Posts Tagged ‘politics’

Definition of gritGrit book cover

from Quartz at Work magazine

 

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

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

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

Youtube comments

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

Plonsky biblio

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

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

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

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

Is ‘grit’ a thing?

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

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

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

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

Criticisms of ‘grit’

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

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

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

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

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

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

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

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

Measuring grit

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

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

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

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

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

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

‘Grit’ and technology

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

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

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

The funding of grit

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

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

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

Postscript

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

References

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

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

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

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

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

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

De Bruyckere, P., Kirschner, P. A. & Hulshof, C. (2020). More Urban Myths about Learning and Education. Routledge.

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

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

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

Gutman, L. M. & Schoon, I. (2013). The impact of non-cognitive skills on outcomes for young people: Literature review. London: Institute of Education, University of London

Hamedani, M. G. & Darling-Hammond, L. (2015). Social Emotional Learning in High School: How Three Urban High Schools Engage, Educate, and Empower Youth. Stanford Center for Opportunity Policy in Education

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

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

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

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

Muenks, K., Wigfield, A., Yang, J. S., & O’Neal, C. R. (2017). How true is grit? Assessing its relations to high school and college students’ personality characteristics, self-regulation, engagement, and achievement. Journal of Educational Psychology, 109, pp. 599–620.

Ravitch, D. (2016). Angela Duckworth, please don’t assess grit. Blog post, 27 March 2016, https://dianeravitch.net/2016/03/27/angela-duckworth-please-dont-assess-grit/

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

Tampio, N. (2016). Teaching ‘grit’ is bad for children, and bad for democracy. Aeon, 2 June: https://aeon.co/ideas/teaching-grit-is-bad-for-children-and-bad-for-democracy

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

Teimouri, Y., Plonsky, L., & Tabandeh, F. (2020). L2 Grit: Passion and perseverance for second-language learning. Language Teaching Research.

Wei, H., Gao, K., & Wang, W. (2019). Understanding the relationship between grit and foreign language performance among middle schools students: The roles of foreign language enjoyment and classroom Environment. Frontiers in Psychology, 10, 1508. doi: 10.3389/fpsyg.2019.01508

Williamson, B. (2017). Decoding ClassDojo: psycho-policy, social-emotional learning and persuasive educational technologies. Learning, Media and Technology, 42 (4): pp. 440-453, DOI: 10.1080/17439884.2017.1278020

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

Yamashita, T. (2018). Grit and second language acquisition: Can passion and perseverance predict performance in Japanese language learning? Unpublished MA thesis, University of Massachusetts, Amherst.

 

Google search resultsUnconditional calls for language teachers to incorporate digital technology into their teaching are common. The reasons that are given are many and typically include the fact that (1) our students are ‘digital natives’ and expect technology to be integrated into their learning, (2) and digital technology is ubiquitous and has so many affordances for learning. Writing on the topic is almost invariably enthusiastic and the general conclusion is that the integration of technology is necessary and essential. Here’s a fairly typical example: digital technology is ‘an essential multisensory extension to the textbook’ (Torben Schmidt and Thomas Strasser in Surkamp & Viebrock, 2018: 221).

 

Teachers who are reluctant or fail to embrace technology are often ‘characterised as technophobic, or too traditional in their teaching style, or reluctant to adopt change’ (Watson, 2001: 253). (It’s those pesky teachers again.)

Claims for the importance of digital technology are often backed up by vague references to research. Michael Carrier, for example, in his introductory chapter to ‘Digital Language Learning and Teaching’ (Carrier et al. 2017: 3) writes that ‘research results […] seem to show conclusively that the use of educational technology adds certain degrees of richness to the learning and teaching process […] at the very least, digital learning seems to provide enhanced motivation for learners’.

Unfortunately, this is simply not true. Neither in language learning / teaching, nor in education more generally, is there any clear evidence of the necessary benefits of introducing educational technology. In the broader context, the ‘PISA analysis of the impact of Information Communication Technology (ICT) on reading, mathematics, and science (OECD, 2015: 3) in countries heavily invested in educational technology showed mixed effects and “no appreciable improvements”’ (Herodotou et al., 2019). Educational technology can or might  ‘add certain degrees of richness’ or ‘provide enhanced motivation’, but that is not the same as saying that it does or will. The shift from can to will, a piece of modal legerdemain used to advocate for educational technology, is neatly illustrated in a quote from the MIT’s Office of Digital Learning, whose remit is to improve learning and teaching across the university via digital learning: ‘Digital Learning technologies can enable students to grasp concepts more quickly [etc….] Digital technologies will enable this in new and better ways and create possibilities beyond the limits of our current imagination’ (quoted by Carrier, 2017: 1).

Before moving on, here’s another example. The introduction to Li Li’s ‘New Technologies and Language Learning’ (Li, 2017: x) states, with a cautious can, that one of the objectives of the book is ‘to provide examples of how technologies can be used in assisting language education’. In the next paragraph, however, caution is thrown to the wind and we are told, unequivocally, that ‘technology is beneficial for language learning’.

Pedagogy before technology

Examples of gratuitous technology use are not hard to find. Mark Warschauer (who, as the founding director of the Digital Learning Lab at the University of California, Irvine, could be fairly described as an edtech enthusiast) describes one example: ‘I remember observing a beginners’ French class a number of years ago, the teacher bragged about how engaged the learners were in creating multimedia in French. However, the students were spending most of their time and energy talking with each other in English about how to make PowerPoints, when, as beginning learners, they really needed to be spending time hearing as much French as possible’ (quoted in the Guardian, May 2014).

As a result, no doubt, of having similar experiences, it seems that many people are becoming a little more circumspect in their enthusiasm for edtech. In the same Guardian article as Warschauer’s recollections, Russell Stannard ‘says the trick is to put the pedagogy first, not the technology. “You’ve got to know why you’re using it. Teachers do need to learn to use new technology, but the driving force should always be the pedagogy behind it’. Nicky Hockly, Gavin Dudeney and Mark Pegrum (Hockly et al., 2013: 45) concur: ‘Content and pedagogy come before technology. We must decide on our content and pedagogical aims before determining whether our students should use pens or keyboards, write essays or blogs, or design posters or videos’. And Graham Stanley (2013: 1) in the introduction to his ‘Language Learning With Technology’ states that his ‘book makes a point of putting pedagogy at the forefront of the lesson, which is why content has been organised around specific learning content goals rather than specific technologies’.

