Multimodality and modals

My attention was recently drawn (thanks to Grzegorz Śpiewak) to a recent free publication from OUP. It’s called ‘Multimodality in ELT: Communication skills for today’s generation’ (Donaghy et al., 2023) and it’s what OUP likes to call a ‘position paper’: it offers ‘evidence-based recommendations to support educators and learners in their future success’. Its topic is multimodal (or multimedia) literacy, a term used to describe the importance for learners of being able ‘not just to understand but to create multimedia messages, integrating text with images, sounds and video to suit a variety of communicative purposes and reach a range of target audiences’ (Dudeney et al., 2013: 13).

Grzegorz noted the author of this paper’s ‘positively charged, unhedged language to describe what is arguably a most complex problem area’. As an example, he takes the summary of the first section and circles questionable and / or unsubstantiated claims. It’s just one example from a text that reads more like a ‘manifesto’ than a balanced piece of evidence-reporting. The verb ‘need’ (in the sense of ‘must’, as in ‘teachers / learners / students need to …’) appears no less than 57 times. The modal ‘should’ (as in ‘teachers / learners / students should …’) clocks up 27 appearances.

What is it then that we all need to do? Essentially, the argument is that English language teachers need to develop their students’ multimodal literacy by incorporating more multimodal texts and tasks (videos and images) in all their lessons. The main reason for this appears to be that, in today’s digital age, communication is more often multimodal than not (i.e. monomodal written or spoken text). As an addendum, we are told that multimodal classroom practices are a ‘fundamental part of inclusive teaching’ in classes with ‘learners with learning difficulties and disabilities’. In case you thought it was ironic that such an argument would be put forward in a flat monomodal pdf, OUP also offers the same content through a multimodal ‘course’ with text, video and interactive tasks.

It might all be pretty persuasive, if it weren’t so overstated. Here are a few of the complex problem areas.

What exactly is multimodal literacy?

We are told in the paper that there are five modes of communication: linguistic, visual, aural, gestural and spatial. Multimodal literacy consists, apparently, of the ability

• to ‘view’ multimodal texts (noticing the different modes, and, for basic literacy, responding to the text on an emotional level, and, for more advanced literacy, respond to it critically)
• to ‘represent’ ideas and information in a multimodal way (posters, storyboards, memes, etc.)

I find this frustratingly imprecise. First: ‘viewing’. Noticing modes and reacting emotionally to a multimedia artefact do not take anyone very far on the path towards multimodal literacy, even if they are necessary first steps. It is only when we move towards a critical response (understanding the relative significance of different modes and problematizing our initial emotional response) that we can really talk about literacy (see the ‘critical literacy’ of Pegrum et al., 2018). We’re basically talking about critical thinking, a concept as vague and contested as any out there. Responding to a multimedia artefact ‘critically’ can mean more or less anything and everything.

Next: ‘representing’. What is the relative importance of ‘viewing’ and ‘representing’? What kinds of representations (artefacts) are important, and which are not? Presumably, they are not all of equal importance. And, whichever artefact is chosen as the focus, a whole range of technical skills will be needed to produce the artefact in question. So, precisely what kind of representing are we talking about?

Priorities in the ELT classroom

The Oxford authors write that ‘the main focus as English language teachers should obviously be on language’. I take this to mean that the ‘linguistic mode’ of communication should be our priority. This seems reasonable, since it’s hard to imagine any kind of digital literacy without some reading skills preceding it. But, again, the question of relative importance rears its ugly head. The time available for language leaning and teaching is always limited. Time that is devoted to the visual, aural, gestural or spatial modes of communication is time that is not devoted to the linguistic mode.

There are, too, presumably, some language teaching contexts (I’m thinking in particular about some adult, professional contexts) where the teaching of multimodal literacy would be completely inappropriate.

Multimodal literacy is a form of digital literacy. Writers about digital literacies like to say things like ‘digital literacies are as important to language learning as […] reading and writing skills’ or it is ‘crucial for language teaching to […] encompass the digital literacies which are increasingly central to learners’ […] lives’ (Pegrum et al, 2022). The question then arises: how important, in relative terms, are the various digital literacies? Where does multimodal literacy stand?

The Oxford authors summarise their view as follows:

There is a need for a greater presence of images, videos, and other multimodal texts in ELT coursebooks and a greater focus on using them as a starting point for analysis, evaluation, debate, and discussion.

My question to them is: greater than what? Typical contemporary courseware is already a whizzbang multimodal jamboree. There seem to me to be more pressing concerns with most courseware than supplementing it with visuals or clickables.

Evidence

The Oxford authors’ main interest is unquestionably in the use of video. They recommend extensive video viewing outside the classroom and digital story-telling activities inside. I’m fine with that, so long as classroom time isn’t wasted on getting to grips with a particular digital tool (e.g. a video editor, which, a year from now, will have been replaced by another video editor).

I’m fine with this because it involves learners doing meaningful things with language, and there is ample evidence to indicate that a good way to acquire language is to do meaningful things with it. However, I am less than convinced by the authors’ claim that such activities will strengthen ‘active and critical viewing, and effective and creative representing’. My scepticism derives firstly from my unease about the vagueness of the terms ‘viewing’ and ‘representing’, but I have bigger reservations.

There is much debate about the extent to which general critical thinking can be taught. General critical viewing has the same problems. I can apply critical viewing skills to some topics, because I have reasonable domain knowledge. In my case, it’s domain knowledge that activates my critical awareness of rhetorical devices, layout, choice of images and pull-out quotes, multimodal add-ons and so on. But without the domain knowledge, my critical viewing skills are likely to remain uncritical.

Perhaps most importantly of all, there is a lack of reliable research about ‘the extent to which language instructors should prioritize multimodality in the classroom’ (Kessler, 2022: 552). There are those, like the authors of this paper, who advocate for a ‘strong version’ of multimodality. Others go for a ‘weak version’ ‘in which non-linguistic modes should only minimally support or supplement linguistic instruction’ (Kessler, 2022: 552). And there are others who argue that multimodal activities may actually detract from or stifle L2 development (e.g. Manchón, 2017). In the circumstances, all the talk of ‘needs to’ and ‘should’ is more than a little premature.

Assessment

The authors of this Oxford paper rightly note that, if we are to adopt a multimodal approach, ‘it is important that assessment requirements take into account the multimodal nature of contemporary communication’. The trouble is that there are no widely used assessments (to my knowledge) that do this (including Oxford’s own tests). English language reading tests (like the Oxford Test of English) measure the comprehension of flat printed texts, as a proxy for reading skills. This is not the place to question the validity of such reading tests. Suffice to say that ‘little consensus exists as to what [the ability to read another language] entails, how it develops, and how progress in development can be monitored and fostered’ (Koda, 2021).

No doubt there are many people beavering away at trying to figure out how to assess multimodal literacy, but the challenges they face are not negligible. Twenty-first century digital (multimodal) literacy includes such things as knowing how to change the language of an online text to your own (and vice versa), how to bring up subtitles, how to convert written text to speech, how to generate audio scripts. All such skills may well be very valuable in this digital age, and all of them limit the need to learn another language.

