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

Critical data literacy #3: learning / teaching resources

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

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

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

A glossary

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

critical-data-literacy-glossaryDownload

A series of interactive videos

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

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

More informational videos

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

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

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

A simple matching task ‘ten principles for online privacy’

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

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

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

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

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

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

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

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

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

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

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

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

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

3 Wrap up with a discussion of these principles.

Hands-on exploration of privacy tools

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

hands-on-exploration-of-privacy-toolsDownload

A graphic novel

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

A website

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

Critical data literacy #2: why and what?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Unpacking ‘the new normal’ in ELT

What is the ‘new normal’?

Among the many words and phrases that have been coined or gained new currency since COVID-19 first struck, I find ‘the new normal’ particularly interesting. In the educational world, its meaning is so obvious that it doesn’t need spelling out. But in case you’re unclear about what I’m referring to, the title of this webinar, run by GENTEFL, the Global Educators Network Association of Teachers of English as a Foreign Language (an affiliate of IATEFL), will give you a hint.

Teaching in a VLE may be overstating it a bit, but you get the picture. ‘The new normal’ is the shift away from face-to-face teaching in bricks-and-mortar institutions, towards online teaching of one kind or another. The Malaysian New Straits Times refers to it as ‘E-learning, new way forward in new norm’. The TEFL Academy says that ‘digital learning is the new normal’, and the New Indian Express prefers the term ‘tech education’.

I’ll come back to these sources in a little while.

Whose new normal?

There is, indeed, a strong possibility that online learning and teaching may become ‘the new normal’ for many people working in education. In corporate training and in higher education, ‘tech education’ will likely become increasingly common. Many universities, especially but not only in the US, Britain and Australia, have been relying on ‘international students’ (almost half a million in the UK in 2018/ 2019), in particular Chinese, to fill their coffers. With uncertainty about how and when these universities will reopen for the next academic year, a successful transition to online is a matter of survival – a challenge that a number of universities will probably not be able to rise to. The core of ELT, private TEFL schools in Inner Circle countries, likewise dependent on visitors from other countries, has also been hard hit. It is not easy for them to transition to online, since the heart of their appeal lies in their physical location.

But elsewhere, the picture is rather different. A recent Reddit discussion began as follows: ‘In Vietnam, [English language] schools have reopened and things have returned to normal almost overnight. There’s actually a teacher shortage at the moment as so many left and interest in online learning is minimal, although most schools are still offering it as an option’. The consensus in the discussion that follows is that bricks-and-mortar schools will take a hit, especially with adult (but not kids’) groups, but that ‘teaching online will not be the new normal’.

By far the greatest number of students studying English around the world are in primary and secondary schools. It is highly unlikely that online study will be the ‘new normal’ for most of these students (although we may expect to see attempts to move towards more blended approaches). There are many reasons for this, but perhaps the most glaringly obvious is that the function of schools is not exclusively educational: child-care, allowing parents to go to work, is the first among these.

We can expect some exceptions. In New York, for example, current plans include a ‘hybrid model’ (a sexed-up term for blended learning), in which students are in schools for part of the time and continue learning remotely for the rest. The idea emerged after Governor Andrew Cuomo ‘convened a committee with the Bill and Melinda Gates Foundation to reimagine education for students when school goes back in session in the fall’. How exactly this will pan out remains to be seen, but, in much of the rest of the world, where the influence of the Gates Foundation is less strong, ‘hybrid schooling’ is likely to be seen as even more unpalatable and unworkable than it is by many in New York.

In short, the ‘new normal’ will affect some sectors of English language teaching much more than others. For some, perhaps the majority, little change can be expected once state schools reopen. Smaller classes, maybe, more blended, but not a wholesale shift to ‘tech education’.

Not so new anyway!

Scott Galloway, a New York professor of marketing and author of the best-selling ‘The Four’ (an analysis of the Big Four tech firms), began a recent blog post as follows:

After COVID-19, nothing will be the same. The previous sentence is bullsh*t. On the contrary, things will never be more the same, just accelerated.

