Twee – an edtech cautionary tale

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

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

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

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

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

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

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

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

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

AI and teacher training

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

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

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

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

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

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

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

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

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

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

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

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

Learning and earning

In the campaign for leadership of the British Conservative party, prime ministerial wannabe, Rishi Sunak, announced that he wanted to phase out all university degrees with low ‘earning potential’. This would mean the end of undergraduate courses in fashion, film, philosophy, English language and media studies. And linguistics. More of an attention-grabbing soundbite than anything else, it reflects a view of education that is shared by his competitor, Liz Truss, who ‘is passionate about giving every child basic maths and science skills’ as a way of driving the contribution of education to the economy.

It’s a view that is shared these days by practically everyone with any power and influence, from national governments to organisations like the EU and the OECD (Schuller, 2000). It is rooted in the belief that what matters most in education are the teachable knowledges, skills and competences that are relevant to economic activity (as the OECD puts it). These competences are seen to be essential to economic growth and competitivity, and essential to individuals to enhance their employment potential. Learning equals earning. The way for societies to push this orientation to education is to allow market forces to respond to the presumed demands of the consumers of education (students and their sponsors), as they seek to obtain the best possible return on their investment in education. Market forces are given more power when education is privatized and uncoupled from the state. For this to happen, the market may need a little help in the form of policies from the likes of Sunak and Truss.

This set of beliefs has a name: human capital theory (Becker, 1993). Human capital refers both to the skills that individuals ‘bring to bear in the economy and the need for capital investment in these’ (Holborow, 2012). It is impossible to overstate just how pervasive this theory in contemporary approaches to education is. See, for example, this selection of articles from Science Direct. It is also very easy to forget how recently the lens of human capital has become practically the only lens through which education is viewed.

Contemporary language teaching is perhaps best understood as a series of initiatives that have been driven by human capital theory. First and foremost, there is the global ‘frenzied rush towards acquiring English’ (Holborow, 2018), driven both by governments and by individuals who see that foreign language competence (especially English) ‘might […]open up new opportunities for students [and] assist them in breaking social barriers’ (Kormos & Kiddle, 2013). Children, at ever younger ages (even pre-school), are pushed towards getting a headstart in the race to acquire human capital, whilst there has been an explosive growth in EMI courses (Lasagabaster, 2022). At the same time, there has been mushrooming interest in so-called 21^st century skills (or ‘life skills’ / ‘global skills’) in the English language curriculum. These skills have been identified by asking employers what skills matter most to them when recruiting staff. Critical and creative thinking skills may be seen as having pre-Human Capital, intrinsic educational worth, but it is their potential contribution to economic productivity that explains their general current acceptance.

Investments in human capital need to be measured and measurable. Language teaching needs to be made accountable. Our preoccupation with learning outcomes is seen in the endless number of competency frameworks, and with new tools for quantifying language proficiency. Technology facilitates this evaluation, promises to deliver language teaching more efficiently, and technological skills are, after English language skills themselves, seen to be the most bankable of 21^st century skills. Current interest in social-emotional learning – growth mindsets, grit, resilience and so on – is also driven by a concern to make learning more efficient.

In all of these aspects of language teaching / learning, the private sector (often in private-public partnerships) is very visible. This is by design. Supported by the state, the market economy of education grows in tandem with the rising influence of the private sector on national educational policy. When education ministers lose their job, they can easily find well-paid consultancies in the private sector (as in the case of Sunak and Truss’s colleague, Gavin Williamson).

One of the powers of market-economy ideologies is that it often seems that ‘there is no alternative’ (TINA). There are, however, good reasons to try to think in alternative terms. To begin with, and limiting ourselves for the moment to language teaching, there is a desperate lack of evidence that starting English language learning at very young ages (in the way that is most typically done) will lead to any appreciable gains in the human capital race. It is generally recognised that EMI is highly problematic in a variety of ways (Lasagabaster, 2022). The focus on 21^st century skills has not led to any significant growth in learning outcomes when these skills are measured. There is a worrying lack of evidence that interventions in schools to promote improvements in critical or creative thinking have had much, if any, impact at all. Similarly, there is a worrying lack of evidence that attention to growth mindsets or grit has led to very much at all. Personalized learning, facilitated by technology, likewise has a dismal track record. At the same time, there is no evidence that the interest in measuring learning outcomes has led to any improvement in those outcomes. For all the millions and millions that have been invested in all these trends, the returns have been very slim. Perhaps we would have done better to look for solutions to those aspects of language teaching which we know to be problematic. The obsession with synthetic syllabuses delivered by coursebooks (or their online equivalents) comes to mind.

But beyond the failure of all these things to deliver on their promises, there are broader issues. Although language skills (usually English) have the potential to enhance employment prospects, Holborow (2018) has noted that they do not necessarily do so (see, for example, Yeung & Gray, 2022). Precisely how important language skills are is very hard to determine. A 2016 survey by Cambridge English found that ‘approximately half of all employers offer a better starting package to applicants with good English language skills’ and a similar number indicate that these skills result in faster career progression. But these numbers need to be treated with caution, not least because Cambridge English is in the business of selling English. More importantly, it seems highly unlikely that the figures that are reported reflect the reality of job markets around the world. The survey observes that banking, finance and law are the sectors with the greatest need for such skills, but these are all usually graduate posts. An average of 39% of the population in OECD countries has tertiary education; the percentage is much lower elsewhere. How many students of a given age cohort will actually work in these sectors? Even in rich countries, like Germany and the Netherlands, between 40 and 60% of workers are employed in what is termed ‘nonstandard forms of work’ (OECD, 2015) where language skills will count for little or nothing. These numbers are growing. Language skills are of most value to those students who are already relatively advantaged. That is not to say that there are no potential benefits to everyone in learning English, but these benefits will not be found in better jobs and wages for the majority. One interesting case study describes how a Swiss airport company exploits the language skills of migrant workers, without any benefits (salary or mobility) accruing to the workers themselves (Duchêne, 2011).

The relationship between learning English and earning more is a lot more complex than is usually presented. The same holds true for learning more generally. In the US, ‘nearly two-thirds of job openings in 2020 required no more than a high school diploma’ (Brown et al., 2022: 222). Earnings for graduates in real terms are in decline, except for those at the very top. For the rest, over $1.3 trillion in student loan debt remains unpaid. Elsewhere in the world, the picture is more mixed, but it is clear that learning does not equal earning in the global gig economy.

This evident uncoupling of learning from earning has led some to conclude that education is ‘a waste of time and money’ (Caplan, 2018), a view that has been gaining traction in the US. It’s not an entirely unreasonable view, if the only reason for education is seen to be its contribution to the economy. More commonly, the reaction has been to double-down on human capital theory. In Spain, for example, with its high levels of youth unemployment, there are calls for closer links between educational institutions, and graduates themselves are blamed for failing to take ‘advantage of the upgrading in the demand for skills’ (Bentolilla et al., 2022). This seems almost wilfully cruel, especially since the authors note that there is global trend in falling economic returns in tertiary education (ILO, 2020).

But, rather than doubling-down on human capital theory (e.g. more vocational training, more efficient delivery of the training), it might be a good idea to question human capital theory itself. Both early and more recent critics have tended to accept without hesitation that education can enhance worker productivity, but argue that, as a theory, it is too simplistic to have much explanatory power, and that the supporting evidence is weak, vague or untestable (Bowles & Gintis, 1975; Fix, 2018). Language skills, like education more generally, do not always lead to better employment prospects and salaries, because ‘wider, systemic social inequalities come into play’ (Holborow, 2018). It is not because black women need to brush up on their 21^st century skills that they earn less than white men.

Until recently, critics of human capital theory have been a minority, and largely unheard, voice. But this appear to be changing. The World Bank, more guilty than anyone for pushing human capital theory on the global stage (see here), has recognised that hoped-for job outcomes do not always materialize after massive investments in training systems (World Bank, 2013). Mainstream critics include the Nobel prize winners Joseph Stiglitz and Amartya Sen, and the recent OUP title, ‘The Death of Human Capital?’ (Brown et al., 2020) is likely to spur debate further. The assumption that human capital theory holds water no longer holds water.

