Archive for the ‘Online learning’ Category

I’d never felt any need for a QR reader on my phone until one day, a few lockdowns ago, I had to scan a code in order to be allowed to sit down outside my nearest breadshop, Anker, to eat a sandwich. Since replacing my phone a week or so ago, it was only this morning that I felt the need to install a new reader. It will come as no surprise to learn that I have never used QR codes in a classroom, and probably never will.

A book that I co-authored a few years ago included QR codes on some pages, and these take you to video recordings of ‘real students’ carrying out tasks from the book. We don’t learn much about these students’ lives, but we can assume that they are learning English in pre-Covid days, when they went into a physical classroom from time to time. But now that the physical classroom is becoming a receding memory, I have to fear for the future of QR codes in language teaching. Who needs a barcode web link when you’re online already?

I’ve seen some fun suggestions for using QR codes in the classroom. Placing QR codes in prominent places around the school – linking to the codes reveals a set of questions or clues in a treasure hunt. Getting learners to prepare their own multimedia material to upload to an interactive map of their school / town / whatever. Other suggestions involve things like sticking QR codes around the walls of the classroom, or walking around with a QR code stuck on your back or your forehead. But they all require physical space, imagining face-to-face contact. And they all require that phones are allowed, which, in turn, requires a whole lot of administration in some places (e.g. with kids). The activities tend to be a bit juvenile.

Some suggestions for using QR codes are decidedly less fun, in my view. Notifying students of their homework assignments by sending them a QR code, for example. Or giving the answers to an exercise when they click on the link.

More ideas can be found here and in ETpedia Technology (Hockly, 2017) and no doubt some other places, too.

(Image from https://www.teacherspayteachers.com/Product/Pirate-Joke-QR-Codes-1262320 )

You can evaluate your own affective response to QR codes in education by pointing your phone at the image above. That’s tricky, of course, if you’re reading this on your phone, and not another device. (Someone is selling this for a dollar.)

So why are they used? According to Cruse and Brereton (2018), they ‘can make classroom activities more engaging and allow students to perform previously impossible or impractical tasks’. Those previously impossible or impractical tasks are, of course, no longer impossible or impractical when the whole class is online. And this leaves us with the main claim of QR advocates: use of these codes leads to more learner engagement. How well does the claim hold up?

With a little encouragement, most people would rather scan a code than manually type in a link. But we don’t really have any evidence that English language learners would be more motivated and engaged if they point their phone at codes. Perhaps, there are some like me who don’t really want to get their phone out. Eye-rollers who find it hard to suppress a groan when someone suggests you use Mentimeter. Of course, the way you feel about using your phone for activities like these may also depend on how good your wifi is (or whether you have any wifi).

Cruse and Brereton’s (2018) first ‘Principle of Good Practice’ is that QR use ‘should not be a gimmick’. If you’re not convinced by the engagement argument, what other reasons could there be? To promote learner autonomy and differentiation? To facilitate asynchronous learning? To support constructivist learning by providing multiple representations of reality and enabling ‘context- and content-dependent knowledge construction’ (Alizadeh, 2019)? To develop digital literacies? Evidence is lacking.

QR codes have soared in global reach since the start of the pandemic, especially for payments and advertising. I also came across a novel use for QR with code stickers designed for tombstones (‘bringing monuments into the 21st century’). I imagine, with a little more investment, scanning the code could generate a realistic hologram of the deceased. But someone needs to come up with a convincing way of using them in online language learning.

References

Alizadeh, M. (2019) Augmented/virtual reality promises for ELT practitioners. In P. Clements, A. Krause, & P. Bennett (Eds.), Diversity and inclusion. Tokyo: JALT.

Hockly, N. (2017) ETpedia Technology. Hove: Pavilion Publishing

Cruse, D. T. H., & Brereton, P. (2018) Integrating QR codes into ELT materials. In P. Clements, A. Krause, & P. Bennett (Eds.), Language teaching in a global age: Shaping the classroom, shaping the world. Tokyo: JALT.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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 21st Century Skills. Newbury, Berks.: Express Publishing. pp. 90 – 98

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

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

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

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

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

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

Sander, I. (2020b). Critical big data literacy tools—Engaging citizens and promoting empowered internet usage. Data & Policy, 2: e5 doi:10.1017/dap.2020.5

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

The ‘Routledge Handbook of Language Learning and Technology’ (eds. Farr and Murray, 2016) claims to be ‘the essential reference’ on the topic and its first two sections are devoted to ‘Historical and conceptual concepts’ and ‘Core issues’. One chapter (‘Limitations and boundaries in language learning and technology’ by Kern and Malinowski) mentions that ‘a growing body of research in intercultural communication and online language learning recognises how all technologies are embedded in cultural and linguistic practices, meaning that a given technological artefact can be used in radically different ways, and for different purposes by different groups of people’ (p.205). However, in terms of critical analyses of technology and language learning, that’s about as far as this book goes. In over 500 pages, there is one passing reference to privacy and a couple of brief mentions of the digital divide. There is no meaningful consideration of the costs, ownership or externalities of EdTech, of the ways in which EdTech is sold and marketed, of the vested interests that profit from EdTech, of the connections between EdTech and the privatisation of education, of the non-educational uses to which data is put, or of the implications of attention tracking, facial analysis and dataveillance in educational settings.

