Posts Tagged ‘investment’

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

Hendrick, C. (2019). The growth mindset problem. Aeon,11 March 2019.

Maslow, A. (1943). A Theory of Human Motivation. Psychological Review, 50: pp. 370-396.

Outes-Leon, I., Sanchez, A. & Vakis, R. (2020). The Power of Believing You Can Get Smarter : The Impact of a Growth-Mindset Intervention on Academic Achievement in Peru (English). Policy Research working paper, no. WPS 9141 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/212351580740956027/The-Power-of-Believing-You-Can-Get-Smarter-The-Impact-of-a-Growth-Mindset-Intervention-on-Academic-Achievement-in-Peru

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

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.

 

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

A brief description

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

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

Programmed English Course

Comparatives strip

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

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

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

Enthusiasm and uptake

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

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

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

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

Lessons to be learned

1 Shaping attitudes

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

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

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

2 Returns on investment

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

Teaching machines (from Goodman)AutoTutor Mk II (from Goodman)

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

3 Research and theory

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

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

4 Content

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

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

5 Motivation

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

The unlearned lessons

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

 

 

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

 

 

 

 

 

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

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

Lava flow Hawaii

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

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

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

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

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

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

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

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

2 Technological innovation is good and necessary

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

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

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

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

3 Technological innovations are best driven by the private sector

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

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

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

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

4 Accountability is crucial

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

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

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

 

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

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

Big_data_Google_Trend

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

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

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

Macmillan_catalogue_2015

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

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

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

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

 

 

 

 

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

jenny-holzer-untitled-protect-me-from-what-i-want-text-displayed-in-times-square-nyc-1982

Jenny Holzer, Protect me from what I want

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

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

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

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

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

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

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

ltsigIt’s hype time again. Spurred on, no doubt, by the current spate of books and articles  about AIED (artificial intelligence in education), the IATEFL Learning Technologies SIG is organising an online event on the topic in November of this year. Currently, the most visible online references to AI in language learning are related to Glossika , basically a language learning system that uses spaced repetition, whose marketing department has realised that references to AI might help sell the product. GlossikaThey’re not alone – see, for example, Knowble which I reviewed earlier this year .

In the wider world of education, where AI has made greater inroads than in language teaching, every day brings more stuff: How artificial intelligence is changing teaching , 32 Ways AI is Improving Education , How artificial intelligence could help teachers do a better job , etc., etc. There’s a full-length book by Anthony Seldon, The Fourth Education Revolution: will artificial intelligence liberate or infantilise humanity? (2018, University of Buckingham Press) – one of the most poorly researched and badly edited books on education I’ve ever read, although that won’t stop it selling – and, no surprises here, there’s a Pearson commissioned report called Intelligence Unleashed: An argument for AI in Education (2016) which is available free.

Common to all these publications is the claim that AI will radically change education. When it comes to language teaching, a similar claim has been made by Donald Clark (described by Anthony Seldon as an education guru but perhaps best-known to many in ELT for his demolition of Sugata Mitra). In 2017, Clark wrote a blog post for Cambridge English (now unavailable) entitled How AI will reboot language learning, and a more recent version of this post, called AI has and will change language learning forever (sic) is available on Clark’s own blog. Given the history of the failure of education predictions, Clark is making bold claims. Thomas Edison (1922) believed that movies would revolutionize education. Radios were similarly hyped in the 1940s and in the 1960s it was the turn of TV. In the 1980s, Seymour Papert predicted the end of schools – ‘the computer will blow up the school’, he wrote. Twenty years later, we had the interactive possibilities of Web 2.0. As each technology failed to deliver on the hype, a new generation of enthusiasts found something else to make predictions about.

But is Donald Clark onto something? Developments in AI and computational linguistics have recently resulted in enormous progress in machine translation. Impressive advances in automatic speech recognition and generation, coupled with the power that can be packed into a handheld device, mean that we can expect some re-evaluation of the value of learning another language. Stephen Heppell, a specialist at Bournemouth University in the use of ICT in Education, has said: ‘Simultaneous translation is coming, making language teachers redundant. Modern languages teaching in future may be more about navigating cultural differences’ (quoted by Seldon, p.263). Well, maybe, but this is not Clark’s main interest.

