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

How to write about AI in language learning

The paragraph above was written by an AI-powered text generator called neuroflash https://app.neuro-flash.com/home which I told to produce a text on the topic ‘AI and education’. As texts on this topic go, it is both remarkable (in that it was not written by a human) and entirely unremarkable (in that it is practically indistinguishable from hundreds of human-written texts on the same subject). Neuroflash uses a neural network technology called GPT-3 – ‘a large language model’ – and ‘one of the most interesting and important AI systems ever produced’ (Chalmers, 2020). Basically, it generates text by predicting sequences of words based on huge databases. The nature of the paragraph above tells you all you need to know about the kinds of content that are usually found in texts about AI and education.

Not dissimilar from the neuroflash paragraph, educational commentary on uses of AI is characterised by (1) descriptions of AI tools already in use (e.g. speech recognition and machine translation) and (2) vague predictions which invariably refer to ‘the promise of personalised learning, adjusting what we give learners according to what they need to learn and keeping them motivated by giving them content that is of interest to them’ (Hughes, 2022). The question of what precisely will be personalised is unanswered: providing learners with optimal sets of resources (but which ones?), providing counselling services, recommendations or feedback for learners and teachers (but of what kind?) (Luckin, 2022). Nearly four years ago, I wrote https://adaptivelearninginelt.wordpress.com/2018/08/13/ai-and-language-teaching/ about the reasons why these questions remain unanswered. The short answer is that AI in language learning requires a ‘domain knowledge model’. This specifies what is to be learnt and includes an analysis of the steps that must be taken to reach that learning goal. This is lacking in SLA, or, at least, there is no general agreement on what it is. Worse, the models that are most commonly adopted in AI-driven programs (e.g. the deliberate learning of discrete items of grammar and vocabulary) are not supported by either current theory or research (see, for example, VanPatten & Smith, 2022).

In 2021, the IATEFL Learning Technologies SIG organised an event dedicated to AI in education. Unsurprisingly, there was a fair amount of input on AI in assessment, but my interest is in how AI might revolutionize how we learn and teach, not how we assess. What concrete examples did speakers provide?

Rose Luckin, the most well-known British expert on AI in education, kicked things off by mentioning three tools. One of these, Carnegie Learning, is a digital language course that looks very much like any of the ELT courses on offer from the big publishers – a fully blendable, multimedia (e.g. flashcards and videos) synthetic syllabus. This ‘blended learning solution’ is personalizable, since ‘no two students learn alike’, and, it claims, will develop a ‘lifelong love of language’. It appears to be premised on the idea of language learning as optimizing the delivery of ‘content’, of this content consisting primarily of discrete items, and of equating input with uptake. Been there, done that.

A second was Alelo Enskill https://www.alelo.com/about-us/ a chatbot / avatar roleplay program, first developed by the US military to teach Iraqi Arabic and aspects of Iraqi culture to Marines. I looked at the limitations of chatbot technology for language learning here https://adaptivelearninginelt.wordpress.com/2016/12/01/chatbots/ . The third tool mentioned by Luckin was Duolingo. Enough said.

Another speaker at this event was the founder and CEO of Edugo.AI https://www.edugo.ai/ , an AI-powered LMS which uses GPT-3. It allows schools to ‘create and upload on the platform any kind of language material (audio, video, text…). Our AI algorithms process and convert it in gamified exercises, which engage different parts of the brain, and gets students eager to practice’. Does this speaker know anything about gamification (for a quick read, I’d recommend Paul Driver (2012)) or neuroscience, I wonder. What, for that matter, does he know about language learning? Apparently, ‘language is not just about words, language is about sentences’ (Tomasello, 2022). Hmm, this doesn’t inspire confidence.

When you look at current uses of AI in language learning, there is very little (outside of testing, translation and speech ↔ text applications) that could justify enthusiastic claims that AI has any great educational potential. Skepticism seems to me a more reasonable and scientific response: de omnibus dubitandum.

Education is not the only field where AI has been talked up. When Covid hit us, AI was seen as the game-changing technology. It ‘could be deployed to make predictions, enhance efficiencies, and free up staff through automation; it could help rapidly process vast amounts of information and make lifesaving decisions’ (Chakravorti, 2022). The contribution of AI to the development of vaccines has been huge, but its role in diagnosing and triaging patients has been another matter altogether. Hundreds of predictive tools were developed: ‘none of them made a real difference, and some were potentially harmful’ (Heaven, 2021). Expectations were unrealistic and led to the deployment of tools before they were properly trialled. Thirty months down the line, a much more sober understanding of the potential of AI has emerged. Here, then, are the main lessons that have been learnt (I draw particularly on Engler, 2020, and Chakravorti, 2022) that are also relevant to education and language learning.

• Anticipate what could go wrong before anticipating what might go right. Engler (2020) writes that ‘a poorly kept secret of AI practitioners is that 96% accuracy is suspiciously high for any machine learning problem’. In language learning, it is highly unlikely that personalized recommendations will ever reach anything even approaching this level of reliability. What are the implications for individual learners whose learning is inappropriately personalised?
• We also know that a significant problem with AI systems is bias (O’Neil, 2016). There is a well-documented history of discriminatory outcomes because of people’s race, gender, social class or disability profile. Bias needs to be addressed proactively, not reactively.
• Acknowledge from the outset that, for AI to work, huge amounts of data related to prior outcomes will be needed. In the cases of both Covid and language learning, much of this data will be personal. This raises immediate questions of privacy and consent, especially for learners who are children. Don’t minimise this issue.
• Common by-products of AI use include insecure data storage, digital surveillance and consolidation of market power (Engler, 2020). Since such issues are so pervasive, assume they will be a problem, and need to be considered in evaluating the potential of any piece of AI edtech.
• Find out what the subject matter experts have to say: ‘AI is only helpful when applied judiciously by subject-matter experts—people with long-standing experience with the problem that they are trying to solve […] Effectively predicting a badly defined problem is worse than doing nothing at all’ (Engler, 2020). It may be that the problem the AI is trying to help solve is the wrong problem and not worth solving at all (e.g. more efficient ‘delivery’ of explicit grammatical knowledge, gamifying gapfills).

