Archive for the ‘ed tech’ Category

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

There’s an aspect of language learning which everyone agrees is terribly important, but no one can quite agree on what to call it. I’m talking about combinations of words, including fixed expressions, collocations, phrasal verbs and idioms. These combinations are relatively fixed, cannot always be predicted from their elements or generated by grammar rules (Laufer, 2022). They are sometimes referred to as formulaic sequences, formulaic expressions, lexical bundles or lexical chunks, among other multiword items. They matter to English language learners because a large part of English consists of such combinations. Hill (2001) suggests this may be up to 70%. More conservative estimates report 58.6% of writing and 52.3% of speech (Erman & Warren, 2000). Some of these combinations (e.g. ‘of course’, ‘at least’) are so common that they fall into lists of the 1000 most frequent lexical items in the language.

By virtue of their ubiquity and frequency, they are important both for comprehension of reading and listening texts and for the speed at which texts can be processed. This is because knowledge of these combinations ‘makes discourse relatively predictable’ (Boers, 2020). Similarly, such knowledge can significantly contribute to spoken fluency because combinations ‘can be retrieved from memory as prefabricated units rather than being assembled at the time of speaking’ (Boer, 2020).

So far, so good, but from here on, the waters get a little muddier. Given their importance, what is the best way for a learner to acquire a decent stock of them? Are they best acquired through incidental learning (through meaning-focused reading and listening) or deliberate learning (e.g. with focused exercises of flashcards)? If the former, how on earth can we help learners to make sure that they get exposure to enough combinations enough times? If the latter, what kind of practice works best and, most importantly, which combinations should be selected? With, at the very least, many tens of thousands of such combinations, life is too short to learn them all in a deliberate fashion. Some sort of triage is necessary, but how should we go about this? Frequency of occurrence would be one obvious criterion, but this merely raises the question of what kind of database should be used to calculate frequency – the spoken discourse of children will reveal very different patterns from the written discourse of, say, applied linguists. On top of that, we cannot avoid consideration of the learners’ reasons for learning the language. If, as is statistically most probable, they are learning English to use as a lingua franca, how important or relevant is it to learn combinations that are frequent, idiomatic and comprehensible in native-speaker cultures, but may be rare and opaque in many English as a Lingua Franca contexts?

There are few, if any, answers to these big questions. Research (e.g. Pellicer-Sánchez, 2020) can give us pointers, but, the bottom line is that we are left with a series of semi-informed options (see O’Keeffe et al., 2007: 58 – 99). So, when an approach comes along that claims to use software to facilitate the learning of English formulaic expressions (Lin, 2022) I am intrigued, to say the least.

The program is, slightly misleadingly, called IdiomsTube (https://www.idiomstube.com). A more appropriate title would have been IdiomaticityTube (as it focuses on ‘speech formulae, proverbs, sayings, similes, binomials, collocations, and so on’), but I guess ‘idioms’ is a more idiomatic word than ‘idiomaticity’. IdiomsTube allows learners to choose any English-captioned video from YouTube, which is then automatically analysed to identify from two to six formulaic expressions that are presented to the learner as learning objects. Learners are shown these items; the items are hyperlinked to (good) dictionary entries; learners watch the video and are then presented with a small variety of practice tasks. The system recommends particular videos, based on an automated analysis of their difficulty (speech rate and a frequency count of the lexical items they include) and on recommendations from previous users. The system is gamified and, for class use, teachers can track learner progress.

When an article by the program’s developer, Phoebe Lin, (in my view, more of an advertising piece than an academic one) came out in the ReCALL journal, she tweeted that she’d love feedback. I reached out but didn’t hear back. My response here is partly an evaluation of Dr Lin’s program, partly a reflection on how far technology can go in solving some of the knotty problems of language learning.

Incidental and deliberate learning

Researchers have long been interested in looking for ways of making incidental learning of lexical items more likely to happen (Boers, 2021: 39 ff.), of making it more likely that learners will notice lexical items while focusing on the content of a text. Most obviously, texts can be selected, written or modified so they contain multiple instances of a particular item (‘input flooding’). Alternatively, texts can be typographically enhanced so that particular items are highlighted in some way. But these approaches are not possible when learners are given the freedom to select any video from YouTube and when the written presentations are in the form of YouTube captions. Instead, IdiomsTube presents the items before the learner watches the video. They are, in effect, told to watch out for these items in advance. They are also given practice tasks after viewing.

The distinction between incidental and deliberate vocabulary learning is not always crystal-clear. In this case, it seems fairly clear that the approach is more slanted to deliberate learning, even though the selection of video by the learner is determined by a focus on content. Whether this works or not will depend on (1) the level-appropriacy of the videos that the learner watches, (2) the effectiveness of the program in recommending / identifying appropriate videos, (3) the ability of the program to identify appropriate formulaic expressions as learning targets in each video, and (4) the ability of the program to generate appropriate practice of these items.

Evaluating the level of YouTube videos

What makes a video easy or hard to understand? IdiomsTube attempts this analytical task by calculating (1) the speed of the speech and (2) the difficulty of the lexis as determined by the corpus frequency of these items. This gives a score out of five for each category (speed and difficulty). I looked at fifteen videos, all of which were recommended by the program. Most of the ones I looked at were scored at Speed #3 and Difficulty #1. One that I looked at, ‘Bruno Mars Carpool Karaoke’, had a speed of #2 and a difficulty of #1 (i.e. one of the easiest). The video is 15 minutes long. Here’s an extract from the first 90 seconds:

Let’s set this party off right, put yo’ pinky rings up to the moon, twenty four karat magic in the air, head to toe soul player, second verse for the hustlas, gangstas, bad bitches and ya ugly ass friends, I gotta show how a pimp get it in, and they waking up the rocket why you mad

Whoa! Without going into details, it’s clear that something has gone seriously wrong. Evaluating the difficulty of language, especially spoken language, is extremely complex (not least because there’s no objective measure of such a thing). It’s not completely dissimilar to the challenge of evaluating the accuracy, appropriacy and level of sophistication of a learner’s spoken language, and we’re a long way from being able to do that with any acceptable level of reliability. At least, we’re a long, long way from being able to do it well when there are no constraints on the kind of text (which is the case when taking the whole of YouTube as a potential source). Especially if we significantly restrict topic and text type, we can train software to do a much better job. However, this will require human input: it cannot be automated without.

The length of these 15 videos ranged from 3.02 to 29.27 minutes, with the mean length being about 10 minutes, and the median 8.32 minutes. Too damn long.

Selecting appropriate learning items

The automatic identification of formulaic language in a text presents many challenges: it is, as O’Keeffe et al. (2007: 82) note, only partially possible. A starting point is usually a list, and IdiomsTube begins with a list of 53,635 items compiled by the developer (Lin, 2022) over a number of years. The software has to match word combinations in the text to items in the list, and has to recognise variant forms. Formulaic language cannot always be identified just by matching to lists of forms: a piece of cake may just be a piece of cake, and therefore not a piece of cake to analyse. 53,365 items may sound like a lot, but a common estimate of the number of idioms in English is 25,000. The number of multiword units is much, much higher. 53,365 is not going to be enough for any reliable capture.

Since any given text is likely to contain a lot of formulaic language, the next task is to decide how to select for presentation (i.e. as learning objects) from those identified. The challenge is, as Lin (2022) remarks, both technical and theoretical: how can frequency and learnability be measured? There are no easy answers, and the approach of IdiomsTube is, by its own admission, crude. The algorithm prioritises longer items that contain lower frequency single items, and which have a low frequency of occurrence in a corpus of 40,000 randomly-sampled YouTube videos. The aim is to focus on formulaic language that is ‘more challenging in terms of composition (i.e. longer and made up of more difficult words) and, therefore, may be easier to miss due to their infrequent appearance on YouTube’. My immediate reaction is to question whether this approach will not prioritise items that are not worth the bother of deliberate learning in the first place.

The proof is in the proverbial pudding, so I looked at the learning items that were offered by my sample of 15 recommended videos. Sadly, IdiomsTube does not even begin to cut the mustard. The rest of this section details why the selection was so unsatisfactory: you may want to skip this and rejoin me at the start of the next section.

