, some 4 billion people do not have any internet access, nearly 2 billion do not use a mobile phone, and almost half a billion live outside areas with a mobile signal.
  • access to the internet is critical, but not sufficient. The digital economy also requires strong regulations that create a vibrant business climate and let firms leverage digital technologies to compete and innovate; skills that allow workers, entrepreneurs, and public servants to seize opportunities in the digital world; and accountable institutions that use the internet to empower citizens.
The Internet promoted development through three mechanisms

The Internet promotes development through three mechanisms

A favourable business climate, strong human capital and good governance are standard requirements for economic growth. But digital technology has two particular roles to play in development:

  • digital technologies amplify the impact of good (and bad) policies, so any failure to reform means falling farther behind those who do reform. With digital technologies, the stakes have risen for developing countries, which have more to gain than high-income countries, but also more to lose;
  • digital technologies can enable and accelerate the impact of these standard requirements for growth, for example, by creating new jobs and business opportunities, through online learning raising the skills level of workers, and by enabling government to make evidence-based decisions.

Comment

This report is a powerful antidote to those who think digital technologies are the silver bullet for increasing equality, improving education, and reducing the gap between rich and poor. What becomes very clear is that digital technology amplifies change: if things are going badly, digital technology will make it happen worse and faster; if things are going well, digital technology will make it better. Thus digital technology is neither cause nor effect in development, but a catalyst that amplifies change.

This means of course that the hard work of making governments and business transparent and accountable, developing an educated workforce, and having an open, well regulated business environment all need to be done. Digital technology can facilitate this, but on its own it will not lead to a better world except for a very few.

One last point. There is a very interesting article in today’s Globe and Mail newspaper by Jim Balsillie, one of the founders of Blackberry, railing against the TPP, the Trans-Pacific Trade Agreement, because it protects incumbent intellectual property holders. This gives a huge advantage to the USA, by protecting them from innovation originating in other countries with a small number of patents, relatively speaking (such as Canada). Balsillie argues that one reason digital technology has led to a greater increase in inequality is because of the distortion of U.S. patent law which makes it very difficult for new entrants to the digital technology market – although China, interestingly, has been smart enough to work around these barriers by its sheer size and more closed culture. Another barrier we see here in Canada is the power of incumbent organizations such as the three telecommunications companies who, through their control of national network infrastructure, can freeze out new competitors.

It sure ain’t a fair world out there, and digital technology is not helping. We need better regulation, and patent reform, that’s for sure, if digital technology is to reap fully its promise.

Developing a next generation online learning assessment system

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Facial recognition

Facial recognition

Universitat Oberta de Catalunya (2016) An Adaptive Trust-based e-assessment system for learning (@TeSLA) Barcelona: UOC

This paper describes a large, collaborative European Commission project headed by the Open University of Catalonia, called TeSLA, (no, not to develop a European electric car, but) a state-of-the-art online assessment system that will be accepted as equal to if not better than traditional face-to-face assessment in higher education.

The challenge

The project argues that at the moment there is no (European?) online assessment system that:

  • has the same level of trust as face-to-face assessment systems
  • that is universally accepted by educational institutions, accreditation agencies and employers
  • incorporates pedagogical as well as technical features
  • integrates with other aspects of teaching and learning
  • provides true and secure ‘authentication’ of authorship.

I added the ‘European’, as I think this claim might come as a surprise to Western Governors’ University, which has been successfully using online proctoring for some time. It is also why I used the term ‘next generation’ in the heading, as the TeSLA project is aiming at something much more technologically advanced than the current WGU system, which consists mainly of a set of web cameras observing learners taking an assessment (click here for a demonstration).

Also, the TeSLA proposal makes a good point when it says any comprehensive online assessment system must also be able to handle formative as well as summative assessment, and that this can be a challenge as formative assessment is often embedded in the day-to-day teaching and learning activities.

But the main reason for this project is that online learning assessment currently lacks the credibility of face-to-face assessment.

The solution

A non-invasive system that is able to provide a quality continuous assessment model, using proportionate and necessary controls that will ensure student identity and authorship [in a way that offers] accrediting agencies and society unambiguous proof of academic progression….

Any solution must work fully online and take into account ‘academic requirements’ for assessment, including enriched feedback, adaptive learning, formative assessment and personalized learning.

This will require the use of technologies that provide reliable and accurate user authentication and identification of authorship, face and voice recognition, and keystroke dynamics recognition (see here for video examples of the proposed techniques).

The solution must result in

a system based on demonstrable trust between the institution and its students. Student trust is continuously updated according to their interaction with the institution, such as analysis of their exercises, peer feedback in cooperative activities or teacher confidence information. Evidence is continuously collected and contrasted in order to provide such unambiguous proof.