He elaborates his point by pointing out that many universities were already in deep trouble before COVID. Big tech had already moved massively into education and healthcare, which are ‘the only two sectors, other than government, that offer the margin dollars required to sate investors’ growth expectations’ (from another recent post by Galloway). Education start-ups have long been attracting cheap capital: COVID has simply sped the process up.

Coming from a very different perspective, Audrey Watters gave a conference presentation over three years ago entitled ‘Education Technology as ‘The New Normal’’. I have been writing about the normalization of digital tools in language teaching for over six years. What is new is the speed, rather than the nature, of the change.

Galloway draws an interesting parallel with the SARS virus, which, he says, ‘was huge for e-commerce in Asia, and it helped Alibaba break out into the consumer space. COVID-19 could be to education in the United States what SARS was to e-commerce in Asia’.

‘The new normal’ as a marketing tool

Earlier in this post, I mentioned three articles that discussed the ‘new normal’ in education. The first of these, from the New Straits Times, looks like a news article, but features extensive quotes from Shereen Chee, chief operating officer of Sunago Education, a Malaysian vendor of online English classes. The article is basically an advert for Sunago: one section includes the following:

Sunago combines digitisation and the human touch to create a personalised learning experience. […] Chee said now is a great time for employers to take advantage of the scheme and equip their team with enhanced English skills, so they can hit the ground running once the Covid-19 slump is over.

The second reference about ‘digital learning is the new normal’ comes from The TEFL Academy, which sells online training courses, particularly targeting prospective teachers who want to work online. The third reference, from the New Indian Express, was written by Ananth Koppar, the founder of Kshema Technologies Pvt Ltd, India’s first venture-funded software company. Koppar is hardly a neutral reporter.

Other examples abound. For example, a similar piece called ‘The ‘New Normal’ in Education’ can be found in FE News (10 June 2020). This was written by Simon Carter, Marketing and Propositions Director of RM Education, an EdTech vendor in the UK. EdTech has a long history of promoting its wares through sponsored content and adverts masquerading as reportage.

It is, therefore, a good idea, whenever you come across the phrase, ‘the new normal’, to adopt a sceptical stance from the outset. I’ll give two more examples to illustrate my point.

A recent article (1 April 2020) in the ELTABB (English Language Teachers Association Berlin Brandenburg) journal is introduced as follows:

With online language teaching being the new normal in ELT, coaching principles can help teachers and students share responsibility for the learning process.

Putting aside, for the moment, my reservations about whether online teaching is, in fact, the new normal in ‘ELT’, I’m happy to accept that coaching principles may be helpful in online teaching. But I can’t help noticing that the article was written by a self-described edupreneur and co-founder of the International Language Coaching Association (€50 annual subscription) which runs three-day training courses (€400).

My second example is a Macmillan webinar by Thom Kiddle called ‘Professional Development for teachers in the ‘new normal’. It’s a good webinar, a very good one in my opinion, but you’ll notice a NILE poster tacked to the wall behind Thom as he speaks. NILE, a highly reputed provider of teacher education courses in the UK, has invested significantly in online teacher education in recent years and is well-positioned to deal with the ‘new normal’. It’s also worth noting that the webinar host, Macmillan, is in a commercial partnership with NILE, the purpose of which is to ‘develop and promote quality teacher education programmes worldwide’. As good as the webinar is, it is also clearly, in part, an advertisement.

The use of the phrase ‘the new normal’ as a marketing hook is not new. Although its first recorded use dates back to the first part of the 20^th century, it became more widespread at the start of the 21^st. One populariser of the phrase was Roger McNamee, a venture capitalist and early investor in technology, including Facebook, who wrote a book called ‘The New Normal: Great Opportunities in a Time of Great Risk’ (2004). Since then, the phrase has been used extensively to refer to the state of the business world after the financial crisis of 2018. (For more about the history of the phrase, see here.) More often than not, users of the phrase are selling the idea (and sometimes a product) that we need to get used to a new configuration of the world, one in which technology plays a greater role.