But, Axel Krommer, of the Friedrich-Alexander University of Erlangen-Nürnberg, has argued that the principle of ‘pedagogy before technology’ is ‘trivial at best’. In a piece for the Goethe Institute he writes ‘a theory with which everyone agrees and whose opposite no-one believes true is meaningless’, although he adds that it may be useful as ‘an admonitory wake-up call when educational institutions risk being blinded by technological possibilities that cause them to neglect pedagogical principles that should really be taken for granted’. It was this piece that set me thinking more about ‘pedagogy before technology’.

Pedagogy before technology (on condition that there is technology)

Another person to lament the placing of technology before pedagogy is Nik Peachey. In an opinion piece for the Guardian, entitled ‘Technology can sometimes be wasted on English language teaching’, he complains about how teachers are left to sort out how to use technology ‘in a pedagogically effective way, often with very little training or support’. He appears to take it as given that technology is a positive force, and argues that it shouldn’t be wasted. The issue, he says, is that better teacher training is needed so that teachers’ ‘digital literacies’ are improved and to ensure that technological potential is fulfilled.

His position, therefore, cannot really be said to be one of ‘pedagogy before technology’. Like the other writers mentioned above, he comes to the pedagogy through and after an interest in the technology. The educational use of digital technology per se is never seriously questioned. The same holds true for almost the entirety of the world of CALL research.

confer

A Canadian conference ‘Pedagogy b4 Technology’ illustrates my point beautifully.

There are occasional exceptions. A recent example which I found interesting was an article by Herodotou et al (2019), in which the authors take as their starting point a set of OECD educational goals (quality of life, including health, civic engagement, social connections, education, security, life satisfaction and the environment), and then investigate the extent to which a variety of learning approaches (formative analytics, teachback, place-based learning, learning with robots, learning with drones, citizen inquiry) – not all of which involve technology – might contribute to the realisation of these goals.

Technology before pedagogy as policy

Some of the high school English teachers I work with have to use tablets in one lesson a week. Some welcome it, some accept it (they can catch up with other duties while the kids are busy with exercises on the tablet), others just roll their eyes at the mention of this policy. In the same school system, English language learning materials can only be bought if they come in digital versions (even if it is the paper versions that are actually used). The digital versions are mostly used for projecting pages onto the IWBs. Meanwhile, budgets and the time available for in-service training have been cut.

Elsewhere, a chain of universities decides that a certain proportion of all courses must be taught online. English language courses, being less prestigious than major subjects, are one of the first to be migrated to platforms. The staff, few of whom have tenure or time to spare, cope as best as they can, with some support from a department head. Training is provided in the mechanics of operating the platform, and, hopefully before too long, more training will become available to optimize the use of the platform for pedagogical purposes. An adequate budget has yet to be agreed.

The reasons why so many educational authorities introduce such policies are, at best, only superficially related to pedagogy. There is a belief, widely held, that technology cannot fail to make things better. In the words of Tony Blair: ‘Technology has revolutionised the way we work and is now set to transform education. Children cannot be effective in tomorrow’s world if they are trained in yesterday’s skills’. But there is also the potential of education technology to scale education up (i.e. increase student numbers), to reduce long-term costs, to facilitate accountability, to increase productivity, to restrict the power of teachers (and their unions), and so on.

In such circumstances, which are not uncommon, it seems to me that there are more pressing things to worry about than teachers who are not sufficiently thinking about the pedagogical uses to which they put the technology that they have to use. Working conditions, pay and hours, are all affected by the digitalisation of education. These things do get talked about (see, for example, Walsh, 2019), but only rarely.

Technology as pedagogy

Blended learning, described by Pete Sharma in 2010 as a ‘buzz word’ in ELT, remains a popular pedagogical approach. In a recent article (2019), he enthuses about the possibilities of blended learning, suggesting that teachers should use it all the time: ‘teaching in this new digital age should use the technologies which students meet in their everyday lives, such as the Internet, laptop, smartphone and tablet’. It’s also, he claims, time-efficient, but other pedagogical justifications are scant: ‘some language areas are really suited to be studied outside the classroom. Extensive reading and practising difficult phonemes, for instance’.

Blended learning and digital technology are inseparable. Hockley (2018) explains the spread of blended learning in ELT as being driven primarily by ‘the twin drivers of economics (i.e. lower costs) and increasingly accessible and affordable hardware and software’. It might be nice to believe that ‘it is pedagogy, rather than technology, that should underpin the design of blended learning programmes’ (McCarthy, 2016, back cover), but the technology is the pedagogy here. Precisely how it is used is almost inevitably an afterthought.

Which pedagogy, anyway?

We can talk about putting pedagogy before technology, but this raises the question of which particular pedagogy we want to put in the driving seat. Presumably not all pedagogies are of equal value.

One of the most common uses of digital technology that has been designed specifically for language learning is the IWB- or platform-delivered coursebook and its accompanying digital workbook. We know that a majority of teachers using online coursebook packages direct their students more readily to tasks with clear right / wrong answers (e.g. drag-and-drop or gap-fill grammar exercises) than they do to the forum facilities where communicative language use is possible. Here, technology is merely replicating and, perhaps (because of its ease of use), encouraging established pedagogical practices. The pedagogy precedes the technology, but it’s probably not the best pedagogy in the world. Nor does it make best use of the technology’s potential. Would the affordances of the technology make a better starting point for course design?

Graham Stanley’s book (2013) offers suggestions for using technology for a variety of purposes, ranging from deliberate practice of grammar and vocabulary to ways of facilitating opportunities for skills practice. It’s an eclectic mix, similar to the range of activities on offer in the average coursebook for adults or teenagers. It is pedagogy-neutral in the sense that it does not offer a set of principles of language learning or teaching, and from these derive a set of practices for using the technology. It is a recipe book for using technological tools and, like all recipe books, prioritises activities over principles. I like the book and I don’t intend these comments as criticism. My point is simply that it’s not easy to take pedagogical principles as a starting point. Does the world of ELT even have generally agreed pedagogical principles?

And what is it that we’re teaching?

One final thought … If we consider how learners are likely to be using the English they are learning in their real-world futures, technology will not be far away: reading online, listening to / watching online material, writing and speaking with messaging apps, writing with text, email or Google Docs … If, in designing pedagogical approaches, we wish to include features of authentic language use, it’s hard to see how we can avoid placing technology fairly near the centre of the stage. Technologically-mediated language use is inseparable from pedagogy: one does not precede the other.

Similarly, if we believe that it is part of the English teacher’s job to develop the digital literacy (e.g. Hockly et al., 2013), visual literacy (e.g. Donaghy, 2015) or multimodal literacy of their students – not, incidentally, a belief that I share – then, again, technology cannot be separated from pedagogy.