  • In total 85 target items were suggested. Of these 39 (just under half) were not fixed expressions. They were single items. Some of these single items (e.g. ‘blog’ and ‘password’ would be extremely easy for most learners). Of the others, 5 were opaque idioms (the most prototypical kind of idiom), the rest were collocations and fixed (but transparent) phrases and frames.
  • Some items (e.g. ‘I rest my case’) are limited in terms of the contexts in which they can be appropriately used.
  • Some items did not appear to be idiomatic in any way. ‘We need to talk’ and ‘able to do it’, for example, are strange selections, compared to others in their respective lists. They are also very ‘easy’: if you don’t readily understand items like these, you wouldn’t have a hope in hell of understanding the video.
  • There were a number of errors in the recommended target items. Errors included duplication of items within one set (‘get in the way’ + ‘get in the way of something’), misreading of an item (‘the shortest’ misread as ‘the shorts’), mislabelling of an item (‘vend’ instead of ‘vending machine’), linking to the wrong dictionary entry (e.g. ‘mini’ links to ‘miniskirt’, although in the video ‘mini’ = ‘small’, or, in another video, ‘stoke’ links to ‘stoked’, which is rather different!).
  • The selection of fixed expressions is sometimes very odd. In one video, the following items have been selected: get into an argument, vend, from the ground up, shovel, we need to talk, prefecture. The video contains others which would seem to be better candidates, including ‘You can’t tell’ (which appears twice), ‘in charge of’, ‘way too’ (which also appears twice), and ‘by the way’. It would seem, therefore, that some inappropriate items are selected, whilst other more appropriate ones are omitted.
  • There is a wide variation in the kind of target item. One set, for example, included: in order to do, friction, upcoming, run out of steam, able to do it, notification. Cross-checking with Pearson’s Global Scale of English, we have items ranging from A2 to C2+.

The challenges of automation

IdiomsTube comes unstuck on many levels. It fails to recommend appropriate videos to watch. It fails to suggest appropriate language to learn. It fails to provide appropriate practice. You wouldn’t know this from reading the article by Phoebe Lin in the ReCALL journal which does, however, suggest that ‘further improvements in the design and functions of IdiomsTube are needed’. Necessary they certainly are, but the interesting question is how possible they are.

My interest in IdiomsTube comes from my own experience in an app project which attempted to do something not completely dissimilar. We wanted to be able to evaluate the idiomaticity of learner-generated language, and this entailed identifying formulaic patterns in a large corpus. We wanted to develop a recommendation engine for learning objects (i.e. the lexical items) by combining measures of frequency and learnability. We wanted to generate tasks to practise collocational patterns, by trawling the corpus for contexts that lent themselves to gapfills. With some of these challenges, we failed. With others, we found a stopgap solution in human curation, writing and editing.

IdiomsTube is interesting, not because of what it tells us about how technology can facilitate language learning. It’s interesting because it tells us about the limits of technological applications to learning, and about the importance of sorting out theoretical challenges before the technical ones. It’s interesting as a case study is how not to go about developing an app: its ‘special enhancement features such as gamification, idiom-of-the-day posts, the IdiomsTube Teacher’s interface and IdiomsTube Facebook and Instagram pages’ are pointless distractions when the key questions have not been resolved. It’s interesting as a case study of something that should not have been published in an academic journal. It’s interesting as a case study of how techno-enthusiasm can blind you to the possibility that some learning challenges do not have solutions that can be automated.

References

Boers, F. (2020) Factors affecting the learning of multiword items. In Webb, S. (Ed.) The Routledge Handbook of Vocabulary Studies. Abingdon: Routledge. pp. 143 – 157

Boers, F. (2021) Evaluating Second Language Vocabulary and Grammar Instruction. Abingdon: Routledge

Erman, B. & Warren, B. (2000) The idiom principle and the open choice principle. Text, 20 (1): pp. 29 – 62

Hill, J. (2001) Revising priorities: from grammatical failure to collocational success. In Lewis, M. (Ed.) Teaching Collocation: further development in the Lexical Approach. Hove: LTP. Pp.47- 69

Laufer, B. (2022) Formulaic sequences and second language learning. In Szudarski, P. & Barclay, S. (Eds.) Vocabulary Theory, Patterning and Teaching. Bristol: Multilingual Matters. pp. 89 – 98

Lin, P. (2022). Developing an intelligent tool for computer-assisted formulaic language learning from YouTube videos. ReCALL 34 (2): pp.185–200.

O’Keeffe, A., McCarthy, M. & Carter, R. (2007) From Corpus to Classroom. Cambridge: Cambridge University Press

Pellicer-Sánchez, A. (2020) Learning single words vs. multiword items. In Webb, S. (Ed.) The Routledge Handbook of Vocabulary Studies. Abingdon: Routledge. pp. 158 – 173

In May of last year, EL Gazette had a story entitled ‘Your new English language teacher is a robot’ that was accompanied by a stock photo of a humanoid robot, Pepper (built by SoftBank Robotics). The story was pure clickbait and the picture had nothing to do with it. The article actually concerned a chatbot (EAP Talk) to practise EAP currently under development at a Chinese university. There’s nothing especially new about chatbots: I last blogged about them in 2016 and interest in them, both research and practical, dates back to the 1970s (Lee et al., 2020). There’s nothing, as far as I can see, especially new about the Chinese EAP chatbot project either. The article concludes by saying that the academic behind the project ‘does not believe that AI can ever replace a human teacher’, but that chatbots might offer some useful benefits.

The benefits are, however, limited – a point that is acknowledged even by chatbot enthusiasts like Lee et al (2020). We are some way from having chatbots that we can actually have meaningful conversations with, but they do appear to have some potential as ‘intelligent tutoring systems’ to provide practice of and feedback on pre-designated bits of language (especially vocabulary and phrases). The main benefit that is usually given, as in the EL Gazette article, is that they are non-judgemental and may, therefore, be appropriate for shy or insecure learners.

Social robots, of the kind used in the illustration for the EL Gazette story, are, of course, not the same as chatbots. Chatbots, like EAP Talk, can be incorporated into all sorts of devices (notably phones, tablets and laptops) and all sorts of applications. If social robots are to be used for language learning, they will clearly need to incorporate chatbots, but in what ways could the other features of robots facilitate language acquisition? Pepper (the robot in the picture) has ‘touch sensors, LEDs and microphones for multimodal interactions’, along with ‘infrared sensors, bumpers, an inertial unit, 2D and 3D cameras, and sonars for omnidirectional and autonomous navigation’. How could these features help language acquisition?

Lee and Lee (2022) attempt to provide an answer to this question. Here’s what they have come up with:

By virtue of their physical embodiment, social robots have been suggested to provide language learners with direct and physical interactions, which is considered one of the basic ingredients for language learning. In addition, as social robots are generally humanoids or anthropomorphized animal shapes, they have been valued for their ability to serve as familiar conversational partners, having potential to lower the affective filter of language learners.

Is there any research evidence to back up these claims? The short answer is no. Motivation and engagement may sometimes be positively impacted, but we can’t say any more than that. As far as learning is concerned, Lee and Lee (2022: 121) write: involving social robots led to statistically similar or even higher [English language learning] outcomes compared with traditional ELT contexts (i.e. no social robot). In other words, social robots did not, on the whole, have a negative impact on learning outcomes. Hardly grounds for wild enthusiasm … Still, Lee and Lee, in the next line, refer to the ‘positive effectiveness of social robots in English teaching’ before proceeding to enumerate the ways in which these robots could be used in English language learning. Doesn’t ELT Journal have editors to pick up on this kind of thing?

So, how could these robots be used? Lee and Lee suggest (for younger learners) one-on-one vocabulary tutoring, dialogue practice, more vocabulary teaching, and personalized feedback. That’s it. It’s worth noting that all of these functions could equally well be carried out by chatbots as by social robots.

Lee and Lee discuss and describe the social robot, NAO6, also built by SoftBank Robotics. It’s a smaller and cheaper cousin of the Pepper robot that illustrates the EL Gazette article. Among Lee and Lee’s reasons for using social robots is that they ‘have become more accessible due to ever-lower costs’: NAO6 costs around £350 a month to rent. Buying it outright is also an option. Eduporium (‘Empowering the future with technology’) has one on offer for $12,990.00. According to the blurb, it helps ‘teach coding, brings literature to life, enhances special education, and allows for training simulations. Plus, its educational solutions include an intuitive interface, remote learning, and various applications for accessibility!’

It’s easy enough to understand why EL Gazette uses clickbait from time to time. I’m less clear about why ELT Journal would print this kind of nonsense. According to Lee and Lee, further research into social robots ‘would initiate a new era of language learning’ in which the robots will become ‘an important addition to the ELT arsenal’. Yeah, right …

References

Lee, H. & Lee, J. H. (2022) Social robots for English language teaching. ELT Journal 76 (1): 119 – 124

Lee, J. H., Yang, H., Shin D. & Kim, H. (2020) Chatbots. ELT Journal 74 (3): 338 – 3444

The pandemic has affected all learners, but the more vulnerable the learner, the harder they have been hit. The evidence is very clear that Covid and the response of authorities to it has, in the words of UNESCO , ‘increased inequalities and exacerbated a pre-existing education crisis’. Learning poverty (a term coined by UNESCO and the World Bank), which refers to the ability to read and understand a simple text by the age of 10, is just one way of looking at these inequalities. Before the pandemic, 53% of children in low and middle income countries (and 9% in high income countries) were living in learning poverty. According to the World Bank (Azevedo et al., 2021), the pandemic will amplify this crisis with the figure rising to somewhere between 63% and 70%. The fear is that the recovery from Covid may be ‘similarly inequitable and that the effects of COVID-19 will be long-lasting’ (ibid.).