The players

The participants in this project include

  • eight universities,
  • four research centres,
  • three educational quality assurance agencies,
  • three technology companies,
  • from twelve different countries.

In total the project will have a team of about 80 professionals and will use large-scale pilots involving over 14,000 European students.

Comment

I think this is a very interesting project and is likely to grab a lot of attention. At the end of the day, there could well be some significant improvements to online assessment that will actually transfer to multiple online courses and programs.

However, I spent many years working on large European Commission projects and I am certainly glad I don’t have to do that any more. Quite apart from the truly mindless bureaucracy that always accompanies such projects (the form-filling is vast and endless), there are real challenges in getting together participants who can truly contribute to such a project. Participants are determined more by political considerations, such as regional representation, rather than technical competence. Such projects in the end are largely driven by two or three key players; the remaining participants are more likely to slow down or inhibit the project, and they certainly divert essential funding away from the those most able to make the project succeed. However, these projects are as much about raising the level of all European countries in terms of learning technologies as becoming a world leader in this field.

These criticisms apply to any of the many European Commission projects, but there are some issues that are particular to this project:

  1. I am not convinced that there is a real problem here, or at least a problem that requires better technology as a solution. Assessment for online learning has been successfully implemented now for more than 20 years, and while it mostly depends on some form of face-to-face invigilation, this has not proved a major acceptability problem or a barrier to online enrolments. There will always be those who do not accept the equivalence of online learning, and the claimed shortcomings of online assessment are just another excuse for non-acceptance of online learning in general.
  2. Many of the problems of authenticity and authorship are the same for face-to-face assessment. Cheating is not exclusive to online learning, nor is there any evidence that it is more prevalent in online learning where it is provided by properly accredited higher education institutions. Such a study is just as likely to reduce rather than increase trust in online learning by focusing attention on an issue that has not been a big problem to date.
  3. Even if this project does result in more ‘trustworthy’ online assessment, there are huge issues of privacy and security of data involved, not to mention the likely cost to institutions. Perhaps the most useful outcome from this project will be a better understanding of these risks, and development of protocols for protecting student privacy and the security of the data collected for this purpose. I wish though that a privacy commissioner was among the eighteen different participants in this project. I fail to see how such a project could be anything but invasive for students, most of whom will be assessed from home.

For all these reasons, this project is well worth tracking. It has the potential to radically change the way we not only assess online learners, but also how we teach them, because assessment always drives learner behaviour. Whether such changes will be on balance beneficial though remains to be seen.

Keyboard dynamics

Keyboard dynamics

An example of online experiential learning: Ryerson University’s Law Practice Program

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Lawyers 2

Alexandris, G., Buontrogianni, M, and Djafarova, N. (2015) Ground-breaking program for Ontario Law School Graduates – Virtual Law Firms, Berlin: OEB Conference

Online, experiential learning

Experiential learning is a very popular concept in education these days, but it is not always well understood, and in particular some see experiential learning and online learning as contradictory. It’s important then to have examples of successful online experiential programs.

Ryerson University in Toronto has one such program. Although hybrid rather than fully online, the online component is both substantial and essential.

Why Ryerson?

One of the many challenges in legal training is moving new law school graduates into the real world of law practice. Although most graduates become articled to a particular law firm, they are often ill-prepared for the actual work, which is much more skills- and context-based than the more theory- and content-based approach in law school.

The Law Society of Upper Canada, which regulates the profession in Ontario, recently introduced changes to its licensing process, requiring a new ‘transition to practice’ training that focuses on skills development. Although Ryerson does not have its own law school, it does have a strong reputation for innovative approaches to skills development in higher education, and as a result in 2013 the Law Society of Upper Canada chose Ryerson to develop the transition to practice program, now called the Law Practice Program (LPP).

The challenge

Ryerson had to develop an experience-based program, drawing initially 220 participants during each of its first two years, spread across the whole province of Ontario and beyond, but also capable of expansion if necessary. The program required developing realistic cases and practices, and a teaching approach that of necessity directly involved ‘real’ law firms and busy, practising lawyers and judges as mentors. At the same time, the training must not interfere with the actual practice of law while participants were engaged in training.

The overall program strategy

Ryerson turned to two of its centres, the Chang School of Continuing Education’s Centre for Digital Education, and the Interpersonal Skills Teaching Centre, which offers simulated learning and teaching of interpersonal communications skills.