Pedagogy before technology, OK??

So, I ask myself what precisely it is that people mean when they say that pedagogy should come before technology. The locutionary force, or referential meaning, usually remains unclear: in the absence of a particular pedagogy and particular contexts, what exactly is being said? The illocutionary force, likewise, is difficult to understand in the absence of a particular addressee: is the message only intended for teachers suffering from Everest Syndrome? And the perlocutionary force is equally intriguing: how are people who make the statement positioning themselves, and in relation to which addressee? Along the lines of green-washing and woke-washing, are we sometimes seeing cases of pedagogy-washing?

REFERENCES

Carrier, M., Damerow, R. M. & Bailey, K. M. (2017) Digital Language Learning and Teaching: Research, theory, and practice. New York: Routledge

Donaghy, K. (2015) Film in Action. Peaslake, Surrey: DELTA Publishing

Herodotou, C., Sharples, M., Gaved, M., Kukulska-Hulme, A., Rienties, B., Scanlon, E. & Whitelock, D. (2019) Innovative Pedagogies of the Future: An Evidence-Based Selection. Frontiers in Education, 4 (113)

Hockly, N. (2018) Blended Learning. ELT Journal 72 (1): pp. 97 – 101

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

Li, L. (2017) New Technologies and Language Learning. London: Palgrave

McCarthy, M. (Ed.) (2016) The Cambridge Guide to Blended Learning for Language Teaching. Cambridge: Cambridge University Press

OECD (2015) Students, Computers and Learning: Making the Connection, PISA. Paris: OECD Publishing

Sharma, P. (2010) Blended Learning. ELT Journal, 64 (4): pp. 456 – 458

Sharma, P. (2019) The Complete Guide to Running a Blended Learning Course. Oxford University Press English Language Teaching Global Blog 17 October 2019. Available at: https://oupeltglobalblog.com/2019/10/17/complete-guidagogyde-blended-learning/

Stanley, G. (2013) Language Learning with Technology. Cambridge: Cambridge University Press

Surkamp, C. & Viebrock, B. (Eds.) (2018) Teaching English as a Foreign Language: An Introduction. Stuttgart: J. B. Metzler

Walsh, P. (2019) Precarity. ELT Journal, 73 (4): pp. 459–462

Watson, D. M. (2001) Pedagogy before Technology: Re-thinking the Relationship between ICT and Teaching. Education and Information Technologies 6:4: pp.251–26

Screenshot_20191011-200743_ChromeOver the last week, the Guardian has been running a series of articles on the global corporations that contribute most to climate change and the way that these vested interests lobby against changes to the law which might protect the planet. Beginning in the 1990s, an alliance of fossil fuel and automobile corporations, along with conservative think tanks and politicians, developed a ‘denial machine’ which sought to undermine the scientific consensus on climate change. Between 2003 and 2010, it has been estimated that over $550 million was received from a variety of sources to support this campaign. The Guardian’s current series is an update and reminder of the research into climate change denial that has been carried out in recent years.

In the past, it was easier to trace where the money came from (e.g. ExxonMobil or Koch Industries), but it appears that the cash is now being channelled through foundations like Donors Trust and Donors Capital, who, in turn, pass it on to other foundations and think tanks (see below) that promote the denial of climate change.

The connection between climate change denial and edtech becomes clear when you look at the organisations behind the ‘denial machine’. I have written about some of these organisations before (see this post ) so when I read the reports in the Guardian, there were some familiar names.

Besides their scepticism about climate change, all of the organisations believe that education should be market-driven, free from governmental interference, and characterised by consumer choice. These aims are facilitated by the deployment of educational technology. Here are some examples.

State Policy Network

The State Policy Network (SPN) is an American umbrella organization for a large group of conservative and libertarian think tanks that seek to influence national and global policies. Among other libertarian causes, it opposes climate change regulations and supports the privatisation of education, in particular the expansion of ‘digital education’.

The Cato Institute

The mission of the Cato Institute, a member of the SPN, ‘is to originate, disseminate, and increase understanding of public policies based on the principles of individual liberty, limited government, free markets, and peace. Our vision is to create free, open, and civil societies founded on libertarian principles’. The Institute has said that it had never been in the business of “promoting climate science denial”; it did not dispute human activity’s impact on the climate, but believed it was minimal. Turning to education, it believes that ‘states should institute school choice on a broad scale, moving toward a competitive education market. The only way to transform the system is to break up the long-standing government monopoly and use the dynamics of the market to create innovations, better methods, and new schools’. Innovations and better methods will, of course, be driven by technology.

FreedomWorks

FreedomWorks, another member of the SPN and another conservative and libertarian advocacy group, is widely associated with the Tea Party Movement . Recent posts on its blog have been entitled ‘The Climate Crisis that Wasn’t: Scientists Agree there is “No Cause for Alarm”’, ‘Climate Protesters: If You Want to Save the Planet, You Should Support Capitalism Not Socialism’ and ‘Electric Vehicle Tax Credit: Nothing But Regressive Cronyism’. Its approach to education is equally uncompromising. It seeks to abolish the US Department of Education, describes American schools as ‘failing’, wants market-driven educational provision and absolute parental choice . Technology will play a fundamental role in bringing about the desired changes: ‘just as computers and the Internet have fundamentally reshaped the way we do business, they will also soon reshape education’ .

The Heritage Foundation

The Heritage Foundation, the last of the SPN members that I’ll mention here, is yet another conservative American think tank which rejects the scientific consensus on climate change . Its line on education is neatly summed up in this extract from a blog post by a Heritage senior policy analyst: ‘Virtual or online learning is revolutionizing American education. It has the potential to dramatically expand the educational opportunities of American students, largely overcoming the geographic and demographic restrictions. Virtual learning also has the potential to improve the quality of instruction, while increasing productivity and lowering costs, ultimately reducing the burden on taxpayers‘.

The Institute of Economic Affairs

Just to show that the ‘denial machine’ isn’t an exclusively American phenomenon, I include ‘the UK’s most influential conservative think tank [which] has published at least four books, as well as multiple articles and papers, over two decades suggesting manmade climate change may be uncertain or exaggerated. In recent years the group has focused more on free-market solutions to reducing carbon emissions’ . It is an ‘associate member of the SPN’ . No surprise to discover that a member of the advisory council of the IEA is James Tooley, a close associate of Michael Barber, formerly Chief Education Advisor at Pearson. Tooley’s articles for the IEA include ‘Education without the State’  and ‘Transforming incentives will unleash the power of entrepreneurship in the education sector’ .