To bring this post to an end, I returned to the AI / GTP-3 driven neuroflash program. This time, I asked it to write a longer piece entitled ‘The dangers of artificial intelligence in education: what could go wrong?’. Here’s what it came up with:

There are a few potential dangers of artificial intelligence in education. One is that AI could be used to replace human teachers. This could lead to a decline in the quality of education, as machines are not yet capable of replicating the full range of human emotions and social interactions that are necessary for teaching. Additionally, AI could be used to create biased or “racist” educational materials, as has already happened with some commercial software products. Finally, AI could be used to track and monitor students in ways that invade their privacy, or that could be used to manipulate their behavior.

All of these dangers are real and should be taken seriously. However, it’s important to remember that AI is still a new technology, and its impact on education is not yet fully understood. As such, we should tread cautiously when implementing AI into our educational systems.

When it comes to education, artificial intelligence could be a double-edged sword. While AI has the potential to revolutionize the field of education and help students learn in more efficient ways, there are also dangers that need to be considered.

One of the dangers of AI in education is that it could lead to a more standardized and homogenized form of learning. If all students are relying on AI-based learning tools, they may all end up learning the same things in the same way. This could lead to a loss of creativity and individuality in education.

Another danger is that AI-based learning tools could become too reliant on data and algorithms. This could lead to a situation where students are simply being taught what the machines think they should know, rather than being allowed to explore and discover new things for themselves.

Finally, there is the danger that AI could eventually replace human teachers altogether. While this may seem like a far-fetched scenario, it is not outside the realm of possibility. If AI-based learning tools become sophisticated enough, they may one day be able to do the job of teaching just as well as humans can.

These are just some of the dangers that need to be considered when it comes to artificial intelligence in education. While AI has the potential to revolutionize education, we need to be careful about how we implement it so that we don’t end up doing more harm than good.

References

Chakravorti, B. (2022) Why AI Failed to Live Up to Its Potential During the Pandemic. Harvard Business Review March 17,2022. https://hbr.org/2022/03/why-ai-failed-to-live-up-to-its-potential-during-the-pandemic

Chalmers, D. (2020) Weinberg, Justin (ed.). “GPT-3 and General Intelligence”. Daily Nous. Philosophers On GPT-3 (updated with replies by GPT-3) July 30, 2020. https://dailynous.com/2020/07/30/philosophers-gpt-3/#chalmers

Driver, P. (2012) The Irony of Gamification. In English Digital Magazine 3, British Council Portugal, pp. 21 – 24 http://digitaldebris.info/digital-debris/2011/12/31/the-irony-of-gamification-written-for-ied-magazine.html

Engler, A. (2020) A guide to healthy skepticism of artificial intelligence and coronavirus. Washington D.C.: Brookings Institution https://www.brookings.edu/research/a-guide-to-healthy-skepticism-of-artificial-intelligence-and-coronavirus/

Heaven, W. D. (2021) Hundreds of AI tools have been built to catch covid. None of them helped. MIT Technology Review, July 30, 2021. https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/

Hughes, G. (2022) What lies at the end of the AI rainbow? IATEFL LTSIG Newsletter Issue April 2022

Luckin, R. (2022) The implications of AI for language learning and teaching. IATEFL LTSIG Newsletter Issue April 2022

O’Neil, C. (2016) Weapons of Math Destruction. London: Allen Lane

Tomasello, G. (2022) Next Generation of AI-Language Education Software:NLP & Language Modules (GPT3). IATEFL LTSIG Newsletter Issue April 2022

VanPatten, B. & Smith, M. (2022) Explicit and Implicit Learning in Second Language Acquisition. Cambridge: Cambridge University Press

VR – the next big thing in ELT

The VR experience is nothing if it is not immersive, and in language learning, the value of immersion in VR is seen to be the way in which it can lead to what we might call ‘engagement’ or ‘flow’. Fully immersed in a VR world, learning can be maximized, or so the thinking goes (Lan, 2020; Chen & Hsu, 2020). ‘By blocking out visual and auditory distractions in the classroom, VR has the potential to help students deeply connect with the material’ (Gadelha, 2018). ‘There are no distracting classroom windows to stare out of when students are directly immersed into the topic they are investigating’ (Bonner & Reinders, 2018: 36). Such is the allure of immersion that it is no surprise to find the word in the names of VR language learning products like Immerse and ImmerseMe (although the nod to bilingual immersion progammes (such as those in Canada) is an added bonus).

There is, however, immersion and immersion. A common categorisation of VR is into:

• non-immersive (e.g. a desktop game with a 2D screen and avatars)
• semi-immersive (e.g. high-end arcade games and flight simulators with large projections)
• fully immersive (e.g. with a head-mounted display, headphones, body sensors)

Taking things a little further is the possibility of directly inducing responses in the nervous system with molecular nanotechnology. We’re some way off that, but, fear not, people are working on it. At this point, it’s worth noting that this hierarchy of immersivity is driven by technological considerations: more tech = more immersion.

In ELT, the most common VR applications are currently at the low end of this scale. Probably the most talked about currently is the use of 360⁰ photography and a very simple headset like Google Cardboard, along with headphones, to take students on virtual field trips – anywhere from a museum or a Disney castle to a coral reef or outer space. See Raquel Ribeiro’s blog post for CUP for more ideas. Then, there are self-study packages, like Velawoods, which is a sort of combination of the SIMS with interaction made possible through speech recognition. The syllabus will be familiar to anyone used to using a contemporary coursebooks.

And, now, up a technological notch or two, is Immerse, which requires an Oculus headset. It appears to be a sort of Second Life where language learners can interact with each other and a trainer in a number of role plays, set in, for example, a garden barbecue, a pool bar, a conference or a deserted island. In addition to interacting with each other, students can interact with virtual objects, picking up darts and throw them at questions they want to focus on, for example. ‘Total physical engagement with the environment’ is how this is described by Immerse’s Chief Product Office. You can find out more in this promotional video.