Final thoughts

I can’t help but wonder why Oxford University Press should bring out a ‘position paper’ that is so at odds with their own publishing and assessing practices, and so at odds with the paper recently published in their flagship journal, ELT Journal. There must be some serious disconnect between the Marketing Department, which commissions papers such as these, and other departments within the company. Why did they allow such overstatement, when it is well known that many ELT practitioners (i.e. their customers) have the view that ‘linguistically based forms are (and should be) the only legitimate form of literacy’ (Choi & Yi, 2016)? Was it, perhaps, the second part of the title of this paper that appealed to the marketing people (‘Communication Skills for Today’s Generation’) and they just thought that ‘multimodality’ had a cool, contemporary ring to it? Or does the use of ‘multimodality’ help the marketing of courses like Headway and English File with additional multimedia bells and whistles? As I say, I can’t help but wonder.

If you want to find out more, I’d recommend the ELT Journal article, which you can access freely without giving your details to the marketing people.

Finally, it is perhaps time to question the logical connection between the fact that much reading these days is multimodal and the idea that multimodal literacy should be taught in a language classroom. Much reading that takes place online, especially with multimodal texts, could be called ‘hyper reading’, characterised as ‘sort of a brew of skimming and scanning on steroids’ (Baron, 2021: 12). Is this the kind of reading that should be promoted with language learners? Baron (2021) argues that the answer to this question depends on the level of reading skills of the learner. The lower the level, the less beneficial it is likely to be. But for ‘accomplished readers with high levels of prior knowledge about the topic’, hyper-reading may be a valuable approach. For many language learners, monomodal deep reading, which demands ‘slower, time-demanding cognitive and reflective functions’ (Baron, 2021: x – xi) may well be much more conducive to learning.

References

Baron, N. S. (2021) How We Read Now. Oxford: Oxford University Press

Choi, J. & Yi, Y. (2016) Teachers’ Integration of Multimodality into Classroom Practices for English Language Learners’ TESOL Journal, 7 (2): 3-4 – 327

Donaghy, K. (author), Karastathi, S. (consultant), Peachey, N. (consultant), (2023). Multimodality in ELT: Communication skills for today’s generation [PDF]. Oxford University Press. https://elt.oup.com/feature/global/expert/multimodality (registration needed)

Dudeney, G., Hockly, N. & Pegrum, M. (2013) Digital Literacies. Harlow: Pearson Education

Kessler, M. (2022) Multimodality. ELT Journal, 76 (4): 551 – 554

Koda, K. (2021) Assessment of Reading. https://doi.org/10.1002/9781405198431.wbeal0051.pub2

Manchón, R. M. (2017) The Potential Impact of Multimodal Composition on Language Learning. Journal of Second Language Writing, 38: 94 – 95

Pegrum, M., Dudeney, G. & Hockly, N. (2018) Digital Literacies Revisited. The European Journal of Applied Linguistics and TEFL, 7 (2): 3 – 24

Pegrum, M., Hockly, N. & Dudeney, G. (2022) Digital Literacies 2nd Edition. New York: Routledge

Mobile learning revisited

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Perspectives on language teaching research

I’ve written about the relationship (or, rather, the lack of one) between language teachers and language teaching research before. I’m talking about the kind of research that is primarily of the ‘what-works’ variety, since that is likely to be of most relevance to teachers. It’s the kind of research that asks questions like: can correction be beneficial to language learners? Or: can spaced repetition be helpful in vocabulary acquisition? Whether teachers find this relevant or not, there is ample evidence that the vast majority rarely look at it (Borg, 2009).

See here, for example, for a discussion of calls from academic researchers for more dialogue between researchers and teachers. The desire, on the part of researchers, for teachers to engage more (or even a little) with research, continues to grow, as shown by two examples. The first is the development of TESOLgraphics, which aims to make research ‘easy to read and understand to ESL, EFL, EAP, ESP, ESOL, EAL, TEFL teachers’ by producing infographic summaries. The second is a proposed special issue of the journal ‘System’ devoted to ‘the nexus of research and practice in and for language teacher education’ and hopes to find ways of promoting more teacher engagement with research. Will either of these initiatives have much impact? I doubt it, and to explain why, I need to take you on a little detour.

The map and the territory

Riffing off an ultra-short story by Jorge Luis Borges (‘On Exactitude in Science’, 1946), the corpus linguist Michael Stubbs (2013) wrote a piece entitled ‘Of Exactitude in Linguistics’, which marked his professional retirement. In it, he described a world where

the craft of Descriptive Linguistics attained such Perfection that the Transcription of a single short Conversation covered the floor of an entire University seminar room, and the Transcription of a Representative Sample of a single Text-Type covered the floor area of a small department to a depth of several feet. In the course of time, especially after the development of narrow phonetic transcription with intonational and proxemic annotation, even these extensive Records were found somehow wanting, and with the advent of fully automatic voice-to-orthography transcription, the weight of the resulting Text Collections threatened severe structural damage to University buildings.

As with all humour, there’s more than a grain of truth behind this Borgesian fantasy. These jokes pick up on what is known as the Richardson Effect, named after a British mathematician who noted that the length of the coastline of Great Britain varies according to the size of the units that are used to measure it – the smaller the unit, the longer the coastline. But at what point does increasing exactitude cease to tell us anything of value?

Both Borges and Lewis Fry Richardson almost certainly knew Lewis Carroll’s novel ‘Sylvie and Bruno Concluded’ (1893) which features a map that has the scale of a mile to a mile. This extraordinarily accurate map is, however, never used, since it is too large to spread out. The cost of increasing exactitude is practical usefulness.

The map of language

Language is rather like a coastline when it comes to drilling down in order to capture its features with smaller and smaller units of measurement. Before very long, you are forced into making decisions about the variety of the language and the contexts of use that you are studying. Precisely what kind of English are you measuring? At some point, you get down to the level of idiolect, but idiolects can be broken down further as they vary depending on the contexts of use. The trouble, of course, is that idiolects tell us little that is of value about the much broader ‘language’ that you set out to measure in the first place. The linguistic map obscures the linguistic terrain.

In ultra close-up, we can no longer distinguish one named language from another just by using linguistic criteria (Makoni & Pennycook, 20077:1). Extending this logic further, it makes little sense to even talk about named languages like English, to talk about first or second languages, about native speakers or about language errors. The close-up view requires us to redefine the thing – language – that we set out to define and describe. English is no longer a fixed and largely territorial system owned by native-speakers, but a dynamic, complex, social, deterritorialized practice owned by its users (May, 2013; Meier, 2017; Li Wei, 2018). In this view, both the purpose and the consequence of describing language in this way is to get away from the social injustice of native-speaker norms, of accentism, and linguistic prejudice.

A load of Ballungs

Language is a fuzzy and context-dependent concept. It is ‘too multifaceted to be measured on a single metric without loss of meaning, and must be represented by a matrix of indices or by several different measures depending on which goals and values are at play’ (Tal, 2020). In the philosophy of measurement, concepts like these are known as ‘Ballung’ concepts (Cartwright & Bradburn, 2011). Much of what is studied by researchers into language learning are also ‘Ballung’ concepts. Language proficiency and language acquisition are ‘Ballung’ concepts, too. As are reading and listening skills, mediation, metacognition and motivation. Critical thinking and digital literacies … the list goes on. Research into all these areas is characterised by multiple and ever-more detailed taxonomies, as researchers struggle to define precisely what it is that they are studying. It is in the nature of most academic study that it strives towards exactitude by becoming more and more specialised in its analysis of ‘ever more particular fractions of our world’ (Pardo-Guerra, 2022: 17).

