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

Bored or boring – instructional grammar videos for flipped learning

Flipped learning undoubtedly has much potential and now, when F2F teaching is not always possible, the case for exploring what it might offer seems greater still. For a variety of reasons (not the least of which are motivational issues), it may not always be possible to flip the classroom, but, if and when it is, how and what should be flipped?

In the most well-known flipped approaches, such as the Khan Academy, students watch instructional videos in their own time, before coming to class where they can work together on practical problems, applying the knowledge they have gained from the instructional video. The flipped part of the learning does not need to be a video (Bergmann et al., 2013), but, in practice, it usually is. But whether video or something else, one of the big questions for me is what, precisely, does it make sense to flip?

In a recently published Cambridge Paper in ELT that I wrote on Flipped Learning, I noted that it is not uncommon for grammar instruction to be flipped. Al-Harbi & Alshumaimeri (2016), for example, describe a Saudi secondary school where the teacher selected a number of grammar areas from the coursebook and then identified instructional videos from YouTube that addressed these areas. Buitrago & Díaz (2018) describe a Colombian university where students were required to watch instructional videos about grammar, some of which were selected from YouTube and others created by members of staff.

To understand better what learners might be doing in their flipped time, I decided to take a look at a selection of YouTube grammar videos. I focussed on one area of grammar only (‘bored’ vs ‘boring’) and from the huge selection available, I prioritised those that were the most popular. Here’s what I found. After a brief commentary on each of the 10 videos, I wrap up with a few observations.

mmmEnglish 1245K views 8.33 minutes

Early on, Emma says ‘These endings are called suffixes and when we add them to the end of a verb, they transform our verb into an adjective, but you need to know how to use each of these types of adjectives and we’re gonna do that right now’. This gives a good taste of what follows. We learn that –ing adjectives refer to ‘the characteristics of a person, a thing, or a situation’ while –ed adjectives refer to an ‘emotion or a feeling’. Bearing in mind that this area of grammar is listed as A2+ (in Pearson’s GSE), explanations of this kind in English may be tricky for many learners. The language grading in explanations like ‘If you say that someone or something is boring, they or it makes you feel bored. Do the thing or the person that is boring is what makes you feel bored. It bores you. OK, there’s our verb’ needs a little attention! On and on goes Emma, until after almost five minutes she reads out a few sentences and students have to decide if the correct adjective has been used. Over a million people have watched this.

Learn English with Let’s Talk 452K views 8.52 minutes

Rashna explains: ‘First, let’s begin by understanding what are adjectives’. My heart sinks. ‘So ‘pretty’ is doing the job of describing or bringing about a quality of the noun ‘girl’, so ‘pretty’ becomes my adjective. So when you’re confused and don’t know how to spot the adjectives, ask the question ‘what kind’. All right. So, if I say I live in a big city, and if I ask what kind of a city, it’s big, so ‘big’ is an adjective that is describing the noun ‘city’. All right. So remember, adjectives are nothing but just words that describe a noun that tell you more about it or bring about some quality.’ Over a quarter of the way through and we haven’t yet got on to –ed and –ing. I recommend watching all the way through to the end just to admire the whiteboard work. You might enjoy the comments, too (e.g. ‘Thanks very much. This lesson was confused me so much.’) Coming up for half a million views.

Alejo Lopera Inglés 428K views 4.07 minutes

The only English here is in the example sentences, with Spanish being used for the rest. The explanation hinges on ‘pienso’ (think) for –ing and ‘sentimiento’ (feeling) for –ed, which only kind of works. Alejo takes us through a few examples using a combination of talking-head video and background slides. His delivery is engaging and using Spanish makes things clearer than English only.

English Lessons with Adam 357K views 5.27 minutes

Standing in front of the whiteboard, Adam says that his video is especially useful for beginners. He rambles on for over 5 minutes in language which is far more complicated than the language he is explaining. Here’s a flavour: Now, what does it mean to be bored and what does it mean to be boring? When we talk about “bored”, we’re describing a feeling. Okay? When we talk about “interested”, we’re describing a feeling. So all of the “ed” adjectives are actually feelings, and you can only use them to talk about people and sometimes animals. Why? Because things, like chairs, or tables, or whatever, they don’t have feelings. […]”I am worried”, now people don’t realize that “worried” can have “worrying” as another adjective. “The situation is worrying” means the situation is making me feel worried. Okay? Maybe the whole global political situation, whatever. Now, hopefully none of you are confused by this lesson because I’m trying to make it not confusing. Okay? Everybody okay with that? […] Now, I just want to point out one other thing: Don’t confuse feeling adjectives with “ed” with actual feelings. Okay? If somebody is loved, does he feel loved? Maybe yes, maybe no. We’re not talking about that person’s feelings.

Crown Academy of English 270K views 26.57 minutes

Using screen capture and voiceover software, the script is mostly read aloud from the screen. There is no attempt to make either the script or the delivery interesting. The approach is as traditional as can be: it focuses first on form, with no shying away from grammatical jargon, and eventually moves on to meaning. And then, if you’re still awake, there’s a discrimination exercise. After over 25 minutes of death-by-Powerpoint, the lesson comes, mercifully, to an end.

 

Learn English with Rebecca 274K views 3.30 minutes

From the same stable as Adam’s video, this is more controlled than his ramble, and with slightly better language grading, but is still hard to follow, in part because no examples are given in written form. As with Adam, Rebecca bangs on about how important it is to get this grammar right, because ‘if you make a mistake you could be saying something very unpleasant about yourself’. It’s hard to tell what level it’s intended for.

Francisco Ochoa Inglés Fácil 64K views 11.02 minutes

Switching between Spanish and English, Pacho rattles non-stop through 6 discrimination sentences, taking the difference between feelings (which take the Spanish ‘estar’) and states (which take the Spanish ‘ser’) as his key explanatory tool. This doesn’t quite work, but following his breakneck delivery is more of a problem. The only thing he doesn’t translate are the commas in his examples. I challenge you not to feel confused / confusing by the time he gets to the third sentence. Even Pacho seems to be struggling. Words like ‘hence’ and tenses like past perfect continuous don’t help his 11 minute monologue. I loved the way that he says at the end that the only way to learn this stuff is by applying the language in the way he has just done.

BBC Learning English 48K views 0.56 minutes

In under a minute, Sam from BBC Learning English achieves much greater clarity than anyone else I watched, helped by a carefully planned script, very controlled language and a split screen showing the key points as she makes them. Towards the end, she rattles through 5 more –ed / -ing pairs rather too quickly. It’s a shame, I thought, that she (or the producers) felt the need to reference the old trope about how boring grammar lessons are.

Shaw English Online 46K views 8.49 minutes

The explanation is mercifully brief and the language of Fanny, the presenter, is well controlled. We could do without the exhortations to listen carefully, etc, ‘because this is very important’, but you can’t have everything. A lot of examples are given, before the explanations are repeated. The repetitions don’t help as Fanny resorts to more complicated language than the language she is explaining (e.g. ‘But when you say the teacher was boring, you are describing the teacher, OK, the teacher made the students feel bored, because he or she was boring’). After nearly 4 minutes of presentation, there are some practice discrimination tasks, but Fanny’s relentless commentary gets seriously in the way. The lesson is rounded off with a few minutes of repeat-after-me pronunciation practice.

Mad English TV 24K views 6.59 minutes

In a surreal opening, the presenter talks about the three different states of H2O, before explaining that people, too, can have different states. Eventually, we get to the idea that ‘boring’ is an accusation, ‘bored’ is a state: ‘If you go up to your teacher and say ‘you’re boring’, that’s an insult’. The language grading is all over the place, as is the explanation itself. As a general rule, the longer the explanation, the less clear it is. At 7 minutes, this video is no exception to the rule. When we get to a mini-test (a useful feature that not all other videos have), the choice is ‘My cat is _______’. To know the answer, you need to know if you’re making an accusation about the cat. Got it?

Flipped learning and grammar

Although grammar instruction might seem a strong candidate for a flipped treatment, videoed explanations are clearly not the way to do it. Many coursebooks have perfectly adequate guided discoveries of this and other standard grammar points. Newer courses on platforms have interactive guided discoveries (and often also offer a more traditional deductive route) that will also do the trick much better than videoed explanations. Would learners not be better off doing something else altogether with their time? Initial vocabulary study, listening, reading, writing, almost anything in fact, is a more appropriate target for flipping than grammar, when approached in this way. Video is not the solution to a problem: on the evidence here, it makes the problem worse.

