Edtech and sustainability

Last September, Cambridge published a ‘Sustainability Framework for ELT’, which attempts to bring together environmental, social and economic sustainability. It’s a kind of 21^st century skills framework and is designed to help teachers ‘to integrate sustainability skills development’ into their lessons. Among the sub-skills that are listed, a handful grabbed my attention:

• Identifying and understanding obstacles to sustainability
• Broadening discussion and including underrepresented voices
• Understanding observable and hidden consequences
• Critically evaluating sustainability claims
• Understanding the bigger picture

Hoping to brush up my skills in these areas, I decided to take a look at the upcoming BETT show in London, which describes itself as ‘the biggest Education Technology exhibition in the world’. BETT and its parent company, Hyve, ‘are committed to redefining sustainability within the event industry and within education’. They are doing this by reducing their ‘onsite printing and collateral’. (‘Event collateral’ is an interesting event-industry term that refers to all the crap that is put into delegate bags, intended to ‘enhance their experience of the event’.) BETT and Hyve are encouraging all sponsors to go paperless, too, ‘switching from seat-drop collateral to QR codes’, and delegate bags will no longer be offered. They are partnering with various charities to donate ‘surplus food and furniture’ to local community projects, they are donating to other food charities that support families in need, and they are recycling all of the aisle banners into tote bags. Keynote speakers will include people like Sally Uren, CEO of ‘Forum for the Future’, who will talk about ‘Transforming carbon neutral education for a just and regenerative future’.

BETT and Hyve want us to take their corporate and social responsibility very seriously. All of these initiatives are very commendable, even though I wouldn’t go so far as to say that they will redefine sustainability within the event industry and education. But there is a problem – and it’s not that the world is already over-saturated with recycled tote bags. As the biggest jamboree of this kind in the world, the show attracts over 600 vendors and over 30,000 visitors, with over 120 countries represented. Quite apart from all the collateral and surplus furniture, the carbon and material footprint of the event cannot be negligible. Think of all those start-up solution-providers flying and driving into town, AirB’n’B-ing for the duration, and Ubering around town after hours, for a start.

But this is not really the problem, either. Much as the event likes to talk about ‘driving impact and improving outcomes for teachers and learners’, the clear and only purpose of the event is to sell stuff. It is to enable the investors in the 600+ edtech solution-providers in the exhibition area to move towards making a return on their investment. If we wanted to talk seriously about sustainability, the question that needs to be asked is: to what extent does all the hardware and software on sale contribute in any positive and sustainable way to education? Is there any meaningful social benefit to be derived from all this hardware and software, or is it all primarily just a part of a speculative, financial game? Is the corporate social responsibility of BETT / Hyve a form of green-washing to disguise the stimulation of more production and consumption? Is it all just a kind of environmentalism of the rich’ (Dauvergne, 2016).

Edtech is not the most pressing of environmental problems – indeed, there are examples of edtech that are likely more sustainable than the non-tech alternatives – but the sustainability question remains. There are at least four environmental costs to edtech:

• The energy-greedy data infrastructures that lie behind digital transactions
• The raw ingredients of digital devices
• The environmentally destructive manufacture and production of digital devices
• The environmental cost of dismantling and disposing digital hardware (Selwyn, 2018)

Some forms of edtech are more environmentally costly than others. First, we might consider the material costs. Going back to pre-internet days, think of the countless tonnes of audio cassettes, VCR tapes, DVDs and CD-ROMs. Think of the discarded playback devices, language laboratories and IWBs. None of these are easily recyclable and most have ended up in landfill, mostly in countries that never used these products. These days the hardware that is used for edtech is more often a device that serves other non-educational purposes, but the planned obsolescence of our phones, tablets and laptops is a huge problem for sustainability.

More important now are probably the energy costs of edtech. Audio and video streaming might seem more environmentally friendly than CDs and DVDs, but, depending on how often the CD or DVD is used, the energy cost of streaming (especially high quality video) can be much higher than using the physical format. AI ups the ante significantly (Brevini, 2022). Five years ago, a standard ‘AI training model in linguistics emit more than 284 tonnes of carbon dioxide equivalent’ (Strubell et al., 2019). With exponentially greater volumes of data now being used, the environmental cost is much, much higher. Whilst VR vendors will tout the environmental benefits of cutting down on travel, getting learners together in a physical room may well have a much lower carbon footprint than meeting in the Metaverse.