The Routledge Handbook is not alone in this respect. Li Li’s ‘New Technologies and Language Learning’ (Palgrave, 2017) is breathlessly enthusiastic about the potential of EdTech. The opening chapter catalogues a series of huge investments in global EdTech, as if the scale of investment was an indication of its wisdom. No mention of the lack of evidence that huge investments into IWBs and PCs in classrooms led to any significant improvement in learning. No mention of how these investments were funded (or which other parts of budgets were cut). Instead, we are told that ‘computers can promote visual, verbal and kinaesthetic learning’ (p.5).

I have never come across a book-length critical analysis of technology and language learning. As the world of language teaching jumps on board Zoom, Google Meet, Microsoft Teams, Skype (aka Microsoft) and the like, the need for a better critical awareness of EdTech and language learning has never been more urgent. Fortunately, there is a growing body of critical literature on technology and general education. Here are my twelve favourites:

Big Data in Education1 Big Data in Education

Ben Williamson (Sage, 2017)

An investigation into the growing digitalization and datafication of education. Williamson looks at how education policy is enacted through digital tools, the use of learning analytics and educational data science. His interest is in the way that technology has reshaped the way we think about education and the book may be read as a critical response to the techno-enthusiasm of Mayer-Schönberger and Cukier’s ‘Learning with Big Data: The Future of Education’ (Houghton Mifflin Harcourt, 2014). Williamson’s blog, Code Acts in Education, is excellent.

 

Distrusting Educational Technology2 Distrusting Educational Technology

Neil Selwyn (Routledge, 2014)

Neil Selwyn is probably the most widely-quoted critical voice in this field, and this book is as good a place to start with his work as any. EdTech, for Selwyn, is a profoundly political affair, and this book explores the gulf between how it could be used, and how it is actually used. Unpacking the ideological agendas of what EdTech is and does, Selwyn covers the reduction of education along data-driven lines, the deskilling of educational labour, the commodification of learning, issues of inequality, and much more. An essential primer.

 

 

The Great American Education Industrial Complex3 The Great American Education-Industrial Complex

Anthony G. Picciano & Joel Spring (Routledge, 2013)

Covering similar ground to both ‘Education Networks’ and ‘Edu.net’ (see below), this book’s subtitle, ‘Ideology, Technology, and Profit’, says it all. Chapter 4 (‘Technology in American Education’) is of particular interest, tracing the recent history of EdTech and the for-profit sector. Chapter 5 provides a wide range of examples of the growing privatization (through EdTech) of American schooling.

 

 

Disruptive Fixation4 Disruptive Fixation

Christo Sims (Princeton University Press, 2017)

The story of a New York school, funded by philanthropists and put together by games designers and educational reformers, that promised to ‘reinvent the classroom for the digital age’. And how it all went wrong … reverting to conventional rote learning with an emphasis on discipline, along with gender and racialized class divisions. A cautionary tale about techno-philanthropism.

 

 

Education Networks5 Education Networks

Joel Spring (Routledge, 2012)

Similar in many ways to ‘Edu.net’ (see below), this is an analysis of the relationships between the interest groups (international agencies, private companies and philanthropic foundations) that are pushing for greater use of EdTech. Spring considers the psychological, social and political implications of the growth of EdTech and concludes with a discussion of the dangers of consumerist approaches to education and dataveillance.

 

 

Edunet6 Edu.net

Stephen J. Ball, Carolina Junemann & Diego Santori (Routledge, 2017)

An account of the ways in which international agencies, private companies (e.g. Bridge International Academies, Pearson) and philanthropic foundations shape global education policies, with a particular focus on India and Ghana. These policies include the standardisation of education, the focus on core subjects, the use of corporate management models and test-based accountability, and are key planks in what has been referred to as the Global Education Reform Movement (GERM). Chapter 4 (‘Following things’) focusses on the role of EdTech in realising GERM goals.

