Mobile learning revisited

In the world of ELT teacher blogs, magazines, webinars and conferences right now, you would be hard pressed to avoid the topic of generative AI. Ten years ago, the hot topic was ‘mobile learning’. Might there be some lessons to be learnt from casting our gaze back a little more than a decade?

One of the first ELT-related conferences about mobile learning took place in Japan in 2006. Reporting on this a year later, Dudeney and Hockly (2007: 156) observed that ‘m-learning appears to be here to stay’. By 2009, Agnes Kukulska-Hulme was asking ‘will mobile learning change language learning?’ Her answer, of course, was yes, but it took a little time for the world of ELT to latch onto this next big thing (besides a few apps). Relatively quick out of the blocks was Caroline Moore with an article in the Guardian (8 March 2011) arguing for wider use of mobile learning in ELT. As is so often the case with early promoters of edtech, Caroline had a vested interest, as a consultant in digital language learning, in advancing her basic argument. This was that the technology was so ubiquitous and so rich in potential that it would be foolish not to make the most of it.

The topic gained traction with an IATEFL LT SIG webinar in December 2011, a full-day pre-conference event at the main IATEFL conference early the following year, along with a ‘Macmillan Education Mobile Learning Debate’. Suddenly, mobile learning was everywhere and, by the end of the year, it was being described as ‘the future of learning’ (Kukulska-Hulme, A., 2012). In early 2013, ELT Journal published a defining article, ‘Mobile Learning’ (Hockly, N., 2013). By this point, it wasn’t just a case of recommending teachers to try out a few apps with their learners. The article concludes by saying that ‘the future is increasingly mobile, and it behoves us to reflect this in our teaching practice’ (Hockly, 2013: 83). The rhetorical force was easier to understand than the logical connection.

It wasn’t long before mobile learning was routinely described as the ‘future of language learning’ and apps, like DuoLingo and Busuu, were said to be ‘revolutionising language learning’. Kukulska-Hulme (Kukulska-Hulme et al., 2017) contributed a chapter entitled ‘Mobile Learning Revolution’ to a handbook of technology and second language learning.

In 2017 (books take a while to produce), OUP brought out ‘Mobile Learning’ by Shaun Wilden (2017). Shaun’s book is the place to go for practical ideas: playing around with photos, using QR codes, audio / video recording and so on. The reasons for using mobile learning continue to grow (developing 21^st century skills like creativity, critical thinking and digital literacy in ‘student-centred, dynamic, and motivating ways’).

Unlike Nicky Hockly’s article (2013), Shaun acknowledges that there may be downsides to mobile technology in the classroom. The major downside, as everybody who has ever been in a classroom where phones are permitted knows, is that the technology may be a bigger source of distraction than it is of engagement. Shaun offers a page about ‘acceptable use policies’ for mobile phones in classrooms, but does not let (what he describes as) ‘media scare stories’ get in the way of his enthusiasm.

There are undoubtedly countless examples of ways in which mobile phones can (and even should) be used to further language learning, although I suspect that the QR reader would struggle to make the list. The problem is that these positive examples are all we ever hear about. The topic of distraction does not even get a mention in the chapter on mobile language learning in ‘The Routledge Handbook of Language Learning and Technology’ (Stockwell, 2016). Neither does it appear in Li Li’s (2017) ‘New Technologies and Language Learning’.

Glenda Morgan (2023) has described this as ‘Success Porn in EdTech’, where success is exaggerated, failures minimized and challenges rendered to the point that they are pretty much invisible. ‘Success porn’ is a feature of conference presentations and blog posts, genres which require relentless positivity and a ‘constructive sense of hope, optimism and ambition’ (Selwyn, 2016). Edtech Kool-Aid (ibid) is also a feature of academic writing. Do a Google Scholar search for ‘mobile learning language learning’ to see what I mean. The first article that comes up is entitled ‘Positive effects of mobile learning on foreign language learning’. Skepticism is in very short supply, as it is in most research into edtech. There are a number of reasons for this, one of which (that ‘locating one’s work in the pro-edtech zeitgeist may be a strategic choice to be part of the mainstream of the field’ (Mertala et al., 2022)) will resonate with colleagues who wish to give conference presentations and write blogs for publishers. The discourse around AI is, of course, no different (see Nemorin et al., 2022).

