Posts Tagged ‘interactive whiteboards’

At a recent ELT conference, a plenary presentation entitled ‘Getting it right with edtech’ (sponsored by a vendor of – increasingly digital – ELT products) began with the speaker suggesting that technology was basically neutral, that what you do with educational technology matters far more than the nature of the technology itself. The idea that technology is a ‘neutral tool’ has a long pedigree and often accompanies exhortations to embrace edtech in one form or another (see for example Fox, 2001). It is an idea that is supported by no less a luminary than Chomsky, who, in a 2012 video entitled ‘The Purpose of Education’ (Chomsky, 2012), said that:

As far as […] technology […] and education is concerned, technology is basically neutral. It’s kind of like a hammer. I mean, […] the hammer doesn’t care whether you use it to build a house or whether a torturer uses it to crush somebody’s skull; a hammer can do either. The same with the modern technology; say, the Internet, and so on.

Womans hammerAlthough hammers are not usually classic examples of educational technology, they are worthy of a short discussion. Hammers come in all shapes and sizes and when you choose one, you need to consider its head weight (usually between 16 and 20 ounces), the length of the handle, the shape of the grip, etc. Appropriate specifications for particular hammering tasks have been calculated in great detail. The data on which these specifications is based on an analysis of the hand size and upper body strength of the typical user. The typical user is a man, and the typical hammer has been designed for a man. The average male hand length is 177.9 mm, that of the average woman is 10 mm shorter (Wang & Cai, 2017). Women typically have about half the upper body strength of men (Miller et al., 1993). It’s possible, but not easy to find hammers designed for women (they are referred to as ‘Ladies hammers’ on Amazon). They have a much lighter head weight, a shorter handle length, and many come in pink or floral designs. Hammers, in other words, are far from neutral: they are highly gendered.

Moving closer to educational purposes and ways in which we might ‘get it right with edtech’, it is useful to look at the smart phone. The average size of these devices has risen in recent years, and is now 5.5 inches, with the market for 6 inch screens growing fast. Why is this an issue? Well, as Caroline Criado Perez (2019: 159) notes, ‘while we’re all admittedly impressed by the size of your screen, it’s a slightly different matter when it comes to fitting into half the population’s hands. The average man can fairly comfortably use his device one-handed – but the average woman’s hand is not much bigger than the handset itself’. This is despite the fact the fact that women are more likely to own an iPhone than men  .

It is not, of course, just technological artefacts that are gendered. Voice-recognition software is also very biased. One researcher (Tatman, 2017) has found that Google’s speech recognition tool is 13% more accurate for men than it is for women. There are also significant biases for race and social class. The reason lies in the dataset that the tool is trained on: the algorithms may be gender- and socio-culturally-neutral, but the dataset is not. It would not be difficult to redress this bias by training the tool on a different dataset.

The same bias can be found in automatic translation software. Because corpora such as the BNC or COCA have twice as many male pronouns as female ones (as a result of the kinds of text that are selected for the corpora), translation software reflects the bias. With Google Translate, a sentence in a language with a gender-neutral pronoun, such as ‘S/he is a doctor’ is rendered into English as ‘He is a doctor’. Meanwhile, ‘S/he is a nurse’ is translated as ‘She is a nurse’ (Criado Perez, 2019: 166).

Datasets, then, are often very far from neutral. Algorithms are not necessarily any more neutral than the datasets, and Cathy O’Neil’s best-seller ‘Weapons of Math Destruction’ catalogues the many, many ways in which algorithms, posing as neutral mathematical tools, can increase racial, social and gender inequalities.

It would not be hard to provide many more examples, but the selection above is probably enough. Technology, as Langdon Winner (Winner, 1980) observed almost forty years ago, is ‘deeply interwoven in the conditions of modern politics’. Technology cannot be neutral: it has politics.