When we turn back to English language teaching, we might benefit from some new thinking. For sure, there will be large numbers of English language learners whose only purpose in studying is utilitarian, whose primary desire is to enhance their human capital. But there are also millions, especially children studying in public schools, for whom things are rather different. A major change in thinking involves a reconceptualization of the point of all this English study. If learning English is not, for the majority, seen primarily as a preparation for the workplace, but as compensation for the realities of (un)employment (Brown et al., 2020: 13), most of the recent innovations in ELT would become highly suspect. We would have a much less impoverished view of ‘the complex and multifaceted nature of language’ (Holborow, 2018) and we would find more space for plurilingual practices. A brake on relentless Englishization might be no bad thing (Wilkinson & Gabriëls, 2021). We might be able to explore more fully artistic and creative uses of language. Who knows? We might finally get round to wider implementation of language teaching approaches that we know have a decent chance of success.

References

Becker, G. S. (1993). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education (3^rd ed.). University of Chicago Press.

Bentolila, S., Felgueroso, F., Jansen, M. et al. (2022). Lost in recessions: youth employment and earnings in Spain. SERIEs 13: 11–49. https://doi.org/10.1007/s13209-021-00244-6

Bowles, S. & Gintis, H. (1975). The Problem with Human Capital Theory – a Marxian critique. The American Economic Review, 65 (2): 74 – 83

Brown, S., Lauder, H. & Cheung, S. Y. (2020). The Death of Human Capital? New York: Oxford University Press

Caplan, B. (2018). The Case against Education: Why the Education System is a Waste of Time and Money. Princeton, NJ: Princeton University Press

Duchêne, A. (2011). Neoliberalism, Social Inequalities, and Multilingualism: The Exploitation of Linguistic Resources and Speakers. Langage et Société, 136 (2): 81 – 108

Fix, B. (2018). The Trouble with Human Capital Theory. Working Papers on Capital as Power, No. 2018/7

Holborow, M. (2012). Neoliberal keywords and the contradictions of an ideology. In Block, D., Gray, J. & Holborow, M. Neoliberalism and Applied Linguistics. Abingdon: Routledge: 33 – 55

Holborow, M. (2018). Language skills as human capital? Challenging the neoliberal frame. Language and Intercultural Communication, 18: (5): 520-532

ILO (2020). Global employment trends for youth, 2020. Geneva: International Labour Organization

Kormos, J., & Kiddle, T. (2013). The role of socio-economic factors in motivation to learn English as a foreign language: the case of Chile. System, 41(2): 399-412

Lasagabaster, D. (2022). English-Medium Instruction in Higher Education. Cambridge: Cambridge University Press

OECD (2015). In It Together, Why Less Inequality Benefits All. Paris: OECD

Schuller, T. (2000). Social and Human Capital: The Search for Appropriate Technomethodology. Policy Studies, 21 (1): 25 – 35

Wilkinson, R., & Gabriëls, R. (Eds.) (2021). The Englishization of Higher Education in Europe. Amsterdam: Amsterdam University Press.

World Bank (2012). World Development Report 2013: Jobs. Washington, DC: World Bank

Yeung, S. & Gray, J. (2022). Neoliberalism, English, and spoiled identity: The case of a high-achieving university graduate in Hong Kong. Language in Society, First View, pp. 1 – 22

How to write about AI in language learning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

The digital future of education (according to the OECD)

On 21 January, I attended the launch webinar of DEFI (the Digital Education Futures Initiative), an initiative of the University of Cambridge, which seeks to work ‘with partners in industry, policy and practice to explore the field of possibilities that digital technology opens up for education’. The opening keynote speaker was Andrea Schleicher, head of education at the OECD. The OECD’s vision of the future of education is outlined in Schleicher’s book, ‘World Class: How to Build a 21st-Century School System’, freely available from the OECD, but his presentation for DEFI offers a relatively short summary. A recording is available here, and this post will take a closer look at some of the things he had to say.

Schleicher is a statistician and the coordinator of the OECD’s PISA programme. Along with other international organisations, such as the World Economic Forum and the World Bank (see my post here), the OECD promotes the global economization and corporatization of education, ‘based on the [human capital] view that developing work skills is the primary purpose of schooling’ (Spring, 2015: 14). In other words, the main proper function of education is seen to be meeting the needs of global corporate interests. In the early days of the COVID-19 pandemic, with the impact of school closures becoming very visible, Schleicher expressed concern about the disruption to human capital development, but thought it was ‘a great moment’: ‘the current wave of school closures offers an opportunity for experimentation and for envisioning new models of education’. Every cloud has a silver lining, and the pandemic has been a godsend for private companies selling digital learning (see my post about this here) and for those who want to reimagine education in a more corporate way.

Schleicher’s presentation for DEFI was a good opportunity to look again at the way in which organisations like the OECD are shaping educational discourse (see my post about the EdTech imaginary and ELT).

He begins by suggesting that, as a result of the development of digital technology (Google, YouTube, etc.) literacy is ‘no longer just about extracting knowledge’. PISA reading scores, he points out, have remained more or less static since 2000, despite the fact that we have invested (globally) more than 15% extra per student in this time. Only 9% of all 15-year-old students in the industrialised world can distinguish between fact and opinion.

To begin with, one might argue about the reliability and validity of the PISA reading scores (Berliner, 2020). One might also argue, as did a collection of 80 education experts in a letter to the Guardian, that the scores themselves are responsible for damaging global education, raising further questions about their validity. One might argue that the increased investment was spent in the wrong way (e.g. on hardware and software, rather than teacher training, for example), because the advice of organisations like OECD has been uncritically followed. And the statistic about critical reading skills is fairly meaningless unless it is compared to comparable metrics over a long time span: there is no reason to believe that susceptibility to fake news is any more of a problem now than it was, say, one hundred years ago. Nor is there any reason to believe that education can solve the fake-news problem (see my post about fake news and critical thinking here). These are more than just quibbles, but the main point that Schleicher is making is that education needs to change.

Schleicher next presents a graph which is designed to show that the amount of time that students spend studying correlates poorly with the amount they learn. His interest is in the (lack of) productivity of educational activities in some contexts. He goes on to argue that there is greater productivity in educational activities when learners have a growth mindset, implying (but not stating) that mindset interventions in schools would lead to a more productive educational environment.

Schleicher appears to confuse what students learn with the things they have learnt that have been measured by PISA. The two are obviously rather different, since PISA is only interested in a relatively small subset of the possible learning outcomes of schooling. His argument for growth mindset interventions hinges on the assumption that such interventions will lead to gains in reading scores. However, his graph demonstrates a correlation between growth mindset and reading scores, not a causal relationship. A causal relationship has not been clearly and empirically demonstrated (see my post about growth mindsets here) and recent work by Carol Dweck and her associates (e.g. Yeager et al., 2016), as well as other researchers (e.g. McPartlan et al, 2020), indicates that the relationship between gains in learning outcomes and mindset interventions is extremely complex.

Schleicher then turns to digitalisation and briefly discusses the positive and negative affordances of technology. He eulogizes platform companies before showing a slide designed to demonstrate that (in the workplace) there is a strong correlation between ICT use and learning. He concludes: ‘the digital world of learning is a hugely empowering world of learning’.

A brief paraphrase of this very disingenuous part of the presentation would be: technology can be good and bad, but I’ll only focus on the former. The discourse appears balanced, but it is anything but.

During the segment, Schleicher argues that technology is empowering, and gives the examples of ‘the most successful companies these days, they’re not created by a big industry, they’re created by a big idea’. This is plainly counterfactual. In the case of Alphabet and Facebook, profits did not follow from a ‘big idea’: the ideas changed as the companies evolved.

Schleicher then sketches a picture of an unpredictable future (pandemics, climate change, AI, cyber wars, etc.) as a way of framing the importance of being open (and resilient) to different futures and how we respond to them. He offers two different kinds of response: maintenance of the status quo, or ‘outsourcing’ of education. The pandemic, he suggests, has made more countries aware that the latter is the way forward.