Paul Driver has suggested that the evolution of VR can be ‘traced back through time as a constant struggle to create more immersive experiences. From the intricate scrolls of twelfth-century China, the huge panoramic paintings of the nineteenth century and early experiments in stereoscopic photography, to the promising but over-hyped 1990s arcade machines (which raised hopes and then dashed expectations for a whole generation), the history of virtual reality has been a meandering march forward, punctuated with long periods of stagnation’. Immerse may be fairly sophisticated as a VR language learning platform, but it has a long way to go as an immersive environment in comparison to games like Meeting Rembrandt: Master of Reality or Project VR Fishing. Its animations are crude and clunky, its scenarios short of detail.

But however ‘lifelike’ games like these are, their immersive potential is extremely limited if you have no interest in Rembrandt or fishing. VR is only as immersive as the intrinsic interest of (1) the ‘real world’ it is attempting to replicate, and (2) what you can do in it. The novelty factor may hold attention for a while, but not for long.

With simpler 360⁰ Google Cardboard versions of VR, you can’t actually do anything in the VR world besides watch, listen and marvel, so the intrinsic interest of the content is even more important. I quite like exploring the Okavango Delta, but I have no interest in rollercoasters or parachute jumps. But, to be immersed, I don’t actually need the 360⁰ experience at all, if the quality of the video is good enough. In many ways, I prefer an old-fashioned screen where my hands are not tied up with holding the phone into the Cardboard and the Cardboard to my nose.

360⁰ videos are usually short, and I can see how they can be used in a language class as a springboard for other work. But as a language learning tool, old-fashioned screens (with good content) may offer more potential than headsets (whether Cardboard or Oculus) because we can do other things (like communicate with other people, use a dictionary or take notes) at the same time.

VR technology in language learning cannot, therefore, (whatever its claims) generate immersion or engagement on its own. For the time being, it can, for some, captivate initial curiosity. For others, already used to high-end Oculus games, programmes like Immerse are more likely to generate a resounding ‘meh’. Engagement in learning is a highly complex phenomenon. Mercer and Dörnyei (2020: 102 ff.) argue that engaging learning materials must be designed for particular groups of learners (in terms of level and interests, for example) and they must get learners emotionally invested. Improvements in VR technology won’t really change anything.

VR is already well established and successful in some forms of education: military, healthcare and engineering, especially. Virtual reality is obviously a good place to learn how to defuse a bomb or carry out keyhole surgery. In other areas, such as soft skills training in corporate contexts, its use is growing, but its effectiveness is much less clear. In language learning, the purported advantages of VR (see, for example, Alizadeh, 2019, which has a useful bibliography, or Lloyd et al., 2017) are not convincing. There is no problem in language learning for which VR is the solution. This doesn’t mean that VR does not have a place in language learning / teaching. VR field trips may offer occasional moments of variety. Conversation in VR worlds like Facebook Spaces may be welcomed by some. And there will be markets for dedicated platforms like Velawoods, Mondly or Immerse.

Predictions about edtech are often thinly disguised attempts to accelerate a predicted future. Four years ago I went to a conference presentation by Saul Nassé, Chief Executive of Cambridge Assessment. All the participants were given a Cambridge branded Google Cardboard. At the time, Nassé wrote the following:

‘The technology is only going to get better and cheaper. In two or three years it will be wireless and cost less than a smart phone. That’s the point when you’ll see whole classrooms equipped with VR. And I like to think we’ll find a way of Cambridge English content being used in those classrooms, with people learning English in a whole new way. It may have been a long time coming, but I think the VR revolution is now truly here to stay’.

The message was echoed in Lloyd et al (2017), all three of whom worked for Cambridge Assessment, and amplified in a series of blog posts and conference presentations around that time. Since then, it has all gone rather quiet. There are still people out there (including the investors who have just pumped $1.5 million into Immerse in Series A funding), who believe that VR will be the next big thing in language learning. But edtech investors have a long track record of turning a blind eye to history. VR, as Saul Nassé observed, ‘has been the next big thing for thirty years’. And maybe for the next thirty years, too.

REFERENCES

Alizadeh, M. (2019). Augmented/virtual reality promises for ELT practitioners. In Clements, P., Krause, A. & Bennett, P. (Eds.), Diversity and inclusion. Tokyo: JALT. https://jalt-publications.org/sites/default/files/pdf-article/jalt2018-pcp-048.pdf

Bonner, E., & Reinders, H. (2018). Augmented and virtual reality in the language classroom: Practical ideas. Teaching English with Technology, 18 (3), pp. 33-53. Retrieved from https://files.eric.ed.gov/fulltext/EJ1186392.pdf

Chen, Y. L. & Hsu, C. C. (2020). Self-regulated mobile game-based English learning in a virtual reality environment. Computers and Education, 154 https://www.sciencedirect.com/science/article/abs/pii/S0360131520301093?dgcid=rss_sd_all

Gadelha, R. (2018). Revolutionizing Education: The promise of virtual reality. Childhood Education, 94 (1), pp. 40-43. doi:10.1080/00094056.2018.1420362

Lan, Y. J. (2020). Immersion, interaction and experience-oriented learning: Bringing virtual reality into FL learning. Language Learning & Technology, 24(1), pp. 1–15. http://hdl.handle.net/10125/44704

Lloyd, A., Rogerson, S. & Stead, G. (2017). Imagining the potential for using Virtual Reality technologies in language learning. In Carrier, M., Damerow, R. M. & Bailey, K. M. (Eds.) Digital Language Learning and Teaching. New York: Routledge. pp. 222 – 234

Mercer, S. & Dörnyei, Z. (2020). Engaging Language Learners in Contemporary Classrooms. Cambridge: Cambridge University Press

The EdTech Imaginary in ELT

In my last post , I asked why it is so easy to believe that technology (in particular, technological innovations) will offer solutions to whatever problems exist in language learning and teaching. A simple, but inadequate, answer is that huge amounts of money have been invested in persuading us. Without wanting to detract from the significance of this, it is clearly not sufficient as an explanation. In an attempt to develop my own understanding, I have been turning more and more to the idea of ‘social imaginaries’. In many ways, this is also an attempt to draw together the various interests that I have had since starting this blog.

