In the last post, I mentioned a lesson plan from an article by Pegrum, M., Dudeney, G. & Hockly, N. (2018. Digital literacies revisited. The European Journal of Applied Linguistics and TEFL, 7 (2), pp. 3-24) in which students discuss the data that is collected by fitness apps and the possibility of using this data to calculate health insurance premiums, before carrying out and sharing online research about companies that track personal data. It’s a nice plan, unfortunately pay-walled, but you could try requesting a copy through Research Gate.
The only other off-the-shelf lesson plan I have been able to find is entitled ‘You and Your Data’ from the British Council. Suitable for level B2, this plan, along with a photocopiable pdf, contains with a vocabulary task (matching), a reading text (you and your data, who uses our data and why, can you protect your data) with true / false and sentence completion tasks) and a discussion (what do you do to protect our data). The material was written to coincide with Safer Internet Day (an EU project), which takes place in early February (next data 9 February 2021). The related website, Better Internet for Kids, contains links to a wide range of educational resources for younger learners.
For other resources, a good first stop is Ina Sander’s ‘A Critically Commented Guide to Data Literacy Tools’ in which she describes and evaluates a wide range of educational online resources for developing critical data literacy. Some of the resources that I discuss below are also evaluated in this guide. Here are some suggestion for learning / teaching resources.
A glossary
This is simply a glossary of terms that are useful in discussing data issues. It could easily be converted into a matching exercise or flashcards.
‘Do not Track’ is an award-winning series of interactive videos, produced by a consortium of broadcasters. In seven parts, the videos consider such issues as who profits from the personal data that we generate online, the role of cookies in the internet economy, how online profiling is done, the data generated by mobile phones and how algorithms interpret the data.
Each episode is between 5 and 10 minutes long, and is therefore ideal for asynchronous viewing. In a survey of critical data literacy tools (Sander, 2020), ‘Do not Track’ proved popular with the students who used it. I highly recommend it, but students will probably need a B2 level or higher.
More informational videos
If you do not have time to watch the ‘Do Not Track’ video series, you may want to use something shorter. There are a huge number of freely available videos about online privacy. I have selected just two which I think would be useful. You may be able to find something better!
1 Students watch a video about how cookies work. This video, from Vox, is well-produced and is just under 7 minutes long. The speaker speaks fairly rapidly, so captions may be helpful.
Students watch a video as an introduction to the topic of surveillance and privacy. This video, ‘Reclaim our Privacy’, was produced by ‘La Quadrature du Net’, a French advocacy group that promotes digital rights and freedoms of citizens. It is short (3 mins) and can be watched with or without captions (English or 6 other languages). Its message is simple: political leaders should ensure that our online privacy is respected.
A simple matching task ‘ten principles for online privacy’
1 Share the image below with all the students and ask them to take a few minutes matching the illustrations to the principles on the right. There is no need for anyone to write or say anything, but it doesn’t matter if some students write the answers in the chat box.
(Note: This image and the other ideas for this activity are adapted from https://teachingprivacy.org/ , a project developed by the International Computer Science Institute and the University of California-Berkeley for secondary school students and undergraduates. Each of the images corresponds to a course module, which contains a wide-range of materials (videos, readings, discussions, etc.) which you may wish to explore more fully.)
2 Share the image below (which shows the answers in abbreviated form). Ask if anyone needs anything clarified.
You’re Leaving Footprints Principle: Your information footprint is larger than you think.
There’s No Anonymity Principle: There is no anonymity on the Internet.
Information Is Valuable Principle: Information about you on the Internet will be used by somebody in their interest — including against you.
Someone Could Listen Principle: Communication over a network, unless strongly encrypted, is never just between two parties.
Sharing Releases Control Principle: Sharing information over a network means you give up control over that information — forever.
Search Is Improving Principle: Just because something can’t be found today, doesn’t mean it can’t be found tomorrow.
Online Is Real Principle: The online world is inseparable from the “real” world.
Identity Isn’t Guaranteed Principle: Identity is not guaranteed on the Internet.
You Can’t Escape Principle: You can’t avoid having an information footprint by not going online.
Privacy Requires Work Principle: Only you have an interest in maintaining your privacy.
3 Wrap up with a discussion of these principles.
Hands-on exploration of privacy tools
Click on the link below to download the procedure for the activity, as well as supporting material.