In his discussion of the maintenance of the status quo, Schleicher talks about the maintenance of educational monopolies. By this, he must be referring to state monopolies on education: this is a favoured way of neoliberals of referring to state-sponsored education. But the extent to which, in 2021 in many OECD countries, the state has any kind of monopoly of education, is very open to debate. Privatization is advancing fast. Even in 2015, the World Education Forum’s ‘Final Report’ wrote that ‘the scale of engagement of nonstate actors at all levels of education is growing and becoming more diversified’. Schleicher goes on to talk about ‘large, bureaucratic school systems’, suggesting that such systems cannot be sufficiently agile, adaptive or responsive. ‘We should ask this question,’ he says, but his own answer to it is totally transparent: ‘changing education can be like moving graveyards’ is the title of the next slide. Education needs to be more like the health sector, he claims, which has been able to develop a COVID vaccine in such a short period of time. We need an education industry that underpins change in the same way as the health industry underpins vaccine development. In case his message isn’t yet clear enough, I’ll spell it out: education needs to be privatized still further.

Schleicher then turns to the ways in which he feels that digital technology can enhance learning. These include the use of AR, VR and AI. Technology, he says, can make learning so much more personalized: ‘the computer can study how you study, and then adapt learning so that it is much more granular, so much more adaptive, so much more responsive to your learning style’. He moves on to the field of assessment, again singing the praises of technology in the ways that it can offer new modes of assessment and ‘increase the reliability of machine rating for essays’. Through technology, we can ‘reunite learning and assessment’. Moving on to learning analytics, he briefly mentions privacy issues, before enthusing at greater length about the benefits of analytics.

Learning styles? Really? The reliability of machine scoring of essays? How reliable exactly? Data privacy as an area worth only a passing mention? The use of sensors to measure learners’ responses to learning experiences? Any pretence of balance appears now to have been shed. This is in-your-face sales talk.

Next up is a graph which purports to show the number of teachers in OECD countries who use technology for learners’ project work. This is followed by another graph showing the number of teachers who have participated in face-to-face and online CPD. The point of this is to argue that online CPD needs to become more common.

I couldn’t understand what point he was trying to make with the first graph. For the second, it is surely the quality of the CPD, rather than the channel, that matters.

Schleicher then turns to two further possible responses of education to unpredictable futures: ‘schools as learning hubs’ and ‘learn-as-you-go’. In the latter, digital infrastructure replaces physical infrastructure. Neither is explored in any detail. The main point appears to be that we should consider these possibilities, weighing up as we do so the risks and the opportunities (see slide below).

Useful ways to frame questions about the future of education, no doubt, but Schleicher is operating with a set of assumptions about the purpose of education, which he chooses not to explore. His fundamental assumption – that the primary purpose of education is to develop human capital in and for the global economy – is not one that I would share. However, if you do take that view, then privatization, economization, digitalization and the training of social-emotional competences are all reasonable corollaries, and the big question about the future concerns how to go about this in a more efficient way.

Schleicher’s (and the OECD’s) views are very much in accord with the libertarian values of the right-wing philanthro-capitalist foundations of the United States (the Gates Foundation, the Broad Foundation and so on), funded by Silicon Valley and hedge-fund managers. It is to the US that we can trace the spread and promotion of these ideas, but it is also, perhaps, to the US that we can now turn in search of hope for an alternative educational future. The privatization / disruption / reform movement in the US has stalled in recent years, as it has become clear that it failed to deliver on its promise of improved learning. The resistance to privatized and digitalized education is chronicled in Diane Ravitch’s latest book, ‘Slaying Goliath’ (2020). School closures during the pandemic may have been ‘a great moment’ for Schleicher, but for most of us, they have underscored the importance of face-to-face free public schooling. Now, with the electoral victory of Joe Biden and the appointment of a new US Secretary for Education (still to be confirmed), we are likely to see, for the first time in decades, an education policy that is firmly committed to public schools. The US is by far the largest contributor to the budget of the OECD – more than twice any other nation. Perhaps a rethink of the OECD’s educational policies will soon be in order?

References

Berliner D.C. (2020) The Implications of Understanding That PISA Is Simply Another Standardized Achievement Test. In Fan G., Popkewitz T. (Eds.) Handbook of Education Policy Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-8343-4_13

McPartlan, P., Solanki, S., Xu, D. & Sato, B. (2020) Testing Basic Assumptions Reveals When (Not) to Expect Mindset and Belonging Interventions to Succeed. AERA Open, 6 (4): 1 – 16 https://journals.sagepub.com/doi/pdf/10.1177/2332858420966994

Ravitch, D. (2020) Slaying Goliath: The Passionate Resistance to Privatization and the Fight to Save America’s Public School. New York: Vintage Books

Schleicher, A. (2018) World Class: How to Build a 21^st-Century School System. Paris: OECD Publishing https://www.oecd.org/education/world-class-9789264300002-en.htm

Spring, J. (2015) Globalization of Education 2nd Edition. New York: Routledge

Yeager, D. S., et al. (2016) Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3), 374–391. https://doi.org/10.1037/edu0000098

Critical data literacy #1: our data and ELT websites

Take the Cambridge Assessment English website, for example. When you connect to the site, you will see, at the bottom of the screen, a familiar (to people in Europe, at least) notification about the site’s use of cookies: the cookies consent.

You probably trust the site, so ignore the notification and quickly move on to find the resource you are looking for. But if you did click on hyperlinked ‘set cookies’, what would you find? The first link takes you to the ‘Cookie policy’ where you will be told that ‘We use cookies principally because we want to make our websites and mobile applications user-friendly, and we are interested in anonymous user behaviour. Generally our cookies don’t store sensitive or personally identifiable information such as your name and address or credit card details’. Scroll down, and you will find out more about the kind of cookies that are used. Besides the cookies that are necessary to the functioning of the site, you will see that there are also ‘third party cookies’. These are explained as follows: ‘Cambridge Assessment works with third parties who serve advertisements or present offers on our behalf and personalise the content that you see. Cookies may be used by those third parties to build a profile of your interests and show you relevant adverts on other sites. They do not store personal information directly but use a unique identifier in your browser or internet device. If you do not allow these cookies, you will experience less targeted content’.

This is not factually inaccurate: personal information is not stored directly. However, it is extremely easy for this information to be triangulated with other information to identify you personally. In addition to the data that you generate by having cookies on your device, Cambridge Assessment will also directly collect data about you. Depending on your interactions with Cambridge Assessment, this will include ‘your name, date of birth, gender, contact data including your home/work postal address, email address and phone number, transaction data including your credit card number when you make a payment to us, technical data including internet protocol (IP) address, login data, browser type and technology used to access this website’. They say they may share this data ‘with other people and/or businesses who provide services on our behalf or at our request’ and ‘with social media platforms, including but not limited to Facebook, Google, Google Analytics, LinkedIn, in pseudonymised or anonymised forms’.

In short, Cambridge Assessment may hold a huge amount of data about you and they can, basically, do what they like with it.

The cookie and privacy policies are fairly standard, as is the lack of transparency in the phrasing of them. Rather more transparency would include, for example, information about which particular ad trackers you are giving your consent to. This information can be found with a browser extension tool like Ghostery, and these trackers can be blocked. As you’ll see below, there are 5 ad trackers on this site. This is rather more than other sites that English language teachers are likely to go to. ETS-TOEFL has 4, Macmillan English and Pearson have 3, CUP ELT and the British Council Teaching English have 1, OUP ELT, IATEFL, BBC Learning English and Trinity College have none. I could only find TESOL, with 6 ad trackers, which has more. The blogs for all these organisations invariably have more trackers than their websites.

The use of numerous ad trackers is probably a reflection of the importance that Cambridge Assessment gives to social media marketing. There is a research paper, produced by Cambridge Assessment, which outlines the significance of big data and social media analytics. They have far more Facebook followers (and nearly 6 million likes) than any other ELT page, and they are proud of their #1 ranking in the education category of social media. The amount of data that can be collected here is enormous and it can be analysed in myriad ways using tools like Ubervu, Yomego and Hootsuite.