The popularity of grammar videos

It’s very hard to watch this stuff and not scoff, but there’s no denying the immense popularity of videos like these. Much as I find it difficult to believe, people must be learning something (or think they are learning something) from watching them. Otherwise, they presumably wouldn’t consume them to such an extent. Perhaps, these videos conform to expectations about what English lessons should be like? Perhaps viewers subscribe to a belief in ‘no pain, no gain’? Perhaps they simply don’t know where to find something that would help them more? Or perhaps they have been told to watch by their flipping teachers?

Emma has had 1.25 million views. Advertising earnings from 1 million YouTube views are generally reckoned to be between $600-$7000, but are likely to be at the higher end of this scale if (1) people watch the video through to the end (which is probably the case here), and (2) viewers interact with the video through likes and comment (for this video Emma has received 2353 comments). Earnings are also higher when you have more subscribers to your channel. Emma can count on 3.25 million subscribers and Rachna of Let’s Talk has 4.77 million subscribers. By way of contrast, Russell Stannard’s Teacher Training Videos has 40,000 subscribers. There’s gold in them thar hills.

Grammar videos and the world of ELT

Free grammar videos, along with self-study apps like Duolingo, are a huge and thriving sector of ELT. They rarely, if ever, feature in research, conference presentations or the broader discourse of ELT, a world, it seems, much more oriented to products you have to pay for.

References

Al-Harbi, S.S., & Alshumaimeri, Y.A. (2016). The flipped classroom impact in grammar class on EFL Saudi secondary school students’ performances and attitudes. English Language Teaching, 9(10): 60–80. Available at: https://files.eric.ed.gov/fulltext/EJ1113506.pdf

Bergmann, J., Overmeyer, J., & Wilie, B. (2013). The flipped class: myth vs. reality. The Daily Riff, July 9, 2013. Available at: http://www.thedailyriff.com/articles/the-flipped-class-conversation-689.php

Buitrago, C. R., & Díaz, J. (2018). Flipping your writing lessons: Optimizing your time in your EFL writing classroom. In Mehring, J., & Leis, A. (Eds.), Innovations in Flipping the Language Classroom. Singapore: Springer, 69–91.

Current trends in ELT (UK)

Around 25 years ago, when I worked at International House London, I used to teach a course called ‘Current Trends in ELT’. I no longer have records of the time so I can’t be 100% sure what was included in the course, but task-based learning, the ‘Lexical Approach’, the use of corpora, English as a Lingua Franca, learner autonomy / centredness, reflective practice and technology (CALL and CD-ROMs) were all probably part of it. I see that IH London still offers this course (next available course in January 2021) and I am struck by how similar the list of contents is. Only ‘emerging language’, CLIL, DOGME and motivation are clearly different from the menu of 25 years ago.

The term ‘current trends’ has always been a good hook to sell a product. Each year, any number of ELT conferences chooses it as their theme. Coursebooks, like ‘Cutting Edge’ or ‘Innovations’, suggest in their titles something fresh and appealing. And, since 2003, the British Council has used its English Language Teaching Innovation Awards to position itself as forward-thinking and innovative.

You could be forgiven for wondering what is especially innovative about many of the ELTon award-winners, or indeed, why neophilia actually matters at all. The problem, in a relatively limited world like language teaching, is that only so much innovation is either possible or desirable.

A year after the ELTons appeared, Adrian Underhill wrote an article about ‘Trends in English Language Teaching Today’. Almost ten years after I was teaching ‘current trends’, Adrian’s list included the use of corpora, English as a Lingua Franca, reflective practice and learner-centredness. His main guess was that practitioners would be working more with ‘the fuzzy, the unclear, the unfinished’. He hadn’t reckoned on the influence of the CEFR, Pearson’s Global Scale of English and our current obsession with measuring everything!

Jump just over ten years and Chia Suan Chong offered a listicle of ‘Ten innovations that have changed English language teaching’ for the British Council. Most of these were technological developments (platforms, online CPD, mobile learning) but a significant newcomer to the list was ‘soft skills’ (especially critical thinking).

Zooming forward nearer to the present, Chia then offered her list of ‘Ten trends and innovations in English language teaching for 2018’ in another post for the British Council. English as a Lingua Franca was still there, but gone were task-based learning and the ‘Lexical Approach’, corpora, learner-centredness and reflective practice. In their place came SpLNs, multi-literacies, inquiry-based learning and, above all, more about technology – platforms, mobile and blended learning, gamification.

I decided to explore current ‘current trends’ by taking a look at the last twelve months of blog posts from the four biggest UK ELT publishers. Posts such as these are interesting in two ways: (1) they are an attempt to capture what is perceived as ‘new’ and therefore more likely to attract clicks, and (2) they are also an attempt to set an agenda – they reflect what these commercial organisations would like us to be talking and thinking about. The posts reflect reasonably well the sorts of topics that are chosen for webinars, whether directly hosted or sponsored.

The most immediate and unsurprising observation is that technology is ubiquitous. No longer one among a number of topics, technology now informs (almost) all other topics. Before I draw a few conclusion, here are more detailed notes.

Pearson English blog

Along with other publishers, Pearson were keen to show how supportive to teachers they were, and the months following the appearance of the pandemic saw a greater number than normal of blog posts that did not focus on particular Pearson products. Over the last twelve months as a whole, Pearson made strenuous efforts to draw attention to their Global Scale of English and the Pearson Test of English. Assessment of one kind or another was never far away. But the other big themes of the last twelve months have been ‘soft / 21^st century skills (creativity, critical thinking, collaboration, leadership, etc.), and aspects of social and emotional learning (especially engagement / motivation, anxiety and mindfulness). Three other topics also featured more than once: mediation, personalization and SpLN (dyslexia).

OUP ELT Global blog

The OUP blog has, on the whole, longer, rather more informative posts than Pearson. They also tend to be less obviously product-oriented, and fewer are written by in-house marketing people. The main message that comes across is the putative importance of ‘soft / 21^st century skills’, which Oxford likes to call ‘global skills’ (along with the assessment of these skills). One post manages to pack three buzzwords into one title: ‘Global Skills – Create Empowered 21st Century Learners’. As with Pearson, ‘engagement / engaging’ is probably the most over-used word in the last twelve months. In the social and emotional area, OUP focuses on teacher well-being, rather than mindfulness (although, of course, mindfulness is a path to this well-being). There is also an interest in inquiry-based learning, literacies (digital and assessment), formative assessment and blended learning.

Macmillan English blog

The Macmillan English ‘Advancing Learning’ blog is a much less corporate beast than the Pearson and OUP blogs. There have been relatively few posts in the last twelve months, and no clear message emerges. The last year has seen posts on the Image Conference, preparing for IELTS, student retention, extensive reading, ELF pronunciation, drama, mindfulness, Zoom, EMI, and collaboration skills.

CUP World of Better Learning blog

The CUP blog, like Macmillan’s, is an eclectic affair, with no clearly discernible messages beyond supporting teachers with tips and tools to deal with the shift to online teaching. Motivation and engagement are fairly prominent (with Sarah Mercer contributing both here and at OUP). Well-being (and the inevitable nod to mindfulness) gets a look-in. Other topics include SpLNs, video and ELF pronunciation (with Laura Patsko contributing both here and at the Macmillan site).

Macro trends

My survey has certainly not been ‘scientific’, but I think it allows us to note a few macro-trends. Here are my thoughts:

• Measurement of language and skills (both learning and teaching skills) has become central to many of our current concerns.
• We are now much less interested in issues which are unique to language learning and teaching (e.g. task-based learning, the ‘Lexical Approach’, corpora) than we used to be.
• Current concerns reflect much more closely the major concerns of general education (measurement, 21^st century skills, social-emotional learning) than they used to. It is no coincidence that these reflect the priorities of those who shape global educational policy (OECD, World Bank, etc.).
• 25 years ago, current trends were more like zones of interest. They were areas to explore, research and critique further. As such, we might think of them as areas of exploratory practice (‘Exploratory Practice’ itself was a ‘current trend’ in the mid 1990s). Current ‘current trends’ are much more enshrined. They are things to be implemented, and exploration of them concerns the ‘how’, not the ‘whether’.