Less a matter of opinion and much closer to the present day is the issue of assessment. AI is becoming ubiquitous in language testing. Cambridge, Pearson, TELC, Babbel and Duolingo are all using or exploring AI in their testing software, and we can expect to see this increase. Current, paper-based systems of testing subject knowledge are, according to Rosemary Luckin and Kristen Weatherby, outdated, ineffective, time-consuming, the cause of great anxiety and can easily be automated (Luckin, R. & Weatherby, K. 2018. ‘Learning analytics, artificial intelligence and the process of assessment’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.253). By capturing data of various kinds throughout a language learner’s course of study and by using AI to analyse learning development, continuous formative assessment becomes possible in ways that were previously unimaginable. ‘Assessment for Learning (AfL)’ or ‘Learning Oriented Assessment (LOA)’ are two terms used by Cambridge English to refer to the potential that AI offers which is described by Luckin (who is also one of the authors of the Pearson paper mentioned earlier). In practical terms, albeit in a still very limited way, this can be seen in the CUP course ‘Empower’, which combines CUP course content with validated LOA from Cambridge Assessment English.

Will this reboot or revolutionise language teaching? Probably not and here’s why. AIED systems need to operate with what is called a ‘domain knowledge model’. This specifies what is to be learnt and includes an analysis of the steps that must be taken to reach that learning goal. Some subjects (especially STEM subjects) ‘lend themselves much more readily to having their domains represented in ways that can be automatically reasoned about’ (du Boulay, D. et al., 2018. ‘Artificial intelligences and big data technologies to close the achievement gap’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.258). This is why most AIED systems have been built to teach these areas. Language are rather different. We simply do not have a domain knowledge model, except perhaps for the very lowest levels of language learning (and even that is highly questionable). Language learning is probably not, or not primarily, about acquiring subject knowledge. Debate still rages about the relationship between explicit language knowledge and language competence. AI-driven formative assessment will likely focus most on explicit language knowledge, as does most current language teaching. This will not reboot or revolutionise anything. It will more likely reinforce what is already happening: a model of language learning that assumes there is a strong interface between explicit knowledge and language competence. It is not a model that is shared by most SLA researchers.

So, one thing that AI can do (and is doing) for language learning is to improve the algorithms that determine the way that grammar and vocabulary are presented to individual learners in online programs. AI-optimised delivery of ‘English Grammar in Use’ may lead to some learning gains, but they are unlikely to be significant. It is not, in any case, what language learners need.

AI, Donald Clark suggests, can offer personalised learning. Precisely what kind of personalised learning this might be, and whether or not this is a good thing, remains unclear. A 2015 report funded by the Gates Foundation found that we currently lack evidence about the effectiveness of personalised learning. We do not know which aspects of personalised learning (learner autonomy, individualised learning pathways and instructional approaches, etc.) or which combinations of these will lead to gains in language learning. The complexity of the issues means that we may never have a satisfactory explanation. You can read my own exploration of the problems of personalised learning starting here .

What’s left? Clark suggests that chatbots are one area with ‘huge potential’. I beg to differ and I explained my reasons eighteen months ago . Chatbots work fine in very specific domains. As Clark says, they can be used for ‘controlled practice’, but ‘controlled practice’ means practice of specific language knowledge, the practice of limited conversational routines, for example. It could certainly be useful, but more than that? Taking things a stage further, Clark then suggests more holistic speaking and listening practice with Amazon Echo, Alexa or Google Home. If and when the day comes that we have general, as opposed to domain-specific, AI, chatting with one of these tools would open up vast new possibilities. Unfortunately, general AI does not exist, and until then Alexa and co will remain a poor substitute for human-human interaction (which is readily available online, anyway). Incidentally, AI could be used to form groups of online language learners to carry out communicative tasks – ‘the aim might be to design a grouping of students all at a similar cognitive level and of similar interests, or one where the participants bring different but complementary knowledge and skills’ (Luckin, R., Holmes, W., Griffiths, M. & Forceir, L.B. 2016. Intelligence Unleashed: An argument for AI in Education. London: Pearson, p.26).