Normalizing ‘the new normal’

Of all the most unlikely sources for a critique of ‘the new normal’, the World Economic Forum has the following to offer in a blog post entitled ‘There’s nothing new about the ‘new normal’. Here’s why’:

The language of a ‘new normal’ is being deployed almost as a way to quell any uncertainty ushered in by the coronavirus. With no cure in sight, everyone from politicians and the media to friends and family has perpetuated this rhetoric as they imagine settling into life under this ‘new normal’. This framing is inviting: it contends that things will never be the same as they were before — so welcome to a new world order. By using this language, we reimagine where we were previously relative to where we are now, appropriating our present as the standard. As we weigh our personal and political responses to this pandemic, the language we employ matters. It helps to shape and reinforce our understanding of the world and the ways in which we choose to approach it. The analytic frame embodied by the persistent discussion of the ‘new normal’ helps bring order to our current turbulence, but it should not be the lens through which we examine today’s crisis.

We can’t expect the World Economic Forum to become too critical of the ‘new normal’ of digital learning, since they have been pushing for it so hard for so long. But the quote from their blog above may usefully be read in conjunction with an article by Jun Yu and Nick Couldry, called ‘Education as a domain of natural data extraction: analysing corporate discourse about educational tracking’ (Information, Communication and Society, 2020, DOI: 10.1080/1369118X.2020.1764604). The article explores the general discursive framing by which the use of big data in education has come to seem normal. The authors looked at the public discourse of eight major vendors of educational platforms that use big data (including Macmillan, Pearson, Knewton and Blackboard). They found that ‘the most fundamental move in today’s dominant commercial discourse is to promote the idea that data and its growth are natural’. In this way, ‘software systems, not teachers, [are] central to education’. Yu and Couldry’s main interest is in the way that discourse shapes the normalization of dataveillance, but, in a more general sense, the phrase, ‘the new normal’, is contributing to the normalization of digital education. If you think that’s fine, I suggest you dip into some of the books I listed in my last blog post.

The EdTech Imaginary in ELT

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

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

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

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

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

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

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

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

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

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

2 Technological innovation is good and necessary

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

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

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

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

3 Technological innovations are best driven by the private sector

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

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

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

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

4 Accountability is crucial

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

 

 

 

 

Big data, learning analytics and language learning / teaching

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

 

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

Adaptive learning in ELT: the current state of play

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

How sticky is adaptivity?

‘Sticky’ – as in ‘sticky learning’ or ‘sticky content’ (as opposed to ‘sticky fingers’ or a ‘sticky problem’) – is itself fast becoming a sticky word. If you check out ‘sticky learning’ on Google Trends, you’ll see that it suddenly spiked in September 2011, following the slightly earlier appearance of ‘sticky content’. The historical rise in this use of the word coincides with the exponential growth in the number of references to ‘big data’.

I am often asked if adaptive learning really will take off as a big thing in language learning. Will adaptivity itself be a sticky idea? When the question is asked, people mean the big data variety of adaptive learning, rather than the much more limited adaptivity of spaced repetition algorithms, which, I think, is firmly here and here to stay. I can’t answer the question with any confidence, but I recently came across a book which suggests a useful way of approaching the question.

‘From the Ivory Tower to the Schoolhouse’ by Jack Schneider (Harvard Education Press, 2014) investigates the reasons why promising ideas from education research fail to get taken up by practitioners, and why other, less-than-promising ideas, from a research or theoretical perspective, become sticky quite quickly. As an example of the former, Schneider considers Robert Sternberg’s ‘Triarchic Theory’. As an example of the latter, he devotes a chapter to Howard Gardner’s ‘Multiple Intelligences Theory’.