The IEA does not disclose its funding, but anyone interested in finding out more should look here ‘Revealed: how the UK’s powerful right-wing think tanks and Conservative MPs work together’ .

Microsoft, Facebook and Google

Let me be clear to start: Microsoft, Facebook and Google are not climate change deniers. However, Facebook and Microsoft are financial backers of the SPN. In a statement, a spokesperson for Microsoft said: “As a large company, Microsoft has great interest in the many policy issues discussed across the country. We have a longstanding record of engaging with a broad assortment of groups on a bipartisan basis, both at the national and local level. In regard to State Policy Network, Microsoft has focused our participation on their technology policy work group because it is valuable forum to hear various perspectives about technology challenges and to share potential solutions” . Google has made substantial contributions to the Competitive Enterprise Institute (a conservative US policy group ‘that was instrumental in convincing the Trump administration to abandon the Paris agreement and has criticised the White House for not dismantling more environmental rules). In the Guardian report, Google ‘defended its contributions, saying that its “collaboration” with organisations such as CEI “does not mean we endorse the organisations’ entire agenda”. “When it comes to regulation of technology, Google has to find friends wherever they can and I think it is wise that the company does not apply litmus tests to who they support,” the source said’ .

You have to wonder what these companies (all of whom support environmental causes in various ways) might consider more important than the future of the planet. Could it be that the libertarian think tanks are important allies in resisting any form of internet governance, in objecting to any constraints on the capture of data?

At a recent ELT conference, a plenary presentation entitled ‘Getting it right with edtech’ (sponsored by a vendor of – increasingly digital – ELT products) began with the speaker suggesting that technology was basically neutral, that what you do with educational technology matters far more than the nature of the technology itself. The idea that technology is a ‘neutral tool’ has a long pedigree and often accompanies exhortations to embrace edtech in one form or another (see for example Fox, 2001). It is an idea that is supported by no less a luminary than Chomsky, who, in a 2012 video entitled ‘The Purpose of Education’ (Chomsky, 2012), said that:

As far as […] technology […] and education is concerned, technology is basically neutral. It’s kind of like a hammer. I mean, […] the hammer doesn’t care whether you use it to build a house or whether a torturer uses it to crush somebody’s skull; a hammer can do either. The same with the modern technology; say, the Internet, and so on.

Womans hammerAlthough hammers are not usually classic examples of educational technology, they are worthy of a short discussion. Hammers come in all shapes and sizes and when you choose one, you need to consider its head weight (usually between 16 and 20 ounces), the length of the handle, the shape of the grip, etc. Appropriate specifications for particular hammering tasks have been calculated in great detail. The data on which these specifications is based on an analysis of the hand size and upper body strength of the typical user. The typical user is a man, and the typical hammer has been designed for a man. The average male hand length is 177.9 mm, that of the average woman is 10 mm shorter (Wang & Cai, 2017). Women typically have about half the upper body strength of men (Miller et al., 1993). It’s possible, but not easy to find hammers designed for women (they are referred to as ‘Ladies hammers’ on Amazon). They have a much lighter head weight, a shorter handle length, and many come in pink or floral designs. Hammers, in other words, are far from neutral: they are highly gendered.

Moving closer to educational purposes and ways in which we might ‘get it right with edtech’, it is useful to look at the smart phone. The average size of these devices has risen in recent years, and is now 5.5 inches, with the market for 6 inch screens growing fast. Why is this an issue? Well, as Caroline Criado Perez (2019: 159) notes, ‘while we’re all admittedly impressed by the size of your screen, it’s a slightly different matter when it comes to fitting into half the population’s hands. The average man can fairly comfortably use his device one-handed – but the average woman’s hand is not much bigger than the handset itself’. This is despite the fact the fact that women are more likely to own an iPhone than men  .

It is not, of course, just technological artefacts that are gendered. Voice-recognition software is also very biased. One researcher (Tatman, 2017) has found that Google’s speech recognition tool is 13% more accurate for men than it is for women. There are also significant biases for race and social class. The reason lies in the dataset that the tool is trained on: the algorithms may be gender- and socio-culturally-neutral, but the dataset is not. It would not be difficult to redress this bias by training the tool on a different dataset.

The same bias can be found in automatic translation software. Because corpora such as the BNC or COCA have twice as many male pronouns as female ones (as a result of the kinds of text that are selected for the corpora), translation software reflects the bias. With Google Translate, a sentence in a language with a gender-neutral pronoun, such as ‘S/he is a doctor’ is rendered into English as ‘He is a doctor’. Meanwhile, ‘S/he is a nurse’ is translated as ‘She is a nurse’ (Criado Perez, 2019: 166).

Datasets, then, are often very far from neutral. Algorithms are not necessarily any more neutral than the datasets, and Cathy O’Neil’s best-seller ‘Weapons of Math Destruction’ catalogues the many, many ways in which algorithms, posing as neutral mathematical tools, can increase racial, social and gender inequalities.

It would not be hard to provide many more examples, but the selection above is probably enough. Technology, as Langdon Winner (Winner, 1980) observed almost forty years ago, is ‘deeply interwoven in the conditions of modern politics’. Technology cannot be neutral: it has politics.

So far, I have focused primarily on the non-neutrality of technology in terms of gender (and, in passing, race and class). Before returning to broader societal issues, I would like to make a relatively brief mention of another kind of non-neutrality: the pedagogic. Language learning materials necessarily contain content of some kind: texts, topics, the choice of values or role models, language examples, and so on. These cannot be value-free. In the early days of educational computer software, one researcher (Biraimah, 1993) found that it was ‘at least, if not more, biased than the printed page it may one day replace’. My own impression is that this remains true today.

Equally interesting to my mind is the fact that all educational technologies, ranging from the writing slate to the blackboard (see Buzbee, 2014), from the overhead projector to the interactive whiteboard, always privilege a particular kind of teaching (and learning). ‘Technologies are inherently biased because they are built to accomplish certain very specific goals which means that some technologies are good for some tasks while not so good for other tasks’ (Zhao et al., 2004: 25). Digital flashcards, for example, inevitably encourage a focus on rote learning. Contemporary LMSs have impressive multi-functionality (i.e. they often could be used in a very wide variety of ways), but, in practice, most teachers use them in very conservative ways (Laanpere et al., 2004). This may be a result of teacher and institutional preferences, but it is almost certainly due, at least in part, to the way that LMSs are designed. They are usually ‘based on traditional approaches to instruction dating from the nineteenth century: presentation and assessment [and] this can be seen in the selection of features which are most accessible in the interface, and easiest to use’ (Lane, 2009).