When doing the calculus of edtech, we need to evaluate the use-value of the technology. Does the tech actually have any clear educational (or other social) benefit, or is its value primarily in terms of its exchange-value?

To illustrate the difference between use-value and exchange-value, I’d like to return again to the beginnings of modern edtech in ELT. As the global market for ELT materials mushroomed in the 1990s, coursebook publishers realised that, for a relatively small investment, they could boost their sales by bringing out ‘new editions’ of best-selling titles. This meant a new cover, replacing a few texts and topics, making minor modifications to other content, and, crucially, adding extra features. As the years went by, these extra features became digital: CD-ROMs, DVDs, online workbooks and downloadables of various kinds. The publishers knew that sales depended on the existence of these shiny new things, even if many buyers made minimal use or zero use of them. But they gave the marketing departments and sales reps a pitch, and justified an increase in unit price. Did these enhanced coursebooks actually represent any increase in use-value? Did learners make better or faster progress in English as a result? On the whole, the answer has to be an unsurprising and resounding no. We should not be surprised if hundreds of megabytes of drag-and-drop grammar practice fail to have much positive impact on learning outcomes. From the start, it was the impact on the exchange-value (sales and profits) of these products that was the driving force.

Edtech vendors have always wanted to position themselves to potential buyers as ‘solution providers’, trumpeting the use-value of what they are selling. When it comes to attracting investors, it’s a different story, one that is all about minimum viable products, scalability and return on investment.

There are plenty of technologies that have undisputed educational use-value in language learning and teaching. Google Docs, Word, Zoom and YouTube come immediately to mind. Not coincidentally, they are not technologies that were designed for educational purposes. But when you look at specifically educational technology, It becomes much harder (though not impossible) to identify unambiguous gains in use-value. Most commonly, the technology holds out the promise of improved learning, but evidence that it has actually achieved this is extremely rare. Sure, a bells-and-whistles LMS offers exciting possibilities for flipped or blended learning, but research that demonstrates the effectiveness of these approaches in the real world is sadly lacking. Sure, VR might seem to offer a glimpse of motivated learners interacting meaningfully in the Metaverse, but I wouldn’t advise you to bet on it.

And betting is what most edtech is all about. An eye-watering $16.1 billion of venture capital was invested in global edtech in 2020. What matters is not that any of these products or services have any use-value, but that they are perceived to have a use-value. Central to this investment is the further commercialisation and privatisation of education (William & Hogan 2020). BETT is a part of this.

Returning to the development of my sustainability skills, I still need to consider the bigger picture. I’ve suggested that it is difficult to separate edtech from a consideration of capitalism, a system that needs to manufacture consumption, to expand production and markets in order to survive (Dauvergne, 2016: 48). Economic growth is the sine qua non of this system, and it is this that makes the British government (and others) so keen on BETT. Education and edtech in particular are rapidly growing markets. But growth is only sustainable, in environmental terms, if it is premised on things that we actually need, rather than things which are less necessary and ecologically destructive (Hickel, 2020). At the very least, as Selwyn (2021) noted, we need more diverse thinking: ‘What if environmental instability cannot be ‘solved’ simply through the expanded application of digital technologies but is actually exacerbated through increased technology use?’

References

Brevini, B. (2022) Is AI Good for the Planet? Cambridge: Polity Press

Dauvergne, P. (2016) Environmentalism of the Rich. Cambridge, Mass.: MIT Press

Hickel, J. (2020) Less Is More. London: William Heinemann

Selwyn, N. (2018) EdTech is killing us all: facing up to the environmental consequences of digital education. EduResearch Matters 22 October, 2018. https://www.aare.edu.au/blog/?p=3293

Selwyn, N. (2021) Ed-Tech Within Limits: Anticipating educational technology in times of environmental crisis. E-Learning and Digital Media, 18 (5): 496 – 510. https://journals.sagepub.com/doi/pdf/10.1177/20427530211022951

Strubell, E., Ganesh, A. & McCallum, A. (2019) Energy and Policy Considerations for Deep Learning in NLP. Cornell University: https://arxiv.org/pdf/1906.02243.pdf

Williamson, B. & Hogan, A. (2020) Commercialisation and privatisation in / of education in the context of Covid-19. Education International

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

Part 8: theory, research and practice

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).

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 21^st 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.

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[1] http://www.digitaljournal.com/article/279940?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+frc+(First+Round+Capital+News)

[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/