The Canadian philosopher, Charles Taylor, describes a ‘social imaginary’ as a ‘common understanding that makes possible common practices and a widely shared sense of legitimacy’ (Taylor, 2004: 23). As a social imaginary develops over time, it ‘begins to define the contours of [people’s] worlds and can eventually come to count as the taken-for-granted shape of things, too obvious to mention’ (Taylor, 2004: 29). It is, however, not just a set of ideas or a shared narrative: it is also a set of social practices that enact those understandings, whilst at the same time modifying or solidifying them. The understandings make the practices possible, and it is the practices that largely carry the understanding (Taylor, 2004: 25). In the process, the language we use is filled with new associations and our familiarity with these associations shapes ‘our perceptions and expectations’ (Worster, 1994, quoted in Moore, 2015: 33). A social imaginary, then, is a complex system that is not technological or economic or social or political or educational, but all of these (Urry, 2016). The image of the patterns of an amorphous mass of moving magma (Castoriadis, 1987), flowing through pre-existing channels, but also, at times, striking out along new paths, may offer a helpful metaphor.

Technology, of course, plays a key role in contemporary social imaginaries and the term ‘sociotechnical imaginary’ is increasingly widely used. The understandings of the sociotechnical imaginary typically express visions of social progress and a desirable future that is made possible by advances in science and technology (Jasanoff & Kim, 2015: 4). In education, technology is presented as capable of overcoming human failings and the dark ways of the past, of facilitating a ‘pedagogical utopia of natural, authentic teaching and learning’ (Friesen, forthcoming). As such understandings become more widespread and as the educational practices (platforms, apps, etc.) which both shape and are shaped by them become equally widespread, technology has come to be seen as a ‘solution’ to the ‘problem’ of education (Friesen, forthcoming). We need to be careful, however, that having shaped the technology, it does not comes to shape us (see Cobo, 2019, for a further exploration of this idea).

As a way of beginning to try to understand what is going on in edtech in ELT, which is not so very different from what is taking place in education more generally, I have sketched a number of what I consider key components of the shared understandings and the social practices that are related to them. These are closely interlocking pieces and each of them is itself embedded in much broader understandings. They evolve over time and their history can be traced quite easily. Taken together, they do, I think, help us to understand a little more why technology in ELT seems so seductive.

1 The main purpose of English language teaching is to prepare people for the workplace

There has always been a strong connection between learning an additional living language (such as English) and preparing for the world of work. The first modern language schools, such as the Berlitz schools at the end of the 19^th century with their native-speaker teachers and monolingual methods, positioned themselves as primarily vocational, in opposition to the kinds of language teaching taking place in schools and universities, which were more broadly humanistic in their objectives. Throughout the 20^th century, and especially as English grew as a global language, the public sector, internationally, grew closer to the methods and objectives of the private schools. The idea that learning English might serve other purposes (e.g. cultural enrichment or personal development) has never entirely gone away, as witnessed by the Council of Europe’s list of objectives (including the promotion of mutual understanding and European co-operation, and the overcoming of prejudice and discrimination) in the Common European Framework, but it is often forgotten.

The clarion calls from industry to better align education with labour markets, present and future, grow louder all the time, often finding expression in claims that ‘education is unfit for purpose.’ It is invariably assumed that this purpose is to train students in the appropriate skills to enhance their ‘human capital’ in an increasingly competitive and global market (Lingard & Gale, 2007). Educational agendas are increasingly set by the world of business (bodies like the OECD or the World Economic Forum, corporations like Google or Microsoft, and national governments which share their priorities (see my earlier post about neo-liberalism and solutionism ).

One way in which this shift is reflected in English language teaching is in the growing emphasis that is placed on ‘21^st century skills’ in teaching material. Sometimes called ‘life skills’, they are very clearly concerned with the world of work, rather than the rest of our lives. The World Economic Forum’s 2018 Future of Jobs survey lists the soft skills that are considered important in the near future and they include ‘creativity’, ‘critical thinking’, ‘emotional intelligence’ and ‘leadership’. (The fact that the World Economic Forum is made up of a group of huge international corporations (e.g. J.P. Morgan, HSBC, UBS, Johnson & Johnson) with a very dubious track record of embezzlement, fraud, money-laundering and tax evasion has not resulted in much serious, public questioning of the view of education expounded by the WEF.)

Without exception, the ELT publishers have brought these work / life skills into their courses, and the topic is an extremely popular one in ELT blogs and magazines, and at conferences. Two of the four plenaries at this year’s international IATEFL conference are concerned with these skills. Pearson has a wide range of related products, including ‘a four-level competency-based digital course that provides engaging instruction in the essential work and life skills competencies that adult learners need’. Macmillan ELT made ‘life skills’ the central plank of their marketing campaign and approach to product design, and even won a British Council ELTon (see below) Award for ‘Innovation in teacher resources) in 2015 for their ‘life skills’ marketing campaign. Cambridge University Press has developed a ‘Framework for Life Competencies’ which allows these skills to be assigned numerical values.

The point I am making here is not that these skills do not play an important role in contemporary society, nor that English language learners may not benefit from some training in them. The point, rather, is that the assumption that English language learning is mostly concerned with preparation for the workplace has become so widespread that it becomes difficult to think in another way.

2 Technological innovation is good and necessary

The main reason that soft skills are deemed to be so important is that we live in a rapidly-changing world, where the unsubstantiated claim that 85% (or whatever other figure comes to mind) of current jobs won’t exist 10 years from now is so often repeated that it is taken as fact . Whether or not this is true is perhaps less important to those who make the claim than the present and the future that they like to envisage. The claim is, at least, true-ish enough to resonate widely. Since these jobs will disappear, and new ones will emerge, because of technological innovations, education, too, will need to innovate to keep up.