 

Education and Technology7 Education and Technology

Neil Selwyn (Continuum, 2011)

Although covering some similar ground to his ‘Distrusting Educational Technology’, this handy volume summarises key issues, including ‘does technology inevitably change education?’, ‘what can history tell us about education and technology?’, ‘does technology improve learning?’, ‘does technology make education fairer?’, ‘will technology displace the teacher?’ and ‘will technology displace the school?’.

 

 

The Evolution of American Educational Technology8 The Evolution of American Educational Technology

Paul Saettler (Information Age, 2004)

A goldmine of historical information, this is the first of three history books on my list. Early educational films from the start of the 20th century, educational radio, teaching machines and programmed instruction, early computer-assisted instruction like the PLATO project, educational broadcasting and television … moving on to interactive video, teleconferencing, and artificial intelligence. A fascinatingly detailed study of educational dreams and obsolescence.

 

Oversold and Underused9 Oversold and Underused

Larry Cuban (Harvard University Press, 2003)

Larry Cuban’s ground-breaking ‘Teachers and Machines: The Classroom Use of Technology since 1920’ (published in 1986, four years before Saettler’s history) was arguably the first critical evaluation of EdTech. In this title, Cuban pursues his interest in the troubled relationship between teachers and technology, arguing that more attention needs to be paid to the civic and social goals of schooling, goals that make the question of how many computers are in classrooms trivial. Larry Cuban’s blog is well worth following.

 

The Flickering Mind10 The Flickering Mind

Todd Oppenheimer (Random House, 2003)

A journalistic account of how approximately $70 billion was thrown at EdTech in American schools at the end of the 20th century in an attempt to improve them. It’s a tale of getting the wrong priorities, technological obsolescence and, ultimately, a colossal waste of money. Technology has changed since the writing of this book, but as the epigram of Alphonse Karr (cited by Oppenheimer in his afterword) puts it – ‘plus ça change, plus c’est la même chose’.

 

 

Teaching Machines11 Teaching Machines

Bill Ferster (John Hopkins University Press, 2014)

This is the third history of EdTech on my list. A critical look at past attempts to automate instruction, and learning from successes and failures as a way of trying to avoid EdTech insanity (‘doing the same thing over and over again and expecting different results’). Not explicitly political, but the final chapter offers a useful framework for ‘making sense of teaching machines’.

 

 

The Technical Fix12 The Technical Fix

Kevin Robbins & Frank Webster (Macmillan, 1989)

Over thirty years old now, this remarkably prescient book situates the push for more EdTech in Britain in the 1980s as a part of broader social and political forces demanding a more market-oriented and entrepreneurial approach to education. The argument that EdTech cannot be extracted from relations of power and the social values that these entail is presented forcefully. Technology, write the authors, ‘is always shaped by, even constitutive of, prevailing values and power distribution’.

 

 

And here’s hoping that Audrey Watters’ new book sees the light of day soon, so it can be added to the list of history books!

 

 

 

 

 

 

Online teaching is big business. Very big business. Online language teaching is a significant part of it, expected to be worth over $5 billion by 2025. Within this market, the biggest demand is for English and the lion’s share of the demand comes from individual learners. And a sizable number of them are Chinese kids.

There are a number of service providers, and the competition between them is hot. To give you an idea of the scale of this business, here are a few details taken from a report in USA Today. VIPKid, is valued at over $3 billion, attracts celebrity investors, and has around 70,000 tutors who live in the US and Canada. 51Talk has 14,800 English teachers from a variety of English-speaking countries. BlingABC gets over 1,000 American applicants a month for its online tutoring jobs. There are many, many others.

Demand for English teachers in China is huge. The Pie News, citing a Chinese state media announcement, reported in September of last year that there were approximately 400,000 foreign citizens working in China as English language teachers, two-thirds of whom were working illegally. Recruitment problems, exacerbated by quotas and more stringent official requirements for qualifications, along with a very restricted desired teacher profile (white, native-speakers from a few countries like the US and the UK), have led more providers to look towards online solutions. Eric Yang, founder of the Shanghai-based iTutorGroup, which operates under a number of different brands and claims to be the ‘largest English-language learning institution in the world’, said that he had been expecting online tutoring to surpass F2F classes within a few years. With coronavirus, he now thinks it will come ‘much earlier’.

Typically, the work does not require much, if anything, in the way of training (besides familiarity with the platform), although a 40-hour TEFL course is usually preferred. Teachers deliver pre-packaged lessons. According to the USA Today report, Chinese students pay between $49 and $80 dollars an hour for the classes.