Predictions about the impact of technology on education have a tendency to be made by people with a vested interest in the technologies. Edison was a businessman who had invested heavily in motion pictures. Donald Clark is an edtech entrepreneur whose company, Wildfire, uses AI in online learning programs. Stephen Heppell is executive chairman of LP+ who are currently developing a Chinese language learning community for 20 million Chinese school students. The reporting of AIED is almost invariably in websites that are paid for, in one way or another, by edtech companies. Predictions need, therefore, to be treated sceptically. Indeed, the safest prediction we can make about hyped educational technologies is that inflated expectations will be followed by disillusionment, before the technology finds a smaller niche.

 

440px-HydraOrganization_HeadLike the mythical monster, the ancient Hydra organisation of Marvel Comics grows two more heads if one is cut off, becoming more powerful in the process. With the most advanced technology on the planet and with a particular focus on data gathering, Hydra operates through international corporations and highly-placed individuals in national governments.
Personalized learning has also been around for centuries. Its present incarnation can be traced to the individualized instructional programmes of the late 19th century which ‘focused on delivering specific subject matter […] based on the principles of scientific management. The intent was to solve the practical problems of the classroom by reducing waste and increasing efficiency, effectiveness, and cost containment in education (Januszewski, 2001: 58). Since then, personalized learning has adopted many different names, including differentiated instruction, individualized instruction, individually guided education, programmed instruction, personalized learning, personalized instruction, and individually prescribed instruction.
Disambiguating the terms has never been easy. In the world of language learning / teaching, it was observed back in the early 1970s ‘that there is little agreement on the description and definition of individualized foreign language instruction’ (Garfinkel, 1971: 379). The point was echoed a few years later by Grittner (1975: 323): it ‘means so many things to so many different people’. A UNESCO document (Chaix & O’Neil, 1978: 6) complained that ‘the term ‘individualization’ and the many expressions using the same root, such as ‘individualized learning’, are much too ambiguous’. Zoom forward to the present day and nothing has changed. Critiquing the British government’s focus on personalized learning, the Institute for Public Policy Research (Johnson, 2004: 17) wrote that it ‘remains difficult to be certain what the Government means by personalised learning’. In the U.S. context, a piece by Sean Cavanagh (2014) in Education Week (which is financially supported by the Gates Foundation) noted that although ‘the term “personalized learning” seems to be everywhere, there is not yet a shared understanding of what it means’. In short, as Arthur Levine  has put it, the words personalized learning ‘generate more heat than light’.
Despite the lack of clarity about what precisely personalized learning actually is, it has been in the limelight of language teaching and learning since before the 1930s when Pendleton (1930: 195) described the idea as being more widespread than ever before. Zoom forward to the 1970s and we find it described as ‘one of the major movements in second-language education at the present time’ (Chastain, 1975: 334). In 1971, it was described as ‘a bandwagon onto which foreign language teachers at all levels are jumping’ (Altman & Politzer, 1971: 6). A little later, in the 1980s, ‘words or phrases such as ‘learner-centered’, ‘student-centered’, ‘personalized’, ‘individualized’, and ‘humanized’ appear as the most frequent modifiers of ‘instruction’ in journals and conferences of foreign language education (Altman & James, 1980). Continue to the present day, and we find that personalized learning is at the centre of the educational policies of governments across the world. Between 2012 and 2015, the U.S. Department of Education threw over half a billion dollars at personalized learning initiatives (Bulger, 2016: 22). At the same time, there is massive sponsorship of personalized learning from the biggest international corporations (the William and Flora Hewlett Foundation, Rogers Family Foundation, Susan and Michael Dell Foundation, and the Eli and Edythe Broad Foundation) (Bulger, 2016: 22). The Bill & Melinda Gates Foundation has invested nearly $175 million in personalized learning development and Facebook’s Mark Zuckerberg is ploughing billions of dollars into it.
There has, however, been one constant: the belief that technology can facilitate the process of personalization (whatever that might be). Technology appears to offer the potential to realise the goal of personalized learning. We have come a long way from Sydney Pressey’s attempts in the 1920s to use teaching machines to individualize instruction. At that time, the machines were just one part of the programme (and not the most important). But each new technology has offered a new range of possibilities to be exploited and each new technology, its advocates argue, ‘will solve the problems better than previous efforts’ (Ferster, 2014: xii). With the advent of data-capturing learning technologies, it has now become virtually impossible to separate advocacy of personalized instruction from advocacy of digitalization in education. As the British Department for Education has put it ‘central to personalised learning is schools’ use of data (DfES (2005) White Paper: Higher Standards, Better Schools for All. London, Department for Education and Skills, para 4.50). When the U.S. Department of Education threw half a billion dollars at personalized learning initiatives, the condition was that these projects ‘use collaborative, data-based strategies and 21st century tools to deliver instruction’ (Bulger, 2016: 22).
Is it just a coincidence that the primary advocates of personalized learning are either vendors of technology or are very close to them in the higher echelons of Hydra (World Economic Forum, World Bank, IMF, etc.)? ‘Personalized learning’ has ‘almost no descriptive value’: it is ‘a term that sounds good without the inconvenience of having any obviously specific pedagogical meaning’ (Feldstein & Hill, 2016: 30). It evokes positive responses, with its ‘nod towards more student-centered learning […], a move that honors the person learning not just the learning institution’ (Watters, 2014). As such, it is ‘a natural for marketing purposes’ since nobody in their right mind would want unpersonalized or depersonalized learning (Feldstein & Hill, 2016: 25). It’s ‘a slogan that nobody’s going to be against, and everybody’s going to be for. Nobody knows what it means, because it doesn’t mean anything. Its crucial value is that it diverts your attention from a question that does mean something: Do you support our policy?’ (Chomsky, 1997).
None of the above is intended to suggest that there might not be goals that come under the ‘personalized learning’ umbrella that are worth working towards. But that’s another story – one I will return to in another post. For the moment, it’s just worth remembering that, in one of the Marvel Comics stories, Captain America, who appeared to be fighting the depersonalized evils of the world, was actually a deep sleeper agent for Hydra.