The argument that educational technology is neutral because it could be put to many different uses, good or bad, is problematic because the likelihood of one particular use is usually much greater than another. There is, however, another way of looking at technological neutrality, and that is to look at its origins. Elsewhere on this blog, in post after post, I have given examples of the ways in which educational technology has been developed, marketed and sold primarily for commercial purposes. Educational values, if indeed there are any, are often an afterthought. The research literature in this area is rich and growing: Stephen Ball, Larry Cuban, Neil Selwyn, Joel Spring, Audrey Watters, etc.

Rather than revisit old ground here, this is an opportunity to look at a slightly different origin of educational technology: the US military. The close connection of the early history of the internet and the Advanced Research Projects Agency (now DARPA) of the United States Department of Defense is fairly well-known. Much less well-known are the very close connections between the US military and educational technologies, which are catalogued in the recently reissued ‘The Classroom Arsenal’ by Douglas D. Noble.

Following the twin shocks of the Soviet Sputnik 1 (in 1957) and Yuri Gagarin (in 1961), the United States launched a massive programme of investment in the development of high-tech weaponry. This included ‘computer systems design, time-sharing, graphics displays, conversational programming languages, heuristic problem-solving, artificial intelligence, and cognitive science’ (Noble, 1991: 55), all of which are now crucial components in educational technology. But it also quickly became clear that more sophisticated weapons required much better trained operators, hence the US military’s huge (and continuing) interest in training. Early interest focused on teaching machines and programmed instruction (branches of the US military were by far the biggest purchasers of programmed instruction products). It was essential that training was effective and efficient, and this led to a wide interest in the mathematical modelling of learning and instruction.

What was then called computer-based education (CBE) was developed as a response to military needs. The first experiments in computer-based training took place at the Systems Research Laboratory of the Air Force’s RAND Corporation think tank (Noble, 1991: 73). Research and development in this area accelerated in the 1960s and 1970s and CBE (which has morphed into the platforms of today) ‘assumed particular forms because of the historical, contingent, military contexts for which and within which it was developed’ (Noble, 1991: 83). It is possible to imagine computer-based education having developed in very different directions. Between the 1960s and 1980s, for example, the PLATO (Programmed Logic for Automatic Teaching Operations) project at the University of Illinois focused heavily on computer-mediated social interaction (forums, message boards, email, chat rooms and multi-player games). PLATO was also significantly funded by a variety of US military agencies, but proved to be of much less interest to the generals than the work taking place in other laboratories. As Noble observes, ‘some technologies get developed while others do not, and those that do are shaped by particular interests and by the historical and political circumstances surrounding their development (Noble, 1991: 4).

According to Noble, however, the influence of the military reached far beyond the development of particular technologies. Alongside the investment in technologies, the military were the prime movers in a campaign to promote computer literacy in schools.

Computer literacy was an ideological campaign rather than an educational initiative – a campaign designed, at bottom, to render people ‘comfortable’ with the ‘inevitable’ new technologies. Its basic intent was to win the reluctant acquiescence of an entire population in a brave new world sculpted in silicon.

The computer campaign also succeeded in getting people in front of that screen and used to having computers around; it made people ‘computer-friendly’, just as computers were being rendered ‘used-friendly’. It also managed to distract the population, suddenly propelled by the urgency of learning about computers, from learning about other things, such as how computers were being used to erode the quality of their working lives, or why they, supposedly the citizens of a democracy, had no say in technological decisions that were determining the shape of their own futures.

Third, it made possible the successful introduction of millions of computers into schools, factories and offices, even homes, with minimal resistance. The nation’s public schools have by now spent over two billion dollars on over a million and a half computers, and this trend still shows no signs of abating. At this time, schools continue to spend one-fifth as much on computers, software, training and staffing as they do on all books and other instructional materials combined. Yet the impact of this enormous expenditure is a stockpile of often idle machines, typically used for quite unimaginative educational applications. Furthermore, the accumulated results of three decades of research on the effectiveness of computer-based instruction remain ‘inconclusive and often contradictory’. (Noble, 1991: x – xi)

Rather than being neutral in any way, it seems more reasonable to argue, along with (I think) most contemporary researchers, that edtech is profoundly value-laden because it has the potential to (i) influence certain values in students; (ii) change educational values in [various] ways; and (iii) change national values (Omotoyinbo & Omotoyinbo, 2016: 173). Most importantly, the growth in the use of educational technology has been accompanied by a change in the way that education itself is viewed: ‘as a tool, a sophisticated supply system of human cognitive resources, in the service of a computerized, technology-driven economy’ (Noble, 1991: 1). These two trends are inextricably linked.

References

Biraimah, K. 1993. The non-neutrality of educational computer software. Computers and Education 20 / 4: 283 – 290

Buzbee, L. 2014. Blackboard: A Personal History of the Classroom. Minneapolis: Graywolf Press

Chomsky, N. 2012. The Purpose of Education (video). Learning Without Frontiers Conference. https://www.youtube.com/watch?v=DdNAUJWJN08

Criado Perez, C. 2019. Invisible Women. London: Chatto & Windus

Fox, R. 2001. Technological neutrality and practice in higher education. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://clt.curtin.edu.au/events/conferences/tlf/tlf2001/fox.html

Laanpere, M., Poldoja, H. & Kikkas, K. 2004. The second thoughts about pedagogical neutrality of LMS. Proceedings of IEEE International Conference on Advanced Learning Technologies, 2004. https://ieeexplore.ieee.org/abstract/document/1357664

Lane, L. 2009. Insidious pedagogy: How course management systems impact teaching. First Monday, 14(10). https://firstmonday.org/ojs/index.php/fm/article/view/2530/2303Lane

Miller, A.E., MacDougall, J.D., Tarnopolsky, M. A. & Sale, D.G. 1993. ‘Gender differences in strength and muscle fiber characteristics’ European Journal of Applied Physiology and Occupational Physiology. 66(3): 254-62 https://www.ncbi.nlm.nih.gov/pubmed/8477683

Noble, D. D. 1991. The Classroom Arsenal. Abingdon, Oxon.: Routledge

Omotoyinbo, D. W. & Omotoyinbo, F. R. 2016. Educational Technology and Value Neutrality. Societal Studies, 8 / 2: 163 – 179 https://www3.mruni.eu/ojs/societal-studies/article/view/4652/4276