Written by Michael Keller and Josh Neufeld, and produced by Al Jazeera, this graphic novel ‘Terms of Service. Understanding our role in the world of Big Data’ provides a good overview of critical data literacy issues, offering lots of interesting, concrete examples of real cases. The language is, however, challenging (C1+). It may be especially useful for trainee teachers.
A website
The Privacy International website is an extraordinary goldmine of information and resources. Rather than recommending anything specific, my suggestion is that you, or your students, use the ‘Search’ function on the homepage and see where you end up.
In the first post in this 3-part series, I focussed on data collection practices in a number of ELT websites, as a way of introducing ‘critical data literacy’. Here, I explore the term in more detail.
Although the term ‘big data’ has been around for a while (see this article and infographic) it’s less than ten years ago that it began to enter everyday language, and found its way into the OED (2013). In the same year, Viktor Mayer-Schönberger and Kenneth Cukier published their best-selling ‘Big Data: A Revolution That Will Transform How We Live, Work, and Think’ (2013) and it was hard to avoid enthusiastic references in the media to the transformative potential of big data in every sector of society.
Since then, the use of big data and analytics has become ubiquitous. Massive data collection (and data surveillance) has now become routine and companies like Palantir, which specialise in big data analytics, have become part of everyday life. Palantir’s customers include the LAPD, the CIA, the US Immigration and Customs Enforcement (ICE) and the British Government. Its recent history includes links with Cambridge Analytica, assistance in an operation to arrest the parents of illegal migrant children, and a racial discrimination lawsuit where the company was accused of having ‘routinely eliminated’ Asian job applicants (settled out of court for $1.7 million).
Unsurprisingly, the datafication of society has not gone entirely uncontested. Whilst the vast majority of people seem happy to trade their personal data for convenience and connectivity, a growing number are concerned about who benefits most from this trade-off. On an institutional level, the EU introduced the General Data Protection Regulation (GDPR), which led to Google being fined Ꞓ50 million for insufficient transparency in their privacy policy and their practices of processing personal data for the purposes of behavioural advertising. In the intellectual sphere, there has been a recent spate of books that challenge the practices of ubiquitous data collection, coining new terms like ‘surveillance capitalism’, ‘digital capitalism’ and ‘data colonialism’. Here are four recent books that I have found particularly interesting.
Beer, D. (2019). The Data Gaze. London: Sage
Couldry, N. & Mejias, U. A. (2019). The Costs of Connection. Stanford: Stanford University Press
Sadowski, J. (2020). Too Smart. Cambridge, Mass.: MIT Press
Zuboff, S. (2019). The Age of Surveillance Capitalism. New York: Public Affairs
The use of big data and analytics in education is also now a thriving industry, with its supporters claiming that these technologies can lead to greater personalization, greater efficiency of instruction and greater accountability. Opponents (myself included) argue that none of these supposed gains have been empirically demonstrated, and that the costs to privacy, equity and democracy outweigh any potential gains. There is a growing critical literature and useful, recent books include:
Bradbury, A. & Roberts-Holmes, G. (2018). The Datafication of Primary and Early Years Education. Abingdon: Routledge
Jarke, J. & Breiter, A. (Eds.) (2020). The Datafication of Education. Abingdon: Routledge
Williamson, B. (2017). Big Data in Education: The digital future of learning, policy and practice. London: Sage
Concomitant with the rapid growth in the use of digital tools for language learning and teaching, and therefore the rapid growth in the amount of data that learners were (mostly unwittingly) giving away, came a growing interest in the need for learners to develop a set of digital competencies, or literacies, which would enable them to use these tools effectively. In the same year that Mayer-Schönberger and Cukier brought out their ‘Big Data’ book, the first book devoted to digital literacies in English language teaching came out (Dudeney et al., 2013). They defined digital literacies as the individual and social skills needed to effectively interpret, manage, share and create meaning in the growing range of digital communication channels (Dudeney et al., 2013: 2). The book contained a couple of activities designed to raise students’ awareness of online identity issues, along with others intended to promote critical thinking about digitally-mediated information (what the authors call ‘information literacy’), but ‘critical literacy’ was missing from the authors’ framework.