References
Altman, H.B. & James, C.V. (eds.) 1980. Foreign Language Teaching: Meeting Individual Needs. Oxford: Pergamon Press
Altman, H.B. & Politzer, R.L. (eds.) 1971. Individualizing Foreign Language Instruction: Proceedings of the Stanford Conference, May 6 – 8, 1971. Washington, D.C.: Office of Education, U.S. Department of Health, Education, and Welfare
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. New York: Data and Society Research Institute.
Cavanagh, S. 2014. ‘What Is ‘Personalized Learning’? Educators Seek Clarity’ Education Week
Chaix, P., & O’Neil, C. 1978. A Critical Analysis of Forms of Autonomous Learning (Autodidaxy and Semi-autonomy in the Field of Foreign Language Learning. Final Report. UNESCO Doc Ed 78/WS/58
Chastain, K. 1975. ‘An Examination of the Basic Assumptions of “Individualized” Instruction’ The Modern Language Journal 59 / 7: 334 – 344
Chomsky, N. 1997. Media Control: The Spectacular Achievements of Propaganda. New York: Seven Stories Press
Feldstein, M. & Hill, P. 2016. ‘Personalized Learning: What it Really is and why it Really Matters’ EduCause Review March / April 2016: 25 – 35
Ferster, B. 2014. Teaching Machines. Baltimore: John Hopkins University Press
Garfinkel, A. 1971. ‘Stanford University Conference on Individualizing Foreign Language Instruction, May 6-8, 1971.’ The Modern Language Journal Vol. 55, No. 6 (Oct., 1971), pp. 378-381
Grittner, F. M. 1975. ‘Individualized Instruction: An Historical Perspective’ The Modern Language Journal 59 / 7: 323 – 333
Januszewski, A. 2001. Educational Technology: The Development of a Concept. Englewood, Colorado: Libraries Unlimited
Johnson, M. 2004. Personalised Learning – an Emperor’s Outfit? London: Institute for Public Policy Research
Pendleton, C. S. 1930. ‘Personalizing English Teaching’ Peabody Journal of Education 7 / 4: 195 – 200
Watters, A. 2014. The problem with ‘personalization’ Hack Education

by Philip Kerr & Andrew Wickham

from IATEFL 2016 Birmingham Conference Selections (ed. Tania Pattison) Faversham, Kent: IATEFL pp. 75 – 78

ELT publishing, international language testing and private language schools are all industries: products are produced, bought and sold for profit. English language teaching (ELT) is not. It is an umbrella term that is used to describe a range of activities, some of which are industries, and some of which (such as English teaching in high schools around the world) might better be described as public services. ELT, like education more generally, is, nevertheless, often referred to as an ‘industry’.