But the perspective on language of Makoni, Pennycook, Li Wei et al is not what we might call the ‘canonical view’, the preferred viewpoint of the majority of people in apprehending the reality of the outside world (Palmer, 1981). Canonical views of language are much less close-up and allow for the unproblematic differentiation of one language from another. Canonical views – whether of social constructs like language or everyday objects like teacups or birds – become canonical because they are more functional for many people for everyday purposes than less familiar perspectives. If you want to know how far it is to walk from A to B along a coastal footpath, the more approximate measure of metres is more useful than one that counts every nook and cranny in microns. Canonical views can, of course, change over time – if the purpose to which they are put changes, too.

Language teaching research

There is a clear preference in academia for quantitative, empirical research where as many variables as possible are controlled. Research into language teaching is no different. It’s not enough to ask, in general terms, about the impact on learning of correction or spaced repetition. ‘What works’ is entirely context-dependent (Al-Hoorie, et al., 2023: 278). Since all languages, language learners and language learning contexts are ‘ultimately different’ (Widdowson, 2023: 397), there’s never any end to the avenues that researchers can explore: it is a ‘self-generating academic area of inquiry’ (ibid.). So we can investigate the impact of correction on the writing (as opposed to the speaking) of a group of Spanish (as opposed to another nationality) university students (as opposed to another age group) in an online setting (as opposed to face-to-face) where the correction is delayed (as opposed to immediate) and delivered by WhatsApp (as opposed to another medium) (see, for example, Murphy et al., 2023). We could carry on playing around with the variables for as long as we like – this kind of research has already been going on for decades.

When it comes to spaced repetition, researchers need to consider the impact of different algorithms (e.g. the length of the spaces) on different kinds of learners (age, level, motivation, self-regulation, etc.) in their acquisition of different kinds of lexical items (frequency, multi-word units, etc.) and how these items are selected and grouped, the nature of this acquisition (e.g. is it for productive use or is it purely recognition?). And so on (see the work of Tatsuya Nakata, for example).

Such attempts to control the variables are a necessary part of scientific enquiry, they are part of the ‘disciplinary agenda’, but they are unlikely to be of much relevance to most teachers. Researchers need precision, but the more they attempt to ‘approximate the complexities of real life, the more unwieldy [their] theories inevitably become’ (Al-Hoorie et al., 2023). Teachers, on the other hand, are typically more interested in canonical views that can lead to general take-aways that can be easily applied in their lessons. It is only secondary research in the form of meta-analyses or literature reviews (of the kind that TESOLgraphics) that can avoid the Richardson Effect and might offer something of help to the average classroom practitioner. But this secondary research, stripped of the contextual variables, can only be fairly vague. It can only really tell us, for example, that some form of written correction or spaced repetition may be helpful to some learners in some contexts some of the time. In need of ‘substantial localization’, it has been argued that the broad-stroke generalisations are often closer to ‘pseudo-applications’ (Al-Hoorie et al., 2023) than anything that is reliably actionable. That is not to say, however, that broad-stroke generalisations are of no value at all.

Finding the right map

Henry Widdowson (e.g. 2023) has declared himself sceptical about the practical relevance of SLA research. Reading journals like ‘Studies in Second Language Acquisition’ or ‘System’, it’s hard not to agree. Attempts to increase the accessibility of research (e.g. open-access or simple summaries) may not have the desired impact since they do not do anything about ‘the tenuous link between research and practice’ (Hwang, 2023). They cannot bridge the ‘gap between two sharply contrasting kinds of knowledge’ (McIntyre, 2006).

There is an alternative: classroom-based action research carried out by teachers. One of the central ideas behind it is that teachers may benefit more from carrying out their own research than from reading someone else’s. Enthusiasm for action research has been around for a long time: it was very fashionable in the 1980s when I trained as a teacher. In the 1990s, there was a series of conferences for English language teachers called ‘Teachers Develop Teachers Research’ (see, for example, Field et al., 1997). Tirelessly promoted by people like Richard Smith, Paula Rebolledo (Smith et al., 2014) and Anne Burns, action research seems to be gaining traction. A recent British Council publication (Burns, 2023) is a fine example of what insights teachers may gain and act on with an exploratory action research approach.

References

Al-Hoorie A. H., Hiver, P., Larsen-Freeman, D. & Lowie, W. (2023) From replication to substantiation: A complexity theory perspective. Language Teaching, 56 (2): pp. 276 – 291

Borg, S. (2009) English language teachers’ conceptions of research. Applied Linguistics, 30 (3): 358 – 88

Burns, A. (Ed.) (2023) Exploratory Action Research in Thai Schools: English teachers identifying problems, taking action and assessing results. Bangkok, Thailand: British Council

Cartwright, N., Bradburn, N. M., & Fuller, J. (2016) A theory of measurement. Working Paper. Centre for Humanities Engaging Science and Society (CHESS), Durham.

Field, J., Graham, A., Griffiths, E. & Head. K. (Eds.) (1997) Teachers Develop Teachers Research 2. Whitstable, Kent: IATEFl

Hwang, H.-B. (2023) Is evidence-based L2 pedagogy achievable? The research–practice dialogue in grammar instruction. The Modern Language Journal, 2023: 1 – 22 https://onlinelibrary.wiley.com/doi/full/10.1111/modl.12864

Li Wei. (2018) Translanguaging as a Practical Theory of Language. Applied Linguistics, 39 (1): 9 – 30

Makoni, S. & Pennycook, A. (Eds.) (2007) Disinventing and Reconstituting Languages. Clevedon: Multilingual Matters

May. S. (Ed.) (2013) The multilingual turn: Implications for SLA, TESOL and Bilingual education. New York: Routledge

McIntyre, D. (2006) Bridging the gap between research and practice. Cambridge Journal of Education 35 (3): 357 – 382

Meier, G. S. (2017) The multilingual turn as a critical movement in education: assumptions, challenges and a need for reflection. Applied Linguistics Review, 8 (1): 131-161

Murphy, B., Mackay J. & Tragant, E. (2023) ‘(Ok I think I was totally wrong: new try!)’: language learning in WhatsApp through the provision of delayed corrective feedback provided during and after task performance’, The Language Learning Journal, DOI: 10.1080/09571736.2023.2223217

Palmer, S.E. et al. (1981) Canonical perspective and the perception of objects. In Longand, J. & Baddeley. A. (Eds.) Attention and Performance IX. Hillsdale, NJ: Erlbaum. pp. 135 – 151

Pardo-Guerra, J. P. (2022) The Quantified Scholar. New York: Columbia University Press

Smith, R., Connelly, T. & Rebolledo, P. (2014). Teacher research as CPD: A project with Chilean secondary school teachers. In D. Hayes (Ed.), Innovations in the continuing professional development of English language teachers (pp. 111–128). The British Council.

Tal, E. “Measurement in Science”, In The Stanford Encyclopedia of Philosophy (Fall 2020 Edition), Edward N. Zalta (Ed.), https://plato.stanford.edu/archives/fall2020/entries/measurement-science/

Widdowson, H. (2023) Webinar on the subject of English and applied linguistics. Language Teaching, 56 (3): 393 – 401

Twee – an edtech cautionary tale

When the internet arrived on our desktops in the 1990s, language teachers found themselves able to access huge amounts of authentic texts of all kinds. It was a true game-changer. But when it came to ELT dedicated websites, the pickings were much slimmer. There was a very small number of good ELT resource sites (onestopenglish stood out from the crowd), but more ubiquitous and more enduring were the sites offering downloadable material shared by teachers. One of these, ESLprintables.com, currently has 1,082,522 registered users, compared to the 700,000+ of onestopenglish.