Inequity was not, of course, the only problem that educational systems faced before the pandemic. Since the turn of the millennium, it has been common to talk about ‘reimagining education’, and use of this phrase peaked in the summer of 2020. Leading the discoursal charge was Andreas Schleicher, head of education at the OECD, who saw the pandemic as ‘a great moment’ for education, since ‘the current wave of school closures offers an opportunity for experimentation and for envisioning new models of education’. Schleicher’s reimagining involves a closely intertwined privatization (by ending state monopolies) and digitalization of education (see this post for more details). Other reimaginings are usually very similar. Yong Zhao, for example, does not share Schleicher’s enthusiasm for standardized tests, but he sees an entrepreneurial, technology-driven, market-oriented approach as the way forward. He outlined this, pre-pandemic, in his book An Education Crisis is a Terrible Thing to Waste (Zhao et al., 2019), and then picked up on the pandemic (Zhao, 2020) to reiterate his ideas and, no doubt, to sell his book – all ‘in the spirit’, he writes, ‘of never wasting a good crisis’.

It was Churchill who first said ‘Never let a good crisis go to waste’, but it is often attributed to Emanuel Rahm, Obama’s Chief of Staff, who said the same thing in reference to the financial crisis of 2009. As we have seen in the last two years, crises can be good opportunities to push through policy changes. Viktor Orbán provides a good example. Crises can also be a way to make a financial killing, a practice known as ‘disaster capitalism’ (Loewenstein, 2017). Sometimes, it’s possible to change policy and turn a tidy profit at the same time. One example from the recent past shows us the way.

Following Hurricane Katrina in 2005, education was massively disrupted in New Orleans and the surrounding areas. Arne Duncan, who became Obama’s Secretary for Education a few years after Katrina, had this to say about the disaster: “Let me be really honest. I think the best thing that happened to the education system in New Orleans was Hurricane Katrina. That education system was a disaster, and it took Hurricane Katrina to wake up the community to say that ‘We have to do better.’” The reform that followed, inspired by Milton Friedman, involved replacing New Orleans’ public school system with privately run charter schools. The change took place with ‘military speed and precision’, compared to the ‘glacial pace’ with which levees and the electricity grid were repaired (Klein, 2007: 5). Nearly 5000 unionized teachers were fired, although some of the younger ones were rehired on reduced salaries. Most of the city’s poorer residents were still in exile when the changes took place: the impact on the most vulnerable students was entirely predictable. ‘The social and economic situation always bleeds into the school, said one researcher into the impact of the catastrophe.

Disaster capitalism may, then, be a useful lens through which to view the current situation (Moore et al., 2021). Betsy DeVos, Trump’s Secretary for Education, stated that the pandemic was an opportunity to ‘look very seriously at the fact that K-12 education for too long has been very static and very stuck in one method of delivering and making instruction available’ (Ferrari, 2020). What DeVos, who was famous for having described public education as a ‘dead end’, meant by this was privatization and digitalization, and privatization through digitalization. Although the pandemic is far from over, we can already begin to ask: has the crisis been wasted?

Turning from the US to Europe, a fascinating report by Zancajo et al (2022) examines the educational policy responses to Covid in a number of European countries. The first point to note is that the recovery plans of these countries is not fundamentally any different from pre-pandemic educational policy. The Covid-19 pandemic has simply ‘served as a catalyst to accelerate preexisting digitization policies in education systems’. Individual states are supported by the European Commission’s Digital Education Action Plan 2021–2027 (European Commission, 2021) which lists three main priorities: making use of technology, the development of digital skills for teachers and learners, and the increased use of data to improve education. The focus of attention of education policy in the national recovery plans of individual EU countries is almost completely monopolized by digitalization. Covid has not led to any reimagining of education: it has simple been ‘a path accelerator contributing to strengthening policy instruments and solutions that were already on the agenda (Zancajo et al., 2022). Less overtly obvious than digitalization has been the creeping privatization that occurs when a greater proportion of national education budgets is spent on technology provided by private companies.

Creeping privatization has been especially noticeable in British universities, which, for years, have been focusing on the most profitable ‘revenue streams’ and on cutting the costs of academic labour. The pandemic has been used by some (Leicester and Manchester, for example) as a justification for further restructuring, cost-cutting and the development of new digitally-driven business models (Nehring, 2021). In schools, the private technology providers were able to jump in quickly because the public sector was unprepared, and, in so doing, position themselves as essential services. The lack of preparedness of the public sector is not, of course, unsurprising, since it has been underfunded for so long. Underfund – create a crisis – privatize the solution: such has long been the ‘Shock Doctrine’ game plan of disaster capitalists. Naomi Klein has observed that where we have ended up in post-Covid education is probably where we would have ended up anyway: Covid accelerated the process by ten years.

Williamson and Hogan (2020) describe the current situation in the following terms:

The pivot to online learning and ‘emergency remote teaching’ has positioned educational technology (edtech) as an integral component of education globally, bringing private sector and commercial organisations into the centre of essential educational services. […] A global education industry of private and commercial organisations has played a significant role in educational provision during the Covid-19 crisis, working at local, national and international scales to insert edtech into educational systems and practices. It has often set the agenda, offered technical solutions for government departments of education to follow, and is actively pursuing long-term reforms whereby private technology companies would be embedded in public education systems during the recovery from the Covid-19 crisis and beyond it in new models of hybrid teaching and learning. […] Supported by multilateral policy influencing organisations and national government departments, these companies have integrated schools, teachers and students into their global cloud systems and online education platforms, raising the prospect of longterm dependencies of public education institutions on private technology infrastructures.

And where is educational equity in all this? Even the OECD is worried – more assessment is needed to identify learning losses, they say! A pandemic tale from California will give us a clue. When schools shut down, 50% of low-income California students lacked the necessary technology to access distance learning (Gutentag, 2020). Big Tech came riding to the rescue: donations from companies like HP, Amazon, Apple, Microsoft and Google made it possible for chromebooks and wifi hotspots to be made available for every student, and California legislators and corporations could congratulate themselves on closing the ‘digital divide’ (ibid.). To compensate for increased problems of homelessness, poverty, hunger, and discrimination, the most vulnerable students now have a laptop or tablet, with which they can generate data to be monetized by the tech vendors (Feathers, 2022).

References

Azevedo, J. P. W., Rogers, F. H., Ahlgren, S. E., Cloutier, M-H., Chakroun, B., Chang, G-C., Mizunoya, S., Reuge,N. J., Brossard, M., & Bergmann, J. L. (2021) The State of the Global Education Crisis : A Path to Recovery (English). Washington, D.C. : World Bank Group. https://documents1.worldbank.org/curated/en/416991638768297704/pdf/The-State-of-the-Global-Education-Crisis-A-Path-to-Recovery.pdf

European Commission. (2021) Digital education action plan 2021-2027. Resetting Education, Brussels

Feathers, T. (2022) This Private Equity Firm Is Amassing Companies That Collect Data on America’s Children. January 11th, 2022 The Markup https://themarkup.org/machine-learning/2022/01/11/this-private-equity-firm-is-amassing-companies-that-collect-data-on-americas-children

Ferrari, K. (2020) Disaster Capitalism Is Coming for Public Education. Jacobin 14 May 2020 https://jacobinmag.com/2020/05/public-education-schools-covid-coronavirus-charter-teachers

Gutentag, A. (2020) The Virtual Education Shock Doctrine. The Bellows https://www.thebellows.org/the-virtual-education-shock-doctrine/

Klein, N. (2007) The Shock Doctrine. New York: Metropolitan Books

Loewenstein, A. (2017) Disaster Capitalism. London: Verso Books

Moore, S. D. M., Jayme, B. D., Black, J. (2021) Disaster capitalism, rampant edtech opportunism, and the advancement of online learning in the era of COVID19. Critical Education, 12(2), 1-21.