It’s a highly profitable business and the biggest cost to the platform providers is the rates they pay the tutors. If you google “Teaching TEFL jobs online”, you’ll quickly find claims that teachers can earn $40 / hour and up. Such claims are invariably found on the sites of recruitment agencies, who are competing for attention. However, although it’s possible that a small number of people might make this kind of money, the reality is that most will get nowhere near it. Scroll down the pages a little and you’ll discover that a more generally quoted and accepted figure is between $14 and $20 / hour. These tutors are, of course, freelancers, so the wages are before tax, and there is no health coverage or pension plan.

Reed job advertVIPKid, for example, considered to be one of the better companies, offers payment in the $14 – $22 / hour range. Others offer considerably less, especially if you are not a white, graduate US citizen. Current rates advertised on OETJobs include work for Ziktalk ($10 – 15 / hour), NiceTalk ($10 – 11 / hour), 247MyTutor ($5 – 8 / hour) and Weblio ($5 – 6 / hour). The number of hours that you get is rarely fixed and tutors need to build up a client base by getting good reviews. They will often need to upload short introductory videos, selling their skills. They are in direct competition with other tutors.

They also need to make themselves available when demand for their services is highest. Peak hours for VIPKid, for example, are between 2 and 8 in the morning, depending on where you live in the US. Weekends, too, are popular. With VIPKid, classes are scheduled in advance, but this is not always the case with other companies, where you log on to show that you are available and hope someone wants you. This is the case with, for example, Cambly (which pays $10.20 / hour … or rather $0.17 / minute) and NiceTalk. According to one review, Cambly has a ‘priority hours system [which] allows teachers who book their teaching slots in advance to feature higher on the teacher list than those who have just logged in, meaning that they will receive more calls’. Teachers have to commit to a set schedule and any changes are heavily penalised. The review states that ‘new tutors on the platform should expect to receive calls for about 50% of the time they’re logged on’.

 

Taking the gig economy to its logical conclusion, there are other companies where tutors can fix their own rates. SkimaTalk, for example, offers a deal where tutors first teach three unpaid lessons (‘to understand how the system works and build up their initial reputation on the platform’), then the system sets $16 / hour as a default rate, but tutors can change this to anything they wish. With another, Palfish, where tutors set their own rate, the typical rate is $10 – 18 / hour, and the company takes a 20% commission. With Preply, here is the deal on offer:

Your earnings depend on the hourly rate you set in your profile and how often you can provide lessons. Preply takes a 100% commission fee of your first lesson payment with every new student. For all subsequent lessons, the commission varies from 33 to 18% and depends on the number of completed lesson hours with students. The more tutoring you do through Preply, the less commission you pay.

Not one to miss a trick, Ziktalk (‘currently focusing on language learning and building global audience’) encourages teachers ‘to upload educational videos in order to attract more students’. Or, to put it another way, teachers provide free content in order to have more chance of earning $10 – 15 / hour. Ah, the joys of digital labour!

And, then, coronavirus came along. With schools shutting down, first in China and then elsewhere, tens of millions of students are migrating online. In Hong Kong, for example, the South China Morning Post reports that schools will remain closed until April 20, at the earliest, but university entrance exams will be going ahead as planned in late March. CNBC reported yesterday that classes are being cancelled across the US, and the same is happening, or is likely to happen, in many other countries.

Shares in the big online providers soared in February, with Forbes reporting that $3.2 billion had been added to the share value of China’s e-Learning leaders. Stock in New Oriental (owners of BlingABC, mentioned above) ‘rose 7.3% last month, adding $190 million to the wealth of its founder Yu Minhong [whose] current net worth is estimated at $3.4 billion’.

DingTalk, a communication and management app owned by Alibaba (and the most downloaded free app in China’s iOS App Store), has been adapted to offer online services for schools, reports Xinhua, the official state-run Chinese news agency. The scale of operations is enormous: more than 10,000 new cloud servers were deployed within just two hours.

Current impacts are likely to be dwarfed by what happens in the future. According to Terry Weng, a Shenzhen-based analyst, ‘The gradual exit of smaller education firms means there are more opportunities for TAL and New Oriental. […] Investors are more keen for their future performance.’ Zhu Hong, CTO of DingTalk, observes ‘the epidemic is like a catalyst for many enterprises and schools to adopt digital technology platforms and products’.

For edtech investors, things look rosy. Smaller, F2F providers are in danger of going under. In an attempt to mop up this market and gain overall market share, many elearning providers are offering weighty discounts and free services. Profits can come later.

For the hundreds of thousands of illegal or semi-legal English language teachers in China, things look doubly bleak. Their situation is likely to become even more precarious, with the online gig economy their obvious fall-back path. But English language teachers everywhere are likely to be affected one way or another, as will the whole world of TEFL.

Now seems like a pretty good time to find out more about precarity (see the Teachers as Workers website) and native-speakerism (see TEFL Equity Advocates).