Critical thinking and critical literacy are not the same thing. Although there is no generally agreed definition of the former (with a small ‘c’), it is focussed primarily on logic and comprehension (Lee, 2011). Paul Dummett and John Hughes (2019: 4) describe it as ‘a mindset that involves thinking reflectively, rationally and reasonably’. The prototypical critical thinking activity involves the analysis of a piece of fake news (e.g. the task where students look at a website about tree octopuses in Dudeney et al. 2013: 198 – 203). Critical literacy, on the other hand, involves standing back from texts and technologies and viewing them as ‘circulating within a larger social and textual context’ (Warnick, 2002). Consideration of the larger social context necessarily entails consideration of unequal power relationships (Leee, 2011; Darvin, 2017), such as that between Google and the average user of Google. And it follows from this that critical literacy has a socio-political emancipatory function.
Critical digital literacy is now a growing field of enquiry (e.g. Pötzsch, 2019) and there is an awareness that digital competence frameworks, such as the Digital Competence Framework of the European Commission, are incomplete and out of date without the inclusion of critical digital literacy. Dudeney et al (2013) clearly recognise the importance of including critical literacy in frameworks of digital literacies. In Pegrum et al. (2018, unfortunately paywalled), they update the framework from their 2013 book, and the biggest change is the inclusion of critical literacy. They divide this into the following:
critical digital literacy – closely related to information literacy
critical mobile literacy – focussing on issues brought to the fore by mobile devices, ranging from protecting privacy through to safeguarding mental and physical health
critical material literacy – concerned with the material conditions underpinning the use of digital technologies, ranging from the socioeconomic influences on technological access to the environmental impacts of technological manufacturing and disposal
critical philosophical literacy – concerned with the big questions posed to and about humanity as our lives become conjoined with the existence of our smart devices, robots and AI
critical academic literacy, which refers to the pressing need to conduct meaningful studies of digital technologies in place of what is at times ‘cookie-cutter’ research
I’m not entirely convinced by the subdivisions, but labelling in this area is still in its infancy. My particular interest here, in critical data literacy, seems to span across a number of their sub-divisions. And the term that I am using, ‘critical data literacy’, which I’ve taken from Tygel & Kirsch (2016), is sometimes referred to as ‘critical big data literacy’ (Sander, 2020a) or ‘personal data literacy’ (Pangrazio & Selwyn, 2019). Whatever it is called, it is the development of ‘informed and critical stances toward how and why [our] data are being used’ (Pangrazio & Selwyn, 2018). One of the two practical activities in the Pegrum at al article (2018) looks at precisely this area (the task requires students to consider the data that is collected by fitness apps). It will be interesting to see, when the new edition of the ‘Digital Literacies’ book comes out (perhaps some time next year), how many other activities take a more overtly critical stance.
In the next post, I’ll be looking at a range of practical activities for developing critical data literacy in the classroom. This involves both bridging the gaps in knowledge (about data, algorithms and online privacy) and learning, practically, how to implement ‘this knowledge for a more empowered internet usage’ (Sander, 2020b).
Without wanting to invalidate the suggestions in the next post, a word of caution is needed. Just as critical thinking activities in the ELT classroom cannot be assumed to lead to any demonstrable increase in critical thinking (although there may be other benefits to the activities), activities to promote critical literacy cannot be assumed to lead to any actual increase in critical literacy. The reaction of many people may well be ‘It’s not like it’s life or death or whatever’ (Pangrazio & Selwyn, 2018). And, perhaps, education is rarely, if ever, a solution to political and social problems, anyway. And perhaps, too, we shouldn’t worry too much about educational interventions not leading to their intended outcomes. Isn’t that almost always the case? But, with those provisos in mind, I’ll come back next time with some practical ideas.