A little more transparency, however, would not go amiss. According to a report in Vox, Apple has announced that some time next year ‘iPhone users will start seeing a new question when they use many of the apps on their devices: Do they want the app to follow them around the internet, tracking their behavior?’ Obviously, Google and Facebook are none too pleased about this and will be fighting back. The implications for ad trackers and online advertising, more generally, are potentially huge. I wrote to Cambridge Assessment about this and was pleased to hear that ‘Cambridge Assessment are currently reviewing the process by which we obtain users consent for the use of cookies with the intention of moving to a much more transparent model in the future’. Let’s hope that other ELT organisations are doing the same.

You may be less bothered than I am by the thought of dozens of ad trackers following you around the net so that you can be served with more personalized ads. But the digital profile about you, to which these cookies contribute, may include information about your ethnicity, disabilities and sexual orientation. This profile is auctioned to advertisers when you visit some sites, allowing them to show you ‘personalized’ adverts based on the categories in your digital profile. Contrary to EU regulations, these categories may include whether you have cancer, a substance-abuse problem, your politics and religion (as reported in Fortune https://fortune.com/2019/01/28/google-iab-sensitive-profiles/ ).

But it’s not these cookies that are the most worrying aspect about our lack of digital privacy. It’s the sheer quantity of personal data that is stored about us. Every time we ask our students to use an app or a platform, we are asking them to divulge huge amounts of data. With ClassDojo, for example, this includes names, usernames, passwords, age, addresses, photographs, videos, documents, drawings, or audio files, IP addresses and browser details, clicks, referring URL’s, time spent on site, and page views (Manolev et al., 2019; see also Williamson, 2019).

It is now widely recognized that the ‘consent’ that is obtained through cookie policies and other end-user agreements is largely spurious. These consent agreements, as Sadowski (2019) observes, are non-negotiated, and non-negotiable; you either agree or you are denied access. What’s more, he adds, citing one study, it would take 76 days, working for 8 hours a day, to read the privacy policies a person typically encounters in a year. As a result, most of us choose not to choose when we accept online services (Cobo, 2019: 25). We have little, if any, control over how the data that is collected is used (Birch et al., 2020). More importantly, perhaps, when we ask our students to sign up to an educational app, we are asking / telling them to give away their personal data, not just ours. They are unlikely to fully understand the consequences of doing so.

The extent of this ignorance is also now widely recognized. In the UK, for example, two reports (cited by Sander, 2020) indicate that ‘only a third of people know that data they have not actively chosen to share has been collected’ (Doteveryone, 2018: 5), and that ‘less than half of British adult internet users are aware that apps collect their location and information on their personal preferences’ (Ofcom, 2019: 14).

The main problem with this has been expressed by programmer and activist, Richard Stallman, in an interview with New York magazine (Kulwin, 2018): Companies are collecting data about people. The data that is collected will be abused. That’s not an absolute certainty, but it’s a practical, extreme likelihood, which is enough to make collection a problem.

The abuse that Smallman is referring to can come in a variety of forms. At the relatively trivial end is the personalized advertising. Much more serious is the way that data aggregation companies will scrape data from a variety of sources, building up individual data profiles which can be used to make significant life-impacting decisions, such as final academic grades or whether one is offered a job, insurance or credit (Manolev et al., 2019). Cathy O’Neil’s (2016) best-selling ‘Weapons of Math Destruction’ spells out in detail how this abuse of data increases racial, gender and class inequalities. And after the revelations of Edward Snowden, we all know about the routine collection by states of huge amounts of data about, well, everyone. Whether it’s used for predictive policing or straightforward repression or something else, it is simply not possible for younger people, our students, to know what personal data they may regret divulging at a later date.

Digital educational providers may try to reassure us that they will keep data private, and not use it for advertising purposes, but the reassurances are hollow. These companies may change their terms and conditions further down the line, and examples exist of when this has happened (Moore, 2018: 210). But even if this does not happen, the data can never be secure. Illegal data breaches and cyber attacks are relentless, and education ranked worst at cybersecurity out of 17 major industries in one recent analysis (Foresman, 2018). One report suggests that one in five US schools and colleges have fallen victim to cyber-crime. Two weeks ago, I learnt (by chance, as I happened to be looking at my security settings on Chrome) that my passwords for Quizlet, Future Learn, Elsevier and Science Direct had been compromised by a data breach. To get a better understanding of the scale of data breaches, you might like to look at the UK’s IT Governance site, which lists detected and publicly disclosed data breaches and cyber attacks each month (36.6 million records breached in August 2020). If you scroll through the list, you’ll see how many of them are educational sites. You’ll also see a comment about how leaky organisations have been throughout lockdown … because they weren’t prepared for the sudden shift online.

Recent years have seen a growing consensus that ‘it is crucial for language teaching to […] encompass the digital literacies which are increasingly central to learners’ […] lives’ (Dudeney et al., 2013). Most of the focus has been on the skills that are needed to use digital media. There also appears to be growing interest in developing critical thinking skills in the context of digital media (e.g. Peachey, 2016) – identifying fake news and so on. To a much lesser extent, there has been some focus on ‘issues of digital identity, responsibility, safety and ethics when students use these technologies’ (Mavridi, 2020a: 172). Mavridi (2020b: 91) also briefly discusses the personal risks of digital footprints, but she does not have the space to explore more fully the notion of critical data literacy. This literacy involves an understanding of not just the personal risks of using ‘free’ educational apps and platforms, but of why they are ‘free’ in the first place. Sander (2020b) suggests that this literacy entails ‘an understanding of datafication, recognizing the risks and benefits of the growing prevalence of data collection, analytics, automation, and predictive systems, as well as being able to critically reflect upon these developments. This includes, but goes beyond the skills of, for example, changing one’s social media settings, and rather constitutes an altered view on the pervasive, structural, and systemic levels of changing big data systems in our datafied societies’.

In my next two posts, I will, first of all, explore in more detail the idea of critical data literacy, before suggesting a range of classroom resources.

(I posted about privacy in March 2014, when I looked at the connections between big data and personalized / adaptive learning. In another post, September 2014, I looked at the claims of the CEO of Knewton who bragged that his company had five orders of magnitude more data about you than Google has. … We literally have more data about our students than any company has about anybody else about anything, and it’s not even close. You might find both of these posts interesting.)

References

Birch, K., Chiappetta, M. & Artyushina, A. (2020). ‘The problem of innovation in technoscientific capitalism: data rentiership and the policy implications of turning personal digital data into a private asset’ Policy Studies, 41:5, 468-487, DOI: 10.1080/01442872.2020.1748264

Cobo, C. (2019). I Accept the Terms and Conditions. https://adaptivelearninginelt.files.wordpress.com/2020/01/41acf-cd84b5_7a6e74f4592c460b8f34d1f69f2d5068.pdf

Doteveryone. (2018). People, Power and Technology: The 2018 Digital Attitudes Report. https://attitudes.doteveryone.org.uk

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

Foresman, B. (2018). Education ranked worst at cybersecurity out of 17 major industries. Edscoop, December 17, 2018. https://edscoop.com/education-ranked-worst-at-cybersecurity-out-of-17-major-industries/

Kulwin, K. (2018). F*ck Them. We Need a Law’: A Legendary Programmer Takes on Silicon Valley, New York Intelligencer, 2018, https://nymag.com/intelligencer/2018/04/richard-stallman-rms-on-privacy-data-and-free-software.html

Manolev, J., Sullivan, A. & Slee, R. (2019). ‘Vast amounts of data about our children are being harvested and stored via apps used by schools’ EduReseach Matters, February 18, 2019. https://www.aare.edu.au/blog/?p=3712

Mavridi, S. (2020a). Fostering Students’ Digital Responsibility, Ethics and Safety Skills (Dress). In Mavridi, S. & Saumell, V. (Eds.) Digital Innovations and Research in Language Learning. Faversham, Kent: IATEFL. pp. 170 – 196

Mavridi, S. (2020b). Digital literacies and the new digital divide. In Mavridi, S. & Xerri, D. (Eds.) English for 21^st Century Skills. Newbury, Berks.: Express Publishing. pp. 90 – 98

Moore, M. (2018). Democracy Hacked. London: Oneworld

Ofcom. (2019). Adults: Media use and attitudes report [Report]. https://www.ofcom.org.uk/__data/assets/pdf_file/0021/149124/adults-media-use-and-attitudes-report.pdf

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

Peachey, N. (2016). Thinking Critically through Digital Media. http://peacheypublications.com/

Sadowski, J. (2019). ‘When data is capital: Datafication, accumulation, and extraction’ Big Data and Society 6 (1) https://doi.org/10.1177%2F2053951718820549

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: e5 doi:10.1017/dap.2020.5

Williamson, B. (2019). ‘Killer Apps for the Classroom? Developing Critical Perspectives on ClassDojo and the ‘Ed-tech’ Industry’ Journal of Professional Learning, 2019 (Semester 2) https://cpl.asn.au/journal/semester-2-2019/killer-apps-for-the-classroom-developing-critical-perspectives-on-classdojo

The Unlearned Lessons of Programmed Learning

From time to time, I have mentioned Programmed Learning (or Programmed Instruction) in this blog (here and here, for example). It felt like time to go into a little more detail about what Programmed Instruction was (and is) and why I think it’s important to know about it.