Nehring, D. (2021) Is COVID-19 Enabling Academic Disaster Capitalism? Social Science Space 21 July 2021 https://www.socialsciencespace.com/2021/07/is-covid-19-enabling-academic-disaster-capitalism/

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

Zancajo, A., Verger, A. & Bolea, P. (2022) Digitalization and beyond: the effects of Covid-19 on post-pandemic educational policy and delivery in Europe, Policy and Society, puab016, https://doi.org/10.1093/polsoc/puab016

Zhao, Y. (2020) COVID-19 as a catalyst for educational change. Prospects 49: 29–33. https://doi.org/10.1007/s11125-020-09477-y

Zhao, Y., Emler, T. E., Snethen, A. & Yin, D. (2019) An Education Crisis is a Terrible Thing to Waste. New York: Teachers College Press

Innovation and ELT

Next week sees the prize ceremony of the nineteenth edition of the British Council’s ELTons awards, celebrating ‘innovation in English language teaching and learning … the newest and most original courses, books, publications, apps, platforms, projects, and more.’ Since the Council launched the ELTons in 2003, it hasn’t been entirely clear what is meant by ‘innovation’. But, reflecting the use of the term in the wider (business) world, ‘innovation’ was seen as a positive value, an inherently good thing, and almost invariably connected to technological innovation. One of the award categories in the ELTons is for ‘digital innovation’, but many of the winners and shortlisted nominations in other categories have been primarily innovative in their use of technology (at first, CD-ROMs, before web-based applications became standard).

Historian Jill Lepore, among others, has traced the mantra of innovation at the start of this century back to renewed interest in the work of mid-20th century Austrian economist, Joseph Schumpeter, in the 1990s. Schumpeter wrote about ‘creative disruption’, and his ideas gained widespread traction with the publication in 1997 of Clayton Christensen’s ‘The Innovator’s Dilemma: The Revolutionary Book that Will Change the Way You Do Business’. Under Christensen, ‘creative disruption’ morphed into ‘disruptive innovation’. The idea was memorably expressed in Facebook’s motto of ‘Move fast and break things’. Disruptive innovation was always centrally concerned with expanding the market for commercial products by leveraging technology to gain access to more customers. Innovation, then, was and is a commercial strategy, and could be used either in product development or simply as an advertising slogan.

From the start of the innovation wave, the British Council has been keen to position itself in the vanguard. It does this for two reasons. Firstly, it needs to promote its own products and, with the cuts to British Council funding, its need to generate more income is increasingly urgent: ELT products are the main source of this income. Secondly, as part of the Council’s role in pushing British ‘soft power’, it seeks to promote Britain as a desirable, and therefore innovative, place to do business or study. This is wonderfully reflected in a series of videos for the Council’s LearnEnglish website called ‘Britain is Great’, subsets of which are entitled ‘Entrepreneurs are GREAT’ and ‘Innovation is GREAT’ with films celebrating the work of people like Richard Branson and James Dyson. For a while, the Council had a ‘Director, English Language Innovation’, and the current senior management team includes a ‘Director Digital, Partnerships and Innovation’ and a ‘Director Transformation’. With such a focus on innovation at the heart of its organisation, it is hardly surprising that the British Council should celebrate the idea in its ELTons awards. The ELTons celebrate the Council itself, and its core message, as much as they do the achievements of the award winners. Finalists in the ELTons receive a ‘promotional kit’ which includes ‘assets for the promotion of products or publications’. These assets (badges, banners, and so on) serve to promote the Council brand at the same time as advertising the shortlisted products themselves.

Innovation and a better world

Innovation, especially ‘disruptive innovation’, is not, however, what it used to be. The work of Clayton Christensen has been largely discredited (Russell & Vinsel, 2016). The Facebook motto has been changed and ‘the Era of “Move Fast and Break Things” Is Over’ (Taneja, 2019). The interest in ‘minimal viable products’ has shifted to an interest in ‘minimal virtuous products’. This is reflected in the marketing of edtech with the growing focus on how product X or Y will make the world a better place in some way. The ELTons introduced ‘Judges’ Commendations’ for ‘Equality, Diversity and Inclusion’ and, this year, a new commendation for ‘Environmental Sustainability and Climate Action’. Innovation is still celebrated, but ‘disruption’ has undergone a slide of meaning, so that it is more likely now to refer to disruption caused by the Covid pandemic, and our responses to it. For example, TESOL Italy’s upcoming annual conference, entitled ‘Disruptive Innovations in ELT’, encourages contributions not only about online study and ‘interactive e-learning platforms’, but also about ‘sustainable development and social justice’, ‘resilience, collaboration, empathy, digital literacy, soft skills, and global competencies’. Innovation is still presented as a good, even necessary, thing.

I am not suggesting that the conflation of innovation with positive social good is purely virtue-signalling, although it is sometimes clearly that. However, the rhetorical shift makes it harder for anyone to criticise innovations, when they are presented as solutions to problems that need to be solved. Allen et al (2021) argue that ‘those who propose solutions are always virtuous because they clearly care about a problem we must solve. Those who suggest the solution will not work, and who have no better solution, are denying the problem the opportunity of the resolution it so desperately needs’.

There are, though, good reasons to be wary of ‘innovation’ in education. First among these is the lessons of history, which teach us that today’s ‘next big thing’ is usually tomorrow’s ‘last next big thing’ (Allen, et al., 2021). On the technology front, from programmed instruction to interactive whiteboards, educational history is littered with artefacts that have been oversold and underused (Cuban, 2001). Away from technology, from Multiple Intelligences to personalized learning, we see the same waves of enthusiasm and widespread adoption, followed by waning interest and abandonment. The waste of money and effort along the way has been colossal, although that is not to say that there have not been some, sometimes significant, gains.

The second big reason to be wary of technological innovations in education is that they focus our attention on products of various kinds. But products are not at the heart of schooling: it is labour, especially the work of teachers, which occupies that place. It is not Zoom that made possible the continuation of education during the pandemic lockdowns. Indeed, in many parts of the world, lower-tech or zero-tech solutions had to be found. It was teachers’ readiness to adapt to the new circumstances that allowed education to stumble onwards during the crisis. Vinsel and Russell’s recent book, ‘The Innovation Delusion’ (2021) compellingly argues that the focus on innovation has led us to ‘devalue the work that underpins modern life’. They point out (Russell and Vinsel, 2016) that ‘feminist theorists have long argued that obsessions with technological novelty obscures all of the labour, including housework, that women, disproportionately, do to keep life on track’. Parallels with the relationship between teachers and technology are hard to avoid. The presentation of innovation as an inherently desirable value ‘rarely asks who benefits, to what end?’

The ‘ELT’ in the ELTons

It’s time to consider the ‘ELT’ part of the ELTons. ‘ELT’ is a hypothetical construct that is often presented as a concrete reality, rather than a loosely-bound constellation of a huge number of different practices and attitudes, many of which have very little in common with each other. This reification of ‘ELT’ can serve a number of purposes, one of which is to frame discourse in particular ways. In a post from a few years ago, Andrew Wickham and I discussed how the framing of ‘ELT’ (and education, more generally) as an industry serves particular interests, but may be detrimental to the interests of others.

Perhaps a useful way of viewing ‘ELT’ is as a discourse community. Borg (2003) argues that ‘membership of a discourse community is usually a matter of choice’. That is to say that you are part of ‘ELT’ if you choose to identify yourself as such. In Europe, huge numbers of English language teachers do not choose to identify themselves primarily as an ‘ELT teacher’: they may see this label as relevant to them, but a more immediate and primary self-identification is often as a ‘school teacher’, a ‘primary school teacher’, a ‘(modern) languages teacher’, a ‘CLIL teacher’, and so on. They work in the state / public sectors. The concerns and interests of those who do not self-identify as ‘ELT practitioners’ are most likely to revolve around their local contexts and issues. Those of us who self-identify as ‘ELT practitioners’ are more likely to be interested in what we share with others who self-identify in the same way in different parts of the globe. The relevance of local contexts and issues is mostly to be found in how they may shed light on more global concerns. If you prioritise the local over the global, your participation in the ‘ELT’ discourse community is likely to be limited. Things like the ELTons are simply off your radar.

Borg (2003) also points out that discourse communities typically have ‘experts who perform gatekeeping roles’. The discourse of ‘ELT’ is enacted in magazines, blogs, videos, webinars and conferences aimed at English language teachers. I exclude from this list academic journals and books which are known to be consulted only rarely by the vast majority of teachers. Similarly, I exclude the more accessible books that have been written specifically for English language teachers, which are mostly sold in minuscule quantities, except for those that are required reading for training courses. The greatest number of contributors to the discourse of ‘ELT’ are authors, developers and publishers of language teaching materials and tools, teachers representing product vendors or (directly or indirectly) promoting their own products, representatives of private teaching / training schools, and organisations, representatives of international examination bodies, and representatives of universities (which, in some countries, essentially function as private institutions (Chowdhury & Ha, 2014)).