Book_coverIn my last post, I looked at shortcomings in edtech research, mostly from outside the world of ELT. I made a series of recommendations of ways in which such research could become more useful. In this post, I look at two very recent collections of ELT edtech research. The first of these is Digital Innovations and Research in Language Learning, edited by Mavridi and Saumell, and published this February by the Learning Technologies SIG of IATEFL. I’ll refer to it here as DIRLL. It’s available free to IATEFL LT SIG members, and can be bought for $10.97 as an ebook on Amazon (US). The second is the most recent edition (February 2020) of the Language Learning & Technology journal, which is open access and available here. I’ll refer to it here as LLTJ.

In both of these collections, the focus is not on ‘technology per se, but rather issues related to language learning and language teaching, and how they are affected or enhanced by the use of digital technologies’. However, they are very different kinds of publication. Nobody involved in the production of DIRLL got paid in any way (to the best of my knowledge) and, in keeping with its provenance from a teachers’ association, has ‘a focus on the practitioner as teacher-researcher’. Almost all of the contributing authors are university-based, but they are typically involved more in language teaching than in research. With one exception (a grant from the EU), their work was unfunded.

The triannual LLTJ is funded by two American universities and published by the University of Hawaii Press. The editors and associate editors are well-known scholars in their fields. The journal’s impact factor is high, close to the impact factor of the paywalled reCALL (published by the University of Cambridge), which is the highest-ranking journal in the field of CALL. The contributing authors are all university-based, many with a string of published articles (in prestige journals), chapters or books behind them. At least six of the studies were funded by national grant-awarding bodies.

I should begin by making clear that there was much in both collections that I found interesting. However, it was not usually the research itself that I found informative, but the literature review that preceded it. Two of the chapters in DIRLL were not really research, anyway. One was the development of a template for evaluating ICT-mediated tasks in CLIL, another was an advocacy of comics as a resource for language teaching. Both of these were new, useful and interesting to me. LLTJ included a valuable literature review of research into VR in FL learning (but no actual new research). With some exceptions in both collections, though, I felt that I would have been better off curtailing my reading after the reviews. Admittedly, there wouldn’t be much in the way of literature reviews if there were no previous research to report …

It was no surprise to see the learners who were the subjects of this research were overwhelmingly university students. In fact, only one article (about a high-school project in Israel, reported in DIRLL) was not about university students. The research areas focused on reflected this bias towards tertiary contexts: online academic reading skills, academic writing, online reflective practices in teacher training programmes, etc.

In a couple of cases, the selection of experimental subjects seemed plain bizarre. Why, if you want to find out about the extent to which Moodle use can help EAP students become better academic readers (in DIRLL), would you investigate this with a small volunteer cohort of postgraduate students of linguistics, with previous experience of using Moodle and experience of teaching? Is a less representative sample imaginable? Why, if you want to investigate the learning potential of the English File Pronunciation app (reported in LLTJ), which is clearly most appropriate for A1 – B1 levels, would you do this with a group of C1-level undergraduates following a course in phonetics as part of an English Studies programme?

More problematic, in my view, was the small sample size in many of the research projects. The Israeli virtual high school project (DIRLL), previously referred to, started out with only 11 students, but 7 dropped out, primarily, it seems, because of institutional incompetence: ‘the project was probably doomed […] to failure from the start’, according to the author. Interesting as this was as an account of how not to set up a project of this kind, it is simply impossible to draw any conclusions from 4 students about the potential of a VLE for ‘interaction, focus and self-paced learning’. The questionnaire investigating experience of and attitudes towards VR (in DIRLL) was completed by only 7 (out of 36 possible) students and 7 (out of 70+ possible) teachers. As the author acknowledges, ‘no great claims can be made’, but then goes on to note the generally ‘positive attitudes to VR’. Perhaps those who did not volunteer had different attitudes? We will never know. The study of motivational videos in tertiary education (DIRLL) started off with 15 subjects, but 5 did not complete the necessary tasks. The research into L1 use in videoconferencing (LLTJ) started off with 10 experimental subjects, all with the same L1 and similar cultural backgrounds, but there was no data available from 4 of them (because they never switched into L1). The author claims that the paper demonstrates ‘how L1 is used by language learners in videoconferencing as a social semiotic resource to support social presence’ – something which, after reading the literature review, we already knew. But the paper also demonstrates quite clearly how L1 is not used by language learners in videoconferencing as a social semiotic resource to support social presence. In all these cases, it is the participants who did not complete or the potential participants who did not want to take part that have the greatest interest for me.