The resources on offer at sites such as these range from texts and scripted dialogues, along with accompanying comprehension questions, to grammar explanations and gapfills, vocabulary matching tasks and gapfills, to lists of prompts for discussions. Almost all of it is unremittingly awful, a terrible waste of the internet’s potential.

Ten years later, interactive online possibilities began to appear. Before long, language teachers found themselves able to use things like blogs, wikis and Google Docs. It was another true game changer. But when it came to ELT dedicated things, the pickings were much slimmer. There is some useful stuff (flashcard apps, for example) out there, but more ubiquitous are interactive versions of the downloadable dross that already existed. Learning platforms, which have such rich possibilities, are mostly loaded with gapfills, drag-and-drop, multiple choice, and so on. Again, it seems such a terrible waste of the technology’s potential. And all of this runs counter to what we know about how people learn another language. It’s as if decades of research into second language acquisition had never taken place.

And now we have AI and large language models like GPT. The possibilities are rich and quite a few people, like Sam Gravell and Svetlana Kandybovich, have already started suggesting interesting and creative ways of using the technology for language teaching. Sadly, though, technology has a tendency to bring out the worst in approaches to language teaching, since there’s always a bandwagon to be jumped on. Welcome to Twee, A.I. powered tools for English teachers, where you can generate your own dross in a matter of seconds. You can generate texts and dialogues, pitched at one of three levels, with or without target vocabulary, and produce comprehension questions (open questions, T / F, or M / C), exercises where vocabulary has to be matched to definitions, word-formation exercises, gapfills. The name of the site has been carefully chosen (Cambridge dictionary defines ‘twee’ as ‘artificially attractive’).

I decided to give it a try. Twee uses the same technology as ChatGPT and the results were unsurprising. I won’t comment in any detail on the intrinsic interest or the accuracy of factual information in the texts. They are what you might expect if you have experimented with ChatGPT. For the same reason, I won’t go into details about the credibility or naturalness of the dialogues. Similarly, the ability of Twee to gauge the appropriacy of texts for particular levels is poor: it hasn’t been trained on a tagged learner corpus. In any case, having only three level bands (A1/A2, B1/B2 and C1/C2) means that levelling is far too approximate. Suffice to say that the comprehension questions, vocabulary-item selection, vocabulary practice activities would all require very heavy editing.

Twee is still in beta, and, no doubt, improvements will come as the large language models on which it draws get bigger and better. Bilingual functionality is a necessary addition, and is doable. More reliable level-matching would be nice, but it’s a huge technological challenge, besides being theoretically problematic. But bigger problems remain and these have nothing to do with technology. Take a look at the examples below of how Twee suggests its reading comprehension tasks (open questions, M / C, T / F) could be used with some Beatles songs.

Is there any point getting learners to look at a ‘dialogue’ (on the topic of yellow submarines) like the one below? Is there any point getting learners to write essays using prompts such as those below?

What possible learning value could tasks such as these have? Is there any credible theory of language learning behind any of this, or is it just stuff that would while away some classroom time? AI meets ESLprintables – what a waste of the technology’s potential!

Edtech vendors like to describe their products as ‘solutions’, but the educational challenges, which these products are supposedly solutions to, often remain unexamined. Poor use of technology can exacerbate these challenges by making inappropriate learning materials more easily available.

Self-paced listening

One of the most common criticisms of schooling is that it typically requires learners to study in lockstep, with everyone expected to use the same learning material at the same pace to achieve the same learning objectives. From everything we know about individual learner differences, this is an unreasonable and unrealisable expectation. It is only natural, therefore, that we should assume that self-paced learning is a better option. Self-paced learning is at the heart of technology-driven personalized learning. Often, it is the only meaningfully personalized aspect of technology-delivered courses.

Unfortunately, almost one hundred years of attempts to introduce elements of self-pacing into formal language instruction have failed to produce conclusive evidence of its benefits. For a more detailed look at the history of these failures, see my blog post on the topic, and for a more detailed look at Programmed Learning, a 1960s attempt to introduce self-pacing, see this post. This is not to say that self-pacing does not have a potentially important role to play. However, history should act as a warning that the simple provision of self-pacing opportunities through technology may be a necessary condition for successful self-pacing, but it is not a sufficient condition.

Of all the different areas of language learning that can be self-paced, I’ve long thought that technology might help the development of listening skills the most. Much contemporary real-world listening is, in any case, self-paced: why should the classroom not be? With online listening, we can use a variety of help options (Cross, 2017) – pause, rewind, speed control, speech-to-text, dictionary look-up, video / visual support – and we control the frequency and timing of this use. Online listening has become a ‘semi-recursive activity, less dependent on transient memory, inching its way closer to reading’ (Robin, 2007: 110). We don’t know which of these help options and which permutations of these options are most likely to lead to gains in listening skills, but it seems reasonable to believe that some of these options have strong potential. It is perhaps unlikely that research could ever provide a definitive answer to the question of optimal help options: different learners have different needs and different preferences (Cárdenas-Claros & Gruba, 2014). But what is clear is that self-pacing is necessary for these options to be used.

Moving away from whole-class lockstep listening practice towards self-paced independent listening has long been advocated by experts. John Field (2008: 47) identified a key advantage of independent listening: a learner ‘can replay the recording as often as she needs (achieving the kind of recursion that reading offers) and can focus upon specific stretches of the input which are difficult for her personally rather than for the class as a whole’. More recently, interest has also turned to the possibility of self-paced listening in assessment practices (Goodwin, 2017).

So, self-paced listening: what’s not to like? I’ve been pushing it with the teachers I work with for some time. But a recent piece of research from Kathrin Eberharter and colleagues (Eberharter et al., 2023) has given me pause for thought. The researchers wanted to know what effect self-pacing would have on the assessment of listening comprehension in a group of young teenage Austrian learners. They were particularly interested in how learners with SpLDs would be affected, and assumed that self-pacing would boost the performance of these learners. Disappointingly, they were wrong. Not only did self-pacing have, on average, no measurable impact on performance, it also seems that self-pacing may have put learners with shorter working-memory capacity and L1 literacy-related challenges at a disadvantage.

This research concerned self-paced listening in assessment (in this case the TOEFL Junior Standard test), not in learning. But might self-paced listening as part of a learning programme not be quite as beneficial as we might hope? The short answer, as ever, is probably that it depends. Eberhart et al speculate that young learners ‘might need explicit training and more practice in regulating their strategic listening behaviour in order to be able to improve their performance with the help of self-pacing’. This probably holds true for many older learners, too. In other words, it’s not the possibility of self-pacing in itself that will make a huge difference: it’s what a learner does or does not do while they are self-pacing that matters. To benefit from the technological affordances of online listening, learners need to know which strategies (and which tools) may help them. They may need ‘explicit training in exploiting the benefits of navigational freedom to enhance their metacognitive strategy use’ (Eberhart et al. 2023: 17). This shouldn’t surprise us: the role of metacognition is well established (Goh & Vandergrift, 2021).

As noted earlier, we do not really know which permutations of help options are likely to be of most help, but it is a relatively straightforward matter to encourage learners to experiment with them. We do, however, have a much clearer idea of the kinds of listening strategies that are likely to have a positive impact, and the most obvious way of providing this training is in the classroom. John Field (2008) suggested many approaches; Richard Cauldwell (2013) offers more; and Sheila Thorn’s recent ‘Integrating Authentic Listening into the Language Classroom’ (2021) adds yet more. If learners’ metacognitive knowledge, effective listening and help-option skills are going to develop, the training will need to involve ‘a cyclic approach […] throughout an entire course’ (Cross, 2017: 557).