English language teaching has not been slow to celebrate innovation. There were coursebooks called ‘Cutting Edge’ (1998) and ‘Innovations’ (2005), but more recently the connections between innovation and technology have become much stronger. The title of the recent ‘Language Hub’ (2019) was presumably chosen, in part, to conjure up images of digital whizzkids in fashionable co-working start-up spaces. Technological innovation is explicitly promoted in the Special Interest Groups of IATEFL and TESOL. Despite a singular lack of research that unequivocally demonstrates a positive connection between technology and language learning, the former’s objective is ‘to raise awareness among ELT professionals of the power of learning technologies to assist with language learning’. There is a popular annual conference, called InnovateELT , which has the tagline ‘Be Part of the Solution’, and the first problem that this may be a solution to is that our students need to be ‘ready to take on challenging new careers’.

Last, but by no means least, there are the annual British Council ELTon awards  with a special prize for digital innovation. Among the British Council’s own recent innovations are a range of digitally-delivered resources to develop work / life skills among teens.

Again, my intention (here) is not to criticise any of the things mentioned in the preceding paragraphs. It is merely to point to a particular structure of feeling and the way that is enacted and strengthened through material practices like books, social groups, conferences and other events.

3 Technological innovations are best driven by the private sector

The vast majority of people teaching English language around the world work in state-run primary and secondary schools. They are typically not native-speakers of English, they hold national teaching qualifications and they are frequently qualified to teach other subjects in addition to English (often another language). They may or may not self-identify as teachers of ‘ELT’ or ‘EFL’, often seeing themselves more as ‘school teachers’ or ‘language teachers’. People who self-identify as part of the world of ‘ELT or ‘TEFL’ are more likely to be native speakers and to work in the private sector (including private or semi-private language schools, universities (which, in English-speaking countries, are often indistinguishable from private sector institutions), publishing companies, and freelancers). They are more likely to hold international (TEFL) qualifications or higher degrees, and they are less likely to be involved in the teaching of other languages.

The relationship between these two groups is well illustrated by the practice of training days, where groups of a few hundred state-school teachers participate in workshops organised by publishing companies and delivered by ELT specialists. In this context, state-school teachers are essentially in a client role when they are in contact with the world of ‘ELT’ – as buyers or potential buyers of educational products, training or technology.

Technological innovation is invariably driven by the private sector. This may be in the development of technologies (platforms, apps and so on), in the promotion of technology (through training days and conference sponsorship, for example), or in training for technology (with consultancy companies like ELTjam or The Consultants-E, which offer a wide range of technologically oriented ‘solutions’).

As in education more generally, it is believed that the private sector can be more agile and more efficient than state-run bodies, which continue to decline in importance in educational policy-setting. When state-run bodies are involved in technological innovation in education, it is normal for them to work in partnership with the private sector.

4 Accountability is crucial

Efficacy is vital. It makes no sense to innovate unless the innovations improve something, but for us to know this, we need a way to measure it. In a previous post , I looked at Pearson’s ‘Asking More: the Path to Efficacy’ by CEO John Fallon (who will be stepping down later this year). Efficacy in education, says Fallon, is ‘making a measurable impact on someone’s life through learning’. ‘Measurable’ is the key word, because, as Fallon claims, ‘it is increasingly possible to determine what works and what doesn’t in education, just as in healthcare.’ We need ‘a relentless focus’ on ‘the learning outcomes we deliver’ because it is these outcomes that can be measured in ‘a systematic, evidence-based fashion’. Measurement, of course, is all the easier when education is delivered online, ‘real-time learner data’ can be captured, and the power of analytics can be deployed.

Data is evidence, and it’s as easy to agree on the importance of evidence as it is hard to decide on (1) what it is evidence of, and (2) what kind of data is most valuable. While those questions remain largely unanswered, the data-capturing imperative invades more and more domains of the educational world.

English language teaching is becoming data-obsessed. From language scales, like Pearson’s Global Scale of English to scales of teacher competences, from numerically-oriented formative assessment practices (such as those used on many LMSs) to the reporting of effect sizes in meta-analyses (such as those used by John Hattie and colleagues), datafication in ELT accelerates non-stop.

The scales and frameworks are all problematic in a number of ways (see, for example, this post on ‘The Mismeasure of Language’) but they have undeniably shaped the way that we are able to think. Of course, we need measurable outcomes! If, for the present, there are privacy and security issues, it is to be hoped that technology will find solutions to them, too.

REFERENCES

Castoriadis, C. (1987). The Imaginary Institution of Society. Cambridge: Polity Press.

Cobo, C. (2019). I Accept the Terms and Conditions. Montevideo: International Development Research Centre / Center for Research Ceibal Foundation. https://adaptivelearninginelt.files.wordpress.com/2020/01/41acf-cd84b5_7a6e74f4592c460b8f34d1f69f2d5068.pdf

Friesen, N. (forthcoming) The technological imaginary in education, or: Myth and enlightenment in ‘Personalized Learning’. In M. Stocchetti (Ed.) The Digital Age and its Discontents. University of Helsinki Press. Available at https://www.academia.edu/37960891/The_Technological_Imaginary_in_Education_or_Myth_and_Enlightenment_in_Personalized_Learning_

Jasanoff, S. & Kim, S.-H. (2015). Dreamscapes of Modernity. Chicago: University of Chicago Press.

Lingard, B. & Gale, T. (2007). The emergent structure of feeling: what does it mean for critical educational studies and research?, Critical Studies in Education, 48:1, pp. 1-23

Moore, J. W. (2015). Capitalism in the Web of Life. London: Verso.

Robbins, K. & Webster, F. (1989]. The Technical Fix. Basingstoke: Macmillan Education.

Taylor, C. (2014). Modern Social Imaginaries. Durham, NC: Duke University Press.