Unsurprisingly, the LLTJ articles had larger sample sizes than those in DIRLL, but in both collections the length of the research was limited. The production of one motivational video (DIRLL) does not really allow us to draw any conclusions about the development of students’ critical thinking skills. Two four-week interventions do not really seem long enough to me to discover anything about learner autonomy and Moodle (DIRLL). An experiment looking at different feedback modes needs more than two written assignments to reach any conclusions about student preferences (LLTJ).

More research might well be needed to compensate for the short-term projects with small sample sizes, but I’m not convinced that this is always the case. Lacking sufficient information about the content of the technologically-mediated tools being used, I was often unable to reach any conclusions. A gamified Twitter environment was developed in one project (DIRLL), using principles derived from contemporary literature on gamification. The authors concluded that the game design ‘failed to generate interaction among students’, but without knowing a lot more about the specific details of the activity, it is impossible to say whether the problem was the principles or the particular instantiation of those principles. Another project, looking at the development of pronunciation materials for online learning (LLTJ), came to the conclusion that online pronunciation training was helpful – better than none at all. Claims are then made about the value of the method used (called ‘innovative Cued Pronunciation Readings’), but this is not compared to any other method / materials, and only a very small selection of these materials are illustrated. Basically, the reader of this research has no choice but to take things on trust. The study looking at the use of Alexa to help listening comprehension and speaking fluency (LLTJ) cannot really tell us anything about IPAs unless we know more about the particular way that Alexa is being used. Here, it seems that the students were using Alexa in an interactive storytelling exercise, but so little information is given about the exercise itself that I didn’t actually learn anything at all. The author’s own conclusion is that the results, such as they are, need to be treated with caution. Nevertheless, he adds ‘the current study illustrates that IPAs may have some value to foreign language learners’.

This brings me onto my final gripe. To be told that IPAs like Alexa may have some value to foreign language learners is to be told something that I already know. This wasn’t the only time this happened during my reading of these collections. I appreciate that research cannot always tell us something new and interesting, but a little more often would be nice. I ‘learnt’ that goal-setting plays an important role in motivation and that gamification can boost short-term motivation. I ‘learnt’ that reflective journals can take a long time for teachers to look at, and that reflective video journals are also very time-consuming. I ‘learnt’ that peer feedback can be very useful. I ‘learnt’ from two papers that intercultural difficulties may be exacerbated by online communication. I ‘learnt’ that text-to-speech software is pretty good these days. I ‘learnt’ that multimodal literacy can, most frequently, be divided up into visual and auditory forms.

With the exception of a piece about online safety issues (DIRLL), I did not once encounter anything which hinted that there may be problems in using technology. No mention of the use to which student data might be put. No mention of the costs involved (except for the observation that many students would not be happy to spend money on the English File Pronunciation app) or the cost-effectiveness of digital ‘solutions’. No consideration of the institutional (or other) pressures (or the reasons behind them) that may be applied to encourage teachers to ‘leverage’ edtech. No suggestion that a zero-tech option might actually be preferable. In both collections, the language used is invariably positive, or, at least, technology is associated with positive things: uncovering the possibilities, promoting autonomy, etc. Even if the focus of these publications is not on technology per se (although I think this claim doesn’t really stand up to close examination), it’s a little disingenuous to claim (as LLTJ does) that the interest is in how language learning and language teaching is ‘affected or enhanced by the use of digital technologies’. The reality is that the overwhelming interest is in potential enhancements, not potential negative effects.

I have deliberately not mentioned any names in referring to the articles I have discussed. I would, though, like to take my hat off to the editors of DIRLL, Sophia Mavridi and Vicky Saumell, for attempting to do something a little different. I think that Alicia Artusi and Graham Stanley’s article (DIRLL) about CPD for ‘remote’ teachers was very good and should interest the huge number of teachers working online. Chryssa Themelis and Julie-Ann Sime have kindled my interest in the potential of comics as a learning resource (DIRLL). Yu-Ju Lan’s article about VR (LLTJ) is surely the most up-to-date, go-to article on this topic. There were other pieces, or parts of pieces, that I liked, too. But, to me, it’s clear that ‘more research is needed’ … much less than (1) better and more critical research, and (2) more digestible summaries of research.

Colloquium

At the beginning of March, I’ll be going to Cambridge to take part in a Digital Learning Colloquium (for more information about the event, see here ). One of the questions that will be explored is how research might contribute to the development of digital language learning. In this, the first of two posts on the subject, I’ll be taking a broad overview of the current state of play in edtech research.

I try my best to keep up to date with research. Of the main journals, there are Language Learning and Technology, which is open access; CALICO, which offers quite a lot of open access material; and reCALL, which is the most restricted in terms of access of the three. But there is something deeply frustrating about most of this research, and this is what I want to explore in these posts. More often than not, research articles end with a call for more research. And more often than not, I find myself saying ‘Please, no, not more research like this!’