If, on the other hand, our approach to listening in the classroom continues to be (as it is in so many coursebooks) one of testing listening through comprehension questions, we should not be too surprised when learners have little idea what strategy to approach when technology allows self-pacing. Self-paced self-testing of listening comprehension is likely to be of limited value.

References

Cárdenas-Claros, M. S. & Gruba, P. A. (2014) Listeners’ interactions with help options in CALL. Computer Assisted Language Learning, 27 (3): 228 – 245

Cauldwell, R. (2013) Phonology for Listening: Teaching the Stream of Speech. Speech in Action

Cross, J. (2017) Help options for L2 listening in CALL: A research agenda. Language Teaching, 50 (4), 544–560. https://doi.org/10.1017/S0261444817000209

Eberharter,K., Kormos, J.,  Guggenbichler, E.,  Ebner, V. S., Suzuki, S.,  Moser-Frötscher, D., Konrad, E. & Kremmel, B. (2023) Investigating the impact of self-pacing on the L2 listening performance of young learner candidates with differing L1 literacy skills. Language Testing 0 10.1177/02655322221149642 https://journals.sagepub.com/doi/epub/10.1177/02655322221149642

Field, J. (2008) Listening in the Language Classroom. Cambridge: Cambridge University Press

Goh, C. C. M. & Vandergrift, L. (2021) Teaching and learning second language listening: Metacognition in action (2nd ed.). Routledge. https://doi.org/10.4324/9780429287749

Goodwin, S. J. (2017) Locus of control in L2 English listening assessment [Doctoral dissertation]. Georgia State University. https://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1037&context=alesl_diss

Robin, R. (2007) Commentary: Learner-based listening and technological authenticity. Language Learning & Technology, 11 (1): 109-115. https://www.lltjournal.org/item/461/

Thorn, S. (2021) Integrating Authentic Listening into the Language Classroom. Shoreham-by-Sea: Pavilion

Design thinking and ELT

There has recently been a spate of articles and blog posts about design thinking and English language teaching. You could try ‘Design Thinking in Digital Language Learning’, by Speex, provider of ‘online coaching and assessment solutions’, ‘Design Thinking Activities in the ESL Classroom’, brought to you by Express Publishing, market leaders in bandwagon-jumping, or a podcast on ‘Design thinking’ from LearnJam. Or, if you happen to be going to the upcoming IATEFL conference, there are three presentations to choose from:

• Design thinking, a sticky side up path to innovators
• ESP course development for cultural creative design with design thinking
• Reimagining teacher-centered professional development – can design thinking help?

The term ‘design thinking’ dates back decades, but really took off in popularity around 2005, and the following year, it was a theme at the World Economic Forum (Woudhuysen, 2011) The Harvard Business Review was pushing the idea in 2008 and The Economist ran a conference on the topic two years later. Judging from Google Trends, its popularity appeared to peak about a year ago, but the current dip might only be temporary. It’s especially popular in Peru and Singapore, for some reason. It is now strongly associated with Stanford University, the spiritual home of Silicon Valley, where you can join a three-and-a-half day design thinking bootcamp if you have $14,000 to spare.

What you would probably get for your money is a better understanding of ‘an approach to problem-solving based on a few easy-to-grasp principles that sound obvious: ‘Show Don’t Tell,’ ‘Focus on Human Values,’ ‘Craft Clarity,’ ‘Embrace Experimentation,’ ‘Mindful of Process,’ ‘Bias Toward Action,’ and ‘Radical Collaboration’’ (Miller, 2015). In the Stanford model of design thinking, which is the most commonly cited, this boils down to five processes: empathize, define, ideate, prototype and test.

I appreciate that this must sound a bit vague. I’d make things clearer if I could, but the problem is that ‘the deeper you dig into Design Thinking, the vaguer it becomes’ (Vinsel, 2017). If one thing is clear, however, it’s that things aren’t very clear (Johansson-Sköldberg et al., 2013), and haven’t been since the bandwagon got rolling. Back in 2010, at the 8^th Design Thinking Research Symposium, Badke-Schaub et al. (2010) entitled their paper ‘Design thinking: a paradigm on its way from dilution to meaninglessness’. At a more recent conference, Bouwman et al. (2019) reported that the term is ‘becoming more and more vague’. So, is it a five-step process or not? According to Marty Neumeier, author of many books on design thinking, it is not: ‘that’s crap design thinking, of which there is plenty, I agree’.

My first direct experience of design thinking was back in 2015/16 when I took part in a meeting with publishers to discuss a new coursebook project. My main recollection of this was brainstorming various ideas, writing them down on Post-its, and adding them to other Post-its on the walls around the room. I think this was a combination of the empathizing and defining stages, but I could be wrong. Some years later, I took part in an online colloquium where we did something similar, except the Post-its were now digitalized using the Miro collaborative whiteboard. On both these occasions, the scepticism was palpable (except on the part of the facilitators), but we could all console ourselves that we were being cutting-edge in our approach to problem-solving.

Not everyone has been quite so ambivalent. Graphic designer, Natasha Jen, entitled her talk ‘Design Thinking is Bullsh*t’ and urged design practitioners to avoid the jargon and buzzwords associated with their field, to engage in more self-criticism, to base their ideas on evidence, and to stop assuming that their five-step process is needed for anything and everything (Vinsel, 2017). Vinsel (2017) likens design thinking to syphilis. Even the Harvard Business Review has changed its tune. Iskander (2018) doesn’t mince her words:

When it comes to design thinking, the bloom is off the rose. Billed as a set of tools for innovation, design thinking has been enthusiastically and, to some extent, uncritically adopted by firms and universities alike as an approach for the development of innovative solutions to complex problems. But skepticism about design thinking has now begun to seep out onto the pages of business magazines and educational publications. The criticisms are several: that design thinking is poorly defined; that the case for its use relies more on anecdotes than data; that it is little more than basic commonsense, repackaged and then marketed for a hefty consulting fee. As some of these design thinking concepts have sloshed into the world of policy, and social change efforts have been re-cast as social innovation, the queasiness around the approach has also begun to surface in the field of public policy.

Design thinking meets all the criteria needed to be called a fad (Brindle & Stearns, 2021). And like all fads from the corporate world, it has arrived in ELT past its sell-by date. Its travelling companions are terms like innovation, disruption, agile, iteration, reframing, hubs, thought leaders and so on. See below for a slide from Natasha Jen’s talk. As fads go, it is fairly harmless, and there may well be some design-thinking-inspired activities that could be useful in a language classroom. But it’s worth remembering that, for all its associations with ‘innovation’, the driving force has always been commercialization (Vinsel, 2017). In ELT, it’s about new products – courses, coursebooks, apps and so on. Whatever else may be intended, use of the term signals alignment with corporate values, an awareness of what is (was?) hip and hot in the start-up world. It’s a discourse-shaper, reframing our questions and concerns as engineering problems, suggesting that solutions to pretty much everything can be found by thinking in the right kind of corporate way. No wonder it was catnip to my publishers.

References

Badke-Schaub, P.G., Roozenburg, N.F.M., & Cardoso, C. (2010) Design thinking: a paradigm on its way from dilution to meaninglessness? In K. Dorst, S. Stewart, I. Staudinger, B. Paton, & A. Dong (Eds.), Proceedings of the 8th Design Thinking Research Symposium (DTRS8) (pp. 39-49). DAB documents.

