November 18, 2017

Are we right to fear computers in education – or in life?

In this post, I’m going to look at some fun fiction about computers, then raise some questions about whether our fears are rational, or whether we really do need to question much more closely our addiction to technology, especially in education. This is not so much focused on specific new developments such as MOOCs (see: My Summer Paranoia) but on what it is reasonable to expect computers to do in education, and what we should not be trying to do with them.

Computers in film and print

There was an interesting article in the Globe and Mail on October 20 about IBM’s super computer, WATSON, being used to ‘help conquer business world challenges.’ Dr. Eric Brown of IBM actually described how WATSON was being used to help with medical diagnosis, or what he called ‘clinical-decision support,’ and how this approach could be extended to other areas in business, such as call-centre support, or financial services to identify ‘problems’ where large amounts of data need to be crunched (did he mean derivatives?)

Just after reading the article, I accidently came across an old 1970 movie on TVO last night, called, ‘Colossus: the Forbin Project‘. It was based upon the 1966 novel Colossus, by Dennis Feltham Jones, about a massive American defense computer, named Colossus, becoming sentient and deciding to assume control of the world. It does not have a good ending (at least for mankind’s freedom).

Colossus was the name given to the first large electronic computer, used to break the German Enigma code in the Second World War. It was located at Bletchley Park, England, not far from where the Open University's headquarters are located.

The date of the movie is interesting, made at the height of the Cold War, but when challenged by the power of in fact two supercomputers (Colossus in the USA and Guardian in the Soviet Union) which decide to communicate with each other and combine their power, the Americans and the Communists come together to fight – unsuccessfully – the mutual threats from the computers, suggesting there is more in common across humanity than there is between humanity and machines.

Of course, this movie came two years after Stanley Kubrik’s masterful 2001: A Space Odyssey, where HAL, the spaceship’s computer, begins to malfunction, kills nearly all the crew, and is finally shut down by the last remaining crew member, Dave Bowman. So we now have a score: humans 1, computers 1.

Then there is my personal favourite, the Matrix (1999). The film depicts a future in which reality as perceived by most humans is actually a simulated reality or cyberspace created by sentient machines to pacify and subdue the human population, while their bodies’ heat and electrical activity are used as an energy source. Upon learning this, computer programmer “Neo” is drawn into a rebellion against the machines, involving other people who have been freed from the “dream world” and into reality. I put this one down to a draw, since there have been two sequels and the battle continues.

Lastly, a new film is coming out in March, 2013, based on Orson Scott Carson’s wonderful book ‘Ender’s Game‘, first published in 1985 and slightly updated in 1991. (If you have teenage boys, this is a must for a Christmas present, especially if they generally hate reading). In preparation for an anticipated third invasion from an insectoid alien species, an international fleet maintains a school to find and train future fleet commanders. The world’s most talented children, including the novel’s protagonist, Ender Wiggin, are taken at a very young age to a training center known as the Battle School. There, teachers train them in the arts of war through increasingly difficult games including ones undertaken in zero gravity in the Battle Room where Ender’s tactical genius is revealed. Again, the book explores the intersection between virtuality and reality.

Computers: promise and reality

It is interesting to look at these old science fiction movies and novels and today’s computer world, and see where progress has been made, and where it hasn’t. Colossus in some ways anticipated the Internet, as the two computers searched for ‘pathways’ through which to communicate with each other. We certainly have much more remote surveillance, especially in the United Kingdom, where almost every public space is now under video surveillance, and where increasingly governments are exerting more monitoring over the Internet, both for protecting individual freedoms, such as monitoring sexual exploitation of minors, and for more insidious purposes, such as industrial and political espionage. Claims have been made that 2011: Space Oduyssey predicted the iPad. Ender’s Game comes very close to representing the complexity and depth of many computer games today, and conspiracy theorists will tell you that the first moon landing was filmed in Hollywood, so close do movies come to presenting fiction as reality.

However, despite Watson and distributed computing, many of the developments in this early science fiction have proved to be much more difficult to implement. In particular, although all these early movies assumed voice recognition, we are still a long way from having the fluency depicted in these movies, even after more than 40 years of research and development. For instance, try communicating with WestJet’s or Telus’s automated answering systems (and in WestJet’s case, it frequently fails to recognize the spoken language of even native English speakers – such as myself!) These ‘voice recognition’ systems manage simple algorithmic decisions (yes or no; options  1-5) but cannot deal with anything that is not predictable, which is often the very reason why you need to communicate with these organizations. In addition to the difficulties of voice recognition, these systems are clearly designed by computer specialists who do not take into account how humans behave, or the reasons they are likely to use the phone to communicate, rather than the Internet.