Digital natives are (nearly) dead and buried. Long live Generation Z!

The idea of ‘digital natives’ emerged at the turn of the century, was popularized by Marc Prensky (2001), and rapidly caught the public imagination, especially the imagination of technology marketers. Its popularity has dwindled a little since then, but is still widely used. Alternative terms include ‘Generation Y’, ‘Millennials’ and the ‘Net Generation’, definitions of which vary slightly from writer to writer. Two examples of the continued currency of the term ‘digital native’ are a 2019 article on the Pearson Global Scale of English website entitled ‘Teaching digital natives to become more human’ and an article in The Pie News (a trade magazine for ‘professionals in international education’), extolling the virtues of online learning for digital natives in times of Covid-19.

Key to understanding ‘digital natives’, according to users of the term, is their fundamental differences from previous generations. They have grown up immersed in technology, have shorter attention spans, and are adept at multitasking. They ‘are no longer the people our educational system was designed to teach’ (Prensky, 2001), so educational systems must change in order to accommodate their needs.

The problem is that ‘digital natives’ are a myth. Prensky’s ideas were not based on any meaningful research: his observations and conclusions, seductive though they might be, were no more than opinions. Kirschner and De Bruyckere (2017) state the research consensus:

There is no such thing as a digital native who is information-skilled simply because (s)he has never known a world that was not digital. […] One of the alleged abilities of students in this generation, the ability to multitask, does not exist and that designing education that assumes the presence of this ability hinders rather than helps learning.

This is neither new (see Bennett et al., 2008) nor contentious. Almost ten years ago, Thomas (2011:3) reported that ‘some researchers have been asked to remove all trace of the term from academic papers submitted to conferences in order to be seriously considered for inclusion’. There are reasons, he added, to consider some uses of the term nothing more than technoevangelism (Thomas, 2011:4). Perhaps someone should tell Pearson and the Pie News? Then, again, perhaps, they wouldn’t care.

The attribution of particular characteristics to ‘digital natives’ / ‘Generation Y’ / ‘Millennials’ is an application of Generation Theory. This can be traced back to a 1928 paper by Karl Mannheim, called ‘Das Problem der Generationen’ which grew in popularity after being translated into English in the 1950s. According to Jauregui et al (2019), the theory was extensively debated in the 1960s and 1970s, but then disappeared from academic study. The theory was not supported by empirical research, was considered to be overly schematised and too culturally-bound, and led inexorably to essentialised and reductive stereotypes.

But Generation Theory gained a new lease of life in the 1990s, following the publication of ‘Generations’ by William Strauss and Neil Howe. The book was so successful that it spawned a slew of other titles leading up to ‘Millennials Rising’ (Howe & Strauss, 2000). This popularity has continued to the present, with fans including Steve Bannon (Kaiser, 2016) who made an ‘apocalyptical and polemical’ documentary film about the 2007 – 2008 financial crisis entitled ‘Generation Zero’. The work of Strauss and Howe has been dismissed as ‘more popular culture than social science’ (Jauregui et al., 2019: 63) and in much harsher terms in two fascinating articles in Jacobin (Hart, 2018) and Aeon (Onion, 2015). The sub-heading of the latter is ‘generational labels are lazy, useless and just plain wrong’. Although dismissed by scholars as pseudo-science, the popularity of such Generation Theory helps explain why Prensky’s paper about ‘digital natives’ fell on such fertile ground. The saying, often falsely attributed to Mark Twain, that we should ‘never let the truth get in the way of a good story’ comes to mind.

But by the end of the first decade of this century, ‘digital natives’ had become problematic in two ways: not only did the term not stand up to close analysis, but it also no longer referred to the generational cohort that pundits and marketers wanted to talk about.

Around January 2018, use of the term ‘Generation Z’ began to soar, and is currently at its highest point ever in the Google Trends graph. As with ‘digital natives’, the precise birth dates of Generation Z vary from writer to writer. After 2001, according to the Cambridge dictionary; slightly earlier according to other sources. The cut-off point is somewhere between the mid and late 2010s. Other terms for this cohort have been proposed, but ‘Generation Z’ is the most popular.

William Strauss died in 2007 and Neil Howe was in his late 60s when ‘Generation Z’ became a thing, so there was space for others to take up the baton. The most successful have probably been Corey Seemiller and Meghan Grace, who, since 2016, have been churning out a book a year devoted to ‘Generation Z’. In the first of these (Seemiller & Grace, 2016), they were clearly keen to avoid some of the criticisms that had been levelled at Strauss and Howe, and they carried out research. This consisted of 1143 responses to a self-reporting questionnaire by students at US institutions of higher education. The survey also collected information about Kolb’s learning styles and multiple intelligences. With refreshing candour, they admit that the sample is not entirely representative of higher education in the US. And, since it only looked at students in higher education, it told us nothing at all about those who weren’t.

In August 2018, Pearson joined the party, bringing out a report entitled ‘Beyond Millennials: The Next Generation of Learners’. Conducted by the Harris Poll, the survey looked at 2,587 US respondents, aged between 14 and 40. The results were weighted for age, gender, race/ethnicity, marital status, household income, and education, so were rather more representative than the Seemiller & Grace research.

In ELT and educational references to ‘Generation Z’, research, of even the very limited kind mentioned above, is rarely cited. When it is, Seemiller and Grace feature prominently (e.g. Mohr & Mohr, 2017). Alternatively, even less reliable sources are used. In an ELT webinar entitled ‘Engaging Generation Z’, for example, information about the characteristics of ‘Generation Z’ learners is taken from an infographic produced by an American office furniture company.

But putting aside quibbles about the reliability of the information, and the fact that it most commonly[1] refers to Americans (who are not, perhaps, the most representative group in global terms), what do the polls tell us?

Despite claims that Generation Z are significantly different from their Millennial predecessors, the general picture that emerges suggests that differences are more a question of degree than substance. These include:

• A preference for visual / video information over text
• A variety of bite-sized, entertaining educational experiences
• Short attention spans and zero tolerance for delay

All of these were identified in 2008 (Williams et al., 2008) as characteristics of the ‘Google Generation’ (a label which usually seems to span Millennials and Generation Z). There is nothing fundamentally different from Prensky’s description of ‘digital natives’. The Pearson report claimed that ‘Generation Z expects experiences both inside and outside the classroom that are more rewarding, more engaging and less time consuming. Technology is no longer a transformative phenomena for this generation, but rather a normal, integral part of life’. However, there is no clear disjuncture or discontinuity between Generation Z and Millennials, any more than there was between ‘digital natives’ and previous generations (Selwyn, 2009: 375). What has really changed is that the technology has moved on (e.g. YouTube was founded in 2005 and the first iPhone was released in 2007).

The discourse surrounding ‘Generation Z’ is now steadily finding its way into the world of English language teaching. The 2^nd TESOL Turkey International ELT Conference took place last November with ‘Teaching Generation Z: Passing on the baton from K12 to University’ as its theme. A further gloss explained that the theme was ‘in reference to the new digital generation of learners with outstanding multitasking skills; learners who can process and absorb information within mere seconds and yet possess the shortest attention span ever’.

 

A few more examples … Cambridge University Press ran a webinar ELT webinar entitled ‘Engaging Generation Z’ and Macmillan Education has a coursebook series called ‘Exercising English for Generation Z’. EBC, a TEFL training provider, ran a blog post in November last year, ‘Teaching English to generation Z students’. And EFL Magazine had an article, ‘Critical Thinking – The Key Competence For The Z Generation’, in February of this year.

The pedagogical advice that results from this interest in Generation Z seems to boil down to: ‘Accept the digital desires of the learners, use lots of video (i.e. use more technology in the classroom) and encourage multi-tasking’.

No one, I suspect, would suggest that teachers should not make use of topics and technologies that appeal to their learners. But recommendations to change approaches to language teaching, ‘based solely on the supposed demands and needs of a new generation of digital natives must be treated with caution’ (Bennett et al., 2008: 782). It is far from clear that generational differences (even if they really exist) are important enough ‘to be considered during the design of instruction or the use of different educational technologies – at this time, the weight of the evidence is negative’ (Reeves, 2008: 21).