In other words, the discourse of ‘ELT’ is shaped to a very significant extent by gatekeepers who have a product to sell. Their customers are often those who do not self-identify in the same way as members of the ‘ELT’ discourse community. The British Council is a key gatekeeper in this discourse and it is a private sector operator par excellence.

The lack of interest in the workers of ‘ELT’ is well documented – see for example the Teachers as Workers blog. It is hardly unexpected, especially in the private sector. The British Council has a long history of labour disputes. At the present time, the Public and Commercial Services Union in the UK is balloting members about strike action against forced redundancies, which ‘are disproportionately targeted at middle to lower graded staff, while at the same time new management positions and a new deputy chief executive officer post are to be created’. One of the aims of the union is to stop the privatisation / outsourcing of Council jobs. The British government’s recent failure to relocate British Council employees in Afghanistan led to over 100,000 people signing a petition demanding action. The public silence of the British Council did little to inspire confidence in their interest in their workers.

The Council is a many-headed beast, and some of these heads do very admirable work in sponsoring or supporting a large variety of valuable projects. I don’t think the ELTons is one of these. The ideology behind them is highly questionable, and their ‘best before’ date has long expired. And given the financial constraints that the Council is now operating under, the money might be better spent elsewhere.

References

Allen, R., Evans, M. & White, B. (2021) The Next Big Thing in School Improvement. Woodbridge: John Catt Educational

Borg, E. (2003) Discourse Community. ELT Journal 57 (4): 398-400

Chowdhury, R. & Ha, P. L. (2014) Desiring TESOL and International Education. Bristol: Multilingual Matters

Christensen, C. M. (1997) The Innovator’s Dilemma: The Revolutionary Book that Will Change the Way You Do Business. Cambridge: Harvard Business Review Press

Cuban, L. (2001) Oversold and Underused: Computers in the Classroom. Cambridge: Harvard University Press

Lepore, J. (2014) The Disruption Machine. The New Yorker, June 16, 2014. https://www.newyorker.com/magazine/2014/06/23/the-disruption-machine

Russell, A. L. & Vinsel, L. (2016) Hail the Maintainers. Aeon, 7 April 2016 https://aeon.co/essays/innovation-is-overvalued-maintenance-often-matters-more

Taneja, H. (2019) The Era of “Move Fast and Break Things” Is Over. Harvard Business Review, January 22, 2019, https://hbr.org/2019/01/the-era-of-move-fast-and-break-things-is-over

Vinsel, L. & Russell, A. L. (2020) The Innovation Delusion. New York: Currency Books

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

In the last post, I mentioned a lesson plan from an article by Pegrum, M., Dudeney, G. & Hockly, N. (2018. Digital literacies revisited. The European Journal of Applied Linguistics and TEFL, 7 (2), pp. 3-24) in which students discuss the data that is collected by fitness apps and the possibility of using this data to calculate health insurance premiums, before carrying out and sharing online research about companies that track personal data. It’s a nice plan, unfortunately pay-walled, but you could try requesting a copy through Research Gate.

The only other off-the-shelf lesson plan I have been able to find is entitled ‘You and Your Data’ from the British Council. Suitable for level B2, this plan, along with a photocopiable pdf, contains with a vocabulary task (matching), a reading text (you and your data, who uses our data and why, can you protect your data) with true / false and sentence completion tasks) and a discussion (what do you do to protect our data). The material was written to coincide with Safer Internet Day (an EU project), which takes place in early February (next data 9 February 2021). The related website, Better Internet for Kids, contains links to a wide range of educational resources for younger learners.

For other resources, a good first stop is Ina Sander’s ‘A Critically Commented Guide to Data Literacy Tools’ in which she describes and evaluates a wide range of educational online resources for developing critical data literacy. Some of the resources that I discuss below are also evaluated in this guide. Here are some suggestion for learning / teaching resources.

A glossary

This is simply a glossary of terms that are useful in discussing data issues. It could easily be converted into a matching exercise or flashcards.

A series of interactive videos

Do not Track’ is an award-winning series of interactive videos, produced by a consortium of broadcasters. In seven parts, the videos consider such issues as who profits from the personal data that we generate online, the role of cookies in the internet economy, how online profiling is done, the data generated by mobile phones and how algorithms interpret the data.

Each episode is between 5 and 10 minutes long, and is therefore ideal for asynchronous viewing. In a survey of critical data literacy tools (Sander, 2020), ‘Do not Track’ proved popular with the students who used it. I highly recommend it, but students will probably need a B2 level or higher.

More informational videos

If you do not have time to watch the ‘Do Not Track’ video series, you may want to use something shorter. There are a huge number of freely available videos about online privacy. I have selected just two which I think would be useful. You may be able to find something better!

1 Students watch a video about how cookies work. This video, from Vox, is well-produced and is just under 7 minutes long. The speaker speaks fairly rapidly, so captions may be helpful.

Students watch a video as an introduction to the topic of surveillance and privacy. This video, ‘Reclaim our Privacy’, was produced by ‘La Quadrature du Net’, a French advocacy group that promotes digital rights and freedoms of citizens. It is short (3 mins) and can be watched with or without captions (English or 6 other languages). Its message is simple: political leaders should ensure that our online privacy is respected.

A simple matching task ‘ten principles for online privacy’

1 Share the image below with all the students and ask them to take a few minutes matching the illustrations to the principles on the right. There is no need for anyone to write or say anything, but it doesn’t matter if some students write the answers in the chat box.

(Note: This image and the other ideas for this activity are adapted from https://teachingprivacy.org/ , a project developed by the International Computer Science Institute and the University of California-Berkeley for secondary school students and undergraduates. Each of the images corresponds to a course module, which contains a wide-range of materials (videos, readings, discussions, etc.) which you may wish to explore more fully.)

2 Share the image below (which shows the answers in abbreviated form). Ask if anyone needs anything clarified.

You’re Leaving Footprints Principle: Your information footprint is larger than you think.

There’s No Anonymity Principle: There is no anonymity on the Internet.

Information Is Valuable Principle: Information about you on the Internet will be used by somebody in their interest — including against you.

Someone Could Listen Principle: Communication over a network, unless strongly encrypted, is never just between two parties.

Sharing Releases Control Principle: Sharing information over a network means you give up control over that information — forever.

Search Is Improving Principle: Just because something can’t be found today, doesn’t mean it can’t be found tomorrow.

Online Is Real Principle: The online world is inseparable from the “real” world.

Identity Isn’t Guaranteed Principle: Identity is not guaranteed on the Internet.

You Can’t Escape Principle: You can’t avoid having an information footprint by not going online.

Privacy Requires Work Principle: Only you have an interest in maintaining your privacy.

3 Wrap up with a discussion of these principles.

Hands-on exploration of privacy tools

Click on the link below to download the procedure for the activity, as well as supporting material.

A graphic novel

Written by Michael Keller and Josh Neufeld, and produced by Al Jazeera, this graphic novel ‘Terms of Service. Understanding our role in the world of Big Data’ provides a good overview of critical data literacy issues, offering lots of interesting, concrete examples of real cases. The language is, however, challenging (C1+). It may be especially useful for trainee teachers.

A website

The Privacy International website is an extraordinary goldmine of information and resources. Rather than recommending anything specific, my suggestion is that you, or your students, use the ‘Search’ function on the homepage and see where you end up.

In the first post in this 3-part series, I focussed on data collection practices in a number of ELT websites, as a way of introducing ‘critical data literacy’. Here, I explore the term in more detail.

Although the term ‘big data’ has been around for a while (see this article and infographic) it’s less than ten years ago that it began to enter everyday language, and found its way into the OED (2013). In the same year, Viktor Mayer-Schönberger and Kenneth Cukier published their best-selling ‘Big Data: A Revolution That Will Transform How We Live, Work, and Think’ (2013) and it was hard to avoid enthusiastic references in the media to the transformative potential of big data in every sector of society.

Since then, the use of big data and analytics has become ubiquitous. Massive data collection (and data surveillance) has now become routine and companies like Palantir, which specialise in big data analytics, have become part of everyday life. Palantir’s customers include the LAPD, the CIA, the US Immigration and Customs Enforcement (ICE) and the British Government. Its recent history includes links with Cambridge Analytica, assistance in an operation to arrest the parents of illegal migrant children, and a racial discrimination lawsuit where the company was accused of having ‘routinely eliminated’ Asian job applicants (settled out of court for $1.7 million).