A brief description

The basic idea behind Programmed Instruction was that subject matter could be broken down into very small parts, which could be organised into an optimal path for presentation to students. Students worked, at their own speed, through a series of micro-tasks, building their mastery of each nugget of learning that was presented, not progressing from one to the next until they had demonstrated they could respond accurately to the previous task.

There were two main types of Programmed Instruction: linear programming and branching programming. In the former, every student would follow the same path, the same sequence of frames. This could be used in classrooms for whole-class instruction and I tracked down a book (illustrated below) called ‘Programmed English Course Student’s Book 1’ (Hill, 1966), which was an attempt to transfer the ideas behind Programmed Instruction to a zero-tech, class environment. This is very similar in approach to the material I had to use when working at an Inlingua school in the 1980s.

An example of how self-paced programming worked is illustrated here, with a section on comparatives.

With branching programming, ‘extra frames (or branches) are provided for students who do not get the correct answer’ (Kay et al., 1968: 19). This was only suitable for self-study, but it was clearly preferable, as it allowed for self-pacing and some personalization. The material could be presented in books (which meant that students had to flick back and forth in their books) or with special ‘teaching machines’, but the latter were preferred.

In the words of an early enthusiast, Programmed Instruction was essentially ‘a device to control a student’s behaviour and help him to learn without the supervision of a teacher’ (Kay et al.,1968: 58). The approach was inspired by the work of Skinner and it was first used as part of a university course in behavioural psychology taught by Skinner at Harvard University in 1957. It moved into secondary schools for teaching mathematics in 1959 (Saettler, 2004: 297).

Enthusiasm and uptake

The parallels between current enthusiasm for the power of digital technology to transform education and the excitement about Programmed Instruction and teaching machines in the 1960s are very striking (McDonald et al., 2005: 90). In 1967, it was reported that ‘we are today on the verge of what promises to be a revolution in education’ (Goodman, 1967: 3) and that ‘tremors of excitement ran through professional journals and conferences and department meetings from coast to coast’ (Kennedy, 1967: 871). The following year, another commentator referred to the way that the field of education had been stirred ‘with an almost Messianic promise of a breakthrough’ (Ornstein, 1968: 401). Programmed instruction was also seen as an exciting business opportunity: ‘an entire industry is just coming into being and significant sales and profits should not be too long in coming’, wrote one hopeful financial analyst as early as 1961 (Kozlowski, 1967: 47).

The new technology seemed to offer a solution to the ‘problems of education’. Media reports in 1963 in Germany, for example, discussed a shortage of teachers, large classes and inadequate learning progress … ‘an ‘urgent pedagogical emergency’ that traditional teaching methods could not resolve’ (Hof, 2018). Individualised learning, through Programmed Instruction, would equalise educational opportunity and if you weren’t part of it, you would be left behind. In the US, two billion dollars were spent on educational technology by the government in the decade following the passing of the National Defense Education Act, and this was added to by grants from private foundations. As a result, ‘the production of teaching machines began to flourish, accompanied by the marketing of numerous ‘teaching units’ stamped into punch cards as well as less expensive didactic programme books and index cards. The market grew dramatically in a short time’ (Hof, 2018).

In the field of language learning, however, enthusiasm was more muted. In the year in which he completed his doctoral studies[1], the eminent linguist, Bernard Spolsky noted that ‘little use is actually being made of the new technique’ (Spolsky, 1966). A year later, a survey of over 600 foreign language teachers at US colleges and universities reported that only about 10% of them had programmed materials in their departments (Valdman, 1968: 1). In most of these cases, the materials ‘were being tried out on an experimental basis under the direction of their developers’. And two years after that, it was reported that ‘programming has not yet been used to any very great extent in language teaching, so there is no substantial body of experience from which to draw detailed, water-tight conclusions’ (Howatt, 1969: 164).

By the early 1970s, Programmed Instruction was already beginning to seem like yesterday’s technology, even though the principles behind it are still very much alive today (Thornbury (2017) refers to Duolingo as ‘Programmed Instruction’). It would be nice to think that language teachers of the day were more sceptical than, for example, their counterparts teaching mathematics. It would be nice to think that, like Spolsky, they had taken on board Chomsky’s (1959) demolition of Skinner. But the widespread popularity of Audiolingual methods suggests otherwise. Audiolingualism, based essentially on the same Skinnerian principles as Programmed Instruction, needed less outlay on technology. The machines (a slide projector and a record or tape player) were cheaper than the teaching machines, could be used for other purposes and did not become obsolete so quickly. The method also lent itself more readily to established school systems (i.e. whole-class teaching) and the skills sets of teachers of the day. Significantly, too, there was relatively little investment in Programmed Instruction for language teaching (compared to, say, mathematics), since this was a smallish and more localized market. There was no global market for English language learning as there is today.

Lessons to be learned

1 Shaping attitudes

It was not hard to persuade some educational authorities of the value of Programmed Instruction. As discussed above, it offered a solution to the problem of ‘the chronic shortage of adequately trained and competent teachers at all levels in our schools, colleges and universities’, wrote Goodman (1967: 3), who added, there is growing realisation of the need to give special individual attention to handicapped children and to those apparently or actually retarded’. The new teaching machines ‘could simulate the human teacher and carry out at least some of his functions quite efficiently’ (Goodman, 1967: 4). This wasn’t quite the same thing as saying that the machines could replace teachers, although some might have hoped for this. The official line was more often that the machines could ‘be used as devices, actively co-operating with the human teacher as adaptive systems and not just merely as aids’ (Goodman, 1967: 37). But this more nuanced message did not always get through, and ‘the Press soon stated that robots would replace teachers and conjured up pictures of classrooms of students with little iron men in front of them’ (Kay et al., 1968: 161).

For teachers, though, it was one thing to be told that the machines would free their time to perform more meaningful tasks, but harder to believe when this was accompanied by a ‘rhetoric of the instructional inadequacies of the teacher’ (McDonald, et al., 2005: 88). Many teachers felt threatened. They ‘reacted against the ‘unfeeling machine’ as a poor substitute for the warm, responsive environment provided by a real, live teacher. Others have seemed to take it more personally, viewing the advent of programmed instruction as the end of their professional career as teachers. To these, even the mention of programmed instruction produces a momentary look of panic followed by the appearance of determination to stave off the ominous onslaught somehow’ (Tucker, 1972: 63).

Some of those who were pushing for Programmed Instruction had a bigger agenda, with their sights set firmly on broader school reform made possible through technology (Hof, 2018). Individualised learning and Programmed Instruction were not just ends in themselves: they were ways of facilitating bigger changes. The trouble was that teachers were necessary for Programmed Instruction to work. On the practical level, it became apparent that a blend of teaching machines and classroom teaching was more effective than the machines alone (Saettler, 2004: 299). But the teachers’ attitudes were crucial: a research study involving over 6000 students of Spanish showed that ‘the more enthusiastic the teacher was about programmed instruction, the better the work the students did, even though they worked independently’ (Saettler, 2004: 299). In other researched cases, too, ‘teacher attitudes proved to be a critical factor in the success of programmed instruction’ (Saettler, 2004: 301).