O’Neil, C. 2016. Weapons of Math Destruction. London: Penguin

Sundström, P. Interpreting the Notion that Technology is Value Neutral. Medicine, Health Care and Philosophy 1, 1998: 42-44

Tatman, R. 2017. ‘Gender and Dialect Bias in YouTube’s Automatic Captions’ Proceedings of the First Workshop on Ethics in Natural Language Processing, pp. 53–59 http://www.ethicsinnlp.org/workshop/pdf/EthNLP06.pdf

Wang, C. & Cai, D. 2017. ‘Hand tool handle design based on hand measurements’ MATEC Web of Conferences 119, 01044 (2017) https://www.matec-conferences.org/articles/matecconf/pdf/2017/33/matecconf_imeti2017_01044.pdf

Winner, L. 1980. Do Artifacts have Politics? Daedalus 109 / 1: 121 – 136

Zhao, Y, Alvarez-Torres, M. J., Smith, B. & Tan, H. S. 2004. The Non-neutrality of Technology: a Theoretical Analysis and Empirical Study of Computer Mediated Communication Technologies. Journal of Educational Computing Research 30 (1 &2): 23 – 55

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

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

41u+NEyWjnL._SY344_BO1,204,203,200_‘From the Ivory Tower to the Schoolhouse’ by Jack Schneider (Harvard Education Press, 2014) investigates the reasons why promising ideas from education research fail to get taken up by practitioners, and why other, less-than-promising ideas, from a research or theoretical perspective, become sticky quite quickly. As an example of the former, Schneider considers Robert Sternberg’s ‘Triarchic Theory’. As an example of the latter, he devotes a chapter to Howard Gardner’s ‘Multiple Intelligences Theory’.

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

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

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

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

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

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

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

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

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

(This post won’t make a lot of sense unless you read the previous one – Researching research: part 1!)

dropoutsI suggested in the previous post that the research of Jayaprakash et al had confirmed something that we already knew concerning the reasons why some students drop out of college. However, predictive analytics are only part of the story. As the authors of this paper point out, they ‘do not influence course completion and retention rates without being combined with effective intervention strategies aimed at helping at-risk students succeed’. The point of predictive analytics is to facilitate the deployment of effective and appropriate interventions strategies, and to do this sooner than would be possible without the use of the analytics. So, it is to these intervention strategies that I now turn.

Interventions to help at-risk students included the following:

  • Sending students messages to inform them that they are at risk of not completing the course (‘awareness messaging’)
  • Making students more aware of the available academic support services (which could, for example, direct them to a variety of campus-based or online resources)
  • Promoting peer-to-peer engagement (e.g. with an online ‘student lounge’ discussion forum)
  • Providing access to self-assessment tools

The design of these interventions was based on the work that had been done at Purdue, which was, in turn, inspired by the work of Vince Tinto, one of the world’s leading experts on student retention issues.

The work done at Purdue had shown that simple notifications to students that they were at risk could have a significant, and positive, effect on student behaviour. Jayaprakash and the research team took the students who had been identified as at-risk by the analytics and divided them into three groups: the first were issued with ‘awareness messages’, the second were offered a combination of the other three interventions in the bullet point list above, and the third, a control group, had no interventions at all. The results showed that the students who were in treatment groups (of either kind of intervention) showed a statistically significant improvement compared to those who received no treatment at all. However, there seemed to be no difference in the effectiveness of the different kinds of intervention.

So far, so good, but, once again, I was left thinking that I hadn’t really learned very much from all this. But then, in the last five pages, the article suddenly got very interesting. Remember that the primary purpose of this whole research project was to find ways of helping not just at-risk students, but specifically socioeconomically disadvantaged at-risk students (such as those receiving Pell Grants). Accordingly, the researchers then focussed on this group. What did they find?

Once again, interventions proved more effective at raising student scores than no intervention at all. However, the averages of final scores are inevitably affected by drop-out rates (since students who drop out do not have final scores which can be included in the averages). At Purdue, the effect of interventions on drop-out rates had not been found to be significant. Remember that Purdue has a relatively well-off student demographic. However, in this research, which focussed on colleges with a much higher proportion of students on Pell Grants, the picture was very different. Of the Pell Grant students who were identified as at-risk and who were given some kind of treatment, 25.6% withdrew from the course. Of the Pell Grant students who were identified as at-risk but who were not ‘treated’ in any way (i.e. those in the control group), only 14.1% withdrew from the course. I recommend that you read those numbers again!

The research programme had resulted in substantially higher drop-out rates for socioeconomically disadvantaged students – the precise opposite of what it had set out to achieve. Jayaprakash et al devote one page of their article to the ethical issues this raises. They suggest that early intervention, resulting in withdrawal, might actually be to the benefit of some students who were going to fail whatever happened. It is better to get a ‘W’ (withdrawal) grade on your transcript than an ‘F’ (fail), and you may avoid wasting your money at the same time. This may be true, but it would be equally true that not allowing at-risk students (who, of course, are disproportionately from socioeconomically disadvantaged backgrounds) into college at all might also be to their ‘benefit’. The question, though, is: who has the right to make these decisions on behalf of other people?

The authors also acknowledge another ethical problem. The predictive analytics which will prompt the interventions are not 100% accurate. 85% accuracy could be considered a pretty good figure. This means that some students who are not at-risk are labelled as at-risk, and other who are at-risk are not identified. Of these two possibilities, I find the first far more worrying. We are talking about the very real possibility of individual students being pushed into making potentially life-changing decisions on the basis of dodgy analytics. How ethical is that? The authors’ conclusion is that the situation forces them ‘to develop the most accurate predictive models possible, as well as to take steps to reduce the likelihood that any intervention would result in the necessary withdrawal of a student’.

I find this extraordinary. It is premised on the assumption that predictive models can be made much, much more accurate. They seem to be confusing prediction and predeterminism. A predictive model is, by definition, only predictive. There will always be error. How many errors are ethically justifiable? And, the desire to reduce the likelihood of unnecessary withdrawals is a long way from the need to completely eliminate the likelihood of unnecessary withdrawals, which seems to me to be the ethical position. More than anything else in the article, this sentence illustrates that the a priori assumption is that predictive analytics can be a force for good, and that the only real problem is getting the science right. If a number of young lives are screwed up along the way, we can at least say that science is getting better.