Perhaps, it would be more useful to turn away from surveys of attitudes and towards more fact-based research. Studies in both the US and the UK have found that myopia and other problems with the eyes is rising fast among the Generation Z cohort, and that there is a link with increased screen time, especially with handheld devices. At the same time, Generation Zers are much more likely than their predecessors to be diagnosed with anxiety disorder and depression. While the connection between technology use and mental health is far from clear, it is possible that  ‘the rise of the smartphone and social media have at least something to do with [the rise in mental health issues]’ (Twenge, 2017).

Should we be using more technology in class because learners say they want or need it? If we follow that logic, perhaps we should also be encouraging the consumption of fast food, energy drinks and Ritalin before and after lessons?

[1] Studies have been carried out in other geographical settings, including Europe (e.g. Triple-a-Team AG, 2016) and China (Tang, 2019).

References

Bennett S., Maton K., & Kervin, L. (2008). The ‘digital natives’ debate: a critical review of the evidence. British Jmournal of Educational Technology, 39 (5):pp. 775-786.

Hart, A. (2018). Against Generational Politics. Jacobin, 28 February 2018. https://jacobinmag.com/2018/02/generational-theory-millennials-boomers-age-history

Howe, N. & Strauss, W. (2000). Millennials Rising: The Next Great Generation. New York, NY: Vintage Books.

Jauregui, J., Watsjold, B., Welsh, L., Ilgen, J. S. & Robins, L. (2019). Generational “othering”: The myth of the Millennial learner. Medical Education,54: pp.60–65. https://onlinelibrary.wiley.com/doi/pdf/10.1111/medu.13795

Kaiser, D. (2016). Donald Trump, Stephen Bannon and the Coming Crisis in American National Life. Time, 18 November 2016. https://time.com/4575780/stephen-bannon-fourth-turning/

Kirschner, P.A. & De Bruyckere P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67: pp. 135-142. https://www.sciencedirect.com/science/article/pii/S0742051X16306692

Mohr, K. A. J. & Mohr, E. S. (2017). Understanding Generation Z Students to Promote a Contemporary Learning Environment. Journal on Empowering Teacher Excellence, 1 (1), Article 9 DOI: https://doi.org/10.15142/T3M05T

Onion, R. (2015). Against generations. Aeon, 19 May, 2015. https://aeon.co/essays/generational-labels-are-lazy-useless-and-just-plain-wrong

Pearson (2018). Beyond Millennials: The Next Generation of Learners. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/news/news-annoucements/2018/The-Next-Generation-of-Learners_final.pdf

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9: pp. 1- 6

Reeves, T.C. (2008). Do Generational Differences Matter in Instructional Design? Athens, GA: University of Georgia, Department of Educational Psychology and Instructional Technology

Seemiller, C. & and Grace, M. (2016). Generation Z Goes to College. San Francisco: Jossey-Bass

Selwyn, N. (2009). The digital native-myth and reality. Perspectives, 61: pp. 364-379

Strauss W. & Howe, N. (1991). Generations: The History of America’s Future, 1584 to 2069. New York, New York: HarperCollins.

Tang F. (2019). A critical review of research on the work-related attitudes of Generation Z in China. Social Psychology and Society, 10 (2): pp. 19—28. Available at: https://psyjournals.ru/files/106927/sps_2019_n2_Tang.pdf

Thomas, M. (2011). Technology, Education, and the Discourse of the Digital Native: Between Evangelists and Dissenters. In Thomas, M. (ed). (2011). Deconstructing Digital Natives: Young people, technology and the new literacies. London: Routledge. pp. 1- 13)

Triple-a-Team AG. (2016). Generation Z Metastudie über die kommende Generation. Biglen, Switzerland. Available at: http://www.sprachenrat.bremen.de/files/aktivitaeten/Generation_Z_Metastudie.pdf

Twenge, J. M. (2017). iGen. New York: Atria Books

Williams, P., Rowlands, I. & Fieldhouse, M. (2008). The ‘Google Generation’ – myths and realities about young people’s digital information behaviour. In Nicholas, D. & Rowlands, I. (eds.) (2008). Digital Consumers. London: Facet Publishers.

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.

 

 

 

 

Digital dictionaries

The most widely-used and popular tool for language learners is the bilingual dictionary (Levy & Steel, 2015), and the first of its kind appeared about 4,000 years ago (2,000 years earlier than the first monolingual dictionaries), offering wordlists in Sumerian and Akkadian (Wheeler, 2013: 9 -11). Technology has come a long way since the clay tablets of the Bronze Age. Good online dictionaries now contain substantially more information (in particular audio recordings) than their print equivalents of a few decades ago. In addition, they are usually quicker and easier to use, more popular, and lead to retention rates that are comparable to, or better than, those achieved with print (Töpel, 2014). The future of dictionaries is likely to be digital, and paper dictionaries may well disappear before very long (Granger, 2012: 2).

English language learners are better served than learners of other languages, and the number of free, online bilingual dictionaries is now enormous. Speakers of less widely-spoken languages may still struggle to find a good quality service, but speakers of, for example, Polish (with approximately 40 million speakers, and a ranking of #33 in the list of the world’s most widely spoken languages) will find over twenty free, online dictionaries to choose from (Lew & Szarowska, 2017). Speakers of languages that are more widely spoken (Chinese, Spanish or Portuguese, for example) will usually find an even greater range. The choice can be bewildering and neither search engine results nor rankings from app stores can be relied on to suggest the product of the highest quality.

Language teachers are not always as enthusiastic about bilingual dictionaries as their learners. Folse (2004: 114 – 120) reports on an informal survey of English teachers which indicated that 11% did not allow any dictionaries in class at all, 37% allowed monolingual dictionaries and only 5% allowed bilingual dictionaries. Other researchers (e.g. Boonmoh & Nesi, 2008), have found a similar situation, with teachers overwhelmingly recommending the use of a monolingual learner’s dictionary: almost all of their students bought one, but the great majority hardly ever used it, preferring instead a digital bilingual version.

Teachers’ preferences for monolingual dictionaries are usually motivated in part by a fear that their students will become too reliant on translation. Whilst this concern remains widespread, much recent suggests that this fear is misguided (Nation, 2013: 424) and that monolingual dictionaries do not actually lead to greater learning gains than their bilingual counterparts. This is, in part, due to the fact that learners typically use these dictionaries in very limited ways – to see if a word exists, check spelling or look up meaning (Harvey & Yuill, 1997). If they made fuller use of the information (about frequency, collocations, syntactic patterns, etc.) on offer, it is likely that learning gains would be greater: ‘it is accessing multiplicity of information that is likely to enhance retention’ (Laufer & Hill, 2000: 77). Without training, however, this is rarely the case.  With lower-level learners, a monolingual learner’s dictionary (even one designed for Elementary level students) can be a frustrating experience, because until they have reached a vocabulary size of around 2,000 – 3,000 words, they will struggle to understand the definitions (Webb & Nation, 2017: 119).

The second reason for teachers’ preference for monolingual dictionaries is that the quality of many bilingual dictionaries is undoubtedly very poor, compared to monolingual learner’s dictionaries such as those produced by Oxford University Press, Cambridge University Press, Longman Pearson, Collins Cobuild, Merriam-Webster and Macmillan, among others. The situation has changed, however, with the rapid growth of bilingualized dictionaries. These contain all the features of a monolingual learner’s dictionary, but also include translations into the learner’s own language. Because of the wealth of information provided by a good bilingualized dictionary, researchers (e.g. Laufer & Hadar, 1997; Chen, 2011) generally consider them preferable to monolingual or normal bilingual dictionaries. They are also popular with learners. Good bilingualized online dictionaries (such as the Oxford Advanced Learner’s English-Chinese Dictionary) are not always free, but many are, and with some language pairings free software can be of a higher quality than services that incur a subscription charge.