Anyway, back to the downside of mobile learning and the ‘media scare stories’. Most language learning takes place in primary and secondary schools. According to a recent report from Common Sense (Radesky et al., 2023), US teens use their smart phones for a median of 4 ½ hours per day, checking for notifications a median of 51 times. Almost all of them (97%) use their phones at school, mostly for social media, videos or gaming. Schools have a variety of policies, and widely varying enforcement within those policies. Your country may not be quite the same as the US, but it’s probably heading that way.

Research suggests that excessive (which is to say typical) mobile phone use has a negative impact on learning outcomes, wellbeing and issues like bullying (see this brief summary of global research). This comes as no surprise to most people – the participants at the 2012 Macmillan debate were aware of these problems. The question that needs to be asked, therefore, is not whether mobile learning can assist language learning, but whether the potential gains outweigh the potential disadvantages. Is language learning a special case?

One in four countries around the world have decided to ban phones in school. A new report from UNESCO (2023) calls for a global smart phone ban in education, pointing out that there is ‘little robust research to demonstrate digital technology inherently added value to education’. The same report delves a little into generative AI, and a summary begins ‘Generative AI may not bring the kind of change in education often discussed. Whether and how AI would be used in education is an open question (Gillani et al., 2023)’ (UNESCO, 2023: 13).

The history of the marketing of edtech has always been ‘this time it’s different’. It relies on a certain number of people repeating the mantra, since the more it is repeated, the more likely it will be perceived to be true (Fazio et al., 2019): this is the illusory truth effect or the ‘Snark rule[1]’. Mobile learning changed things for the better for some learners in some contexts: claims that it was the future of, or would revolutionize, language learning have proved somewhat exaggerated. Indeed, the proliferation of badly-designed language learning apps suggests that much mobile learning reinforces the conventional past of language learning (drilling, gamified rote learning, native-speaker models, etc.) rather than leading to positive change (see Kohn, 2023). The history of edtech is a history of broken promises and unfulfilled potential and there is no good reason why generative AI will be any different.

Perhaps, then, it behoves us to be extremely sceptical about the current discourse surrounding generative AI in ELT. Like mobile technology, it may well be an extremely useful tool, but the chances that it will revolutionize language teaching are extremely slim – much like the radio, TV, audio / video recording and playback, the photocopier, the internet and VR before it. A few people will make some money for a while, but truly revolutionary change in teaching / learning will not come about through technological innovation.

References

Dudeney, G. & Hockly, N. (2007) How to Teach English with Technology. Harlow: Pearson Education

Fazio, L. K., Rand, D. G. & Pennycook, G. (2019) Repetition increases perceived truth equally for plausible and implausible statements. Psychonomic Bulletin and Review 26: 1705–1710. https://doi.org/10.3758/s13423-019-01651-4

Hockly, N. (2013) Mobile Learning. ELT Journal, 67 (1): 80 – 84

Kohn, A. (2023) How ‘Innovative’ Ed Tech Actually Reinforces Convention. Education Week, 19 September 2023.

Kukulska-Hulme, A. (2009) Will Mobile Learning Change Language Learning? reCALL, 21 (2): 157 – 165

Kukulska-Hulme, A. (2012) Mobile Learning and the Future of Learning. International HETL Review, 2: 13 – 18

Kukulska-Hulme, A., Lee, H. & Norris, L. (2017) Mobile Learning Revolution: Implications for Language Pedagogy. In Chapelle, C. A. & Sauro, S. (Eds.) The Handbook of Technology and Second Language Teaching and Learning. John Wiley & Sons

Li, L. (2017) New Technologies and Language Learning. London: Palgrave

Mertala, P., Moens, E. & Teräs, M. (2022) Highly cited educational technology journal articles: a descriptive and critical analysis, Learning, Media and Technology, DOI: 10.1080/17439884.2022.2141253

Nemorin, S., Vlachidis, A., Ayerakwa, H. M. & Andriotis, P. (2022): AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development, Learning, Media and Technology, DOI: 10.1080/17439884.2022.2095568

Radesky, J., Weeks, H.M., Schaller, A., Robb, M., Mann, S., and Lenhart, A. (2023) Constant Companion: A Week in the Life of a Young Person’s Smartphone Use. San Francisco, CA: Common Sense.