In the authors’ final conclusion, they describe the results of their research as ‘promising’. They do not elaborate on who it is promising for. They say that relatively simple intervention strategies can positively impact student learning outcomes, but they could equally well have said that relatively simple intervention strategies can negatively impact learning outcomes. They could have said that predictive analytics and intervention programmes are fine for the well-off, but more problematic for the poor. Remembering once more that the point of the study was to look at the situation of socioeconomically disadvantaged at-risk students, it is striking that there is no mention of this group in the researchers’ eight concluding points. The vast bulk of the paper is devoted to technical descriptions of the design and training of the software; the majority of the conclusions are about the validity of that design and training. The ostensibly intended beneficiaries have got lost somewhere along the way.

How and why is it that a piece of research such as this can so positively slant its results? In the third and final part of this mini-series, I will turn my attention to answering that question.

Back in the Neanderthal days before Web 2.0, iPhones, tablets, the cloud, learning analytics and so on, Chris Bigum and Jane Kenway wrote a paper called ‘New Information Technologies and the Ambiguous Future of Schooling’. Although published in 1998, it remains relevant and can be accessed here.

They analysed the spectrum of discourse that was concerned with new technologies in education. At one end of this spectrum was a discourse community which they termed ‘boosters’. Then, as now, the boosters were far and away the dominant voices. Bigum and Kenway characterized the boosters as having an ‘unswerving faith in the technology’s capacity to improve education and most other things in society’. I discussed the boosterist discourse in my post on this blog, ‘Saving the World (adaptive marketing)’, focussing on the language of Knewton, as a representative example.

At the other end of Bigum and Kenway’s spectrum was what they termed ‘doomsters’ – ‘unqualified opponents of new technologies’ who see inevitable damage to society and education if we uncritically accept these new technologies.

Since starting this blog, I have been particularly struck by two things. The first of these is that I have had to try to restrain my aversion to the excesses of boosterist discourse – not always, it must be said, with complete success. The second is that I have found myself characterized by some people (perhaps those who have only superficially read a post of two) as an anti-technology doomsterist. At the same time, I have noticed that the debate about adaptive learning and educational technology, in general, tends to become polarized into booster and doomster camps.

To some extent, such polarization is inevitable. When a discourse is especially dominant, anyone who questions it risks finding themselves labelled as the extreme opposite. In some parts of the world, for example, any critique of neoliberal doxa is likely to be critiqued, in its turn, as ‘socialist, or worse’: ‘if you’re not with us, you’re against us’.

GramsciWhen it comes to adaptive learning, one can scoff at the adspeak of Knewton or the gapfills of Voxy, without having a problem with the technology per se. But, given the dominance of the booster discourse, one can’t really be neutral. Neil Selwyn (yes, him again!) suggests that the best way of making full sense of educational technology is to adopt a pessimistic perspective. ‘If nothing else,’ he writes, ‘a pessimistic view remains true to the realities of what has actually taken place with regards to higher education and digital technology over the past thirty years (to be blunt, things have clearly not been transformed or improved by digital technology so far, so why should we expect anything different in the near future?)’. This is not an ‘uncompromising pessimism’, but ‘a position akin to Gramsci’s notion of being ‘a pessimist because of intelligence, but an optimist because of will’’.

Note: The quotes from Neil Selwyn here are taken from his new book Digital Technology and the Contemporary University (2014, Abingdon: Routledge). In the autumn of this year, there will be an online conference, jointly organised by the Learning Technologies and Global Issues Special Interest Groups of IATEFL, during which I will be interviewing Neil Selwyn. I’ll keep you posted.

In Part 9 of the ‘guide’ on this blog (neo-liberalism and solutionism), I suggested that the major advocates of adaptive learning form a complex network of vested neo-liberal interests. Along with adaptive learning and the digital delivery of educational content, they promote a free-market, for-profit, ‘choice’-oriented (charter schools in the US and academies in the UK) ideology. The discourses of these advocates are explored in a fascinating article by Neil Selwyn, ‘Discourses of digital ‘disruption’ in education: a critical analysis’ which can be accessed here.

Stephen Ball includes a detailed chart of this kind of network in his ‘Global Education Inc.’ (Routledge 2012). I thought it would be interesting to attempt a similar, but less ambitious, chart of my own. Sugata Mitra’s plenary talk at the IATEFL conference yesterday has generated a lot of discussion, so I thought it would be interesting to focus on him. What such charts demonstrate very clearly is that there is a very close interlinking between EdTech advocacy and a wider raft of issues on the neo-liberal wish list. Adaptive learning developments (or, for example, schools in the cloud) need to be understood in a broader context … in the same way that Mitra, Tooley, Gates et al understand these technologies.

In order to understand the chart, you will need to look at the notes below. Many more nodes could be introduced, but I have tried my best to keep things simple. All of the information here is publicly available, but I found Stephen Ball’s work especially helpful.

mitra chart

People

Bill Gates is the former chief executive and chairman of Microsoft, co-chair of the Bill and Melinda Gates Foundation.

James Tooley is the Director of the E.G. West Centre. He is a founder of the Educare Trust, founder and chairman of Omega Schools, president of Orient Global, chairman of Rumi School of Excellence, and a former consultant to the International Finance Corporation. He is also a member of the advisory council of the Institute of Economic Affairs and was responsible for creating the Education and Training Unit at the Institute.

Michael Barber is Pearson’s Chief Education Advisor and Chairman of Pearson’s $15 million Affordable Learning Fund. He is also an advisor on ‘deliverology’ to the International Finance Corporation.

Sugata Mitra is Professor of Educational Technology at the E.G. West Centre and he is Chief Scientist, Emeritus, at NIIT. He is best known for his “Hole in the Wall” experiment. In 2013, he won the $1 million TED Prize to develop his idea of a ‘school-in-the-cloud’.

Institutions

Hiwel (Hole-in-the-Wall Education Limited) is the company behind Mitra’s “Hole in the Wall” experiment. It is a subsidiary of NIIT.

NIIT Limited is an Indian company based in Gurgaon, India that operates several for-profit higher education institutions.

Omega Schools is a privately held chain of affordable, for-profit schools based in Ghana.There are currently 38 schools educating over 20,000 students.

Orient Global is a Singapore-based investment group, which bought a $48 million stake in NIIT.

Pearson is … Pearson. Pearson’s Affordable Learning Fund was set up to invest in private companies committed to innovative approaches. Its first investment was a stake in Omega Schools.

Rumi Schools of Excellence is Orient Global’s chain of low-cost private schools in India, which aims to extend access and improve educational quality through affordable private schooling.

School-in-the-cloud is described by Mitra as’ a learning lab in India, where children can embark on intellectual adventures by engaging and connecting with information and mentoring online’. Microsoft are the key sponsors.