As Dr. Eric Brown of IBM admits, ‘When you try to create computer systems that can understand natural language, given all the nuance and ambiguity, it becomes a very significant problem.’ As he rightly says, human language is often implicit and tacit, using signs and meanings which humans have learned to almost automatically and most times correctly interpret, but which are very difficult for computers to interpret. Indeed, in recent years, more progress seems to have been made on face recognition than voice recognition, no doubt driven by security concerns.

Face recognition has made more progress than voice recognition

The biggest challenge though that computers face is in the field of artificial intelligence, and in particular how humans think and make decisions. As already noted, computers can handle algorithms very well, but this is a comparatively small component of human decision-making. Humans tend to be inductive or intuitive thinkers, rather than deductive or algorithmic thinkers. Computers tend to operate in absolute terms. If part of the algorithm fails, then the computer is likely to crash. Humans however are more qualitative and probabilistic in their thinking. They handle ambiguity better, are willing to make decisions on less than perfect information, and continue to operate even though they may be wrong in their thinking or actions – they tend to be much more self-correcting than computers.

Can we and should we?

This raises two important questions:

  • will it be possible to design machines that can think like humans?
  • And more importantly, if we can do this, should we?

These questions have particular significance for education, because as Dr. Brown of IBM said, ‘to build these kinds of systems you actually need to leverage learning, automatic learning and machine learning in a variety of ways.’

At the moment, even though WATSON, the world’s largest computer, can beat experts at chess, can outperform humans in memory games such as Jeopardy, and can support certain kinds of decision-making, such as medical diagnosis, it still struggles with non-algorithmic thinking. One human brain has many more nodes and networks than the largest computers today. According to Dharmendra Modha, director of cognitive computing at the IBM Almaden Research Center:

We have no computers today that can begin to approach the awesome power of the human mind. A computer comparable to the human brain would need to be able to perform more than 38 thousand trillion operations per second and hold about 3,584 terabytes of memory. (IBM’s BlueGene supercomputer, one of the worlds’ most powerful, has a computational capability of 92 trillion operations per second and 8 terabytes of storage.)

However, research and development in psychology probably will lead to developments in artificial intelligence that will enable very powerful computers, probably using networked distributed computing, to eventually outperform humans in more intuitive and less certain forms of thinking. Dr. Modha went on to predict that we’ll be able to simulate the workings of the brain by 2018. I’m not so sure. If we still haven’t satisfactorily cracked voice recognition after 40 years, it may take a little more than six years to tackle intuitive thinking. Nevertheless, I do believe eventually it will be possible to replicate in machines much of what is now performed by human brains. The issue then becomes whether this is practical or cost-efficient, compared with using humans for similar tasks, who in turn often have to be educated or trained at high cost to do these activities well.

Answering the second question – whether we should replace human thinking with computers – though is much more difficult. Machines have been replacing human activity since at least the Renaissance. The printing press put a lot of monks out of business. So won’t computers start making teachers redundant?

This assumes though that teaching and learning is purely about logic and reasoning. If only it were. So much of learning requires understanding of emotion and feelings, the ability of students to relate to their teachers and their fellow students, and above all, is about fostering, developing and supporting values, especially freedom, security, and well-being. Indeed, even some computer scientists such as Dr. Brown argue that computers are most valuable when they are used to support rather than replace human activities: ‘It’s technology to help humans do their jobs better, faster, more effectively, more efficiently‘. And, as in films such as Colossus and the Matrix, it’s about computers supporting humanity, not the other way round.

The implications for teaching and learning

Thus my belief (how will a computer handle that?) is that computers are wonderful tools for supporting teaching and learning, and as cognitive and computer scientists become more knowledgeable, computers will increase in value in meeting this purpose as time goes on, . However it means that these scientists need to work collaboratively, and more importantly as equals, with teachers and indeed learners, to ensure that computers are used in ways that respect not only the complexity of teaching and learning, but also the value systems that underpin a liberal education.

And it is here that I have the most concerns. There is, especially in the United States of America, a growing ideology that considers teachers to be ineffective or redundant and which seeks means to replace teachers with computers. Coursera-style MOOCs are just one example. Multiple-choice testing and open educational resources in the format of iTunes and OpenCourseWare are other examples.Once it’s ‘up there’, there are some who believe that the recorded lecture is the ‘teacher.’ It is not: it is a transmitter of content, which is not the same as a teacher.

Another concern for us, as humans, is to be continually aware of the difference between virtuality and reality. This is not to criticize the use of virtual reality for teaching, but it is to ensure that learners understand the significance of their actions when they transfer skills from a virtual to a real world, and to be able to distinguish which world they are in. This is not yet a major problem because virtual reality is disappointingly under-used in education, but it is increasingly a feature of the lives of young people. This sensitivity to the difference between virtuality and reality will become an increasingly important life skill, as we begin to merge them, for instance in the remote control of robot welders in pipelines. It’s important to know the difference between training (virtual reality) and life, when a mistake can lead to an explosion or an oil leak, which has very real consequences.