Unsurprisingly, the datafication of society has not gone entirely uncontested. Whilst the vast majority of people seem happy to trade their personal data for convenience and connectivity, a growing number are concerned about who benefits most from this trade-off. On an institutional level, the EU introduced the General Data Protection Regulation (GDPR), which led to Google being fined Ꞓ50 million for insufficient transparency in their privacy policy and their practices of processing personal data for the purposes of behavioural advertising. In the intellectual sphere, there has been a recent spate of books that challenge the practices of ubiquitous data collection, coining new terms like ‘surveillance capitalism’, ‘digital capitalism’ and ‘data colonialism’. Here are four recent books that I have found particularly interesting.

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

Couldry, N. & Mejias, U. A. (2019). The Costs of Connection. Stanford: Stanford University Press

Sadowski, J. (2020). Too Smart. Cambridge, Mass.: MIT Press

Zuboff, S. (2019). The Age of Surveillance Capitalism. New York: Public Affairs

The use of big data and analytics in education is also now a thriving industry, with its supporters claiming that these technologies can lead to greater personalization, greater efficiency of instruction and greater accountability. Opponents (myself included) argue that none of these supposed gains have been empirically demonstrated, and that the costs to privacy, equity and democracy outweigh any potential gains. There is a growing critical literature and useful, recent books include:

Bradbury, A. & Roberts-Holmes, G. (2018). The Datafication of Primary and Early Years Education. Abingdon: Routledge

Jarke, J. & Breiter, A. (Eds.) (2020). The Datafication of Education. Abingdon: Routledge

Williamson, B. (2017). Big Data in Education: The digital future of learning, policy and practice. London: Sage

Concomitant with the rapid growth in the use of digital tools for language learning and teaching, and therefore the rapid growth in the amount of data that learners were (mostly unwittingly) giving away, came a growing interest in the need for learners to develop a set of digital competencies, or literacies, which would enable them to use these tools effectively. In the same year that Mayer-Schönberger and Cukier brought out their ‘Big Data’ book, the first book devoted to digital literacies in English language teaching came out (Dudeney et al., 2013). They defined digital literacies as the individual and social skills needed to effectively interpret, manage, share and create meaning in the growing range of digital communication channels (Dudeney et al., 2013: 2). The book contained a couple of activities designed to raise students’ awareness of online identity issues, along with others intended to promote critical thinking about digitally-mediated information (what the authors call ‘information literacy’), but ‘critical literacy’ was missing from the authors’ framework.

Critical thinking and critical literacy are not the same thing. Although there is no generally agreed definition of the former (with a small ‘c’), it is focussed primarily on logic and comprehension (Lee, 2011). Paul Dummett and John Hughes (2019: 4) describe it as ‘a mindset that involves thinking reflectively, rationally and reasonably’. The prototypical critical thinking activity involves the analysis of a piece of fake news (e.g. the task where students look at a website about tree octopuses in Dudeney et al. 2013: 198 – 203). Critical literacy, on the other hand, involves standing back from texts and technologies and viewing them as ‘circulating within a larger social and textual context’ (Warnick, 2002). Consideration of the larger social context necessarily entails consideration of unequal power relationships (Leee, 2011; Darvin, 2017), such as that between Google and the average user of Google. And it follows from this that critical literacy has a socio-political emancipatory function.

Critical digital literacy is now a growing field of enquiry (e.g. Pötzsch, 2019) and there is an awareness that digital competence frameworks, such as the Digital Competence Framework of the European Commission, are incomplete and out of date without the inclusion of critical digital literacy. Dudeney et al (2013) clearly recognise the importance of including critical literacy in frameworks of digital literacies. In Pegrum et al. (2018, unfortunately paywalled), they update the framework from their 2013 book, and the biggest change is the inclusion of critical literacy. They divide this into the following:

  • critical digital literacy – closely related to information literacy
  • critical mobile literacy – focussing on issues brought to the fore by mobile devices, ranging from protecting privacy through to safeguarding mental and physical health
  • critical material literacy – concerned with the material conditions underpinning the use of digital technologies, ranging from the socioeconomic influences on technological access to the environmental impacts of technological manufacturing and disposal
  • critical philosophical literacy – concerned with the big questions posed to and about humanity as our lives become conjoined with the existence of our smart devices, robots and AI
  • critical academic literacy, which refers to the pressing need to conduct meaningful studies of digital technologies in place of what is at times ‘cookie-cutter’ research

I’m not entirely convinced by the subdivisions, but labelling in this area is still in its infancy. My particular interest here, in critical data literacy, seems to span across a number of their sub-divisions. And the term that I am using, ‘critical data literacy’, which I’ve taken from Tygel & Kirsch (2016), is sometimes referred to as ‘critical big data literacy’ (Sander, 2020a) or ‘personal data literacy’ (Pangrazio & Selwyn, 2019). Whatever it is called, it is the development of ‘informed and critical stances toward how and why [our] data are being used’ (Pangrazio & Selwyn, 2018). One of the two practical activities in the Pegrum at al article (2018) looks at precisely this area (the task requires students to consider the data that is collected by fitness apps). It will be interesting to see, when the new edition of the ‘Digital Literacies’ book comes out (perhaps some time next year), how many other activities take a more overtly critical stance.

In the next post, I’ll be looking at a range of practical activities for developing critical data literacy in the classroom. This involves both bridging the gaps in knowledge (about data, algorithms and online privacy) and learning, practically, how to implement ‘this knowledge for a more empowered internet usage’ (Sander, 2020b).

Without wanting to invalidate the suggestions in the next post, a word of caution is needed. Just as critical thinking activities in the ELT classroom cannot be assumed to lead to any demonstrable increase in critical thinking (although there may be other benefits to the activities), activities to promote critical literacy cannot be assumed to lead to any actual increase in critical literacy. The reaction of many people may well be ‘It’s not like it’s life or death or whatever’ (Pangrazio & Selwyn, 2018). And, perhaps, education is rarely, if ever, a solution to political and social problems, anyway. And perhaps, too, we shouldn’t worry too much about educational interventions not leading to their intended outcomes. Isn’t that almost always the case? But, with those provisos in mind, I’ll come back next time with some practical ideas.

REFERENCES

Darvin R. (2017). Language, Ideology, and Critical Digital Literacy. In: Thorne S., May S. (eds) Language, Education and Technology. Encyclopedia of Language and Education (3rd ed.). Springer, Cham. pp. 17 – 30 https://doi.org/10.1007/978-3-319-02237-6_35

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

Dummett, P. & Hughes, J. (2019). Critical Thinking in ELT. Boston: National Geographic Learning

Lee, C. J. (2011). Myths about critical literacy: What teachers need to unlearn. Journal of Language and Literacy Education [Online], 7 (1), 95-102. Available at http://www.coa.uga.edu/jolle/2011_1/lee.pdf

Mayer-Schönberger, V. & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray

Pangrazio, L. & Selwyn, N. (2018). ‘It’s not like it’s life or death or whatever’: young people’s understandings of social media data. Social Media + Society, 4 (3): pp. 1–9. https://journals.sagepub.com/doi/pdf/10.1177/2056305118787808

Pangrazio, L. & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media and Society, 21 (2): pp. 419 – 437

Pegrum, M., Dudeney, G. & Hockly, N. (2018). Digital literacies revisited. The European Journal of Applied Linguistics and TEFL, 7 (2), pp. 3-24

Pötzsch, H. (2019). Critical Digital Literacy: Technology in Education Beyond Issues of User Competence and Labour-Market Qualifications. tripleC: Communication, Capitalism & Critique, 17: pp. 221 – 240 Available at https://www.triple-c.at/index.php/tripleC/article/view/1093

Sander, I. (2020a). What is critical big data literacy and how can it be implemented? Internet Policy Review, 9 (2). DOI: 10.14763/2020.2.1479 https://www.econstor.eu/bitstream/10419/218936/1/2020-2-1479.pdf

Sander, I. (2020b). Critical big data literacy tools – Engaging citizens and promoting empowered internet usage. Data & Policy, 2: DOI: https://doi.org/10.1017/dap.2020.5

Tygel, A. & Kirsch, R. (2016). Contributions of Paulo Freire for a Critical Data Literacy: a Popular Education Approach. The Journal of Community Informatics, 12 (3). Available at http://www.ci-journal.net/index.php/ciej/article/view/1296

Warnick, B. (2002). Critical Literacy in a Digital Era. Mahwah, NJ, Lawrence Erlbaum Associates

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Definition of gritGrit book cover

from Quartz at Work magazine

 

Grit is on the up. You may have come across articles like ‘How to Be Gritty in the Time of COVID-19’ or ‘Rediscovering the meaning of grit during COVID-19’ . If you still want more, there are new videos from Angela Duckworth herself where we can learn how to find our grit in the face of the pandemic.

Schools and educational authorities love grit. Its simple, upbeat message (‘Yes, you can’) has won over hearts and minds. Back in 2014, the British minister for education announced a £5million plan to encourage teaching ‘character and resilience’ in schools – specifically looking at making Britain’s pupils ‘grittier’. The spending on grit hasn’t stopped since.