2 Returns on investment

Pricing a hyped edtech product is a delicate matter. Vendors need to see a relatively quick return on their investment, before a newer technology knocks them out of the market. Developments in computing were fast in the late 1960s, and the first commercially successful personal computer, the Altair 8800, appeared in 1974. But too high a price carried obvious risks. In 1967, the cheapest teaching machine in the UK, the Tutorpack (from Packham Research Ltd), cost £7 12s (equivalent to about £126 today), but machines like these were disparagingly referred to as ‘page-turners’ (Higgins, 1983: 4). A higher-end linear programming machine cost twice this amount. Branching programme machines cost a lot more. The Mark II AutoTutor (from USI Great Britain Limited), for example, cost £31 per month (equivalent to £558), with eight reels of programmes thrown in (Goodman, 1967: 26). A lower-end branching machine, the Grundytutor, could be bought for £ 230 (worth about £4140 today).

This was serious money, and any institution splashing out on teaching machines needed to be confident that they would be well used for a long period of time (Nordberg, 1965). The programmes (the software) were specific to individual machines and the content could not be updated easily. At the same time, other technological developments (cine projectors, tape recorders, record players) were arriving in classrooms, and schools found themselves having to pay for technical assistance and maintenance. The average teacher was ‘unable to avail himself fully of existing aids because, to put it bluntly, he is expected to teach for too many hours a day and simply has not the time, with all the administrative chores he is expected to perform, either to maintain equipment, to experiment with it, let alone keeping up with developments in his own and wider fields. The advent of teaching machines which can free the teacher to fulfil his role as an educator will intensify and not diminish the problem’ (Goodman, 1967: 44). Teaching machines, in short, were ‘oversold and underused’ (Cuban, 2001).

3 Research and theory

Looking back twenty years later, B. F. Skinner conceded that ‘the machines were crude, [and] the programs were untested’ (Skinner, 1986: 105). The documentary record suggests that the second part of this statement is not entirely true. Herrick (1966: 695) reported that ‘an overwhelming amount of research time has been invested in attempts to determine the relative merits of programmed instruction when compared to ‘traditional’ or ‘conventional’ methods of instruction. The results have been almost equally overwhelming in showing no significant differences’. In 1968, Kay et al (1968: 96) noted that ‘there has been a definite effort to examine programmed instruction’. A later meta-analysis of research in secondary education (Kulik et al.: 1982) confirmed that ‘Programmed Instruction did not typically raise student achievement […] nor did it make students feel more positively about the subjects they were studying’.

It was not, therefore, the case that research was not being done. It was that many people were preferring not to look at it. The same holds true for theoretical critiques. In relation to language learning, Spolsky (1966) referred to Chomsky’s (1959) rebuttal of Skinner’s arguments, adding that ‘there should be no need to rehearse these inadequacies, but as some psychologists and even applied linguists appear to ignore their existence it might be as well to remind readers of a few’. Programmed Instruction might have had a limited role to play in language learning, but vendors’ claims went further than that and some people believed them: ‘Rather than addressing themselves to limited and carefully specified FL tasks – for example the teaching of spelling, the teaching of grammatical concepts, training in pronunciation, the acquisition of limited proficiency within a restricted number of vocabulary items and grammatical features – most programmers aimed at self-sufficient courses designed to lead to near-native speaking proficiency’ (Valdman, 1968: 2).

4 Content

When learning is conceptualised as purely the acquisition of knowledge, technological optimists tend to believe that machines can convey it more effectively and more efficiently than teachers (Hof, 2018). The corollary of this is the belief that, if you get the materials right (plus the order in which they are presented and appropriate feedback), you can ‘to a great extent control and engineer the quality and quantity of learning’ (Post, 1972: 14). Learning, in other words, becomes an engineering problem, and technology is its solution.

One of the problems was that technology vendors were, first and foremost, technology specialists. Content was almost an afterthought. Materials writers needed to be familiar with the technology and, if not, they were unlikely to be employed. Writers needed to believe in the potential of the technology, so those familiar with current theory and research would clearly not fit in. The result was unsurprising. Kennedy (1967: 872) reported that ‘there are hundreds of programs now available. Many more will be published in the next few years. Watch for them. Examine them critically. They are not all of high quality’. He was being polite.

5 Motivation

As is usually the case with new technologies, there was a positive novelty effect with Programmed Instruction. And, as is always the case, the novelty effect wears off: ‘students quickly tired of, and eventually came to dislike, programmed instruction’ (McDonald et al.: 89). It could not really have been otherwise: ‘human learning and intrinsic motivation are optimized when persons experience a sense of autonomy, competence, and relatedness in their activity. Self-determination theorists have also studied factors that tend to occlude healthy functioning and motivation, including, among others, controlling environments, rewards contingent on task performance, the lack of secure connection and care by teachers, and situations that do not promote curiosity and challenge’ (McDonald et al.: 93). The demotivating experience of using these machines was particularly acute with younger and ‘less able’ students, as was noted at the time (Valdman, 1968: 9).

The unlearned lessons

I hope that you’ll now understand why I think the history of Programmed Instruction is so relevant to us today. In the words of my favourite Yogi-ism, it’s like deja vu all over again. I have quoted repeatedly from the article by McDonald et al (2005) and I would highly recommend it – available here. Hopefully, too, Audrey Watters’ forthcoming book, ‘Teaching Machines’, will appear before too long, and she will, no doubt, have much more of interest to say on this topic.

References

Chomsky N. 1959. ‘Review of Skinner’s Verbal Behavior’. Language, 35:26–58.

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

Goodman, R. 1967. Programmed Learning and Teaching Machines 3rd edition. (London: English Universities Press)

Herrick, M. 1966. ‘Programmed Instruction: A critical appraisal’ The American Biology Teacher, 28 (9), 695 -698

Higgins, J. 1983. ‘Can computers teach?’ CALICO Journal, 1 (2)

Hill, L. A. 1966. Programmed English Course Student’s Book 1. (Oxford: Oxford University Press)

Hof, B. 2018. ‘From Harvard via Moscow to West Berlin: educational technology, programmed instruction and the commercialisation of learning after 1957’ History of Education, 47:4, 445-465

Howatt, A. P. R. 1969. Programmed Learning and the Language Teacher. (London: Longmans)

Kay, H., Dodd, B. & Sime, M. 1968. Teaching Machines and Programmed Instruction. (Harmondsworth: Penguin)

Kennedy, R.H. 1967. ‘Before using Programmed Instruction’ The English Journal, 56 (6), 871 – 873

Kozlowski, T. 1961. ‘Programmed Teaching’ Financial Analysts Journal, 17 / 6, 47 – 54

Kulik, C.-L., Schwalb, B. & Kulik, J. 1982. ‘Programmed Instruction in Secondary Education: A Meta-analysis of Evaluation Findings’ Journal of Educational Research, 75: 133 – 138

McDonald, J. K., Yanchar, S. C. & Osguthorpe, R.T. 2005. ‘Learning from Programmed Instruction: Examining Implications for Modern Instructional Technology’ Educational Technology Research and Development, 53 / 2, 84 – 98

Nordberg, R. B. 1965. Teaching machines-six dangers and one advantage. In J. S. Roucek (Ed.), Programmed teaching: A symposium on automation in education (pp. 1–8). (New York: Philosophical Library)

Ornstein, J. 1968. ‘Programmed Instruction and Educational Technology in the Language Field: Boon or Failure?’ The Modern Language Journal, 52 / 7, 401 – 410

Post, D. 1972. ‘Up the programmer: How to stop PI from boring learners and strangling results’. Educational Technology, 12(8), 14–1

Saettler, P. 2004. The Evolution of American Educational Technology. (Greenwich, Conn.: Information Age Publishing)

Skinner, B. F. 1986. ‘Programmed Instruction Revisited’ The Phi Delta Kappan, 68 (2), 103 – 110

Spolsky, B. 1966. ‘A psycholinguistic critique of programmed foreign language instruction’ International Review of Applied Linguistics in Language Teaching, Volume 4, Issue 1-4: 119–130

Thornbury, S. 2017. Scott Thornbury’s 30 Language Teaching Methods. (Cambridge: Cambridge University Press)

Tucker, C. 1972. ‘Programmed Dictation: An Example of the P.I. Process in the Classroom’. TESOL Quarterly, 6(1), 61-70

Valdman, A. 1968. ‘Programmed Instruction versus Guided Learning in Foreign Language Acquisition’ Die Unterrichtspraxis / Teaching German, 1 (2), 1 – 14

 

 

 

[1] Spolsky’ doctoral thesis for the University of Montreal was entitled ‘The psycholinguistic basis of programmed foreign language instruction’.