Urry, J. (2016). What is the Future? Cambridge: Polity Press.

 

Platforms and big data in ELT – a look back at the last decade

At the start of the last decade, ELT publishers were worried, Macmillan among them. The financial crash of 2008 led to serious difficulties, not least in their key Spanish market. In 2011, Macmillan’s parent company was fined ₤11.3 million for corruption. Under new ownership, restructuring was a constant. At the same time, Macmillan ELT was getting ready to move from its Oxford headquarters to new premises in London, a move which would inevitably lead to the loss of a sizable proportion of its staff. On top of that, Macmillan, like the other ELT publishers, was aware that changes in the digital landscape (the first 3G iPhone had appeared in June 2008 and wifi access was spreading rapidly around the world) meant that they needed to shift away from the old print-based model. With her finger on the pulse, Caroline Moore, wrote an article in October 2010 entitled ‘No Future? The English Language Teaching Coursebook in the Digital Age’ . The publication (at the start of the decade) and runaway success of the online ‘Touchstone’ course, from arch-rivals, Cambridge University Press, meant that Macmillan needed to change fast if they were to avoid being left behind.

Macmillan already had a platform, Campus, but it was generally recognised as being clunky and outdated, and something new was needed. In the summer of 2012, Macmillan brought in two new executives – people who could talk the ‘creative-disruption’ talk and who believed in the power of big data to shake up English language teaching and publishing. At the time, the idea of big data was beginning to reach public consciousness and ‘Big Data: A Revolution that Will Transform how We Live, Work, and Think’ by Viktor Mayer-Schönberger and Kenneth Cukier, was a major bestseller in 2013 and 2014. ‘Big data’ was the ‘hottest trend’ in technology and peaked in Google Trends in October 2014. See the graph below.

Not long after taking up their positions, the two executives began negotiations with Knewton, an American adaptive learning company. Knewton’s technology promised to gather colossal amounts of data on students using Knewton-enabled platforms. Its founder, Jose Ferreira, bragged that Knewton had ‘more data about our students than any company has about anybody else about anything […] We literally know everything about what you know and how you learn best, everything’. This data would, it was claimed, enable publishers to multiply, by orders of magnitude, the efficacy of learning materials, allowing publishers, like Macmillan, to provide a truly personalized and optimal offering to learners using their platform.

The contract between Macmillan and Knewton was agreed in May 2013 ‘to build next-generation English Language Learning and Teaching materials’. Perhaps fearful of being left behind in what was seen to be a winner-takes-all market (Pearson already had a financial stake in Knewton), Cambridge University Press duly followed suit, signing a contract with Knewton in September of the same year, in order ‘to create personalized learning experiences in [their] industry-leading ELT digital products’. Things moved fast because, by the start of 2014 when Macmillan’s new catalogue appeared, customers were told to ‘watch out for the ‘Big Tree’’, Macmillans’ new platform, which would be powered by Knewton. ‘The power that will come from this world of adaptive learning takes my breath away’, wrote the international marketing director.

Not a lot happened next, at least outwardly. In the following year, 2015, the Macmillan catalogue again told customers to ‘look out for the Big Tree’ which would offer ‘flexible blended learning models’ which could ‘give teachers much more freedom to choose what they want to do in the class and what they want the students to do online outside of the classroom’.

But behind the scenes, everything was going wrong. It had become clear that a linear model of language learning, which was a necessary prerequisite of the Knewton system, simply did not lend itself to anything which would be vaguely marketable in established markets. Skills development, not least the development of so-called 21^st century skills, which Macmillan was pushing at the time, would not be facilitated by collecting huge amounts of data and algorithms offering personalized pathways. Even if it could, teachers weren’t ready for it, and the projections for platform adoptions were beginning to seem very over-optimistic. Costs were spiralling. Pushed to meet unrealistic deadlines for a product that was totally ill-conceived in the first place, in-house staff were suffering, and this was made worse by what many staffers thought was a toxic work environment. By the end of 2014 (so, before the copy for the 2015 catalogue had been written), the two executives had gone.

For some time previously, skeptics had been joking that Macmillan had been barking up the wrong tree, and by the time that the 2016 catalogue came out, the ‘Big Tree’ had disappeared without trace. The problem was that so much time and money had been thrown at this particular tree that not enough had been left to develop new course materials (for adults). The whole thing had been a huge cock-up of an extraordinary kind.

Cambridge, too, lost interest in their Knewton connection, but were fortunate (or wise) not to have invested so much energy in it. Language learning was only ever a small part of Knewton’s portfolio, and the company had raised over $180 million in venture capital. Its founder, Jose Ferreira, had been a master of marketing hype, but the business model was not delivering any better than the educational side of things. Pearson pulled out. In December 2016, Ferreira stepped down and was replaced as CEO. The company shifted to ‘selling digital courseware directly to higher-ed institutions and students’ but this could not stop the decline. In September of 2019, Knewton was sold for something under $17 million dollars, with investors taking a hit of over $160 million. My heart bleeds.

It was clear, from very early on (see, for example, my posts from 2014 here and here) that Knewton’s product was little more than what Michael Feldstein called ‘snake oil’. Why and how could so many people fall for it for so long? Why and how will so many people fall for it again in the coming decade, although this time it won’t be ‘big data’ that does the seduction, but AI (which kind of boils down to the same thing)? The former Macmillan executives are still at the game, albeit in new companies and talking a slightly modified talk, and Jose Ferreira (whose new venture has already raised $3.7 million) is promising to revolutionize education with a new start-up which ‘will harness the power of technology to improve both access and quality of education’ (thanks to Audrey Watters for the tip). Investors may be desperate to find places to spread their portfolio, but why do the rest of us lap up the hype? It’s a question to which I will return.

 

 

 

 

Personalized Learning and Educational Genomics

Learners are different, the argument goes, so learning paths will be different, too. And, the argument continues, if learners will benefit from individualized learning pathways, so instruction should be based around an analysis of the optimal learning pathways for individuals and tailored to match them. In previous posts, I have questioned whether such an analysis is meaningful or reliable and whether the tailoring leads to any measurable learning gains. In this post, I want to focus primarily on the analysis of learner differences.