REFERENCES
Darvin R. (2017). Language, Ideology, and Critical Digital Literacy. In: Thorne S., May S. (eds) Language, Education and Technology. Encyclopedia of Language and Education (3rd ed.). Springer, Cham. pp. 17 – 30 https://doi.org/10.1007/978-3-319-02237-6_35
Dudeney, G., Hockly, N. & Pegrum, M. (2013). Digital Literacies. Harlow: Pearson Education
Dummett, P. & Hughes, J. (2019). Critical Thinking in ELT. Boston: National Geographic Learning
Lee, C. J. (2011). Myths about critical literacy: What teachers need to unlearn. Journal of Language and Literacy Education [Online], 7 (1), 95-102. Available at http://www.coa.uga.edu/jolle/2011_1/lee.pdf
Mayer-Schönberger, V. & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray
Pangrazio, L. & Selwyn, N. (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. (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
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
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
A week or so ago, someone in the Macmillan marketing department took it upon themselves to send out this tweet. What grabbed my attention was the claim that it is ‘a well-known fact’ that teaching students a growth mindset makes them perform better academically over time. The easily demonstrable reality (which I’ll come on to) is that this is not a fact. It’s fake news, being used for marketing purposes. The tweet links to a blog post of over a year ago. In it, Chia Suan Chong offers five tips for developing a growth mindset in students: educating students about neuroplasticity, delving deeper into success stories, celebrating challenges and mistakes, encouraging students to go outside their comfort zones, and giving ‘growth-mindset-feedback’. All of which, she suggests, might help our students. Indeed, it might, and, even if it doesn’t, it might be worth a try anyway. Chia doesn’t make any claims beyond the potential of the suggested strategies, so I wonder where the Macmillan Twitter account person got the ‘well-known fact’.
If you google ‘mindset ELT’, you will find webpage after webpage offering tips about how to promote growth mindset in learners. It’s rare for the writers of these pages to claim that the positive effects of mindset interventions are a ‘fact’, but it’s even rarer to come across anyone who suggests that mindset interventions might be an à la mode waste of time and effort. Even in more serious literature (e.g. Mercer, S. & Ryan, S. (2010). A mindset for EFL: learners’ beliefs about the role of natural talent. ELT Journal, 64 (4): 436 – 444), the approach is fundamentally enthusiastic, with no indication that there might be a problem with mindset theory. Given that this enthusiasm is repeated so often, perhaps we should not blame the Macmillan tweeter for falling victim to the illusory truth effect. After all, it appears that 98% of teachers in the US feel that growth mindset approaches should be adopted in schools (Hendrick, 2019).
Chia suggests that we can all have fixed mindsets in certain domains (e.g. I know all about that, there’s nothing more I can learn). One domain where it seems that fixed mindsets are prevalent is mindset theory itself. This post is an attempt to nudge towards more ‘growth’ and, in trying to persuade you to be more sceptical, I will quote as much as possible from Carol Dweck, the founder of mindset theory, and her close associates.
Carol Dweck’s book ‘Mindset: The New Psychology of Success’ appeared in 2006. In it, she argued that people can be placed on a continuum between those who have ‘a fixed mindset–those who believe that abilities are fixed—[and who] are less likely to flourish [and] those with a growth mindset–those who believe that abilities can be developed’ (from the back cover of the updated (2007) version of the book). There was nothing especially new about the idea. It is very close to Bandura’s (1982) theory of self-efficacy, which will be familiar to anyone who has read Zoltán Dörnyei’s more recent work on motivation in language learning. It’s closely related to Carl Roger’s (1969) ideas about self-concept and it’s not a million miles removed, either, from Maslow’s (1943) theory of self-actualization. The work of Rogers and Maslow was at the heart of the ‘humanistic turn’ in ELT in the latter part of the 20th century (see, for example, Early, 1981), so mindset theory is likely to resonate with anyone who was inspired by the humanistic work of people like Moskowitz, Stevick or Rinvolucri. The appeal of mindset theory is easy to see. Besides its novelty value, it resonates emotionally with the values that many teachers share, writes Tom Bennett: it feels right that you don’t criticise the person, but invite them to believe that, through hard work and persistence, you can achieve.
We might even trace interest in the importance of self-belief back to the Stoics (who, incidentally but not coincidentally, are experiencing a revival of interest), but Carol Dweck introduced a more modern flavour to the old wine and packaged it skilfully and accessibly in shiny new bottles. Her book was a runaway bestseller, with sales in the millions, and her TED Talk has now had over 11 million views. It was in education that mindset theory became particularly popular. As a mini-industry it is now worth millions and millions. Just one research project into the efficacy of one mindset product has received 3.5 million dollars in US federal funding.