Selwyn, N. (2016) Minding our Language: Why Education and Technology is Full of Bullshit … and What Might be Done About it. Learning, Media and Technology, 41 (3): 437–443

Stockwell, G. (2016) Mobile Language Learning. In Farr, F. & Murray, L. (Eds.) The Routledge Handbook of Language Learning and Technology. Abingdon: Routledge. pp. 296 – 307

UNESCO (2023) Global Education Monitoring Report 2023: Technology in Education – A Tool on whose Terms?Paris: UNESCO

Wilden, S. (2017) Mobile Learning. Oxford: OUP

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[1] Named after Lewis Carroll’s poem ‘The Hunting of the Snark’ in which the Bellman cries ‘I have said it thrice: What I tell you three times is true.’

The fun they had

Back in the middle of the last century, the first interactive machines for language teaching appeared. Previously, there had been phonograph discs and wire recorders (Ornstein, 1968: 401), but these had never really taken off. This time, things were different. Buoyed by a belief in the power of technology, along with the need (following the Soviet Union’s successful Sputnik programme) to demonstrate the pre-eminence of the United States’ technological expertise, the interactive teaching machines that were used in programmed instruction promised to revolutionize language learning (Valdman, 1968: 1). From coast to coast, ‘tremors of excitement ran through professional journals and conferences and department meetings’ (Kennedy, 1967: 871). The new technology was driven by hard science, supported and promoted by the one of the most well-known and respected psychologists and public intellectuals of the day (Skinner, 1961).

In classrooms, the machines acted as powerfully effective triggers in generating situational interest (Hidi & Renninger, 2006). Even more exciting than the mechanical teaching machines were the computers that were appearing on the scene. ‘Lick’ Licklider, a pioneer in interactive computing at the Advanced Research Projects Agency in Arlington, Virginia, developed an automated drill routine for learning German by hooking up a computer, two typewriters, an oscilloscope and a light pen (Noble, 1991: 124). Students loved it, and some would ‘go on and on, learning German words until they were forced by scheduling to cease their efforts’. Researchers called the seductive nature of the technology ‘stimulus trapping’, and Licklider hoped that ‘before [the student] gets out from under the control of the computer’s incentives, [they] will learn enough German words’ (Noble, 1991: 125).

With many of the developed economies of the world facing a critical shortage of teachers, ‘an urgent pedagogical emergency’ (Hof, 2018), the new approach was considered to be extremely efficient and could equalise opportunity in schools across the country. It was ‘here to stay: [it] appears destined to make progress that could well go beyond the fondest dreams of its originators […] an entire industry is just coming into being and significant sales and profits should not be too long in coming’ (Kozlowski, 1961: 47).

Unfortunately, however, researchers and entrepreneurs had massively underestimated the significance of novelty effects. The triggered situational interest of the machines did not lead to intrinsic individual motivation. Students quickly tired of, and eventually came to dislike, programmed instruction and the machines that delivered it (McDonald et al.: 2005: 89). What’s more, the machines were expensive and ‘research studies conducted on its effectiveness showed that the differences in achievement did not constantly or substantially favour programmed instruction over conventional instruction (Saettler, 2004: 303). Newer technologies, with better ‘stimulus trapping’, were appearing. Programmed instruction lost its backing and disappeared, leaving as traces only its interest in clearly defined learning objectives, the measurement of learning outcomes and a concern with the efficiency of learning approaches.

Hot on the heels of programmed instruction came the language laboratory. Futuristic in appearance, not entirely unlike the deck of the starship USS Enterprise which launched at around the same time, language labs captured the public imagination and promised to explore the final frontiers of language learning. As with the earlier teaching machines, students were initially enthusiastic. Even today, when language labs are introduced into contexts where they may be perceived as new technology, they can lead to high levels of initial motivation (e.g. Ramganesh & Janaki, 2017).