The E.G. West Centre of the University of Newcastle is dedicated to generating knowledge and understanding about how markets and self organising systems work in education.

The Educare Trustis a non-profit agency, formed in 2002 by Professor James Tooley of the University of Newcastle Upon Tyne, England, and other members associated with private unaided schools in India.It is advised by an international team from the University of Newcastle. It services include the running of a loan scheme for schools to improve their infrastructure and facilities.

The Institute of Economic Affairs is a right-wing free market think tank in London whose stated mission is to improve understanding of the fundamental institutions of a free society by analysing and expounding the role of markets in solving economic and social problems.

The International Finance Corporation is an international financial institution which offers investment, advisory, and asset management services to encourage private sector development in developing countries. The IFC is a member of the World Bank Group.

The Templeton Foundation is a philanthropic organization that funds inter-disciplinary research about human purpose and ultimate reality. Described by Barbara Ehrenreich as a ‘right wing venture’, it has a history of supporting the Cato Institute (publishers of Tooley’s most well-known book) , a libertarian think-tank, as well as projects at major research centers and universities that explore themes related to free market economics.

Additional connections

Barber is an old friend of Tooley’s from when both men were working in Zimbabwe in the 1990s.

Omega Schools are taking part in Sugata Mitra’s TED Prize Schools in the Cloud project.

Omega Schools use textbooks developed by Pearson.

Orient Global sponsored an Education Development fund at Newcastle University. The project leaders were Tooley and Mitra. They also sponsored the Hole-in-the-Wall experiment.

Pearson, the Pearson Foundation, Microsoft and the Gates Foundation work closely together on a wide variety of projects.

Some of Tooley’s work for the Educare Trust was funded by the Templeton Trust. Tooley was also winner of the 2006 Templeton Freedom Prize for Excellence.

The International Finance Corporation and the Gates Foundation are joint sponsors of a $60 million project to improve health in Nigeria.

The International Finance Corporation was another sponsor of the Hole-in-the-Wall experiment.

Adaptive learning is likely to impact on the lives of language teachers very soon. In my work as a writer of education materials, it has already dramatically impacted on mine. This impact has affected the kinds of things I am asked to write, the way in which I write them and my relationship with the editors and publishers I am writing for. I am as dismissive as Steve Jobs[1] was of the idea that technology can radically transform education, but in the short term it can radically disrupt it. Change is not necessarily progress.

Teachers and teacher trainers need to be very alert to what is going on if they don’t want to wake up one morning and find themselves out of work, or in a very different kind of job. The claims for adaptive language learning need to be considered in the bright light of particular, local contexts. Teachers and teacher trainers can even take a lesson from the proponents of adaptive learning who rail against the educational approach of one-size-fits-all. One size, whether it’s face-to-face with a print coursebook or whether it’s a blended adaptive program, will never fit all. We need to be very skeptical of the publishers and software providers who claim in a TED-style, almost evangelical way that they are doing the right thing for students, our society, or our world. There is a real risk that adaptive learning may be leading simply to ‘a more standardised, minimalist product targeted for a mass market, [that] will further ‘box in’ and ‘dumb down’ education’ (Selwyn, Education and Technology 2011, p.101).

There is nothing wrong, per se, with adaptive learning. It could be put to some good uses, but how likely is this? In order to understand how it may impact on our working lives, we need to be better informed. A historical perspective is often a good place to start and Larry Cuban’s Teachers and Machines: The Classroom Use of Technology since 1920 (New York: Teachers College Press, 1986) is still well worth reading.

81WEOH4yyOL

To get a good picture of where big data and analytics are now and where they are heading, Mayer-Schonberger & Cukier’s Big Data (London: John Murray, 2013) is informative and entertaining reading. If you are ‘an executive looking to integrate analytics in your decision making or a manager seeking to generate better conversations with the quants in your organisation’, I’d recommend Keeping up with the Quants by Thomas H. Davenport and Jinho Kim (Harvard Business School, 2013). Or you could just read ‘The Economist’ for this kind of thing.

If you want to follow up the connections between educational technology and neo-liberalism, the books by Stephen Ball (Global Education Inc., Abingdon, Oxon: Routledge, 2012), Neil Selwyn (Education and Technology, London: Continuum, 2011; Education in a Digital World, New York: Routledge, 2013; Distrusting Educational Technology, New York: Routledge, 2013), Diane Ravitch (Reign of Error, New York: Knopf, 2013) and Joel Spring (Education Networks, New York: Routledge, 2012; The Great American Education-Industrial Complex with Anthony G. Picciano, Routledge, 2013) are all good reads. And keep a look out for anything new from these writers.

Finally, to keep up to date with recent developments, the eltjam blog http://www.eltjam.com/ is a good one to follow, as is Richard Whiteside’s Scoop.it! page http://www.scoop.it/t/elt-publishing-by-richard-whiteside

I’ll be continuing to post things here from time to time! Thanks for following me so far.


[1] Jobs, however, did set his sights ‘on the $8 billion a year textbook industry, which he saw as ‘ripe for digital destruction’. His first instinct seems to have been to relieve kids from having to carry around heavy backpacks crammed with textbooks: ‘The iPad would solve that,’ he said, ever practical’ (Fullan, Stratosphere 2013, p.61).

There is a good chance that many readers will have only the haziest idea of what adaptive learning is. There is a much better chance that most English language teachers, especially those working in post-secondary education, will feel the impact of adaptive learning on their professional lives in the next few years. According to Time magazine, it is a ‘hot concept, embraced by education reformers‘, which is ‘poised to reshape education’[1]. According to the educational news website, Education Dive, there is ‘no hotter segment in ed tech right now’[2]. All the major ELT publishers are moving away from traditional printed coursebooks towards the digital delivery of courses that will contain adaptive learning elements. Their investments in the technology are colossal. Universities in many countries, especially the US, are moving in the same direction, again with huge investments. National and regional governments, intergovernmental organisations (such as UNESCO, the OECD, the EU and the World Bank), big business and hugely influential private foundations (such as the Bill and Melinda Gates Foundation) are all lined up in support of the moves towards the digital delivery of education, which (1) will inevitably involve elements of adaptive learning, and (2) will inevitably impact massively on the world of English language teaching.

The next 13 posts will, together, form a guide to adaptive learning in ELT.

1 Introduction

2 Simple models of adaptive learning

3 Gamification

4 Big data, analytics and adaptive learning

5 Platforms and more complex adaptive learning systems

6 The selling points of adaptive learning

7 Ten predictions for the future

8 Theory, research and practice
9 Neo liberalism and solutionism
10 Learn more