Family / social background and previous educational experiences are obvious ways in which learners differ when they embark on any course of study. The way they impact on educational success is well researched and well established. Despite this research, there are some who disagree. For example, Dominic Cummings (former adviser to Michael Gove when he was UK Education minister and former campaign director of the pro-Brexit Vote Leave group) has argued  that genetic differences, especially in intelligence, account for more than 50% of the differences in educational achievement.

Cummings got his ideas from Robert Plomin , one of the world’s most cited living psychologists. Plomin, in a recent paper in Nature, ‘The New Genetics of Intelligence’ , argues that ‘intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait’. In an earlier paper, ‘Genetics affects choice of academic subjects as well as achievement’, Plomin and his co-authors argued that ‘choosing to do A-levels and the choice of subjects show substantial genetic influence, as does performance after two years studying the chosen subjects’. Environment matters, says Plomin , but it’s possible that genes matter more.

All of which leads us to the field known as ‘educational genomics’. In an article of breathless enthusiasm entitled ‘How genetics could help future learners unlock hidden potential’ , University of Sussex psychologist, Darya Gaysina, describes educational genomics as the use of ‘detailed information about the human genome – DNA variants – to identify their contribution to particular traits that are related to education [… ] it is thought that one day, educational genomics could enable educational organisations to create tailor-made curriculum programmes based on a pupil’s DNA profile’. It could, she writes, ‘enable schools to accommodate a variety of different learning styles – both well-worn and modern – suited to the individual needs of the learner [and] help society to take a decisive step towards the creation of an education system that plays on the advantages of genetic background. Rather than the current system, that penalises those individuals who do not fit the educational mould’.

The goal is not just personalized learning. It is ‘Personalized Precision Education’ where researchers ‘look for patterns in huge numbers of genetic factors that might explain behaviors and achievements in individuals. It also focuses on the ways that individuals’ genotypes and environments interact, or how other “epigenetic” factors impact on whether and how genes become active’. This will require huge amounts of ‘data gathering from learners and complex analysis to identify patterns across psychological, neural and genetic datasets’. Why not, suggests Darya Gaysina, use the same massive databases that are being used to identify health risks and to develop approaches to preventative medicine?

If I had a spare 100 Euros, I (or you) could buy Darya Gaysina’s book, ‘Behavioural Genetics for Education’ (Palgrave Macmillan, 2016) and, no doubt, I’d understand the science better as a result. There is much about the science that seems problematic, to say the least (e.g. the definition and measurement of intelligence, the lack of reference to other research that suggests academic success is linked to non-genetic factors), but it isn’t the science that concerns me most. It’s the ethics. I don’t share Gaysina’s optimism that ‘every child in the future could be given the opportunity to achieve their maximum potential’. Her utopianism is my fear of Gattaca-like dystopias. IQ testing, in its early days, promised something similarly wonderful, but look what became of that. When you already have reporting of educational genomics using terms like ‘dictate’, you have to fear for the future of Gaysina’s brave new world.

Educational genomics could equally well lead to expectations of ‘certain levels of achievement from certain groups of children – perhaps from different socioeconomic or ethnic groups’ and you can be pretty sure it will lead to ‘companies with the means to assess students’ genetic identities [seeking] to create new marketplaces of products to sell to schools, educators and parents’. The very fact that people like Dominic Cummings (described by David Cameron as a ‘career psychopath’ ) have opted to jump on this particular bandwagon is, for me, more than enough cause for concern.

Underlying my doubts about educational genomics is a much broader concern. It’s the apparent belief of educational genomicists that science can provide technical solutions to educational problems. It’s called ‘solutionism’ and it doesn’t have a pretty history.

Adaptive learning markets: talking Turkey

It’s a good time to be in Turkey if you have digital ELT products to sell. Not so good if you happen to be an English language learner. This post takes a look at both sides of the Turkish lira.

OUP, probably the most significant of the big ELT publishers in Turkey, recorded ‘an outstanding performance’ in the country in the last financial year, making it their 5^th largest ELT market. OUP’s annual report for 2013 – 2014 describes the particularly strong demand for digital products and services, a demand which is now influencing OUP’s global strategy for digital resources. When asked about the future of ELT, Peter Marshall , Managing Director of OUP’s ELT Division, suggested that Turkey was a country that could point us in the direction of an answer to the question. Marshall and OUP will be hoping that OUP’s recently launched Digital Learning Platform (DLP) ‘for the global distribution of adult and secondary ELT materials’ will be an important part of that future, in Turkey and elsewhere. I can’t think of any good reason for doubting their belief.

OUP aren’t the only ones eagerly checking the pound-lira exchange rates. For the last year, CUP also reported ‘significant sales successes’ in Turkey in their annual report . For CUP, too, it was a year in which digital development has been ‘a top priority’. CUP’s Turkish success story has been primarily driven by a deal with Anadolu University (more about this below) to provide ‘a print and online solution to train 1.7 million students’ using their Touchstone course. This was the biggest single sale in CUP’s history and has inspired publishers, both within CUP and outside, to attempt to emulate the deal. The new blended products will, of course, be adaptive.

Just how big is the Turkish digital ELT pie? According to a 2014 report from Ambient Insight , revenues from digital ELT products reached $32.0 million in 2013. They are forecast to more than double to $72.6 million in 2018. This is a growth rate of 17.8%, a rate which is practically unbeatable in any large economy, and Turkey is the 17^th largest economy in the world, according to World Bank statistics .

So, what makes Turkey special?