Bouwman, S., Voorendt, J., Eisenbart, B. & McKilligan, S. (2019) Design Thinking: An Approach with Various Perceptions. Proceedings of the Design Society: International Conference on Engineering Design. Cambridge: Cambridge University Press

Brindle, M. C. & Stearns, P. N. (2001) Facing up to Management Faddism: A New Look at an Old Force. Westport, CT: Quorum Books

Iskander, N. (2018) Design Thinking Is Fundamentally Conservative and Preserves the Status Quo. Harvard Business Review, September 5, 2018

Johansson-Sköldberg, U., Woodilla, J. & Çetinkaya, M. (2013) Design Thinking: Past, Present and Possible Futures. Creativity and Innovation Management, 22 (2): 121 – 146

Miller, P. N. (2015) Is ‘Design Thinking’ the New Liberal Arts? The Chronicle of Higher Education March 26, 2015

Vinsel, L. (2017) Design Thinking is Kind of Like Syphilis — It’s Contagious and Rots Your Brains. Medium Dec 6, 2018

Woudhuysen, J. (2011) The Craze for Design Thinking: Roots, A Critique, and toward an Alternative. Design Principles And Practices: An International Journal, Vol. 5

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

Computer-assisted native-speakerism (CAPTs)

Recent years have seen a proliferation of computer-assisted pronunciations trainers (CAPTs), both as stand-alone apps and as a part of broader language courses. The typical CAPT records the learner’s voice, compares this to a model of some kind, detects differences between the learner and the model, and suggests ways that the learner may more closely approximate to the model (Agarwal & Chakraborty, 2019). Most commonly, the focus is on individual phonemes, rather than, as in Richard Cauldwell’s ‘Cool Speech’ (2012), on the features of fluent natural speech (Rogerson-Revell, 2021).

The fact that CAPTs are increasingly available and attractive ‘does not of course ensure their pedagogic value or effectiveness’ … ‘many are technology-driven rather than pedagogy-led’ (Rogerson-Revell, 2021). Rogerson-Revell (2021) points to two common criticisms of CAPTs. Firstly, their pedagogic accuracy sometimes falls woefully short. He gives the example of a unit on intonation in one app, where users are told that ‘when asking questions in English, our voice goes up in pitch’ and ‘we lower the pitch of our voice at the end of questions’. Secondly, he observes that CAPTs often adopt a one-size-fits-all approach, despite the fact that we know that issues of pronunciation are extremely context-sensitive: ‘a set of learners in one context will need certain features that learners in another context do not’ (Levis, 2018: 239).

There are, in addition, technical challenges that are not easy to resolve. Many CAPTs rely on automatic speech recognition (ASR), which can be very accurate with some accents, but much less so with other accents (including many non-native-speaker accents) (Korzekwa et al., 2022). Anyone using a CAPT will experience instances of the software identifying pronunciation problems that are not problems, and failing to identify potentially more problematic issues (Agarwal & Chakraborty, 2019).

We should not, therefore, be too surprised if these apps don’t always work terribly well. Some apps, like the English File Pronunciation app, have been shown to be effective in helping the perception and production of certain phonemes by a very unrepresentative group of Spanish learners of English (Fouz-González, 2020), but this tells us next to nothing about the overall effectiveness of the app. Most CAPTs have not been independently reviewed, and, according to a recent meta-analysis of CAPTs (Mahdi & Al Khateeb, 2019), the small number of studies are ‘all of very low quality’. This, unfortunately, renders their meta-analysis useless.

Even if the studies in the meta-analysis had not been of very low quality, we would need to pause before digesting any findings about CAPTs’ effectiveness. Before anything else, we need to develop a good understanding of what they might be effective at. It’s here that we run headlong into the problem of native-speakerism (Holliday, 2006; Kiczkowiak, 2018).

The pronunciation model that CAPTs attempt to push learners towards is a native-speaker model. In the case of ELSA Speak, for example, this is a particular kind of American accent, although ‘British and other accents’ will apparently soon be added. Xavier Anguera, co-founder and CTO of ELSA Speak, in a fascinating interview with Paul Raine of TILTAL, happily describes his product as ‘an app that is for accent reduction’. Accent reduction is certainly a more accurate way of describing CAPTs than accent promotion.

Accent reduction, or the attempt to mimic an imagined native-speaker pronunciation, is now ‘rarely put forward by teachers or researchers as a worthwhile goal’ (Levis, 2018: 33) because it is only rarely achievable and, in many contexts, inappropriate. In addition, accent reduction cannot easily be separated from accent prejudice. Accent reduction courses and products ‘operate on the assumption that some accents are more legitimate than others’ (Ennser-Kananen, et al., 2021) and there is evidence that they can ‘reinscribe racial inequalities’ (Ramjattan, 2019). Accent reduction is quintessentially native-speakerist.

Rather than striving towards a native-speaker accentedness, there is a growing recognition among teachers, methodologists and researchers that intelligibility may be a more appropriate learning goal (Levis, 2018) than accentedness. It has been over 20 years since Jennifer Jenkins (2000) developed her Lingua Franca Core (LFC), a relatively short list of pronunciation features that she considered central to intelligibility in English as a Lingua Franca contexts (i.e. the majority of contexts in which English is used). Intelligibility as the guiding principle of pronunciation teaching continues to grow in influence, spurred on by the work of Walker (2010), Kiczkowiak & Lowe (2018), Patsko & Simpson (2019) and Hancock (2020), among others.

Unfortunately, intelligibility is a deceptively simple concept. What exactly it is, is ‘not an easy question to answer’ writes John Levis (2018) before attempting his own answer in the next 250 pages. As admirable as the LFC may be as an attempt to offer a digestible and actionable list of key pronunciation features, it ‘remains controversial in many of its recommendations. It lacks robust empirical support, assumes that all NNS contexts are similar, and does not take into account the importance of stigma associated with otherwise intelligible pronunciations’ (Levis, 2018: 47). Other attempts to list features of intelligibility fare no better in Levis’s view: they are ‘a mishmash of incomplete and contradictory recommendations’ (Levis, 2018: 49).

Intelligibility is also complex because of the relationship between intelligibility and comprehensibility, or the listener’s willingness to understand – their attitude or stance towards the speaker. Comprehensibility is a mediation concept (Ennser-Kananen, et al., 2021). It is a two-way street, and intelligibility-driven approaches need to take this into account (unlike the accent-reduction approach which places all the responsibility for comprehensibility on the shoulders of the othered speaker).

The problem of intelligibility becomes even more thorny when it comes to designing a pronunciation app. Intelligibility and comprehensibility cannot easily be measured (if at all!), and an app’s algorithms need a concrete numerically-represented benchmark towards which a user / learner can be nudged. Accentedness can be measured (even if the app has to reify a ‘native-speaker accent’ to do so). Intelligibility / Comprehensibility is simply not something, as Xavier Anguera acknowledges, that technology can deal with. In this sense, CAPTs cannot avoid being native-speakerist.