If a good bilingualized dictionary is available, there is no longer any compelling reason to use a monolingual learner’s dictionary, unless it contains features which cannot be found elsewhere. In order to compete in a crowded marketplace, many of the established monolingual learner’s dictionaries do precisely that. Examples of good, free online dictionaries include:

• Merriam-Webster Learner’s Dictionary : quizzes, ‘Word of the Day’ and ‘Ask the Editor’
• Macmillan Dictionary : puzzles and quizzes, lesson plans and ‘Words in the News’
• Oxford Learner’s Dictionaries : word lists, teaching tips and ‘Word of the Month’
• Longman Dictionary of Contemporary English (LDOCE Online):  practice exercises, exam preparation and ‘Word of the Day’
• Cambridge Dictionary : word lists, quizzes, ‘Word of the Day’ and new words
• Collins Online English Dictionary : quizzes, word lists, trending words and ‘Word of the Day’

Students need help in selecting a dictionary that is right for them. Without this, many end up using as a dictionary a tool such as Google Translate , which, for all its value, is of very limited use as a dictionary. They need to understand that the most appropriate dictionary will depend on what they want to use it for (receptive, reading purposes or productive, writing purposes). Teachers can help in this decision-making process by addressing the issue in class (see the activity below).

In addition to the problem of selecting an appropriate dictionary, it appears that many learners have inadequate dictionary skills (Niitemaa & Pietilä, 2018). In one experiment (Tono, 2011), only one third of the vocabulary searches in a dictionary that were carried out by learners resulted in success. The reasons for failure include focussing on only the first meaning (or translation) of a word that is provided, difficulty in finding the relevant information in long word entries, an inability to find the lemma that is needed, and spelling errors (when they had to type in the word) (Töpel, 2014). As with monolingual dictionaries, learners often only check the meaning of a word in a bilingual dictionary and fail to explore the wider range of information (e.g. collocation, grammatical patterns, example sentences, synonyms) that is available (Laufer & Kimmel, 1997; Laufer & Hill, 2000; Chen, 2010). This information is both useful and may lead to improved retention.

Most learners receive no training in dictionary skills, but would clearly benefit from it. Nation (2013: 333) suggests that at least four or five hours, spread out over a few weeks, would be appropriate. He suggests (ibid: 419 – 421) that training should encourage learners, first, to look closely at the context in which an unknown word is encountered (in order to identify the part of speech, the lemma that needs to be looked up, its possible meaning and to decide whether it is worth looking up at all), then to help learners in finding the relevant entry or sub-entry (by providing information about common dictionary abbreviations (e.g. for parts of speech, style and register)), and, finally, to check this information against the original context.

Two good resource books full of practical activities for dictionary training are available: ‘Dictionary Activities’ by Cindy Leaney (Cambridge: Cambridge University Press, 2007) and ‘Dictionaries’ by Jon Wright (Oxford: Oxford University Press, 1998). Many of the good monolingual dictionaries offer activity guides to promote effective dictionary use and I have suggested a few activities here.

Activity: Understanding a dictionary

Outline: Students explore the use of different symbols in good online dictionaries.

Level: All levels, but not appropriate for very young learners. The activity ‘Choosing a dictionary’ is a good follow-up to this activity.

1 Distribute the worksheet and ask students to follow the instructions.

2 Check the answers.

Activity: Choosing a dictionary

Outline: Students explore and evaluate the features of different free, online bilingual dictionaries.

Level: All levels, but not appropriate for very young learners. The text in stage 3 is appropriate for use with levels A2 and B1. For some groups of learners, you may want to adapt (or even translate) the list of features. It may be useful to do the activity ‘Understanding a dictionary’ before this activity.

1 Ask the class which free, online bilingual dictionaries they like to use. Write some of their suggestions on the board.

2 Distribute the list of features. Ask students to work individually and tick the boxes that are important for them. Ask students to work with a partner to compare their answers.

3 Give students a list of free, online bilingual (English and the students’ own language) dictionaries. You can use suggestions from the list below, add the suggestions that your students made in stage 1, or add your own ideas. (For many language pairings, better resources are available than those in the list below.) Give the students the following short text and ask the students to use two of these dictionaries to look up the underlined words. Ask the students to decide which dictionary they found most useful and / or easiest to use.

4 Conduct feedback with the whole class.

Activity: Getting more out of a dictionary

Outline: Students use a dictionary to help them to correct a text

Level: Levels B1 and B2, but not appropriate for very young learners. For higher levels, a more complex text (with less obvious errors) would be appropriate.

1 Distribute the worksheet below and ask students to follow the instructions.

2 Check answers with the whole class. Ask how easy it was to find the information in the dictionary that they were using.

Key

When you are reading, you probably only need a dictionary when you don’t know the meaning of a word and you want to look it up. For this, a simple bilingual dictionary is good enough. But when you are writing or editing your writing, you will need something that gives you more information about a word: grammatical patterns, collocations (the words that usually go with other words), how formal the word is, and so on. For this, you will need a better dictionary. Many of the better dictionaries are monolingual (see the box), but there are also some good bilingual ones.

Use one (or more) of the online dictionaries in the box (or a good bilingual dictionary) and make corrections to this text. There are eleven mistakes (they have been underlined) in total.

References

Boonmoh, A. & Nesi, H. 2008. ‘A survey of dictionary use by Thai university staff and students with special reference to pocket electronic dictionaries’ Horizontes de Linguística Aplicada , 6(2), 79 – 90

Chen, Y. 2011. ‘Studies on Bilingualized Dictionaries: The User Perspective’. International Journal of Lexicography, 24 (2): 161–197

Folse, K. 2004. Vocabulary Myths. Ann Arbor: University of Michigan Press

Granger, S. 2012. Electronic Lexicography. Oxford: Oxford University Press

Harvey, K. & Yuill, D. 1997. ‘A study of the use of a monolingual pedagogical dictionary by learners of English engaged in writing’ Applied Linguistics, 51 (1): 253 – 78

Laufer, B. & Hadar, L. 1997. ‘Assessing the effectiveness of monolingual, bilingual and ‘bilingualized’ dictionaries in the comprehension and production of new words’. Modern Language Journal, 81 (2): 189 – 96

Laufer, B. & M. Hill 2000. ‘What lexical information do L2 learners select in a CALL dictionary and how does it affect word retention?’ Language Learning & Technology 3 (2): 58–76

Laufer, B. & Kimmel, M. 1997. ‘Bilingualised dictionaries: How learners really use them’, System, 25 (3): 361 -369

Leaney, C. 2007. Dictionary Activities. Cambridge: Cambridge University Press

Levy, M. and Steel, C. 2015. ‘Language learner perspectives on the functionality and use of electronic language dictionaries’. ReCALL, 27(2): 177–196

Lew, R. & Szarowska, A. 2017. ‘Evaluating online bilingual dictionaries: The case of popular free English-Polish dictionaries’ ReCALL 29(2): 138–159

Nation, I.S.P. 2013. Learning Vocabulary in Another Language 2^nd edition. Cambridge: Cambridge University Press

Niitemaa, M.-L. & Pietilä, P. 2018. ‘Vocabulary Skills and Online Dictionaries: A Study on EFL Learners’ Receptive Vocabulary Knowledge and Success in Searching Electronic Sources for Information’, Journal of Language Teaching and Research, 9 (3): 453-462

Tono, Y. 2011. ‘Application of eye-tracking in EFL learners’ dictionary look-up process research’, International Journal of Lexicography 24 (1): 124–153

Töpel, A. 2014. ‘Review of research into the use of electronic dictionaries’ in Müller-Spitzer, C. (Ed.) 2014. Using Online Dictionaries. Berlin: De Gruyter, pp. 13 – 54

Webb, S. & Nation, P. 2017. How Vocabulary is Learned. Oxford: Oxford University Press

Wheeler, G. 2013. Language Teaching through the Ages. New York: Routledge

Wright, J. 1998. Dictionaries. Oxford: Oxford University Press

Big data, learning analytics and language learning / teaching

The use of big data and analytics in education continues to grow.