The publishers of Duckworth’s book paid a seven-figure sum to acquire the US rights, and sales have proved the wisdom of the investment. Her TED talk has had over 6.5 million views on YouTube, although it’s worth looking at the comments to see why many people have been watching it.

Youtube comments

The world of English language teaching, always on the lookout for a new bandwagon to jump onto, is starting to catch up with the wider world of education. Luke Plonsky, an eminent SLA scholar, specialist in meta-analyses and grit enthusiast, has a bibliography of grit studies related to L2 learning, that he deems worthy of consideration. Here’s a summary, by year, of those publications. More details will follow in the next section.

Plonsky biblio

We can expect interest in ‘grit’ to continue growing, and this may be accelerated by the publication this year of Engaging Language Learners in Contemporary Classrooms by Sarah Mercer and Zoltán Dörnyei. In this book, the authors argue that a ‘facilitative mindset’ is required for learner engagement. They enumerate five interrelated principles for developing a ‘facilitative mindset’: promote a sense of competence, foster a growth mindset, promote learners’ sense of ownership and control, develop proactive learners and, develop gritty learners. After a brief discussion of grit, they write: ‘Thankfully, grit can be learnt and developed’ (p.38).

Unfortunately, they don’t provide any evidence at all for this. Unfortunately, too, this oversight is easy to explain. Such evidence as there is does not lend unequivocal support to the claim. Two studies that should have been mentioned in this book are ‘Much ado about grit: A meta-analytic synthesis of the grit literature’ (Credé et al, 2017) and ‘What shall we do about grit? A critical review of what we know and what we don’t know’ (Credé, 2018). The authors found that ‘grit as it is currently measured does not appear to be particularly predictive of success and performance’ (Credé et al, 2017) and that there is no support for the claim that ‘grit is likely to be responsive to interventions’ (Credé, 2018). In the L2 learning context, Teimouri et al (2020) concluded that more research in SLA substantiating the role of grit in L2 contexts was needed before any grit interventions can be recommended.

It has to be said that such results are hardly surprising. If, as Duckworth claims, ‘grit’ is a combination of passion and persistence, how on earth can the passion part of it be susceptible to educational interventions? ‘If there is one thing that cannot be learned, it’s passion. A person can have it and develop it, but learn it? Sadly, not’. (De Bruyckere et al., 2020: 83)

Even Duckworth herself is not convinced. In an interview on a Freakonomics podcast, she states that she hopes it’s something people can learn, but also admits not having enough proof to confirm that they can (Kirschner & Neelen, 2016)!

Is ‘grit’ a thing?

Marc Jones, in a 2016 blog post entitled ‘Gritty Politti: Grit, Growth Mindset and Neoliberal Language Teaching’, writes that ‘Grit is so difficult to define that it takes Duckworth (2016) the best part of a book to describe it adequately’. Yes, ‘grit’ is passion and persistence (or perseverance), but it’s also conscientiousness, practice and hope. Credé et al (2017) found that ‘grit is very strongly correlated with conscientiousness’ (which has already been widely studied in the educational literature). Why lump this together with passion? Another study (Muenks et al., 2017) found that ‘Students’ grit overlapped empirically with their concurrently reported self-control, self-regulation, and engagement. Students’ perseverance of effort (but not their consistency of interests) predicted their later grades, although other self-regulation and engagement variables were stronger predictors of students’ grades than was grit’. Credé (2018) concluded that ‘there appears to be no reason to accept the combination of perseverance and passion for long-term goals into a single grit construct’.

The L2 learning research listed in Plonsky’s bibliography does not offer much in support of ‘grit’, either. Many of the studies identified problems with ‘grit’ as a construct, but, even when accepting it, did not find it to be of much value. Wei et al. (2019) found a positive but weak correlation between grit and English language course grades. Yamashita (2018) found no relationship between learners’ grit and their course grades. Taşpinar & Külekçi (2018) found that students’ grit levels and academic achievement scores did not relate to each other (but still found that ‘grit, perseverance, and tenacity are the essential elements that impact learners’ ability to succeed to be prepared for the demands of today’s world’!).

There are, then, grounds for suspecting that Duckworth and her supporters have fallen foul of the ‘jangle fallacy’ – the erroneous assumption that two identical or almost identical things are different because they are labelled differently. This would also help to explain the lack of empirical support for the notion of ‘grit’. Not only are the numerous variables insufficiently differentiated, but the measures of ‘grit’ (such as Duckworth’s Grit-S measure) do not adequately target some of these variables (e.g. long-term goals, where ‘long-term’ is not defined) (Muenks et al., 2017). In addition, these measures are self-reporting and not, therefore, terribly reliable.

Referring to more general approaches to character education, one report (Gutman & Schoon, 2012) has argued that there is little empirical evidence of a causal relationship between self-concept and educational outcomes. Taking this one step further, Kathryn Ecclestone (Ecclestone, 2012) suggests that at best, the concepts and evidence that serve as the basis of these interventions are inconclusive and fragmented; ‘at worst, [they are] prey to ‘advocacy science’ or, in [their] worst manifestations, to simple entrepreneurship that competes for publicly funded interventions’ (cited in Cabanas & Illouz, 2019: 80).

Criticisms of ‘grit’

Given the lack of supporting research, any practical application of ‘grit’ ideas is premature. Duckworth herself, in an article entitled ‘Don’t Believe the Hype About Grit, Pleads the Scientist Behind the Concept’ (Dahl, 2016), cautions against hasty applications:

[By placing too much emphasis on grit, the danger is] that grit becomes a scapegoat — another reason to blame kids for not doing well, or to say that we don’t have a responsibility as a society to help them. [She worries that some interpretations of her work might make a student’s failure seem like his problem, as if he just didn’t work hard enough.] I think to separate and pit against each other character strengths on the one hand — like grit — and situational opportunities on the other is a false dichotomy […] Kids need to develop character, and they need our support in doing so.

Marc Jones, in the blog mentioned above, writes that ‘to me, grit is simply another tool for attacking the poor and the other’. You won’t win any prizes for guessing which kinds of students are most likely to be the targets of grit interventions. A clue: think of the ‘no-nonsense’ charters in the US and academies in the UK. This is what Kenneth Saltzman has to say:

‘Grit’ is a pedagogy of control that is predicated upon a promise made to poor children that if they learnt the tools of self-control and learnt to endure drudgery, then they can compete with rich children for scarce economic resources. (Saltzman, 2017: 38)

[It] is a behaviourist form of learned self-control targeting poor students of color and has been popularized post-crisis in the wake of educational privatization and defunding as the cure for poverty. [It] is designed to suggest that individual resilience and self-reliance can overcome social violence and unsupportive social contexts in the era of the shredded social state. (Saltzman, 2017: 15)

Grit is misrepresented by proponents as opening a world of individual choices rather than discussed as a mode of educational and social control in the austere world of work defined by fewer and fewer choices as secure public sector work is scaled back, unemployment continuing at high levels. (Saltzman, 2017: 49)

Whilst ‘grit’ is often presented as a way of dealing with structural inequalities in schools, critics see it as more of a problem than a solution: ‘It’s the kids who are most impacted by, rebel against, or criticize the embedded racism and classism of their institutions that are being told to have more grit, that school is hard for everyone’ (EquiTEA, 2018). A widely cited article by Nicholas Tampio (2016) points out that ‘Duckworth celebrates educational models such as Beast at West Point that weed out people who don’t obey orders’. He continues ‘that is a disastrous model for education in a democracy. US schools ought to protect dreamers, inventors, rebels and entrepreneurs – not crush them in the name of grit’.

If you’re interested in reading more critics of grit, the blog ‘Debunked!’ is an excellent source of links.

Measuring grit

Analyses of emotional behaviour have become central to economic analysis and, beginning in the 1990s, there have been constant efforts to create ‘formal instruments of classification of emotional behaviour and the elaboration of the notion of emotional competence’ (Illouz, 2007: 64). The measurement and manipulation of various aspects of ‘emotional intelligence’ have become crucial as ways ‘to control, predict, and boost performance’ (Illouz, 2007: 65). An article in the Journal of Benefit-Cost Analysis (Belfield et al., 2015) makes the economic importance of emotions very clear. Entitled ‘The Economic Value of Social and Emotional Learning’, it examines the economic value of these skills within a benefit-cost analysis (BCA) framework, and finds that the benefits of [social and emotional learning] interventions substantially outweigh the costs.