Education in a neoliberal world

The framing of ELT as an industry is both a reflection of how we understand the term and a force that shapes our understanding. Associated with the idea of ‘industry’ is a constellation of other ideas and words (such as efficacy, productivity, privatization, marketization, consumerization, digitalization and globalization) which become a part of ELT once it is framed as an industry. Repeated often enough, ‘ELT as an industry’ can become a metaphor that we think and live by. Those activities that fall under the ELT umbrella, but which are not industries, become associated with the desirability of industrial practices through such discourse.

The shift from education, seen as a public service, to educational managerialism (where education is seen in industrial terms with a focus on efficiency, free market competition, privatization and a view of students as customers) can be traced to the 1980s and 1990s (Gewirtz, 2001). In 1999, under pressure from developed economies, the General Agreement on Trade in Services (GATS) transformed education into a commodity that could be traded like any other in the marketplace (Robertson, 2006). The global industrialisation and privatization of education continues to be promoted by transnational organisations (such as the World Bank and the OECD), well-funded free-market think-tanks (such as the Cato Institute), philanthro-capitalist foundations (such as the Gates Foundation) and educational businesses (such as Pearson) (Ball, 2012).

Efficacy and learning outcomes

Managerialist approaches to education require educational products and services to be measured and compared. In ELT, the most visible manifestation of this requirement is the current ubiquity of learning outcomes. Contemporary coursebooks are full of ‘can-do’ statements, although these are not necessarily of any value to anyone. Examples from one unit of one best-selling course include ‘Now I can understand advice people give about hotels’ and ‘Now I can read an article about unique hotels’ (McCarthy et al. 2014: 74). However, in a world where accountability is paramount, they are deemed indispensable. The problem from a pedagogical perspective is that teaching input does not necessarily equate with learning uptake. Indeed, there is no reason why it should.

Drawing on the Common European Framework of Reference for Languages (CEFR) for inspiration, new performance scales have emerged in recent years. These include the Cambridge English Scale and the Pearson Global Scale of English. Moving away from the broad six categories of the CEFR, such scales permit finer-grained measurement and we now see individual vocabulary and grammar items tagged to levels. Whilst such initiatives undoubtedly support measurements of efficacy, the problem from a pedagogical perspective is that they assume that language learning is linear and incremental, as opposed to complex and jagged.

Given the importance accorded to the measurement of language learning (or what might pass for language learning), it is unsurprising that attention is shifting towards the measurement of what is probably the most important factor impacting on learning: the teaching. Teacher competency scales have been developed by Cambridge Assessment, the British Council and EAQUALS (Evaluation and Accreditation of Quality Language Services), among others.

The backwash effects of the deployment of such scales are yet to be fully experienced, but the likely increase in the perception of both language learning and teacher learning as the synthesis of granularised ‘bits of knowledge’ is cause for concern.

Digital technology

Digital technology may offer advantages to both English language teachers and learners, but its rapid growth in language learning is the result, primarily but not exclusively, of the way it has been promoted by those who stand to gain financially. In education, generally, and in English language teaching, more specifically, advocacy of the privatization of education is always accompanied by advocacy of digitalization. The global market for digital English language learning products was reported to be $2.8 billion in 2015 and is predicted to reach $3.8 billion by 2020 (Ambient Insight, 2016).

In tandem with the increased interest in measuring learning outcomes, there is fierce competition in the market for high-stakes examinations, and these are increasingly digitally delivered and marked. In the face of this competition and in a climate of digital disruption, companies like Pearson and Cambridge English are developing business models of vertical integration where they can provide and sell everything from placement testing, to courseware (either print or delivered through an LMS), teaching, assessment and teacher training. Huge investments are being made in pursuit of such models. Pearson, for example, recently bought GlobalEnglish, Wall Street English, and set up a partnership with Busuu, thus covering all aspects of language learning from resources provision and publishing to off- and online training delivery.

As regards assessment, the most recent adult coursebook from Cambridge University Press (in collaboration with Cambridge English Language Assessment), ‘Empower’ (Doff, et. Al, 2015) sells itself on a combination of course material with integrated, validated assessment.