At this point, we might ride off indignantly into the sunset, but a couple of further observations are in order. First of all, accentedness and comprehensibility are not mutually exclusive categories. Anguera notes that intelligibility can be partly improved by reducing accentedness, and some of the research cited by Levis (2018) backs him up on this. But precisely how much and what kind of accent reduction improves intelligibility is not knowable, so the use of CAPTs is something of an optimistic stab in the dark. Like all stabs in the dark, there are dangers. Secondly, individual language learners may be forgiven for not wanting to wait for accent prejudice to become a thing of the past: if they feel that they will suffer less from prejudice by attempting here and now to reduce their ‘foreign’ accent, it is not for me, I think, to pass judgement. The trouble, of course, is that CAPTs contribute to the perpetuation of the prejudices.

There is, however, one area where the digital evaluation of accentedness is, I think, unambiguously unacceptable. According to Rogerson-Revell (2021), ‘Australia’s immigration department uses the Pearson Test of English (PTE) Academic as one of five tests. The PTE tests speaking ability using voice recognition technology and computer scoring of test-takers’ audio recordings. However, L1 English speakers and highly proficient L2 English speakers have failed the oral fluency section of the English test, and in some cases it appears that L1 speakers achieve much higher scores if they speak unnaturally slowly and carefully’. Human evaluations are not necessarily any better.

References

Agarwal, C. & Chakraborty, P. (2019) A review of tools and techniques for computer aided pronunciation training (CAPT) in English. Education and Information Technologies, 24: 3731–3743. https://doi.org/10.1007/s10639-019-09955-7

Cauldwell, R (2012) Cool Speech app. Available at: http://www.speechinaction.org/cool-speech-2

Fouz-González, J (2020) Using apps for pronunciation training: An empirical evaluation of the English File Pronunciation app. Language Learning & Technology, 24(1): 62–85.

Ennser-Kananen, J., Halonen, M. & Saarinen, T. (2021) “Come Join Us and Lose Your Accent!” Accent Modification Courses as Hierarchization of International Students. Journal of International Students 11 (2): 322 – 340

Holliday, A. (2006) Native-speakerism. ELT Journal, 60 (4): 385 – 387

Jenkins. J. (2000) The Phonology of English as a Lingua Franca. Oxford: Oxford University Press

Hancock, M. (2020) 50 Tips for Teaching Pronunciation. Cambridge: Cambridge University Press

Kiczkowiak, M. (2018) Native Speakerism in English Language Teaching: Voices From Poland. Doctoral dissertation.

Kiczkowiak, M & Lowe, R. J. (2018) Teaching English as a Lingua Franca. Stuttgart: DELTA Publishing

Korzekwa, D., Lorenzo-Trueba, J., Thomas Drugman, T. & Kostek, B. (2022) Computer-assisted pronunciation training—Speech synthesis is almost all you need. Speech Communication, 142: 22 – 33

Levis, J. M. (2018) Intelligibility, Oral Communication, and the Teaching of Pronunciation. Cambridge: Cambridge University Press

Mahdi, H. S. & Al Khateeb, A. A. (2019) The effectiveness of computer-assisted pronunciation training: A meta-analysis. Review of Education, 7 (3): 733 – 753

Patsko, L. & Simpson, K. (2019) How to Write Pronunciation Activities. ELT Teacher 2 Writer https://eltteacher2writer.co.uk/our-books/how-to-write-pronunciation-activities/

Ramjattan, V. A. (2019) Racializing the problem of and solution to foreign accent in business. Applied Linguistics Review, 13 (4). https://doi.org/10.1515/applirev2019-0058

Rogerson-Revell, P. M. (2021) Computer-Assisted Pronunciation Training (CAPT): Current Issues and Future Directions. RELC Journal, 52(1), 189–205. https://doi.org/10.1177/0033688220977406

Walker, R. (2010) Teaching the Pronunciation of English as a Lingua Franca. Oxford: Oxford University Press

Generative AI and ELT Materials

I must begin by apologizing for my last, flippant blog post where I spun a tale about using generative AI to produce ‘teacher development content’ and getting rid of teacher trainers. I had just watched a webinar by Giuseppe Tomasello of edugo.ai, ‘Harnessing Generative AI to Supercharge Language Education’, and it felt as if much of the script of this webinar had been generated by ChatGPT: ‘write a relentlessly positive product pitch for a language teaching platform in the style of a typical edtech vendor’.

In the webinar, Tomasello talked about using GPT-3 to generate texts for language learners. Yes, it’s doable, the results are factually and linguistically accurate, but dull in the extreme (see Steve Smith’s experiment with some texts for French learners). André Hedlund summarises: ‘limited by a rigid structure … no big differences in style or linguistic devices. They follow a recipe, a formula.’ Much like many coursebook / courseware texts, in other words.

More interesting texts can be generated with GPT-3 when the prompts are crafted in careful detail, but this requires creative human imagination. Crafting such prompts requires practice, trial and error, and it requires knowledge of the intended readers.

AI can be used to generate dull texts at a certain level (B1, B2, C1, etc.), but reliability is a problem. So many variables (starting with familiarity with the subject matter and the learner’s first language) impact on levels of reading difficulty that automation can never satisfactorily resolve this challenge.

However, the interesting question is not whether we can quickly generate courseware-like texts using GPT-3, but whether we should even want to. What is the point of using such texts? Giuseppe Tomasello’s demonstration made it clear that the way these texts should be exploited is by using (automatically generating) a series of comprehension questions and a list of key words which can also be (automatically) converted into a variety of practice tasks (flashcards, gapfills, etc.). Like courseware texts, then, these texts are language objects (TALOs), to be tested for comprehension and mined for items to be deliberately learnt. They are not, in any meaningful way, texts as vehicles of information (TAVIs) – see here for more about TALOs and TAVIs.

Comprehension testing is a standard feature of language teaching, but there are good reasons to believe that it will do little, if anything, to improve a learner’s reading skills (see, for example, Swan & Walter, 2017; Grabe, W. & Yamashita, J., 2022) or language competence. It has nothing to do with the kind of extensive reading (see, for example, Leather & Uden, 2021) that is known to be such a powerful force in language acquisition. This use of text is all about deliberate language study. We are talking here about a synthetic approach to language learning.

The problem, of course, is that deliberate language study is far from uncontested. Synthetic approaches wrongly assume that ‘the explicit teaching of declarative knowledge (knowledge about the L2) will lead to the procedural knowledge learners need in order to successfully use the L2 for communicative purpose’ (see Geoff Jordan on this topic). Even if it were true that some explicit teaching was of value, it’s widely agreed that explicit teaching should not form the core of a language learning syllabus. The edugo.ai product, however, is basically all about explicit teaching.

Judging from the edugo.ai presentation on YouTube, the platform offers a wide range of true / false, multiple choice questions, gapfills, dictations and so on, all of which can be gamified. The ‘methodology’ is called ‘Flip and Flop the Classroom’. In this approach, learners do some self-study (presumably of some pre-selected discrete language item(s), practise it in the synchronous lesson, and then have more self-study where this language is reviewed. In the ‘flop’ section, the learner’s spoken contribution to the live lesson is recorded, transcribed and analysed by the system which identifies aspects of the learner’s language which can be improved through personalized practice.

The focus is squarely on accuracy, and the importance of accuracy is underlined by the presenter’s observation that Communicative Language Teaching does not focus enough on accuracy. Accuracy is also to the fore in another task type, strangely called ‘chunking’ (see below). Apparently, this will lead to fluency: ‘The goal of this template is that they can repeat it a lot of times and reach fluency at the end’.