But, much like other ideas that have done a roaring trade in popular psychology (Howard Gardner’s ‘multiple intelligences theory, for example) which seem to offer simple solutions to complex problems, there was soon pushback. It wasn’t hard for critics to scoff at motivational ‘yes-you-can’ posters in classrooms or accounts of well-meaning but misguided teacher interventions, like this one reported by Carl Hendrick:
One teacher [took] her children out into the pristine snow covering the school playground, she instructed them to walk around, taking note of their footprints. “Look at these paths you’ve been creating,” the teacher said. “In the same way that you’re creating new pathways in the snow, learning creates new pathways in your brain.”
Carol Dweck was sympathetic to the critics. She has described the early reaction to her book as ‘uncontrollable’. She freely admits that she and her colleagues had underestimated the issues around mindset interventions in the classrooms and that such interventions were ‘not yet evidence-based’. She identified two major areas where mindset interventions have gone awry. The first of these is when a teacher teaches the concept of mindsets to students, but does not change other policies and practices in the classroom. The second is that some teachers have focussed too much on praising their learners’ efforts. Teachers have taken mindset recipes and tips, without due consideration. She says:
Teachers have to ask, what exactly is the evidence suggesting? They have to realise it takes deep thought and deep experimentation on their part in the classroom to see how best the concept can be implemented there. This should be a group enterprise, in which they share what worked, what did not work, for whom and when. People need to recognise we are researchers, we have produced a body of evidence that says under these conditions this is what happened. We have not explored all the conditions that are possible. Teacher feedback on what is working and not working is hugely valuable to us to tell us what we have not done and what we need to do.
Critics like Dylan William, Carl Hendrick and Timothy Bates found that it was impossible to replicate Dweck’s findings, and that there were at best weak correlations between growth mindset and academic achievement, and between mindset interventions and academic gains. They were happy to concede that typical mindset interventions would not do any harm, but asked whether the huge amounts of money being spent on mindset would not be better invested elsewhere.
Carol Dweck seems to like the phrase ‘not yet’. She argues, in her TED Talk, that simply using the words ‘not yet’ can build students’ confidence, and her tip is often repeated by others. She also talks about mindset interventions being ‘not yet evidence-based’, which is a way of declaring her confidence that they soon will be. But, with huge financial backing, Dweck and her colleagues have recently been carrying out a lot of research and the results are now coming in. There are a small number of recent investigations that advocates of mindset interventions like to point to. For reasons of space, I’ll refer to two of them.
The first (Outes-Leon, et al., 2020) of these looked at an intervention with children in the first grades in a few hundred Peruvian secondary schools. The intervention consisted of students individually reading a text designed to introduce them to the concept of growth-mindset. This was followed by a group debate about the text, before students had to write individually a reflective letter to a friend/relative describing what they had learned. In total, this amounted to about 90 minutes of activity. Subsequently, teachers made a subjective assessment of the ‘best’ letters and attached these to the classroom wall, along with a growth mindset poster, for the rest of the school year. Teachers were also asked to take a picture of the students alongside the letters and the poster and to share this picture by email.
Academic progress was measured 2 and 14 months after the intervention and compared to a large control group. The short-term (2 months) impact of the intervention was positive for mathematics, but less so for reading comprehension. (Why?) These gains were only visible in regional schools, not at all in metropolitan schools. Similar results were found when looking at the medium-term (14 month) impact. The reasons for this are unclear. It is hypothesized that the lower-achieving students in regional schools might benefit more from the intervention. Smaller class sizes in regional schools might also be a factor. But, of course, many other explanations are possible.
The paper is entitled The Power of Believing You Can Get Smarter. The authors make it clear that they were looking for positive evidence of the intervention and they were supported by mindset advocates (e.g. David Yeager) from the start. It was funded by the World Bank, which is a long-standing advocate of growth mindset interventions. (Rather jumping the gun, the World Bank’s Mindset Team wrote in 2014 that teaching growth mindset is not just another policy fad. It is backed by a burgeoning body of empirical research.) The paper’s authors conclude that ‘the benefits of the intervention were relevant and long-lasting in the Peruvian context’, and they focus strongly on the low costs of the intervention. They acknowledge that the way the tool is introduced (design of the intervention) and the context in which this occurs (i.e., school and teacher characteristics) both matter to understand potential gains. But without understanding the role of the context, we haven’t really learned anything practical that we can take away from the research. Our understanding of the power of believing you can get smarter has not been meaningfully advanced.