A vast apparatus of measurement is being developed to underpin national education systems, institutions and the actions of the individuals who occupy them. […] The presence of digital data and software in education is being amplified through massive financial and political investment in educational technologies, as well as huge growth in data collection and analysis in policymaking practices, extension of performance measurement technologies in the management of educational institutions, and rapid expansion of digital methodologies in educational research. To a significant extent, many of the ways in which classrooms function, educational policy departments and leaders make decisions, and researchers make sense of data, simply would not happen as currently intended without the presence of software code and the digital data processing programs it enacts. (Williamson, 2017: 4)

The most common and successful use of this technology so far has been in the identification of students at risk of dropping out of their courses (Jørno & Gynther, 2018: 204). The kind of analytics used in this context may be called ‘academic analytics’ and focuses on educational processes at the institutional level or higher (Gelan et al, 2018: 3). However, ‘learning analytics’, the capture and analysis of learner and learning data in order to personalize learning ‘(1) through real-time feedback on online courses and e-textbooks that can ‘learn’ from how they are used and ‘talk back’ to the teacher, and (2) individualization and personalization of the educational experience through adaptive learning systems that enable materials to be tailored to each student’s individual needs through automated real-time analysis’ (Mayer-Schönberger & Cukier, 2014) has become ‘the main keyword of data-driven education’ (Williamson, 2017: 10). See my earlier posts on this topic here and here and here.

Near the start of Mayer-Schönberger and Cukier’s enthusiastic sales pitch (Learning with Big Data: The Future of Education) for the use of big data in education, there is a discussion of Duolingo. They quote Luis von Ahn, the founder of Duolingo, as saying ‘there has been little empirical work on what is the best way to teach a foreign language’. This is so far from the truth as to be laughable. Von Ahn’s comment, along with the Duolingo product itself, is merely indicative of a lack of awareness of the enormous amount of research that has been carried out. But what could the data gleaned from the interactions of millions of users with Duolingo tell us of value? The example that is given is the following. Apparently, ‘in the case of Spanish speakers learning English, it’s common to teach pronouns early on: words like “he,” “she,” and “it”.’ But, Duolingo discovered, ‘the term “it” tends to confuse and create anxiety for Spanish speakers, since the word doesn’t easily translate into their language […] Delaying the introduction of “it” until a few weeks later dramatically improves the number of people who stick with learning English rather than drop out.’ Was von Ahn unaware of the decades of research into language transfer effects? Did von Ahn (who grew up speaking Spanish in Guatemala) need all this data to tell him that English personal pronouns can cause problems for Spanish learners of English? Was von Ahn unaware of the debates concerning the value of teaching isolated words (especially grammar words!)?

The area where little empirical research has been done is not in different ways of learning another language: it is in the use of big data and learning analytics to assist language learning. Claims about the value of these technologies in language learning are almost always speculative – they are based on comparison to other school subjects (especially, mathematics). Gelan et al (2018: 2), who note this lack of research, suggest that ‘understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways’ (my italics). Reinders (2018: 81) writes ‘that analysis of prior experiences with certain groups or certain courses may help to identify key moments at which students need to receive more or different support. Analysis of student engagement and performance throughout a course may help with early identification of learning problems and may prompt early intervention’ (italics added). But there is some research out there, and it’s worth having a look at. Most studies that have collected learner-tracking data concern glossary use for reading comprehension and vocabulary retention (Gelan et al, 2018: 5), but a few have attempted to go further in scope.

Volk et al (2015) looked at the behaviour of the 20,000 students per day using the platform which accompanies ‘More!’ (Gerngross et al. 2008) to do their English homework for Austrian lower secondary schools. They discovered that

• the exercises used least frequently were those that are located further back in the course book
• usage is highest from Monday to Wednesday, declining from Thursday, with a rise again on Sunday
• most interaction took place between 3:00 and 5:00 pm.
• repetition of exercises led to a strong improvement in success rate
• students performed better on multiple choice and matching exercises than they did where they had to produce some language

The authors of this paper conclude by saying that ‘the results of this study suggest a number of new avenues for research. In general, the authors plan to extend their analysis of exercise results and applied exercises to the population of all schools using the online learning platform more-online.at. This step enables a deeper insight into student’s learning behaviour and allows making more generalizing statements.’ When I shared these research findings with the Austrian lower secondary teachers that I work with, their reaction was one of utter disbelief. People get paid to do this research? Why not just ask us?

More useful, more actionable insights may yet come from other sources. For example, Gu Yueguo, Pro-Vice-Chancellor of the Beijing Foreign Studies University has announced the intention to set up a national Big Data research center, specializing in big data-related research topics in foreign language education (Yu, 2015). Meanwhile, I’m aware of only one big research project that has published its results. The EC Erasmus+ VITAL project (Visualisation Tools and Analytics to monitor Online Language Learning & Teaching) was carried out between 2015 and 2017 and looked at the learning trails of students from universities in Belgium, Britain and the Netherlands. It was discovered (Gelan et al, 2015) that:

• students who did online exercises when they were supposed to do them were slightly more successful than those who were late carrying out the tasks
• successful students logged on more often, spent more time online, attempted and completed more tasks, revisited both exercises and theory pages more frequently, did the work in the order in which it was supposed to be done and did more work in the holidays
• most students preferred to go straight into the assessed exercises and only used the theory pages when they felt they needed to; successful students referred back to the theory pages more often than unsuccessful students
• students made little use of the voice recording functionality
• most online activity took place the day before a class and the day of the class itself

EU funding for this VITAL project amounted to 274,840 Euros[1]. The technology for capturing the data has been around for a long time. In my opinion, nothing of value, or at least nothing new, has been learnt. Publishers like Pearson and Cambridge University Press who have large numbers of learners using their platforms have been capturing learning data for many years. They do not publish their findings and, intriguingly, do not even claim that they have learnt anything useful / actionable from the data they have collected. Sure, an exercise here or there may need to be amended. Both teachers and students may need more support in using the more open-ended functionalities of the platforms (e.g. discussion forums). But are they getting ‘unprecedented insights into what works and what doesn’t’ (Mayer-Schönberger & Cukier, 2014)? Are they any closer to building better pedagogies? On the basis of what we know so far, you wouldn’t want to bet on it.

It may be the case that all the learning / learner data that is captured could be used in some way that has nothing to do with language learning. Show me a language-learning app developer who does not dream of monetizing the ‘behavioural surplus’ (Zuboff, 2018) that they collect! But, for the data and analytics to be of any value in guiding language learning, it must lead to actionable insights. Unfortunately, as Jørno & Gynther (2018: 198) point out, there is very little clarity about what is meant by ‘actionable insights’. There is a danger that data and analytics ‘simply gravitates towards insights that confirm longstanding good practice and insights, such as “students tend to ignore optional learning activities … [and] focus on activities that are assessed” (Jørno & Gynther, 2018: 211). While this is happening, the focus on data inevitably shapes the way we look at the object of study (i.e. language learning), ‘thereby systematically excluding other perspectives’ (Mau, 2019: 15; see also Beer, 2019). The belief that tech is always the solution, that all we need is more data and better analytics, remains very powerful: it’s called techno-chauvinism (Broussard, 2018: 7-8).

References

Beer, D. 2019. The Data Gaze. London: Sage

Broussard, M. 2018. Artificial Unintelligence. Cambridge, Mass.: MIT Press

Gelan, A., Fastre, G., Verjans, M., Martin, N., Jansenswillen, G., Creemers, M., Lieben, J., Depaire, B. & Thomas, M. 2018. ‘Affordances and limitations of learning analytics for computer­assisted language learning: a case study of the VITAL project’. Computer Assisted Language Learning. pp. 1­26. http://clok.uclan.ac.uk/21289/

Gerngross, G., Puchta, H., Holzmann, C., Stranks, J., Lewis-Jones, P. & Finnie, R. 2008. More! 1 Cyber Homework. Innsbruck, Austria: Helbling

Jørno, R. L. & Gynther, K. 2018. ‘What Constitutes an “Actionable Insight” in Learning Analytics?’ Journal of Learning Analytics 5 (3): 198 – 221

Mau, S. 2019. The Metric Society. Cambridge: Polity Press

Mayer-Schönberger, V. & Cukier, K. 2014. Learning with Big Data: The Future of Education. New York: Houghton Mifflin Harcourt

Reinders, H. 2018. ‘Learning analytics for language learning and teaching’. JALT CALL Journal 14 / 1: 77 – 86 https://files.eric.ed.gov/fulltext/EJ1177327.pdf

Volk, H., Kellner, K. & Wohlhart, D. 2015. ‘Learning Analytics for English Language Teaching.’ Journal of Universal Computer Science, Vol. 21 / 1: 156-174 http://www.jucs.org/jucs_21_1/learning_analytics_for_english/jucs_21_01_0156_0174_volk.pdf

Williamson, B. 2017. Big Data in Education. London: Sage

Yu, Q. 2015. ‘Learning Analytics: The next frontier for computer assisted language learning in big data age’ SHS Web of Conferences, 17 https://www.shs-conferences.org/articles/shsconf/pdf/2015/04/shsconf_icmetm2015_02013.pdf

Zuboff, S. 2019. The Age of Surveillance Capitalism. London: Profile Books

 

[1] See https://ec.europa.eu/programmes/erasmus-plus/sites/erasmusplus2/files/ka2-2015-he_en.pdf

ELT as an Industry

by Philip Kerr & Andrew Wickham

from IATEFL 2016 Birmingham Conference Selections (ed. Tania Pattison) Faversham, Kent: IATEFL pp. 75 – 78

ELT publishing, international language testing and private language schools are all industries: products are produced, bought and sold for profit. English language teaching (ELT) is not. It is an umbrella term that is used to describe a range of activities, some of which are industries, and some of which (such as English teaching in high schools around the world) might better be described as public services. ELT, like education more generally, is, nevertheless, often referred to as an ‘industry’.