The YouTube presentation is called ‘How to structure your language course using popular pedagogical approaches’ and suggests that you can mix’n’match bits from ‘Grammar Translation’, ‘Direct Method’ and ‘CLT’. Sorry, Giuseppe, you can mix’n’match all you like, but you can’t be methodologically agnostic. This is a synthetic approach all the way down the line. As such, it’s not going to supercharge language education. It’s basically more of what we already have too much of.

Let’s assume, though, for a moment that what we really want is a kind of supercharged, personalizable, quickly generated combination of vocabulary flashcards and ‘English Grammar in Use’ loaded with TALOs. How does this product stand up? We’ll need to consider two related questions: (1) its reliability, and (2) how time-saving it actually is.

As far as I can judge from the YouTube presentation, reliability is not exactly optimal. There’s the automated key word identifier that identified ‘Denise’, ‘the’ and ‘we’ as ‘key words’ in an extract of learner-produced text (‘Hello, my name is Denise and I founded my makeup company in 2021. We produce skin care products using 100% natural ingredients.’). There’s the automated multiple choice / translation generator which tests your understanding of ‘Denise’ (see below), and there’s the voice recognition software which transcribed ‘it cost’ as ‘they cost’.

In the more recent ‘webinar’ (i.e commercial presentation) that has not yet been uploaded to YouTube, participants identified a number of other bloopers. In short, reliability is an issue. This shouldn’t surprise anyone. Automation of some of these tasks is extremely difficult (see my post about the automated generation of vocabulary learning materials). Perhaps impossible … but how much error is acceptable?

edugo.ai does not sell content: they sell a platform for content-generation, course-creation and selling. Putative clients are institutions wanting to produce and sell learning content of the synthetic kind. The problem with a lack of reliability, any lack of reliability, is that you immediately need skilled staff to work with the platform, to check for error, to edit, to improve on the inevitable limitations of the AI (starting, perhaps, with the dull texts it has generated). It is disingenuous to suggest that anyone can do this without substantial training / supervision. Generative AI only offers a time-saving route, which does not sacrifice reliability, if a skilled and experienced writer is working with it.

edugo.ai is a young start-up that raised $345K in first round funding in September of last year. The various technologies they are using are expensive, and a lot more funding will be needed to make the improvements and additions (such as spaced repetition) that are necessary. In both presentations, there was lots of talk that the platform will be able to do this and will be able to do that. For the moment, though, nothing has been proved, and my suspicion is that some of the problems they are trying to solve do not have technological solutions. First of all, they’ll need a better understanding of what these problems are, and, for that, there has to be a coherent and credible theory of second language acquisition. There are all sorts of good uses that GPT-3 / AI could be put to in language teaching. I doubt this is one of them.

To wrap up, here’s a little question. What are the chances that edugo.ai’s claims that the product will lead to ‘+50% student engagement’ and ‘5X Faster creating language courses’ were also generated by GPT-3?

References

Grabe, W. & Yamashita, J. (2022) Reading in a Second Language 2^nd edition. New York: Cambridge University Press

Leather, S. & Uden, J. (2021) Extensive Reading. New York: Routledge

Swan, M. & Walter, C. (2017) Misunderstanding comprehension. ELT Journal, 71 (2): 228 – 236

AI and teacher training

Who can tell where a blog post might lead? Over six years ago I wrote about adaptive professional development for teachers. I imagined the possibility of bite-sized, personalized CPD material. Now my vision is becoming real.

For the last two years, I have been working with a start-up that has been using AI to generate text using GPT-3 large language models. GPT-3 has recently been in the news because of the phenomenal success of the newly released ChatGPT. The technology certainly has a wow factor, but it has been around for a while now. ChatGPT can generate texts of various genres on any topic (with a few exceptions like current affairs) and the results are impressive. Imagine, then, how much more impressive the results can be when the kind of text is limited by genre and topic, allowing the software to be trained much more reliably.

This is what we have been working on. We took as our training corpus a huge collection of English language teaching teacher development texts that we could access online: blogs from all the major publishers, personal blogs, transcriptions from recorded conference presentations and webinars, magazine articles directed at teachers, along with books from publishers such as DELTA and Pavilion ELT, etc. We identified topics that seemed to be of current interest and asked our AI to generate blog posts. Later, we were able to get suggestions of topics from the software itself.

We then contacted a number of teachers and trainers who contribute to the publishers’ blogs and contracted them, first, to act as human trainers for the software, and, second, to agree to their names being used as the ‘authors’ of the blog posts we generated. In one or two cases, the authors thought that they had actually written the posts themselves! Next we submitted these posts to the marketing departments of the publishers (who run the blogs). Over twenty were submitted in this way, including:

• What do teachers need to know about teaching 21^st century skills in the English classroom?
• 5 top ways of improving the well-being of English teachers
• Teaching leadership skills in the primary English classroom
• How can we promote eco-literacy in the English classroom?
• My 10 favourite apps for English language learners

We couldn’t, of course, tell the companies that AI had been used to write the copy, but once we were sure that nobody had ever spotted the true authorship of this material, we were ready to move to the next stage of the project. We approached the marketing executives of two publishers and showed how we could generate teacher development material at a fraction of the current cost and in a fraction of the time. Partnerships were quickly signed.

Blog posts were just the beginning. We knew that we could use the same technology to produce webinar scripts, using learning design insights to optimise the webinars. The challenge we faced was that webinars need a presenter. We experimented with using animations, but feedback indicated that participants like to see a face. This is eminently doable, using our contracted authors and deep fake technology, but costs are still prohibitive. It remains cheaper and easier to use our authors delivering the scripts we have generated. This will no doubt change before too long.

The next obvious step was to personalize the development material. Large publishers collect huge amounts of data about visitors to their sites using embedded pixels. It is also relatively cheap and easy to triangulate this data with information from the customer databases and from activity on social media (especially Facebook). We know what kinds of classes people teach, and we know which aspects of teacher development they are interested in.

Publishers have long been interested in personalizing marketing material, and the possibility of extending this to the delivery of real development content is clearly exciting. (See below an email I received this week from the good folks at OUP marketing.)

Earlier this year one of our publishing partners began sending links to personalized materials of the kind we were able to produce with AI. The experiment was such a success that we have already taken it one stage further.

One of the most important clients of our main publishing partner employs hundreds of teachers to deliver online English classes using courseware that has been tailored to the needs of the institution. With so many freelance teachers working for them, along with high turnover of staff, there is inevitably a pressing need for teacher training to ensure optimal delivery. Since the classes are all online, it is possible to capture precisely what is going on. Using an AI-driven tool that was inspired by the Visible Classroom app (informed by the work of John Hattie), we can identify the developmental needs of the teachers. What kinds of activities are they using? How well do they exploit the functionalities of the platform? What can be said about the quality of their teacher talk? We combine this data with everything else and our proprietary algorithms determine what kinds of training materials each teacher receives. It doesn’t stop there. We can also now evaluate the effectiveness of these materials by analysing the learning outcomes of the students.

Teaching efficacy can by massively increased, whilst the training budget of the institution can be slashed. If all goes well, there will be no further need for teacher trainers at all. We won’t be stopping there. If results such as these can be achieved in teacher training, there’s no reason why the same technology cannot be leveraged for the teaching itself. Most of our partner’s teaching and testing materials are now quickly and very cheaply generated using GPT-3.5. If you want to see how this is done, check out the work of edugo.AI (a free trial is available) which can generate gapfills and comprehension test questions in a flash. As for replacing the teachers, we’re getting there. For the time being, though, it’s more cost-effective to use freelancers and to train them up.