The second of these studies (Yeager et al., 2019) took many thousands of lower-achieving American 9th graders from a representative sample of schools. It is a very well-designed and thoroughly reported piece of research. The intervention consisted of two 25-minute online sessions, 20 days apart, which sought to reduce the negative effort beliefs of students (the belief that having to try hard or ask for help means you lack ability), fixed-trait attributions (the attribution that failure stems from low ability) and performance avoidance goals (the goal of never looking stupid). An analysis of academic achievement at the end of the school year indicated clearly that the intervention led to improved performance. These results lead to very clear grounds for optimism about the potential of growth mindset interventions, but the report is careful to avoid overstatement. We have learnt about one particular demographic with one particular intervention, but it would be wrong to generalise beyond that. The researchers had hoped that the intervention would help to compensate for unsupportive school norms, but found that this was not the case. Instead, they found that it was when the peer norm supported the adoption of intellectual challenges that the intervention promoted sustained benefits. Context, as in the Peruvian study, was crucial. The authors write:
We emphasize that not all forms of growth mindset interventions can be expected to increase grades or advanced course-taking, even in the targeted subgroups. New growth mindset interventions that go beyond the module and population tested here will need to be subjected to rigorous development and validation processes.
I think that a reasonable conclusion from reading this research is that it may well be worth experimenting with growth mindset interventions in English language classes, but without any firm expectation of any positive impact. If nothing else, the interventions might provide useful, meaningful practice of the four skills. First, though, it would make sense to read two other pieces of research (Sisk et al., 2018; Burgoyne et al., 2020). Unlike the projects I have just discussed, these were not carried out by researchers with an a priori enthusiasm for growth-mindset interventions. And the results were rather different.
The first of these (Sisk et al., 2018) was a meta-analysis of the literature. It found that there was only a weak correlation between mindset and academic achievement, and only a weak correlation between mindset interventions and academic gains. It did, however, lend support to one of the conclusions of Yeager et al (2019), that such interventions may benefit students who are academically at risk.
The second (Burgoyne et al., 2020) found that the foundations of mind-set theory are not firm and that bold claims about mind-set appear to be overstated. Other constructs such as self-efficacy and need for achievement, [were] found to correlate much more strongly with presumed associates of mind-set.
So, where does this leave us? We are clearly a long way from ‘facts’; mindset interventions are ‘not yet evidence-based’. Carl Hendrick (2019) provides a useful summary:
The truth is we simply haven’t been able to translate the research on the benefits of a growth mindset into any sort of effective, consistent practice that makes an appreciable difference in student academic attainment. In many cases, growth mindset theory has been misrepresented and miscast as simply a means of motivating the unmotivated through pithy slogans and posters. […] Recent evidence would suggest that growth mindset interventions are not the elixir of student learning that many of its proponents claim it to be. The growth mindset appears to be a viable construct in the lab, which, when administered in the classroom via targeted interventions, doesn’t seem to work at scale. It is hard to dispute that having a self-belief in their own capacity for change is a positive attribute for students. Paradoxically, however, that aspiration is not well served by direct interventions that try to instil it.
References
Bandura, Albert (1982). Self-efficacy mechanism in human agency. American Psychologist, 37 (2): pp. 122–147. doi:10.1037/0003-066X.37.2.122.
Burgoyne, A. P., Hambrick, D. Z., & Macnamara, B. N. (2020). How Firm Are the Foundations of Mind-Set Theory? The Claims Appear Stronger Than the Evidence. Psychological Science, 31(3), 258–267. https://doi.org/10.1177/0956797619897588
Early, P. (Ed.) ELT Documents 1113 – Humanistic Approaches: An Empirical View. London: The British Council
Dweck, C. S. (2006). Mindset: The New Psychology of Success. New York: Ballantine Books
Rogers, C. R. (1969). Freedom to Learn: A View of What Education Might Become. Columbus, Ohio: Charles Merill
Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29, 549–571. doi:10.1177/0956797617739704
Yeager, D.S., Hanselman, P., Walton, G.M. et al. (2019). A national experiment reveals where a growth mindset improves achievement. Nature 573, 364–369. https://doi.org/10.1038/s41586-019-1466-y
Adaptive Learning in ELT 