Externally Ryerson partnered with the Ontario Bar Association. This enabled Ryerson to annually engage over 250 lawyers across the province as mentors and contributors to the program, and 220 law firms and organizations for work placements. This also allowed the program to integrate technology and legal resources already used in the law profession.

The program adopts a hybrid approach, with a four month practical training period consisting of 14 weeks online and three separate weeks on campus. During these seventeen weeks, candidates work on simulated files developed by practising lawyers. This training is then followed by a four month work placement, where participants work on actual files.

Curriculum

The practical training component consists of developing skills and competency in the following areas:

  • professionalism and ethics
  • analytical skills
  • research
  • oral and written communication
  • client management
  • practice management.

using seven practice areas of law:

  • administrative law
  • business law
  • civil litigation
  • criminal law
  • family law
  • real estate law
  • wills and estates law.

Program design

This is where the program becomes unique and innovative. There are several components of the design.

a. Virtual ‘firms’

Virtual firms are created with four participants, and an external lawyer as a mentor. Each firm also has multiple clients, actors specially trained to play a specific role. There are weekly firm meetings, often in virtual, but real-time, format.

b. Specially designed learning resources

Participants have access to more than 90 pieces of simulated legal correspondence, several specialized legal applications and databases, 40 custom-made videos, and 20 learning modules.

LPP presentation 2

A number of multiple choice assessments and interactive learning objects have been designed to facilitate comprehension and understanding of legal issues and the development of skills.

There are also in-person and virtual presentations by experts in key competency and substantive legal areas, and participants also have to meet virtually and in-person with clients, other lawyers and judges.

c. Communication

A wide variety of tools are used for communication between participants, mentors and clients, including:

  • a standard learning management system
  • online communications tools used within the legal profession (Clio, Webex)

d. Assessment

Participants are assessed through their interaction with lawyers and judges during the program, including live legal presentations and argument.

Conclusion

The main success of the program, now in its second year, has been the ability of the participants ‘to hit the ground running’ when they join a law firm/legal employer. Employers’ responses to the program have been generally highly favourable (see here), although no formal evaluation of the program has yet been conducted. The strong involvement of lawyers and judges as well as law firms has ensured that the training is both relevant and practical, while the firms benefit from better prepared future employees.

The creation of virtual cases, processes and procedures, the use of simulations and virtual meetings and virtual firms, and work placements under supervision, have combined to provide a strong, experience-based approach to learning which both participants and mentors have found highly motivating.

Lastly the ability for participants and mentors to work primarily online has provided the flexibility necessary for busy, working professionals.

There are of course many other online experiential learning programs, such as the virtual reality-based program on custom border services for Canada Border Service Agents at Loyalist College, Ontario. I would welcome other contributions or examples for future blog posts.

LPP case 2

Disclaimer

Since 1st January 2016 I am a Distinguished Visiting Professor at Ryerson University, but I have not been engaged in any way with the design, development or delivery of this program. I am though indebted to Gina Alexandris, the program director for the LPP, for her help and advice in preparing this post.

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

State support for public higher education is declining in the USA

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Young Invincibles 2

Young Invincibles (2015) 2016 State Report Cards Washington DC: Young Invincibles

This is a very interesting state by state report card on the support for public higher education in the USA since the economic recession of 2008. Key results:

  • states have cut per student spending by 21 percent since 2008. Only two states spend as much as they did before the recession (Alaska and North Dakota). Six states are now spending less than two thirds of what they were spending in 2008
  • tuition and fees at both 4-year and 2-year institutions rose 28 percent since 2008 (inflation rose 14%).
  • in 2008, students and families paid approximately 36 percent of the cost of public college; in 2014 that percentage increased to 50 per cent.
  • the gap between white non-Hispanic adults and Latino adults with postsecondary degrees grew by 2.2 percentage points between 2007 and 2015

As interesting as the result is the organization that did the study. Young Invincibles is:

a national organization, representing the interests of 18 to 34 year-olds and making sure that our perspective is heard wherever decisions about our collective future are being made. We do this through conducting cutting-edge policy research and analysis, sharing the stories of young adults, designing campaigns to educate on important issue areas, informing and mobilizing our generation and advocating to change the status quo.

It can be seen that state funding of public higher education in the USA has declined significantly over the last six years, even though the economy in general has more than recovered (U.S. GDP in 2015 was $1.5 billion higher than before the recession kicked in).

This is clear evidence in the decline of political support at a state level for publicly funded higher education in the USA over the last six years. Once again it is young people who are paying the price.

In Canada we didn’t suffer as badly during and following the recession and I suspect public funding of universities is if anything slightly higher today in most provinces per capita than it was in 2008. However, can anyone give me the exact figures?