So far, I have focused primarily on the non-neutrality of technology in terms of gender (and, in passing, race and class). Before returning to broader societal issues, I would like to make a relatively brief mention of another kind of non-neutrality: the pedagogic. Language learning materials necessarily contain content of some kind: texts, topics, the choice of values or role models, language examples, and so on. These cannot be value-free. In the early days of educational computer software, one researcher (Biraimah, 1993) found that it was ‘at least, if not more, biased than the printed page it may one day replace’. My own impression is that this remains true today.

Equally interesting to my mind is the fact that all educational technologies, ranging from the writing slate to the blackboard (see Buzbee, 2014), from the overhead projector to the interactive whiteboard, always privilege a particular kind of teaching (and learning). ‘Technologies are inherently biased because they are built to accomplish certain very specific goals which means that some technologies are good for some tasks while not so good for other tasks’ (Zhao et al., 2004: 25). Digital flashcards, for example, inevitably encourage a focus on rote learning. Contemporary LMSs have impressive multi-functionality (i.e. they often could be used in a very wide variety of ways), but, in practice, most teachers use them in very conservative ways (Laanpere et al., 2004). This may be a result of teacher and institutional preferences, but it is almost certainly due, at least in part, to the way that LMSs are designed. They are usually ‘based on traditional approaches to instruction dating from the nineteenth century: presentation and assessment [and] this can be seen in the selection of features which are most accessible in the interface, and easiest to use’ (Lane, 2009).

The argument that educational technology is neutral because it could be put to many different uses, good or bad, is problematic because the likelihood of one particular use is usually much greater than another. There is, however, another way of looking at technological neutrality, and that is to look at its origins. Elsewhere on this blog, in post after post, I have given examples of the ways in which educational technology has been developed, marketed and sold primarily for commercial purposes. Educational values, if indeed there are any, are often an afterthought. The research literature in this area is rich and growing: Stephen Ball, Larry Cuban, Neil Selwyn, Joel Spring, Audrey Watters, etc.

Rather than revisit old ground here, this is an opportunity to look at a slightly different origin of educational technology: the US military. The close connection of the early history of the internet and the Advanced Research Projects Agency (now DARPA) of the United States Department of Defense is fairly well-known. Much less well-known are the very close connections between the US military and educational technologies, which are catalogued in the recently reissued ‘The Classroom Arsenal’ by Douglas D. Noble.

Following the twin shocks of the Soviet Sputnik 1 (in 1957) and Yuri Gagarin (in 1961), the United States launched a massive programme of investment in the development of high-tech weaponry. This included ‘computer systems design, time-sharing, graphics displays, conversational programming languages, heuristic problem-solving, artificial intelligence, and cognitive science’ (Noble, 1991: 55), all of which are now crucial components in educational technology. But it also quickly became clear that more sophisticated weapons required much better trained operators, hence the US military’s huge (and continuing) interest in training. Early interest focused on teaching machines and programmed instruction (branches of the US military were by far the biggest purchasers of programmed instruction products). It was essential that training was effective and efficient, and this led to a wide interest in the mathematical modelling of learning and instruction.

What was then called computer-based education (CBE) was developed as a response to military needs. The first experiments in computer-based training took place at the Systems Research Laboratory of the Air Force’s RAND Corporation think tank (Noble, 1991: 73). Research and development in this area accelerated in the 1960s and 1970s and CBE (which has morphed into the platforms of today) ‘assumed particular forms because of the historical, contingent, military contexts for which and within which it was developed’ (Noble, 1991: 83). It is possible to imagine computer-based education having developed in very different directions. Between the 1960s and 1980s, for example, the PLATO (Programmed Logic for Automatic Teaching Operations) project at the University of Illinois focused heavily on computer-mediated social interaction (forums, message boards, email, chat rooms and multi-player games). PLATO was also significantly funded by a variety of US military agencies, but proved to be of much less interest to the generals than the work taking place in other laboratories. As Noble observes, ‘some technologies get developed while others do not, and those that do are shaped by particular interests and by the historical and political circumstances surrounding their development (Noble, 1991: 4).