First, though, I would like to turn to a more reader-friendly source of research findings. Systematic reviews are, basically literature reviews which can save people like me from having to plough through endless papers on similar subjects, all of which contain the same (or similar) literature review in the opening sections. If only there were more of them. Others agree with me: the conclusion of one systematic review of learning and teaching with technology in higher education (Lillejord et al., 2018) was that more systematic reviews were needed.

Last year saw the publication of a systematic review of research on artificial intelligence applications in higher education (Zawacki-Richter, et al., 2019) which caught my eye. The first thing that struck me about this review was that ‘out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis’. In other words, only just over 5% of the research was considered worthy of inclusion.

The review did not paint a very pretty picture of the current state of AIEd research. As the second part of the title of this review (‘Where are the educators?’) makes clear, the research, taken as a whole, showed a ‘weak connection to theoretical pedagogical perspectives’. This is not entirely surprising. As Bates (2019) has noted: ‘since AI tends to be developed by computer scientists, they tend to use models of learning based on how computers or computer networks work (since of course it will be a computer that has to operate the AI). As a result, such AI applications tend to adopt a very behaviourist model of learning: present / test / feedback.’ More generally, it is clear that technology adoption (and research) is being driven by technology enthusiasts, with insufficient expertise in education. The danger is that edtech developers ‘will simply ‘discover’ new ways to teach poorly and perpetuate erroneous ideas about teaching and learning’ (Lynch, 2017).

This, then, is the first of my checklist of things that, collectively, researchers need to do to improve the value of their work. The rest of this list is drawn from observations mostly, but not exclusively, from the authors of systematic reviews, and mostly come from reviews of general edtech research. In the next blog post, I’ll look more closely at a recent collection of ELT edtech research (Mavridi & Saumell, 2020) to see how it measures up.

1 Make sure your research is adequately informed by educational research outside the field of edtech

Unproblematised behaviourist assumptions about the nature of learning are all too frequent. References to learning styles are still fairly common. The most frequently investigated skill that is considered in the context of edtech is critical thinking (Sosa Neira, et al., 2017), but this is rarely defined and almost never problematized, despite a broad literature that questions the construct.

2 Adopt a sceptical attitude from the outset

Know your history. Decades of technological innovation in education have shown precious little in the way of educational gains and, more than anything else, have taught us that we need to be sceptical from the outset. ‘Enthusiasm and praise that are directed towards ‘virtual education, ‘school 2.0’, ‘e-learning and the like’ (Selwyn, 2014: vii) are indications that the lessons of the past have not been sufficiently absorbed (Levy, 2016: 102). The phrase ‘exciting potential’, for example, should be banned from all edtech research. See, for example, a ‘state-of-the-art analysis of chatbots in education’ (Winkler & Söllner, 2018), which has nothing to conclude but ‘exciting potential’. Potential is fine (indeed, it is perhaps the only thing that research can unambiguously demonstrate – see section 3 below), but can we try to be a little more grown-up about things?

3 Know what you are measuring

Measuring learning outcomes is tricky, to say the least, but it’s understandable that researchers should try to focus on them. Unfortunately, ‘the vast array of literature involving learning technology evaluation makes it challenging to acquire an accurate sense of the different aspects of learning that are evaluated, and the possible approaches that can be used to evaluate them’ (Lai & Bower, 2019). Metrics such as student grades are hard to interpret, not least because of the large number of variables and the danger of many things being conflated in one score. Equally, or possibly even more, problematic, are self-reporting measures which are rarely robust. It seems that surveys are the most widely used instrument in qualitative research (Sosa Neira, et al., 2017), but these will tell us little or nothing when used for short-term interventions (see point 5 below).

4 Ensure that the sample size is big enough to mean something

In most of the research into digital technology in education that was analysed in a literature review carried out for the Scottish government (ICF Consulting Services Ltd, 2015), there were only ‘small numbers of learners or teachers or schools’.

5 Privilege longitudinal studies over short-term projects

The Scottish government literature review (ICF Consulting Services Ltd, 2015), also noted that ‘most studies that attempt to measure any outcomes focus on short and medium term outcomes’. The fact that the use of a particular technology has some sort of impact over the short or medium term tells us very little of value. Unless there is very good reason to suspect the contrary, we should assume that it is a novelty effect that has been captured (Levy, 2016: 102).