Given the huge investments into these labs, it’s unfortunate that initial interest waned fast. By 1969, many of these rooms had turned into ‘“electronic graveyards,” sitting empty and unused, or perhaps somewhat glorified study halls to which students grudgingly repair to don headphones, turn down the volume, and prepare the next period’s history or English lesson, unmolested by any member of the foreign language faculty’ (Turner, 1969: 1, quoted in Roby, 2003: 527). ‘Many second language students shudder[ed] at the thought of entering into the bowels of the “language laboratory” to practice and perfect the acoustical aerobics of proper pronunciation skills. Visions of sterile white-walled, windowless rooms, filled with endless bolted-down rows of claustrophobic metal carrels, and overseen by a humorless, lab director, evoke[d] fear in the hearts of even the most stout-hearted prospective second-language learners (Wiley, 1990: 44).

By the turn of this century, language labs had mostly gone, consigned to oblivion by the appearance of yet newer technology: the internet, laptops and smartphones. Education had been on the brink of being transformed through new learning technologies for decades (Laurillard, 2008: 1), but this time it really was different. It wasn’t just one technology that had appeared, but a whole slew of them: ‘artificial intelligence, learning analytics, predictive analytics, adaptive learning software, school management software, learning management systems (LMS), school clouds. No school was without these and other technologies branded as ‘superintelligent’ by the late 2020s’ (Macgilchrist et al., 2019). The hardware, especially phones, was ubiquitous and, therefore, free. Unlike teaching machines and language laboratories, students were used to using the technology and expected to use their devices in their studies.

A barrage of publicity, mostly paid for by the industry, surrounded the new technologies. These would ‘meet the demands of Generation Z’, the new generation of students, now cast as consumers, who ‘were accustomed to personalizing everything’.  AR, VR, interactive whiteboards, digital projectors and so on made it easier to ‘create engaging, interactive experiences’. The ‘New Age’ technologies made learning fun and easy,  ‘bringing enthusiasm among the students, improving student engagement, enriching the teaching process, and bringing liveliness in the classroom’. On top of that, they allowed huge amounts of data to be captured and sold, whilst tracking progress and attendance. In any case, resistance to digital technology, said more than one language teaching expert, was pointless (Styring, 2015).

At the same time, technology companies increasingly took on ‘central roles as advisors to national governments and local districts on educational futures’ and public educational institutions came to be ‘regarded by many as dispensable or even harmful’ (Macgilchrist et al., 2019).

But, as it turned out, the students of Generation Z were not as uniformly enthusiastic about the new technology as had been assumed, and resistance to digital, personalized delivery in education was not long in coming. In November 2018, high school students at Brooklyn’s Secondary School for Journalism staged a walkout in protest at their school’s use of Summit Learning, a web-based platform promoting personalized learning developed by Facebook. They complained that the platform resulted in coursework requiring students to spend much of their day in front of a computer screen, that made it easy to cheat by looking up answers online, and that some of their teachers didn’t have the proper training for the curriculum (Leskin, 2018). Besides, their school was in a deplorable state of disrepair, especially the toilets. There were similar protests in Kansas, where students staged sit-ins, supported by their parents, one of whom complained that ‘we’re allowing the computers to teach and the kids all looked like zombies’ before pulling his son out of the school (Bowles, 2019). In Pennsylvania and Connecticut, some schools stopped using Summit Learning altogether, following protests.

But the resistance did not last. Protesters were accused of being nostalgic conservatives and educationalists kept largely quiet, fearful of losing their funding from the Chan Zuckerberg Initiative (Facebook) and other philanthro-capitalists. The provision of training in grit, growth mindset, positive psychology and mindfulness (also promoted by the technology companies) was ramped up, and eventually the disaffected students became more quiescent. Before long, the data-intensive, personalized approach, relying on the tools, services and data storage of particular platforms had become ‘baked in’ to educational systems around the world (Moore, 2018: 211). There was no going back (except for small numbers of ultra-privileged students in a few private institutions).