According to Noble, however, the influence of the military reached far beyond the development of particular technologies. Alongside the investment in technologies, the military were the prime movers in a campaign to promote computer literacy in schools.

Computer literacy was an ideological campaign rather than an educational initiative – a campaign designed, at bottom, to render people ‘comfortable’ with the ‘inevitable’ new technologies. Its basic intent was to win the reluctant acquiescence of an entire population in a brave new world sculpted in silicon.

The computer campaign also succeeded in getting people in front of that screen and used to having computers around; it made people ‘computer-friendly’, just as computers were being rendered ‘used-friendly’. It also managed to distract the population, suddenly propelled by the urgency of learning about computers, from learning about other things, such as how computers were being used to erode the quality of their working lives, or why they, supposedly the citizens of a democracy, had no say in technological decisions that were determining the shape of their own futures.

Third, it made possible the successful introduction of millions of computers into schools, factories and offices, even homes, with minimal resistance. The nation’s public schools have by now spent over two billion dollars on over a million and a half computers, and this trend still shows no signs of abating. At this time, schools continue to spend one-fifth as much on computers, software, training and staffing as they do on all books and other instructional materials combined. Yet the impact of this enormous expenditure is a stockpile of often idle machines, typically used for quite unimaginative educational applications. Furthermore, the accumulated results of three decades of research on the effectiveness of computer-based instruction remain ‘inconclusive and often contradictory’. (Noble, 1991: x – xi)

Rather than being neutral in any way, it seems more reasonable to argue, along with (I think) most contemporary researchers, that edtech is profoundly value-laden because it has the potential to (i) influence certain values in students; (ii) change educational values in [various] ways; and (iii) change national values (Omotoyinbo & Omotoyinbo, 2016: 173). Most importantly, the growth in the use of educational technology has been accompanied by a change in the way that education itself is viewed: ‘as a tool, a sophisticated supply system of human cognitive resources, in the service of a computerized, technology-driven economy’ (Noble, 1991: 1). These two trends are inextricably linked.

References

Biraimah, K. 1993. The non-neutrality of educational computer software. Computers and Education 20 / 4: 283 – 290

Buzbee, L. 2014. Blackboard: A Personal History of the Classroom. Minneapolis: Graywolf Press

Chomsky, N. 2012. The Purpose of Education (video). Learning Without Frontiers Conference. https://www.youtube.com/watch?v=DdNAUJWJN08

Criado Perez, C. 2019. Invisible Women. London: Chatto & Windus

Fox, R. 2001. Technological neutrality and practice in higher education. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://clt.curtin.edu.au/events/conferences/tlf/tlf2001/fox.html

Laanpere, M., Poldoja, H. & Kikkas, K. 2004. The second thoughts about pedagogical neutrality of LMS. Proceedings of IEEE International Conference on Advanced Learning Technologies, 2004. https://ieeexplore.ieee.org/abstract/document/1357664

Lane, L. 2009. Insidious pedagogy: How course management systems impact teaching. First Monday, 14(10). https://firstmonday.org/ojs/index.php/fm/article/view/2530/2303Lane

Miller, A.E., MacDougall, J.D., Tarnopolsky, M. A. & Sale, D.G. 1993. ‘Gender differences in strength and muscle fiber characteristics’ European Journal of Applied Physiology and Occupational Physiology. 66(3): 254-62 https://www.ncbi.nlm.nih.gov/pubmed/8477683

Noble, D. D. 1991. The Classroom Arsenal. Abingdon, Oxon.: Routledge

Omotoyinbo, D. W. & Omotoyinbo, F. R. 2016. Educational Technology and Value Neutrality. Societal Studies, 8 / 2: 163 – 179 https://www3.mruni.eu/ojs/societal-studies/article/view/4652/4276

O’Neil, C. 2016. Weapons of Math Destruction. London: Penguin

Sundström, P. Interpreting the Notion that Technology is Value Neutral. Medicine, Health Care and Philosophy 1, 1998: 42-44