 

 

 

 

 

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

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

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

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

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

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

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

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

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

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

 

 

 

 

The fun they had

Back in the middle of the last century, the first interactive machines for language teaching appeared. Previously, there had been phonograph discs and wire recorders (Ornstein, 1968: 401), but these had never really taken off. This time, things were different. Buoyed by a belief in the power of technology, along with the need (following the Soviet Union’s successful Sputnik programme) to demonstrate the pre-eminence of the United States’ technological expertise, the interactive teaching machines that were used in programmed instruction promised to revolutionize language learning (Valdman, 1968: 1). From coast to coast, ‘tremors of excitement ran through professional journals and conferences and department meetings’ (Kennedy, 1967: 871). The new technology was driven by hard science, supported and promoted by the one of the most well-known and respected psychologists and public intellectuals of the day (Skinner, 1961).

In classrooms, the machines acted as powerfully effective triggers in generating situational interest (Hidi & Renninger, 2006). Even more exciting than the mechanical teaching machines were the computers that were appearing on the scene. ‘Lick’ Licklider, a pioneer in interactive computing at the Advanced Research Projects Agency in Arlington, Virginia, developed an automated drill routine for learning German by hooking up a computer, two typewriters, an oscilloscope and a light pen (Noble, 1991: 124). Students loved it, and some would ‘go on and on, learning German words until they were forced by scheduling to cease their efforts’. Researchers called the seductive nature of the technology ‘stimulus trapping’, and Licklider hoped that ‘before [the student] gets out from under the control of the computer’s incentives, [they] will learn enough German words’ (Noble, 1991: 125).

With many of the developed economies of the world facing a critical shortage of teachers, ‘an urgent pedagogical emergency’ (Hof, 2018), the new approach was considered to be extremely efficient and could equalise opportunity in schools across the country. It was ‘here to stay: [it] appears destined to make progress that could well go beyond the fondest dreams of its originators […] an entire industry is just coming into being and significant sales and profits should not be too long in coming’ (Kozlowski, 1961: 47).

Unfortunately, however, researchers and entrepreneurs had massively underestimated the significance of novelty effects. The triggered situational interest of the machines did not lead to intrinsic individual motivation. Students quickly tired of, and eventually came to dislike, programmed instruction and the machines that delivered it (McDonald et al.: 2005: 89). What’s more, the machines were expensive and ‘research studies conducted on its effectiveness showed that the differences in achievement did not constantly or substantially favour programmed instruction over conventional instruction (Saettler, 2004: 303). Newer technologies, with better ‘stimulus trapping’, were appearing. Programmed instruction lost its backing and disappeared, leaving as traces only its interest in clearly defined learning objectives, the measurement of learning outcomes and a concern with the efficiency of learning approaches.

Hot on the heels of programmed instruction came the language laboratory. Futuristic in appearance, not entirely unlike the deck of the starship USS Enterprise which launched at around the same time, language labs captured the public imagination and promised to explore the final frontiers of language learning. As with the earlier teaching machines, students were initially enthusiastic. Even today, when language labs are introduced into contexts where they may be perceived as new technology, they can lead to high levels of initial motivation (e.g. Ramganesh & Janaki, 2017).

Given the huge investments into these labs, it’s unfortunate that initial interest waned fast. By 1969, many of these rooms had turned into ‘“electronic graveyards,” sitting empty and unused, or perhaps somewhat glorified study halls to which students grudgingly repair to don headphones, turn down the volume, and prepare the next period’s history or English lesson, unmolested by any member of the foreign language faculty’ (Turner, 1969: 1, quoted in Roby, 2003: 527). ‘Many second language students shudder[ed] at the thought of entering into the bowels of the “language laboratory” to practice and perfect the acoustical aerobics of proper pronunciation skills. Visions of sterile white-walled, windowless rooms, filled with endless bolted-down rows of claustrophobic metal carrels, and overseen by a humorless, lab director, evoke[d] fear in the hearts of even the most stout-hearted prospective second-language learners (Wiley, 1990: 44).

By the turn of this century, language labs had mostly gone, consigned to oblivion by the appearance of yet newer technology: the internet, laptops and smartphones. Education had been on the brink of being transformed through new learning technologies for decades (Laurillard, 2008: 1), but this time it really was different. It wasn’t just one technology that had appeared, but a whole slew of them: ‘artificial intelligence, learning analytics, predictive analytics, adaptive learning software, school management software, learning management systems (LMS), school clouds. No school was without these and other technologies branded as ‘superintelligent’ by the late 2020s’ (Macgilchrist et al., 2019). The hardware, especially phones, was ubiquitous and, therefore, free. Unlike teaching machines and language laboratories, students were used to using the technology and expected to use their devices in their studies.

A barrage of publicity, mostly paid for by the industry, surrounded the new technologies. These would ‘meet the demands of Generation Z’, the new generation of students, now cast as consumers, who ‘were accustomed to personalizing everything’.  AR, VR, interactive whiteboards, digital projectors and so on made it easier to ‘create engaging, interactive experiences’. The ‘New Age’ technologies made learning fun and easy,  ‘bringing enthusiasm among the students, improving student engagement, enriching the teaching process, and bringing liveliness in the classroom’. On top of that, they allowed huge amounts of data to be captured and sold, whilst tracking progress and attendance. In any case, resistance to digital technology, said more than one language teaching expert, was pointless (Styring, 2015).

At the same time, technology companies increasingly took on ‘central roles as advisors to national governments and local districts on educational futures’ and public educational institutions came to be ‘regarded by many as dispensable or even harmful’ (Macgilchrist et al., 2019).

But, as it turned out, the students of Generation Z were not as uniformly enthusiastic about the new technology as had been assumed, and resistance to digital, personalized delivery in education was not long in coming. In November 2018, high school students at Brooklyn’s Secondary School for Journalism staged a walkout in protest at their school’s use of Summit Learning, a web-based platform promoting personalized learning developed by Facebook. They complained that the platform resulted in coursework requiring students to spend much of their day in front of a computer screen, that made it easy to cheat by looking up answers online, and that some of their teachers didn’t have the proper training for the curriculum (Leskin, 2018). Besides, their school was in a deplorable state of disrepair, especially the toilets. There were similar protests in Kansas, where students staged sit-ins, supported by their parents, one of whom complained that ‘we’re allowing the computers to teach and the kids all looked like zombies’ before pulling his son out of the school (Bowles, 2019). In Pennsylvania and Connecticut, some schools stopped using Summit Learning altogether, following protests.

But the resistance did not last. Protesters were accused of being nostalgic conservatives and educationalists kept largely quiet, fearful of losing their funding from the Chan Zuckerberg Initiative (Facebook) and other philanthro-capitalists. The provision of training in grit, growth mindset, positive psychology and mindfulness (also promoted by the technology companies) was ramped up, and eventually the disaffected students became more quiescent. Before long, the data-intensive, personalized approach, relying on the tools, services and data storage of particular platforms had become ‘baked in’ to educational systems around the world (Moore, 2018: 211). There was no going back (except for small numbers of ultra-privileged students in a few private institutions).

By the middle of the century (2155), most students, of all ages, studied with interactive screens in the comfort of their homes. Algorithmically-driven content, with personalized, adaptive tests had become the norm, but the technology occasionally went wrong, leading to some frustration. One day, two young children discovered a book in their attic. Made of paper with yellow, crinkly pages, where ‘the words stood still instead of moving the way they were supposed to’. The book recounted the experience of schools in the distant past, where ‘all the kids from the neighbourhood came’, sitting in the same room with a human teacher, studying the same things ‘so they could help one another on the homework and talk about it’. Margie, the younger of the children at 11 years old, was engrossed in the book when she received a nudge from her personalized learning platform to return to her studies. But Margie was reluctant to go back to her fractions. She ‘was thinking about how the kids must have loved it in the old days. She was thinking about the fun they had’ (Asimov, 1951).