Lastly, I also have some concerns about the ‘open culture’ of web 2.0. In general, as readers will know, I am a great supporter of web 2.0 tools in education, and of open access in particular. However, this does not apply to all web 2.0 tools, or all ways in which they are used. Jared Lanier, one of the founders of virtual reality, says:

 “I know quite a few people … who are proud to say that they have accumulated thousands of friends on Facebook. Obviously, this statement can only be true if the idea of friendship is reduced.

Also, while in general Lanier supports the use of crowd sourcing and the ‘wisdom of the crowd’ that underlies moves towards cMOOCs and Siemen’s theory of connectivism, he criticizes:

the odd lack of curiosity about the limits of crowd wisdom. This is an indication of the faith-based motivations behind such schemes. Numerous projects have looked at how to improve specific markets and other crowd wisdom systems, but too few projects have framed the question in more general terms or tested general hypotheses about how crowd systems work.’

None of these concerns undermine my belief that computers, when used appropriately, can and do bring enormous benefits to teaching and learning. We shouldn’t anthropomorphize computers (they don’t like it) but, as I learned from ‘Downton Abbey’, like all good servants, they need to know their place.


1. Do you believe that ‘we’ll be able to simulate the workings of the brain by 2018’? I’d like to hear from brain scientists if they agree – too often what’s reported in science is not what the majority of scientists think.

2. If we could ‘simulate the workings of the brain’, what impact would it have on teaching and learning?

3. Do you believe that there is a desire in some countries to replace teachers with computers? Do you see Coursera and xMOOCs as part of this conspiracy?

4. Do you think I am being irrational in my concerns about computers in teaching?

Further reading

HAL 9000 (2012) Wikipedia

Houpt, S. (2012) IBM hones Watson the supercomputer’s skills to help conquer business world challenges The Globe and Mail, October 20

Lanier, J. (2010) You Are Not a Gadget New York: Alfred A. Knopf

Orson Scott Card (1994) Ender’s Game New York: Tor

Colossus: The Forbin Project 

Desire2Learn moving to ‘predictive analytics’ with IBM

Sorry for the gap in posts over the last week. Funny how work gets in the way of blogging. I’ve been visiting some universities in Québec to catch up on e-learning in Canada’s francophone world, and I am also working on a contract for the design of a virtual university in Mexico. More on this later.

Howitt, C. (2012) Desire2Learn partners with IBM on e-learning Guelph, April 13

This announcement caught my eye, as it suggests a move to link big data and big data analysis directly into online learning. It is just an announcement at this stage of an agreement to work together on developing predictive analytics for online learning. This seems to be a move beyond just trawling through the student information system and LMS to building predictive models of online behaviour.

Watch this space for more discussion about learning analytics. I have a number of questions about who is designing the algorithims and the questions they are intended to answer, what assumptions are driving the design, who has access to the data, what rights students and instructors will have, and how institutions plan to use analytics from online teaching. However, I need some time to do this, so expect something later next month.

Analytics: the next buzz word in e-learning?

MIT Sloan Management Review (2010) Analytics: the New Path to Value Cambridge MA: MIT/IBM

At the moment, this is a publication likely to be of interest to a small number of people in the e-learning community. However, the ability now to drill down and across multiple databases, collect and analyse data, then show it graphically in the form of charts or even simulations that project the future, is becoming an important tool for improving productivity, setting or changing strategies, and for measuring performance in business. These tools are not just for the head honchos, but are now being used on a daily basis by people on the front line.

In education, such analytical tools would enable academic program managers to make choices about the mix of technologies and forms of delivery to suit for instance the changing demographics of students taking the program. It would enable data from learning management systems to be combined with student demographic data to indicate differences in learning styles, what tools are used by what kinds of students, and much more.

This publication from the MIT Sloan management School and IBM shows how analytics are being used in business. If you want to get ahead of the game, take a look at this report then think how analytics could be applied to teaching and learning.

Competing in a Global Economy Through Open Education

Michael King, Vice President, IBM Education Industry will be a keynote at Online Educa Berlin 2008. He will be speaking on the following topic.

On a global scale, the U.S., European Union, Japan, China and India will face critical shortfalls of 32 million technically specialised professionals. Throughout the world, the demand for educated professionals is growing faster than the populations of those people with the required skills.

Unfortunately, today’s educational systems – typically underfunded and under pressure to do more with less – are ill-equipped to close the skills gap. Despite billions of dollars in spending, technology has produced inconsistent results.  Siloed enterprise applications, lack of data interoperability, high software licensing fees, escalating total cost of ownership, absence of industry standards – they all contribute to inefficient processes, creating barriers to collaboration and innovation.

To address these challenges, the education industry must commit itself to becoming more open. That means more open access to educational opportunities for more students, more open data and processes within and across institutions, and a more open culture of collaboration and sharing.

For more information on this session, click here