Schneider argues that educational ideas need to possess four key attributes in order for teachers to sit up, take notice and adopt them.

1. perceived significance: the idea must answer a question central to the profession – offering a big-picture understanding rather than merely one small piece of a larger puzzle
2. philosophical compatibility: the idea must clearly jibe with closely held [teacher] beliefs like the idea that teachers are professionals, or that all children can learn
3. occupational realism: it must be possible for the idea to be put easily into immediate use
4. transportability: the idea needs to find its practical expression in a form that teachers can access and use at the time that they need it – it needs to have a simple core that can travel through pre-service coursework, professional development seminars, independent study and peer networks

To what extent does big data adaptive learning possess these attributes? It certainly comes up trumps with respect to perceived significance. The big question that it attempts to answer is the question of how we can make language learning personalized / differentiated / individualised. As its advocates never cease to remind us, adaptive learning holds out the promise of moving away from a one-size-fits-all approach. The extent to which it can keep this promise is another matter, of course. For it to do so, it will never be enough just to offer different pathways through a digitalised coursebook (or its equivalent). Much, much more content will be needed: at least five or six times the content of a one-size-fits-all coursebook. At the moment, there is little evidence of the necessary investment into content being made (quite the opposite, in fact), but the idea remains powerful nevertheless.

When it comes to philosophical compatibility, adaptive learning begins to run into difficulties. Despite the decades of edging towards more communicative approaches in language teaching, research (e.g. the research into English teaching in Turkey described in a previous post), suggests that teachers still see explanation and explication as key functions of their jobs. They believe that they know their students best and they know what is best for them. Big data adaptive learning challenges these beliefs head on. It is no doubt for this reason that companies like Knewton make such a point of claiming that their technology is there to help teachers. But Jose Ferreira doth protest too much, methinks. Platform-delivered adaptive learning is a direct threat to teachers’ professionalism, their salaries and their jobs.

Occupational realism is more problematic still. Very, very few language teachers around the world have any experience of truly blended learning, and it’s very difficult to envisage precisely what it is that the teacher should be doing in a classroom. Publishers moving towards larger-scale blended adaptive materials know that this is a big problem, and are actively looking at ways of packaging teacher training / teacher development (with a specific focus on blended contexts) into the learner-facing materials that they sell. But the problem won’t go away. Education ministries have a long history of throwing money at technological ‘solutions’ without thinking about obtaining the necessary buy-in from their employees. It is safe to predict that this is something that is unlikely to change. Moreover, learning how to become a blended teacher is much harder than learning, say, how to make good use of an interactive whiteboard. Since there are as many different blended adaptive approaches as there are different educational contexts, there cannot be (irony of ironies) a one-size-fits-all approach to training teachers to make good use of this software.

Finally, how transportable is big data adaptive learning? Not very, is the short answer, and for the same reasons that ‘occupational realism’ is highly problematic.

Looking at things through Jack Schneider’s lens, we might be tempted to come to the conclusion that the future for adaptive learning is a rocky path, at best. But Schneider doesn’t take political or economic considerations into account. Sternberg’s ‘Triarchic Theory’ never had the OECD or the Gates Foundation backing it up. It never had millions and millions of dollars of investment behind it. As we know from political elections (and the big data adaptive learning issue is a profoundly political one), big bucks can buy opinions.

It may also prove to be the case that the opinions of teachers don’t actually matter much. If the big adaptive bucks can win the educational debate at the highest policy-making levels, teachers will be the first victims of the ‘creative disruption’ that adaptivity promises. If you don’t believe me, just look at what is going on in the U.S.

There are causes for concern, but I don’t want to sound too alarmist. Nobody really has a clue whether big data adaptivity will actually work in language learning terms. It remains more of a theory than a research-endorsed practice. And to end on a positive note, regardless of how sticky it proves to be, it might just provide the shot-in-the-arm realisation that language teachers, at their best, are a lot more than competent explainers of grammar or deliverers of gap-fills.