References

Asimov, I. 1951. The Fun They Had. Accessed September 20, 2019. http://web1.nbed.nb.ca/sites/ASD-S/1820/J%20Johnston/Isaac%20Asimov%20-%20The%20fun%20they%20had.pdf

Bowles, N. 2019. ‘Silicon Valley Came to Kansas Schools. That Started a Rebellion’ The New York Times, April 21. Accessed September 20, 2019. https://www.nytimes.com/2019/04/21/technology/silicon-valley-kansas-schools.html

Hidi, S. & Renninger, K.A. 2006. ‘The Four-Phase Model of Interest Development’ Educational Psychologist, 41 (2), 111 – 127

Hof, B. 2018. ‘From Harvard via Moscow to West Berlin: educational technology, programmed instruction and the commercialisation of learning after 1957’ History of Education, 47 (4): 445-465

Kennedy, R.H. 1967. ‘Before using Programmed Instruction’ The English Journal, 56 (6), 871 – 873

Kozlowski, T. 1961. ‘Programmed Teaching’ Financial Analysts Journal, 17 (6): 47 – 54

Laurillard, D. 2008. Digital Technologies and their Role in Achieving our Ambitions for Education. London: Institute for Education.

Leskin, P. 2018. ‘Students in Brooklyn protest their school’s use of a Zuckerberg-backed online curriculum that Facebook engineers helped build’ Business Insider, 12.11.18 Accessed 20 September 2019. https://www.businessinsider.de/summit-learning-school-curriculum-funded-by-zuckerberg-faces-backlash-brooklyn-2018-11?r=US&IR=T

McDonald, J. K., Yanchar, S. C. & Osguthorpe, R.T. 2005. ‘Learning from Programmed Instruction: Examining Implications for Modern Instructional Technology’ Educational Technology Research and Development, 53 (2): 84 – 98

Macgilchrist, F., Allert, H. & Bruch, A. 2019. ‚Students and society in the 2020s. Three future ‘histories’ of education and technology’. Learning, Media and Technology, https://www.tandfonline.com/doi/full/10.1080/17439884.2019.1656235 )

Moore, M. 2018. Democracy Hacked. London: Oneworld

Noble, D. D. 1991. The Classroom Arsenal. London: The Falmer Press

Ornstein, J. 1968. ‘Programmed Instruction and Educational Technology in the Language Field: Boon or Failure?’ The Modern Language Journal, 52 (7), 401 – 410

Ramganesh, E. & Janaki, S. 2017. ‘Attitude of College Teachers towards the Utilization of Language Laboratories for Learning English’ Asian Journal of Social Science Studies; Vol. 2 (1): 103 – 109

Roby, W.B. 2003. ‘Technology in the service of foreign language teaching: The case of the language laboratory’ In D. Jonassen (ed.), Handbook of Research on Educational Communications and Technology, 2nd ed.: 523 – 541. Mahwah, NJ.: Lawrence Erlbaum Associates

Saettler, P. 2004. The Evolution of American Educational Technology. Greenwich, Conn.: Information Age Publishing

Skinner, B. F. 1961. ‘Teaching Machines’ Scientific American, 205(5), 90-107

Styring, J. 2015. Engaging Generation Z. Cambridge English webinar 2015 https://www.youtube.com/watch?time_continue=4&v=XCxl4TqgQZA

Valdman, A. 1968. ‘Programmed Instruction versus Guided Learning in Foreign Language Acquisition’ Die Unterrichtspraxis / Teaching German, 1 (2), 1 – 14.

Wiley, P. D. 1990. ‘Language labs for 1990: User-friendly, expandable and affordable’. Media & Methods, 27(1), 44–47)

Jenny Holzer, Protect me from what I want

EdTech: a cautionary tale

When the startup, AltSchool, was founded in 2013 by Max Ventilla, the former head of personalization at Google, it quickly drew the attention of venture capitalists and within a few years had raised $174 million from the likes of the Zuckerberg Foundation, Peter Thiel, Laurene Powell Jobs and Pierre Omidyar. It garnered gushing articles in a fawning edtech press which enthused about ‘how successful students can be when they learn in small, personalized communities that champion project-based learning, guided by educators who get a say in the technology they use’. It promised ‘a personalized learning approach that would far surpass the standardized education most kids receive’.

Ventilla was an impressive money-raiser who used, and appeared to believe, every cliché in the edTech sales manual. Dressed in regulation jeans, polo shirt and fleece, he claimed that schools in America were ‘stuck in an industrial-age model, [which] has been in steady decline for the last century’ . What he offered, instead, was a learner-centred, project-based curriculum providing real-world lessons. There was a focus on social-emotional learning activities and critical thinking was vital.

The key to the approach was technology. From the start, software developers, engineers and researchers worked alongside teachers everyday, ‘constantly tweaking the Personalized Learning Plan, which shows students their assignments for each day and helps teachers keep track of and assess student’s learning’. There were tablets for pre-schoolers, laptops for older kids and wall-mounted cameras to record the lessons. There were, of course, Khan Academy videos. Ventilla explained that “we start with a representation of each child”, and even though “the vast majority of the learning should happen non-digitally”, the child’s habits and preferences gets converted into data, “a digital representation of the important things that relate to that child’s learning, not just their academic learning but also their non-academic learning. Everything logistic that goes into setting up the experience for them, whether it’s who has permission to pick them up or their allergy information. You name it.” And just like Netflix matches us to TV shows, “If you have that accurate and actionable representation for each child, now you can start to personalize the whole experience for that child. You can create that kind of loop you described where because we can represent a child well, we can match them to the right experiences.”

AltSchool seemed to offer the possibility of doing something noble, of transforming education, ‘bringing it into the digital age’, and, at the same time, a healthy return on investors’ money. Expanding rapidly, nine AltSchool microschools were opened in New York and the Bay Area, and plans were afoot for further expansion in Chicago. But, by then, it was already clear that something was going wrong. Five of the schools were closed before they had really got started and the attrition rate in some classrooms had reached about 30%. Revenue in 2018 was only $7 million and there were few buyers for the AltSchool platform. Quoting once more from the edTech bible, Ventilla explained the situation: ‘Our whole strategy is to spend more than we make,’ he says. Since software is expensive to develop and cheap to distribute, the losses, he believes, will turn into steep profits once AltSchool refines its product and lands enough customers.

The problems were many and apparent. Some of the buildings were simply not appropriate for schools, with no playgrounds or gyms, malfunctioning toilets, among other issues. Parents were becoming unhappy and accused AltSchool of putting ‘its ambitions as a tech company above its responsibility to teach their children. […] We kind of came to the conclusion that, really, AltSchool as a school was kind of a front for what Max really wants to do, which is develop software that he’s selling,’ a parent of a former AltSchool student told Business Insider. ‘We had really mediocre educators using technology as a crutch,’ said one father who transferred his child to a different private school after two years at AltSchool. […] We learned that it’s almost impossible to really customize the learning experience for each kid.’ Some parents began to wonder whether AltSchool had enticed families into its program merely to extract data from their children, then toss them aside?

With the benefit of hindsight, it would seem that the accusations were hardly unfair. In June of this year, AltSchool announced that its four remaining schools would be operated by a new partner, Higher Ground Education (a well-funded startup founded in 2016 which promotes and ‘modernises’ Montessori education). Meanwhile, AltSchool has been rebranded as Altitude Learning, focusing its ‘resources on the development and expansion of its personalized learning platform’ for licensing to other schools across the country.

Quoting once more from the edTech sales manual, Ventilla has said that education should drive the tech, not the other way round. Not so many years earlier, before starting AltSchool, Ventilla also said that he had read two dozen books on education and emerged a fan of Sir Ken Robinson. He had no experience as a teacher or as an educational administrator. Instead, he had ‘extensive knowledge of networks, and he understood the kinds of insights that can be gleaned from big data’.