Education in a neoliberal world

The framing of ELT as an industry is both a reflection of how we understand the term and a force that shapes our understanding. Associated with the idea of ‘industry’ is a constellation of other ideas and words (such as efficacy, productivity, privatization, marketization, consumerization, digitalization and globalization) which become a part of ELT once it is framed as an industry. Repeated often enough, ‘ELT as an industry’ can become a metaphor that we think and live by. Those activities that fall under the ELT umbrella, but which are not industries, become associated with the desirability of industrial practices through such discourse.

The shift from education, seen as a public service, to educational managerialism (where education is seen in industrial terms with a focus on efficiency, free market competition, privatization and a view of students as customers) can be traced to the 1980s and 1990s (Gewirtz, 2001). In 1999, under pressure from developed economies, the General Agreement on Trade in Services (GATS) transformed education into a commodity that could be traded like any other in the marketplace (Robertson, 2006). The global industrialisation and privatization of education continues to be promoted by transnational organisations (such as the World Bank and the OECD), well-funded free-market think-tanks (such as the Cato Institute), philanthro-capitalist foundations (such as the Gates Foundation) and educational businesses (such as Pearson) (Ball, 2012).

Efficacy and learning outcomes

Managerialist approaches to education require educational products and services to be measured and compared. In ELT, the most visible manifestation of this requirement is the current ubiquity of learning outcomes. Contemporary coursebooks are full of ‘can-do’ statements, although these are not necessarily of any value to anyone. Examples from one unit of one best-selling course include ‘Now I can understand advice people give about hotels’ and ‘Now I can read an article about unique hotels’ (McCarthy et al. 2014: 74). However, in a world where accountability is paramount, they are deemed indispensable. The problem from a pedagogical perspective is that teaching input does not necessarily equate with learning uptake. Indeed, there is no reason why it should.

Drawing on the Common European Framework of Reference for Languages (CEFR) for inspiration, new performance scales have emerged in recent years. These include the Cambridge English Scale and the Pearson Global Scale of English. Moving away from the broad six categories of the CEFR, such scales permit finer-grained measurement and we now see individual vocabulary and grammar items tagged to levels. Whilst such initiatives undoubtedly support measurements of efficacy, the problem from a pedagogical perspective is that they assume that language learning is linear and incremental, as opposed to complex and jagged.

Given the importance accorded to the measurement of language learning (or what might pass for language learning), it is unsurprising that attention is shifting towards the measurement of what is probably the most important factor impacting on learning: the teaching. Teacher competency scales have been developed by Cambridge Assessment, the British Council and EAQUALS (Evaluation and Accreditation of Quality Language Services), among others.

The backwash effects of the deployment of such scales are yet to be fully experienced, but the likely increase in the perception of both language learning and teacher learning as the synthesis of granularised ‘bits of knowledge’ is cause for concern.

Digital technology

Digital technology may offer advantages to both English language teachers and learners, but its rapid growth in language learning is the result, primarily but not exclusively, of the way it has been promoted by those who stand to gain financially. In education, generally, and in English language teaching, more specifically, advocacy of the privatization of education is always accompanied by advocacy of digitalization. The global market for digital English language learning products was reported to be $2.8 billion in 2015 and is predicted to reach $3.8 billion by 2020 (Ambient Insight, 2016).

In tandem with the increased interest in measuring learning outcomes, there is fierce competition in the market for high-stakes examinations, and these are increasingly digitally delivered and marked. In the face of this competition and in a climate of digital disruption, companies like Pearson and Cambridge English are developing business models of vertical integration where they can provide and sell everything from placement testing, to courseware (either print or delivered through an LMS), teaching, assessment and teacher training. Huge investments are being made in pursuit of such models. Pearson, for example, recently bought GlobalEnglish, Wall Street English, and set up a partnership with Busuu, thus covering all aspects of language learning from resources provision and publishing to off- and online training delivery.

As regards assessment, the most recent adult coursebook from Cambridge University Press (in collaboration with Cambridge English Language Assessment), ‘Empower’ (Doff, et. Al, 2015) sells itself on a combination of course material with integrated, validated assessment.

Besides its potential for scalability (and therefore greater profit margins), the appeal (to some) of platform-delivered English language instruction is that it facilitates assessment that is much finer-grained and actionable in real time. Digitization and testing go hand in hand.

Few English language teachers have been unaffected by the move towards digital. In the state sectors, large-scale digitization initiatives (such as the distribution of laptops for educational purposes, the installation of interactive whiteboards, the move towards blended models of instruction or the move away from printed coursebooks) are becoming commonplace. In the private sectors, online (or partially online) language schools are taking market share from the traditional bricks-and-mortar institutions.

These changes have entailed modifications to the skill-sets that teachers need to have. Two announcements at this conference reflect this shift. First of all, Cambridge English launched their ‘Digital Framework for Teachers’, a matrix of six broad competency areas organised into four levels of proficiency. Secondly, Aqueduto, the Association for Quality Education and Training Online, was launched, setting itself up as an accreditation body for online or blended teacher training courses.

Teachers’ pay and conditions

In the United States, and likely soon in the UK, the move towards privatization is accompanied by an overt attack on teachers’ unions, rights, pay and conditions (Selwyn, 2014). As English language teaching in both public and private sectors is commodified and marketized it is no surprise to find that the drive to bring down costs has a negative impact on teachers worldwide. Gwynt (2015), for example, catalogues cuts in funding, large-scale redundancies, a narrowing of the curriculum, intensified workloads (including the need to comply with ‘quality control measures’), the deskilling of teachers, dilapidated buildings, minimal resources and low morale in an ESOL department in one British further education college. In France, a large-scale study by Wickham, Cagnol, Wright and Oldmeadow (Linguaid, 2015; Wright, 2016) found that EFL teachers in the very competitive private sector typically had multiple employers, limited or no job security, limited sick pay and holiday pay, very little training and low hourly rates that were deteriorating. One of the principle drivers of the pressure on salaries is the rise of online training delivery through Skype and other online platforms, using offshore teachers in low-cost countries such as the Philippines. This type of training represents 15% in value and up to 25% in volume of all language training in the French corporate sector and is developing fast in emerging countries. These examples are illustrative of a broad global trend.

Implications

Given the current climate, teachers will benefit from closer networking with fellow professionals in order, not least, to be aware of the rapidly changing landscape. It is likely that they will need to develop and extend their skill sets (especially their online skills and visibility and their specialised knowledge), to differentiate themselves from competitors and to be able to demonstrate that they are in tune with current demands. More generally, it is important to recognise that current trends have yet to run their full course. Conditions for teachers are likely to deteriorate further before they improve. More than ever before, teachers who want to have any kind of influence on the way that marketization and industrialization are shaping their working lives will need to do so collectively.