Tatman, R. 2017. ‘Gender and Dialect Bias in YouTube’s Automatic Captions’ Proceedings of the First Workshop on Ethics in Natural Language Processing, pp. 53–59 http://www.ethicsinnlp.org/workshop/pdf/EthNLP06.pdf

Wang, C. & Cai, D. 2017. ‘Hand tool handle design based on hand measurements’ MATEC Web of Conferences 119, 01044 (2017) https://www.matec-conferences.org/articles/matecconf/pdf/2017/33/matecconf_imeti2017_01044.pdf

Winner, L. 1980. Do Artifacts have Politics? Daedalus 109 / 1: 121 – 136

Zhao, Y, Alvarez-Torres, M. J., Smith, B. & Tan, H. S. 2004. The Non-neutrality of Technology: a Theoretical Analysis and Empirical Study of Computer Mediated Communication Technologies. Journal of Educational Computing Research 30 (1 &2): 23 – 55

One could be forgiven for thinking that there are no problems associated with adaptive learning in ELT. Type the term into a search engine and you’ll mostly come up with enthusiasm or sales talk. There are, however, a number of reasons to be deeply skeptical about the whole business. In the post after this, I will be considering the political background.

1. Learning theory

Jose Fereira, the CEO of Knewton, spoke, in an interview with Digital Journal[1] in October 2009, about getting down to the ‘granular level’ of learning. He was referencing, in an original turn of phrase, the commonly held belief that learning is centrally concerned with ‘gaining knowledge’, knowledge that can be broken down into very small parts that can be put together again. In this sense, the adaptive learning machine is very similar to the ‘teaching machine’ of B.F. Skinner, the psychologist who believed that learning was a complex process of stimulus and response. But how many applied linguists would agree, firstly, that language can be broken down into atomised parts (rather than viewed as a complex, dynamic system), and, secondly, that these atomised parts can be synthesized in a learning program to reform a complex whole? Human cognitive and linguistic development simply does not work that way, despite the strongly-held contrary views of ‘folk’ theories of learning (Selwyn Education and Technology 2011, p.3).

machine

Furthermore, even if an adaptive system delivers language content in personalized and interesting ways, it is still premised on a view of learning where content is delivered and learners receive it. The actual learning program is not personalized in any meaningful way: it is only the way that it is delivered that responds to the algorithms. This is, again, a view of learning which few educationalists (as opposed to educational leaders) would share. Is language learning ‘simply a technical business of well managed information processing’ or is it ‘a continuing process of ‘participation’ (Selwyn, Education and Technology 2011, p.4)?

Finally, adaptive learning is also premised on the idea that learners have particular learning styles, that these can be identified by the analytics (even if they are not given labels), and that actionable insights can be gained from this analysis (i.e. the software can decide on the most appropriate style of content delivery for an individual learner). Although the idea that teaching programs can be modified to cater to individual learning styles continues to have some currency among language teachers (e.g. those who espouse Neuro-Linguistic Programming or Multiple Intelligences Theory), it is not an idea that has much currency in the research community.

It might be the case that adaptive learning programs will work with some, or even many, learners, but it would be wise to carry out more research (see the section on Research below) before making grand claims about its efficacy. If adaptive learning can be shown to be more effective than other forms of language learning, it will be either because our current theories of language learning are all wrong, or because the learning takes place despite the theory, (and not because of it).

2. Practical problems

However good technological innovations may sound, they can only be as good, in practice, as the way they are implemented. Language laboratories and interactive whiteboards both sounded like very good ideas at the time, but they both fell out of favour long before they were technologically superseded. The reasons are many, but one of the most important is that classroom teachers did not understand sufficiently the potential of these technologies or, more basically, how to use them. Given the much more radical changes that seem to be implied by the adoption of adaptive learning, we would be wise to be cautious. The following is a short, selected list of questions that have not yet been answered.