In recent years, the OECD has commissioned a number of reports on social and emotional learning and, as with everything connected with the OECD, is interested in measuringnon-cognitive skills such as perseverance (“grit”), conscientiousness, self-control, trust, attentiveness, self-esteem and self-efficacy, resilience to adversity, openness to experience, empathy, humility, tolerance of diverse opinions and the ability to engage productively in society’ (Kautz et al., 2014: 9). The measurement of personality factors will feature in the OECD’s PISA programme. Elsewhere, Ben Williamson reports that ‘US schools [are] now under pressure—following the introduction of the Every Student Succeeds Act in 2015—to provide measurable evidence of progress on the development of students’ non-academic learning’ (Williamson, 2017).

Grit, which ‘starts and ends with the lone individual as economic actor, worker, and consumer’ (Saltzman, 2017: 50), is a recent addition to the categories of emotional competence, and it should come as no surprise that educational authorities have so wholeheartedly embraced it. It is the claim that something (i.e. ‘grit’) can be taught and developed that leads directly to the desire to measure it. In a world where everything must be accountable, we need to know how effective and cost-effective our grit interventions have been.

The idea of measuring personality constructs like ‘grit’ worries even Angela Duckworth. She writes (Duckworth, 2016):

These days, however, I worry I’ve contributed, inadvertently, to an idea I vigorously oppose: high-stakes character assessment. New federal legislation can be interpreted as encouraging states and schools to incorporate measures of character into their accountability systems. This year, nine California school districts will begin doing this. But we’re nowhere near ready — and perhaps never will be — to use feedback on character as a metric for judging the effectiveness of teachers and schools. We shouldn’t be rewarding or punishing schools for how students perform on these measures.

Diane Ravitch (Ravitch, 2016) makes the point rather more forcefully: ‘The urge to quantify the unmeasurable must be recognized for what it is: stupid; arrogant; harmful; foolish, yet another way to standardize our beings’. But, like it or not, attempts to measure ‘grit’ and ‘grit’ interventions are unlikely to go away any time soon.

‘Grit’ and technology

Whenever there is talk about educational measurement and metrics, we are never far away from the world of edtech. It may not have escaped your notice that the OECD and the US Department of State for Education, enthusiasts for promoting ‘grit’, are also major players in the promotion of the datafication of education. The same holds true for organisations like the World Education Forum, the World Bank and the various philanthro-capitalist foundations to which I have referred so often in this blog. Advocacy of social and emotional learning goes hand in hand with edtech advocacy.

Two fascinating articles by Ben Williamson (2017; 2019) focus on ClassDojo, which, according to company information, reaches more than 10 million children globally every day. The founding directors of ClassDojo, writes Ben Williamson (2017), ‘explicitly describe its purpose as promoting ‘character development’ in schools and it is underpinned by particular psychological concepts from character research. Its website approvingly cites the journalist Paul Tough, author of two books on promoting ‘grit’ and ‘character’ in children, and is informed by character research conducted with the US network of KIPP charter schools (Knowledge is Power Program)’. In a circular process, ClassDojo has also ‘helped distribute and popularise concepts such as growth mindset, grit and mindfulness’ (Williamson, 2019).

The connections between ‘grit’ and edtech are especially visible when we focus on Stanford and Silicon Valley. ClassDojo was born in Palo Alto. Duckworth was a consulting scholar at Stanford 2014 -15, where Carol Dweck is a Professor of Psychology. Dweck is the big name behind growth mindset theory, which, as Sarah Mercer and Zoltán Dörnyei indicate, is closely related to ‘grit’. Dweck is also the co-founder of MindsetWorks, whose ‘Brainology’ product is ‘an online interactive program in which middle school students learn about how the brain works, how to strengthen their own brains, and how to ….’. Stanford is also home to the Stanford Lytics Lab, ‘which has begun applying new data analytics techniques to the measurement of non-cognitive learning factors including perseverance, grit, emotional state, motivation and self-regulation’, as well as the Persuasive Technologies Lab, ‘which focuses on the development of machines designed to influence human beliefs and behaviors across domains including health, business, safety, and education’ (Williamson, 2017). The Professor of Education Emeritus at Stanford is Linda Darling-Hammond, one of the most influential educators in the US. Darling-Hammond is known, among many other things, for collaborating with Pearson to develop the edTPA, ‘a nationally available, performance-based assessment for measuring the effectiveness of teacher candidates’. She is also a strong advocate of social-emotional learning initiatives and extols the virtues of ‘developing grit and a growth mindset’ (Hamadi & Darling-Hammond, 2015).

The funding of grit

Angela Duckworth’s Character Lab (‘Our mission is to advance scientific insights that help kids thrive’) is funded by, among others, the Chan Zuckerberg Initiative, the Bezos Family Foundation and Stanford’s Mindset Scholars Network. Precisely how much money Character Lab has is difficult to ascertain, but the latest grant from the Chan Zuckerberg Initiative was worth $1,912,000 to cover the period 2018 – 2021. Covering the same period, the John Templeton Foundation, donated $3,717,258 , the purpose of the grant being to ‘make character development fast, frictionless, and fruitful’.

In an earlier period (2015 – 2018), the Walton Family Foundation pledged $6.5 millionto promote and measure character education, social-emotional learning, and grit’, with part of this sum going to Character Lab and part going to similar research at Harvard Graduate School of Education. Character Lab also received $1,300,000 from the Overdeck Family Foundation for the same period.

It is not, therefore, an overstatement to say that ‘grit’ is massively funded. The funders, by and large, are the same people who have spent huge sums promoting personalized learning through technology (see my blog post Personalized learning: Hydra and the power of ambiguity). Whatever else it might be, ‘grit’ is certainly ‘a commercial tech interest’ (as Ben Williamson put it in a recent tweet).

Postscript

In the 2010 Cohen brothers’ film, ‘True Grit’, the delinquent ‘kid’, Moon, is knifed by his partner, Quincy. Turning to Rooster Cogburn, the man of true grit, Moon begs for help. In response, Cogburn looks at the dying kid and deadpans ‘I can do nothing for you, son’.

References

Belfield, C., Bowden, A., Klapp, A., Levin, H., Shand, R., & Zander, S. (2015). The Economic Value of Social and Emotional Learning. Journal of Benefit-Cost Analysis, 6(3), pp. 508-544. doi:10.1017/bca.2015.55

Cabanas, E. & Illouz, E. (2019). Manufacturing Happy Citizens. Cambridge: Polity Press.

Chaykowski, K. (2017). How ClassDojo Built One Of The Most Popular Classroom Apps By Listening To Teachers. Forbes, 22 May, 2017. https://www.forbes.com/sites/kathleenchaykowski/2017/05/22/how-classdojo-built-one-of-the-most-popular-classroom-apps-by-listening-to-teachers/#ea93d51e5ef5

Credé, M. (2018). What shall we do about grit? A critical review of what we know and what we don’t know. Educational Researcher, 47(9), 606-611.

Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492. doi:10.1037/pspp0000102

Dahl, M. (2016). Don’t Believe the Hype About Grit, Pleads the Scientist Behind the Concept. The Cut, May 9, 2016. https://www.thecut.com/2016/05/dont-believe-the-hype-about-grit-pleads-the-scientist-behind-the-concept.html

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Duckworth, A. (2016). Don’t Grade Schools on Grit. New York Times, March 26, 2016 https://www.nytimes.com/2016/03/27/opinion/sunday/dont-grade-schools-on-grit.html?auth=login-google&smid=nytcore-ipad-share&smprod=nytcore-ipad

Ecclestone, K. (2012). From emotional and psychological well-being to character education: Challenging policy discourses of behavioural science and ‘vulnerability’. Research Papers in Education, 27 (4), pp. 463-480

EquiTEA (2018). The Problem with Teaching ‘Grit’. Medium, 11 December 2018. https://medium.com/@eec/the-problem-with-teaching-grit-8b37ce43a87e

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Kirschner, P.A. & Neelen, M. (2016). To Grit Or Not To Grit: That’s The Question. 3-Star Learning Experiences, July 5, 2016 https://3starlearningexperiences.wordpress.com/2016/07/05/to-grit-or-not-to-grit-thats-the-question/

Illouz, E. (2007). Cold Intimacies: The making of emotional capitalism. Cambridge: Polity Press

Kautz, T., Heckman, J. J., Diris, R., ter Weel, B & Borghans, L. (2014). Fostering and Measuring Skills: Improving Cognitive and Non-cognitive Skills to Promote Lifetime Success. OECD Education Working Papers 110, OECD Publishing.

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

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Saltzman, K. J. (2017). Scripted Bodies. Routledge.

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Taşpinar, K., & Külekçi, G. (2018). GRIT: An Essential Ingredient of Success in the EFL Classroom. International Journal of Languages’ Education and Teaching, 6, 208-226.

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