References

Ambient Insight. 2016. The 2015-2020 Worldwide Digital English Language Learning Market. http://www.ambientinsight.com/Resources/Documents/AmbientInsight_2015-2020_Worldwide_Digital_English_Market_Sample.pdf

Ball, S. J. 2012. Global Education Inc. Abingdon, Oxon.: Routledge

Doff, A., Thaine, C., Puchta, H., Stranks, J. and P. Lewis-Jones 2015. Empower. Cambridge: Cambridge University Press

Gewirtz, S. 2001. The Managerial School: Post-welfarism and Social Justice in Education. Abingdon, Oxon.: Routledge

Gwynt, W. 2015. ‘The effects of policy changes on ESOL’. Language Issues 26 / 2: 58 – 60

McCarthy, M., McCarten, J. and H. Sandiford 2014. Touchstone 2 Student’s Book Second Edition. Cambridge: Cambridge University Press

Linguaid, 2015. Le Marché de la Formation Langues à l’Heure de la Mondialisation. Guildford: Linguaid

Robertson, S. L. 2006. ‘Globalisation, GATS and trading in education services.’ published by the Centre for Globalisation, Education and Societies, University of Bristol, Bristol BS8 1JA, UK at http://www.bris.ac.uk/education/people/academicStaff/edslr/publications/04slr

Selwyn, N. 2014. Distrusting Educational Technology. New York: Routledge

Wright, R. 2016. ‘My teacher is rich … or not!’ English Teaching Professional 103: 54 – 56

 

 

Adaptive learning in ELT: the current state of play

About two and a half years ago when I started writing this blog, there was a lot of hype around adaptive learning and the big data which might drive it. Two and a half years are a long time in technology. A look at Google Trends suggests that interest in adaptive learning has been pretty static for the last couple of years. It’s interesting to note that 3 of the 7 lettered points on this graph are Knewton-related media events (including the most recent, A, which is Knewton’s latest deal with Hachette) and 2 of them concern McGraw-Hill. It would be interesting to know whether these companies follow both parts of Simon Cowell’s dictum of ‘Create the hype, but don’t ever believe it’.

A look at the Hype Cycle (see here for Wikipedia’s entry on the topic and for criticism of the hype of Hype Cycles) of the IT research and advisory firm, Gartner, indicates that both big data and adaptive learning have now slid into the ‘trough of disillusionment’, which means that the market has started to mature, becoming more realistic about how useful the technologies can be for organizations.

A few years ago, the Gates Foundation, one of the leading cheerleaders and financial promoters of adaptive learning, launched its Adaptive Learning Market Acceleration Program (ALMAP) to ‘advance evidence-based understanding of how adaptive learning technologies could improve opportunities for low-income adults to learn and to complete postsecondary credentials’. It’s striking that the program’s aims referred to how such technologies could lead to learning gains, not whether they would. Now, though, with the publication of a report commissioned by the Gates Foundation to analyze the data coming out of the ALMAP Program, things are looking less rosy. The report is inconclusive. There is no firm evidence that adaptive learning systems are leading to better course grades or course completion. ‘The ultimate goal – better student outcomes at lower cost – remains elusive’, the report concludes. Rahim Rajan, a senior program office for Gates, is clear: ‘There is no magical silver bullet here.’

The same conclusion is being reached elsewhere. A report for the National Education Policy Center (in Boulder, Colorado) concludes: Personalized Instruction, in all its many forms, does not seem to be the transformational technology that is needed, however. After more than 30 years, Personalized Instruction is still producing incremental change. The outcomes of large-scale studies and meta-analyses, to the extent they tell us anything useful at all, show mixed results ranging from modest impacts to no impact. Additionally, one must remember that the modest impacts we see in these meta-analyses are coming from blended instruction, which raises the cost of education rather than reducing it (Enyedy, 2014: 15 -see reference at the foot of this post). In the same vein, a recent academic study by Meg Coffin Murray and Jorge Pérez (2015, ‘Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning’) found that ‘adaptive learning systems have negligible impact on learning outcomes’.

In the latest educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Reimagining the Role of Technology in Education’, 2016) the only mentions of the word ‘adaptive’ are in the context of testing. And the latest OECD report on ‘Students, Computers and Learning: Making the Connection’ (2015), finds, more generally, that information and communication technologies, when they are used in the classroom, have, at best, a mixed impact on student performance.

There is, however, too much money at stake for the earlier hype to disappear completely. Sponsored cheerleading for adaptive systems continues to find its way into blogs and national magazines and newspapers. EdSurge, for example, recently published a report called ‘Decoding Adaptive’ (2016), sponsored by Pearson, that continues to wave the flag. Enthusiastic anecdotes take the place of evidence, but, for all that, it’s a useful read.

In the world of ELT, there are plenty of sales people who want new products which they can call ‘adaptive’ (and gamified, too, please). But it’s striking that three years after I started following the hype, such products are rather thin on the ground. Pearson was the first of the big names in ELT to do a deal with Knewton, and invested heavily in the company. Their relationship remains close. But, to the best of my knowledge, the only truly adaptive ELT product that Pearson offers is the PTE test.

Macmillan signed a contract with Knewton in May 2013 ‘to provide personalized grammar and vocabulary lessons, exam reviews, and supplementary materials for each student’. In December of that year, they talked up their new ‘big tree online learning platform’: ‘Look out for the Big Tree logo over the coming year for more information as to how we are using our partnership with Knewton to move forward in the Language Learning division and create content that is tailored to students’ needs and reactive to their progress.’ I’ve been looking out, but it’s all gone rather quiet on the adaptive / platform front.

In September 2013, it was the turn of Cambridge to sign a deal with Knewton ‘to create personalized learning experiences in its industry-leading ELT digital products for students worldwide’. This year saw the launch of a major new CUP series, ‘Empower’. It has an online workbook with personalized extra practice, but there’s nothing (yet) that anyone would call adaptive. More recently, Cambridge has launched the online version of the 2^nd edition of Touchstone. Nothing adaptive there, either.

Earlier this year, Cambridge published The Cambridge Guide to Blended Learning for Language Teaching, edited by Mike McCarthy. It contains a chapter by M.O.Z. San Pedro and R. Baker on ‘Adaptive Learning’. It’s an enthusiastic account of the potential of adaptive learning, but it doesn’t contain a single reference to language learning or ELT!

So, what’s going on? Skepticism is becoming the order of the day. The early hype of people like Knewton’s Jose Ferreira is now understood for what it was. Companies like Macmillan got their fingers badly burnt when they barked up the wrong tree with their ‘Big Tree’ platform.

Noel Enyedy captures a more contemporary understanding when he writes: Personalized Instruction is based on the metaphor of personal desktop computers—the technology of the 80s and 90s. Today’s technology is not just personal but mobile, social, and networked. The flexibility and social nature of how technology infuses other aspects of our lives is not captured by the model of Personalized Instruction, which focuses on the isolated individual’s personal path to a fixed end-point. To truly harness the power of modern technology, we need a new vision for educational technology (Enyedy, 2014: 16).

Adaptive solutions aren’t going away, but there is now a much better understanding of what sorts of problems might have adaptive solutions. Testing is certainly one. As the educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Re-imagining the Role of Technology in Education’, 2016) puts it: Computer adaptive testing, which uses algorithms to adjust the difficulty of questions throughout an assessment on the basis of a student’s responses, has facilitated the ability of assessments to estimate accurately what students know and can do across the curriculum in a shorter testing session than would otherwise be necessary. In ELT, Pearson and EF have adaptive tests that have been well researched and designed.

Vocabulary apps which deploy adaptive technology continue to become more sophisticated, although empirical research is lacking. Automated writing tutors with adaptive corrective feedback are also developing fast, and I’ll be writing a post about these soon. Similarly, as speech recognition software improves, we can expect to see better and better automated adaptive pronunciation tutors. But going beyond such applications, there are bigger questions to ask, and answers to these will impact on whatever direction adaptive technologies take. Large platforms (LMSs), with or without adaptive software, are already beginning to look rather dated. Will they be replaced by integrated apps, or are apps themselves going to be replaced by bots (currently riding high in the Hype Cycle)? In language learning and teaching, the future of bots is likely to be shaped by developments in natural language processing (another topic about which I’ll be blogging soon). Nobody really has a clue where the next two and a half years will take us (if anywhere), but it’s becoming increasingly likely that adaptive learning will be only one very small part of it.

 

Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning. Boulder, CO: National Education Policy Center. Retrieved 17.07.16 from http://nepc.colorado.edu/publication/personalized-instruction