  • Language teachers often struggle with mixed ability classes. If adaptive programs (as part of a blended program) allow students to progress at their own speed, the range of abilities in face-to-face lessons may be even more marked. How will teachers cope with this? Teacher – student ratios are unlikely to improve!
  • Who will pay for the training that teachers will need to implement effective blended learning and when will this take place?
  • How will teachers respond to a technology that will be perceived by some as a threat to their jobs and their professionalism and as part of a growing trend towards the accommodation of commercial interests (see the next post)?
  • How will students respond to online (adaptive) learning when it becomes the norm, rather than something ‘different’?

3 Research

Technological innovations in education are rarely, if ever, driven by solidly grounded research, but they are invariably accompanied by grand claims about their potential. Motion pictures, radio, television and early computers were all seen, in their time, as wonder technologies that would revolutionize education (Cuban, Teachers and Machines: The Classroom Use of Technology since 1920 1986). Early research seemed to support the claims, but the passage of time has demonstrated all too clearly the precise opposite. The arrival on the scene of e-learning in general, and adaptive learning in particular, has also been accompanied by much cheer-leading and claims of research support.

Examples of such claims of research support for adaptive learning in higher education in the US and Australia include an increase in pass rates of between 7 and 18%, a decrease of between 14 and 47% in student drop-outs, and an acceleration of 25% in the time needed to complete courses[2]. However, research of this kind needs to be taken with a liberal pinch of salt. First of all, the research has usually been commissioned, and sometimes carried out, by those with vested commercial interests in positive results. Secondly, the design of the research study usually guarantees positive results. Finally, the results cannot be interpreted to have any significance beyond their immediate local context. There is no reason to expect that what happened in a particular study into adaptive learning in, say, the University of Arizona would be replicated in, say, the Universities of Amman, Astana or anywhere else. Very often, when this research is reported, the subject of the students’ study is not even mentioned, as if this were of no significance.

The lack of serious research into the effectiveness of adaptive learning does not lead us to the conclusion that it is ineffective. It is simply too soon to say, and if the examples of motion pictures, radio and television are any guide, it will be a long time before we have any good evidence. By that time, it is reasonable to assume, adaptive learning will be a very different beast from what it is today. Given the recency of this kind of learning, the lack of research is not surprising. For online learning in general, a meta-analysis commissioned by the US Department of Education (Means et al, Evaluation of Evidence-Based Practice in Online Learning 2009, p.9) found that there were only a small number of rigorous published studies, and that it was not possible to attribute any gains in learning outcomes to online or blended learning modes. As the authors of this report were aware, there are too many variables (social, cultural and economic) to compare in any direct way the efficacy of one kind of learning with another. This is as true of attempts to compare adaptive online learning with face-to-face instruction as it is with comparisons of different methodological approaches in purely face-to-face teaching. There is, however, an irony in the fact that advocates of adaptive learning (whose interest in analytics leads them to prioritise correlational relationships over causal ones) should choose to make claims about the causal relationship between learning outcomes and adaptive learning.

Perhaps, as Selwyn (Education and Technology 2011, p.87) suggests, attempts to discover the relative learning advantages of adaptive learning are simply asking the wrong question, not least as there cannot be a single straightforward answer. Perhaps a more useful critique would be to look at the contexts in which the claims for adaptive learning are made, and by whom. Selwyn also suggests that useful insights may be gained from taking a historical perspective. It is worth noting that the technicist claims for adaptive learning (that ‘it works’ or that it is ‘effective’) are essentially the same as those that have been made for other education technologies. They take a universalising position and ignore local contexts, forgetting that ‘pedagogical approach is bound up with a web of cultural assumption’ (Wiske, ‘A new culture of teaching for the 21st century’ in Gordon, D.T. (ed.) The Digital Classroom: How Technology is Changing the Way we teach and Learn 2000, p.72). Adaptive learning might just possibly be different from other technologies, but history advises us to be cautious.


[2] These figures are quoted in Learning to Adapt: A Case for Accelerating Adaptive Learning in Higher Education, a booklet produced in March 2013 by Education Growth Advisors, an education consultancy firm. Their research is available at http://edgrowthadvisors.com/research/