Besides its potential for scalability (and therefore greater profit margins), the appeal (to some) of platform-delivered English language instruction is that it facilitates assessment that is much finer-grained and actionable in real time. Digitization and testing go hand in hand.

Few English language teachers have been unaffected by the move towards digital. In the state sectors, large-scale digitization initiatives (such as the distribution of laptops for educational purposes, the installation of interactive whiteboards, the move towards blended models of instruction or the move away from printed coursebooks) are becoming commonplace. In the private sectors, online (or partially online) language schools are taking market share from the traditional bricks-and-mortar institutions.

These changes have entailed modifications to the skill-sets that teachers need to have. Two announcements at this conference reflect this shift. First of all, Cambridge English launched their ‘Digital Framework for Teachers’, a matrix of six broad competency areas organised into four levels of proficiency. Secondly, Aqueduto, the Association for Quality Education and Training Online, was launched, setting itself up as an accreditation body for online or blended teacher training courses.

Teachers’ pay and conditions

In the United States, and likely soon in the UK, the move towards privatization is accompanied by an overt attack on teachers’ unions, rights, pay and conditions (Selwyn, 2014). As English language teaching in both public and private sectors is commodified and marketized it is no surprise to find that the drive to bring down costs has a negative impact on teachers worldwide. Gwynt (2015), for example, catalogues cuts in funding, large-scale redundancies, a narrowing of the curriculum, intensified workloads (including the need to comply with ‘quality control measures’), the deskilling of teachers, dilapidated buildings, minimal resources and low morale in an ESOL department in one British further education college. In France, a large-scale study by Wickham, Cagnol, Wright and Oldmeadow (Linguaid, 2015; Wright, 2016) found that EFL teachers in the very competitive private sector typically had multiple employers, limited or no job security, limited sick pay and holiday pay, very little training and low hourly rates that were deteriorating. One of the principle drivers of the pressure on salaries is the rise of online training delivery through Skype and other online platforms, using offshore teachers in low-cost countries such as the Philippines. This type of training represents 15% in value and up to 25% in volume of all language training in the French corporate sector and is developing fast in emerging countries. These examples are illustrative of a broad global trend.

Implications

Given the current climate, teachers will benefit from closer networking with fellow professionals in order, not least, to be aware of the rapidly changing landscape. It is likely that they will need to develop and extend their skill sets (especially their online skills and visibility and their specialised knowledge), to differentiate themselves from competitors and to be able to demonstrate that they are in tune with current demands. More generally, it is important to recognise that current trends have yet to run their full course. Conditions for teachers are likely to deteriorate further before they improve. More than ever before, teachers who want to have any kind of influence on the way that marketization and industrialization are shaping their working lives will need to do so collectively.

References

Ambient Insight. 2016. The 2015-2020 Worldwide Digital English Language Learning Market. http://www.ambientinsight.com/Resources/Documents/AmbientInsight_2015-2020_Worldwide_Digital_English_Market_Sample.pdf

Ball, S. J. 2012. Global Education Inc. Abingdon, Oxon.: Routledge

Doff, A., Thaine, C., Puchta, H., Stranks, J. and P. Lewis-Jones 2015. Empower. Cambridge: Cambridge University Press

Gewirtz, S. 2001. The Managerial School: Post-welfarism and Social Justice in Education. Abingdon, Oxon.: Routledge

Gwynt, W. 2015. ‘The effects of policy changes on ESOL’. Language Issues 26 / 2: 58 – 60

McCarthy, M., McCarten, J. and H. Sandiford 2014. Touchstone 2 Student’s Book Second Edition. Cambridge: Cambridge University Press

Linguaid, 2015. Le Marché de la Formation Langues à l’Heure de la Mondialisation. Guildford: Linguaid

Robertson, S. L. 2006. ‘Globalisation, GATS and trading in education services.’ published by the Centre for Globalisation, Education and Societies, University of Bristol, Bristol BS8 1JA, UK at http://www.bris.ac.uk/education/people/academicStaff/edslr/publications/04slr

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

Wright, R. 2016. ‘My teacher is rich … or not!’ English Teaching Professional 103: 54 – 56