• Turkey has a large and young population that is growing by about 1.4% each year, which is equivalent to approximately 1 million people. According to the Turkish Ministry of Education, there are currently about 5.5 million students enrolled in upper-secondary schools. Significant growth in numbers is certain.
• Turkey is currently in the middle of a government-sponsored $990 million project to increase the level of English proficiency in schools. The government’s target is to position the country as one of the top ten global economies by 2023, the centenary of the Turkish Republic, and it believes that this position will be more reachable if it has a population with the requisite foreign language (i.e. English) skills. As part of this project, the government has begun to introduce English in the 1^st grade (previously it was in the 4^th grade).
• The level of English in Turkey is famously low and has been described as a ‘national weakness’. In October/November 2011, the Turkish research institute SETA and the Turkish Ministry for Youth and Sports conducted a large survey across Turkey of 10,174 young citizens, aged 15 to 29. The result was sobering: 59 per cent of the young people said they “did not know any foreign language.” A recent British Council report (2013) found the competence level in English of most (90+%) students across Turkey was evidenced as rudimentary – even after 1000+ hours (estimated at end of Grade 12) of English classes. This is, of course, good news for vendors of English language learning / teaching materials.
• Turkey has launched one of the world’s largest educational technology projects: the FATIH Project (The Movement to Enhance Opportunities and Improve Technology). One of its objectives is to provide tablets for every student between grades 5 and 12. At the same time, according to the Ambient report , the intention is to ‘replace all print-based textbooks with digital content (both eTextbooks and online courses).’
• Purchasing power in Turkey is concentrated in a relatively small number of hands, with the government as the most important player. Institutions are often very large. Anadolu University, for example, is the second largest university in the world, with over 2 million students, most of whom are studying in virtual classrooms. There are two important consequences of this. Firstly, it makes scalable, big-data-driven LMS-delivered courses with adaptive software a more attractive proposition to purchasers. Secondly, it facilitates the B2B sales model that is now preferred by vendors (including the big ELT publishers).
• Turkey also has a ‘burgeoning private education sector’, according to Peter Marshall, and a thriving English language school industry. According to Ambient ‘commercial English language learning in Turkey is a $400 million industry with over 600 private schools across the country’. Many of these are grouped into large chains (see the bullet point above).
• Turkey is also ‘in the vanguard of the adoption of educational technology in ELT’, according to Peter Marshall. With 36 million internet users, the 5^th largest internet population in Europe, and the 3^rd highest online engagement in Europe, measured by time spent online, (reported by Sina Afra ), the country’s enthusiasm for educational technology is not surprising. Ambient reports that ‘the growth rate for mobile English educational apps is 27.3%’. This enthusiasm is reflected in Turkey’s thriving ELT conference scene. The most popular conference themes and conference presentations are concerned with edtech. A keynote speech by Esat Uğurlu at the ISTEK schools 3^rd international ELT conference at Yeditepe in April 2013 gives a flavour of the current interests. The talk was entitled ‘E-Learning: There is nothing to be afraid of and plenty to discover’.

All of the above makes Turkey a good place to be if you’re selling digital ELT products, even though the competition is pretty fierce. If your product isn’t adaptive, personalized and gamified, you may as well not bother.

What impact will all this have on Turkey’s English language learners? A report co-produced by TEPAV (the Economic Policy Research Foundation of Turkey) and the British Council in November 2013 suggests some of the answers, at least in the school population. The report  is entitled ‘Turkey National Needs Assessment of State School English Language Teaching’ and its Executive Summary is brutally frank in its analysis of the low achievements in English language learning in the country. It states:

The teaching of English as a subject and not a language of communication was observed in all schools visited. This grammar-based approach was identified as the first of five main factors that, in the opinion of this report, lead to the failure of Turkish students to speak/ understand English on graduation from High School, despite having received an estimated 1000+ hours of classroom instruction.

In all classes observed, students fail to learn how to communicate and function independently in English. Instead, the present teacher-centric, classroom practice focuses on students learning how to answer teachers’ questions (where there is only one, textbook-type ‘right’ answer), how to complete written exercises in a textbook, and how to pass a grammar-based test. Thus grammar-based exams/grammar tests (with right/wrong answers) drive the teaching and learning process from Grade 4 onwards. This type of classroom practice dominates all English lessons and is presented as the second causal factor with respect to the failure of Turkish students to speak/understand English.

The problem, in other words, is the curriculum and the teaching. In its recommendations, the report makes this crystal clear. Priority needs to be given to developing a revised curriculum and ‘a comprehensive and sustainable system of in-service teacher training for English teachers’. Curriculum renewal and programmes of teacher training / development are the necessary prerequisites for the successful implementation of a programme of educational digitalization. Unfortunately, research has shown again and again that these take a long time and outcomes are difficult to predict in advance.

By going for digitalization first, Turkey is taking a huge risk. What LMSs, adaptive software and most apps do best is the teaching of language knowledge (grammar and vocabulary), not the provision of opportunities for communicative practice (for which there is currently no shortage of opportunity … it is just that these opportunities are not being taken). There is a real danger, therefore, that the technology will push learning priorities in precisely the opposite direction to that which is needed. Without significant investments in curriculum reform and teacher training, how likely is it that the transmission-oriented culture of English language teaching and learning will change?

Even if the money for curriculum reform and teacher training were found, it is also highly unlikely that effective country-wide approaches to blended learning for English would develop before the current generation of tablets and their accompanying content become obsolete.

Sadly, the probability is, once more, that educational technology will be a problem-changer, even a problem-magnifier, rather than a problem-solver. I’d love to be wrong.

“What isn’t measured can’t be managed” – further reading

I already mentioned Evgeny Morozov’s To Save Everything, Click Here when I discussed his idea of ‘solutionism’. Even if you don’t agree with everything he writes, he is always interesting to read. In a recent review article for the New York Times he looks at two new books, The Naked Future: What Happens in a World That Anticipates Your Every Move? by Patrick Tucker, and Social Physics: How Good Ideas Spread — The Lessons From a New Science by Alex Pentland.

Morozov takes a critical, philosophical look at the way that big data might impact on our lives, and the article is well worth a read. For an entertaining fictional take on Big Data, Dave Eggers’ dystopia, The Circle, is a novel worth packing in your suitcase next time you have to go somewhere.