By the middle of the century (2155), most students, of all ages, studied with interactive screens in the comfort of their homes. Algorithmically-driven content, with personalized, adaptive tests had become the norm, but the technology occasionally went wrong, leading to some frustration. One day, two young children discovered a book in their attic. Made of paper with yellow, crinkly pages, where ‘the words stood still instead of moving the way they were supposed to’. The book recounted the experience of schools in the distant past, where ‘all the kids from the neighbourhood came’, sitting in the same room with a human teacher, studying the same things ‘so they could help one another on the homework and talk about it’. Margie, the younger of the children at 11 years old, was engrossed in the book when she received a nudge from her personalized learning platform to return to her studies. But Margie was reluctant to go back to her fractions. She ‘was thinking about how the kids must have loved it in the old days. She was thinking about the fun they had’ (Asimov, 1951).

References

Asimov, I. 1951. The Fun They Had. Accessed September 20, 2019. http://web1.nbed.nb.ca/sites/ASD-S/1820/J%20Johnston/Isaac%20Asimov%20-%20The%20fun%20they%20had.pdf

Bowles, N. 2019. ‘Silicon Valley Came to Kansas Schools. That Started a Rebellion’ The New York Times, April 21. Accessed September 20, 2019. https://www.nytimes.com/2019/04/21/technology/silicon-valley-kansas-schools.html

Hidi, S. & Renninger, K.A. 2006. ‘The Four-Phase Model of Interest Development’ Educational Psychologist, 41 (2), 111 – 127

Hof, B. 2018. ‘From Harvard via Moscow to West Berlin: educational technology, programmed instruction and the commercialisation of learning after 1957’ History of Education, 47 (4): 445-465

Kennedy, R.H. 1967. ‘Before using Programmed Instruction’ The English Journal, 56 (6), 871 – 873

Kozlowski, T. 1961. ‘Programmed Teaching’ Financial Analysts Journal, 17 (6): 47 – 54

Laurillard, D. 2008. Digital Technologies and their Role in Achieving our Ambitions for Education. London: Institute for Education.

Leskin, P. 2018. ‘Students in Brooklyn protest their school’s use of a Zuckerberg-backed online curriculum that Facebook engineers helped build’ Business Insider, 12.11.18 Accessed 20 September 2019. https://www.businessinsider.de/summit-learning-school-curriculum-funded-by-zuckerberg-faces-backlash-brooklyn-2018-11?r=US&IR=T

McDonald, J. K., Yanchar, S. C. & Osguthorpe, R.T. 2005. ‘Learning from Programmed Instruction: Examining Implications for Modern Instructional Technology’ Educational Technology Research and Development, 53 (2): 84 – 98

Macgilchrist, F., Allert, H. & Bruch, A. 2019. ‚Students and society in the 2020s. Three future ‘histories’ of education and technology’. Learning, Media and Technology, https://www.tandfonline.com/doi/full/10.1080/17439884.2019.1656235 )

Moore, M. 2018. Democracy Hacked. London: Oneworld

Noble, D. D. 1991. The Classroom Arsenal. London: The Falmer Press

Ornstein, J. 1968. ‘Programmed Instruction and Educational Technology in the Language Field: Boon or Failure?’ The Modern Language Journal, 52 (7), 401 – 410

Ramganesh, E. & Janaki, S. 2017. ‘Attitude of College Teachers towards the Utilization of Language Laboratories for Learning English’ Asian Journal of Social Science Studies; Vol. 2 (1): 103 – 109

Roby, W.B. 2003. ‘Technology in the service of foreign language teaching: The case of the language laboratory’ In D. Jonassen (ed.), Handbook of Research on Educational Communications and Technology, 2nd ed.: 523 – 541. Mahwah, NJ.: Lawrence Erlbaum Associates

Saettler, P. 2004. The Evolution of American Educational Technology. Greenwich, Conn.: Information Age Publishing

Skinner, B. F. 1961. ‘Teaching Machines’ Scientific American, 205(5), 90-107

Styring, J. 2015. Engaging Generation Z. Cambridge English webinar 2015 https://www.youtube.com/watch?time_continue=4&v=XCxl4TqgQZA

Valdman, A. 1968. ‘Programmed Instruction versus Guided Learning in Foreign Language Acquisition’ Die Unterrichtspraxis / Teaching German, 1 (2), 1 – 14.

Wiley, P. D. 1990. ‘Language labs for 1990: User-friendly, expandable and affordable’. Media & Methods, 27(1), 44–47)

Jenny Holzer, Protect me from what I want

The neutrality of educational technology

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.

Although 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

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