6 Don’t forget the content

The starting point of much edtech research is the technology, but most edtech, whether it’s a flashcard app or a full-blown Moodle course, has content. Research reports rarely give details of this content, assuming perhaps that it’s just fine, and all that’s needed is a little tech to ‘present learners with the ‘right’ content at the ‘right’ time’ (Lynch, 2017). It’s a foolish assumption. Take a random educational app from the Play Store, a random MOOC or whatever, and the chances are you’ll find it’s crap.

7 Avoid anecdotal accounts of technology use in quasi-experiments as the basis of a ‘research article’

Control (i.e technology-free) groups may not always be possible but without them, we’re unlikely to learn much from a single study. What would, however, be extremely useful would be a large, collated collection of such action-research projects, using the same or similar technology, in a variety of settings. There is a marked absence of this kind of work.

8 Enough already of higher education contexts

Researchers typically work in universities where they have captive students who they can carry out research on. But we have a problem here. The systematic review of Lundin et al (2018), for example, found that ‘studies on flipped classrooms are dominated by studies in the higher education sector’ (besides lacking anchors in learning theory or instructional design). With some urgency, primary and secondary contexts need to be investigated in more detail, not just regarding flipped learning.

9 Be critical

Very little edtech research considers the downsides of edtech adoption. Online safety, privacy and data security are hardly peripheral issues, especially with younger learners. Ignoring them won’t make them go away.

More research?

So do we need more research? For me, two things stand out. We might benefit more from, firstly, a different kind of research, and, secondly, more syntheses of the work that has already been done. Although I will probably continue to dip into the pot-pourri of articles published in the main CALL journals, I’m looking forward to a change at the CALICO journal. From September of this year, one issue a year will be thematic, with a lead article written by established researchers which will ‘first discuss in broad terms what has been accomplished in the relevant subfield of CALL. It should then outline which questions have been answered to our satisfaction and what evidence there is to support these conclusions. Finally, this article should pose a “soft” research agenda that can guide researchers interested in pursuing empirical work in this area’. This will be followed by two or three empirical pieces that ‘specifically reflect the research agenda, methodologies, and other suggestions laid out in the lead article’.

But I think I’ll still have a soft spot for some of the other journals that are coyer about their impact factor and that can be freely accessed. How else would I discover (it would be too mean to give the references here) that ‘the effective use of new technologies improves learners’ language learning skills’? Presumably, the ineffective use of new technologies has the opposite effect? Or that ‘the application of modern technology represents a significant advance in contemporary English language teaching methods’?

References

Bates, A. W. (2019). Teaching in a Digital Age Second Edition. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from https://pressbooks.bccampus.ca/teachinginadigitalagev2/

ICF Consulting Services Ltd (2015). Literature Review on the Impact of Digital Technology on Learning and Teaching. Edinburgh: The Scottish Government. https://dera.ioe.ac.uk/24843/1/00489224.pdf

Lai, J.W.M. & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133(1), 27-42. Elsevier Ltd. Retrieved January 14, 2020 from https://www.learntechlib.org/p/207137/

Levy, M. 2016. Researching in language learning and technology. In Farr, F. & Murray, L. (Eds.) The Routledge Handbook of Language Learning and Technology. Abingdon, Oxon.: Routledge. pp.101 – 114

Lillejord S., Børte K., Nesje K. & Ruud E. (2018). Learning and teaching with technology in higher education – a systematic review. Oslo: Knowledge Centre for Education https://www.forskningsradet.no/siteassets/publikasjoner/1254035532334.pdf

Lundin, M., Bergviken Rensfeldt, A., Hillman, T. et al. (2018). Higher education dominance and siloed knowledge: a systematic review of flipped classroom research. International Journal of Educational Technology in Higher Education 15, 20 (2018) doi:10.1186/s41239-018-0101-6

Lynch, J. (2017). How AI Will Destroy Education. Medium, November 13, 2017. https://buzzrobot.com/how-ai-will-destroy-education-20053b7b88a6

Mavridi, S. & Saumell, V. (Eds.) (2020). Digital Innovations and Research in Language Learning. Faversham, Kent: IATEFL

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

Sosa Neira, E. A., Salinas, J. and de Benito Crosetti, B. (2017). Emerging Technologies (ETs) in Education: A Systematic Review of the Literature Published between 2006 and 2016. International Journal of Emerging Technologies in Education, 12 (5). https://online-journals.org/index.php/i-jet/article/view/6939

Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA. https://www.alexandria.unisg.ch/254848/1/JML_699.pdf

Zawacki-Richter, O., Bond, M., Marin, V. I. And Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education 2019

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.

Lava flow Hawaii

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 19th 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 20th 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 ‘21st 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.

 

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.

Big_data_Google_Trend

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’.

Macmillan_catalogue_2015

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 21st 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.