Posts Tagged ‘marketing’

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Jargon buster

Posted: January 18, 2019 in Discourse, ed tech
Tags:

With the 2019 educational conference show season about to start, here’s a handy guide to gaining a REAL understanding of the words you’re likely to come across. Please feel free to add in the comments anything I’ve omitted.

iatefl conference

accountability

Keeping the money-people happy.

AI (artificial intelligence)

Ooh! Aah! Yes, please.

analytics (as in learning analytics)

The analysis of student data to reveal crucial insights such as the fact that students who work more, make more progress. Cf. data

AR (augmented reality)

Out-of-date interactive technology with no convincing classroom value. cf. interactive

benchmark

A word for standard that makes you sound like you know what you’re talking about.

blended (as in blended learning)

Homework. Or, if you want to sound more knowledgeable, the way e-learning is being combined with traditional classroom methods and independent study to create a new, hybrid teaching methodology that is shown by research to facilitate better learning outcomes.

bot

A non-unionized, cheap teacher for the masses.

brain-friendly

A word used by people who haven’t read enough neuro-science.

collaborative

Getting other people to help you, and getting praised for doing so.

CPD (continuous professional development)

Unpaid training.

creativity

A good excuse to get out your guitar, recite a few poems and show how sensitive you are. Cf. 21st century skills

curated (as in curated learning content)

Stuff nicked from other websites. A way of getting more personalization for less investment.

customer

The correct way to refer to students. Cf. markets

data

Information about students that can be sold to advertising companies.

design (as in learning design)

Used to mean curriculum by people selling edtech products who aren’t sure what curriculum means.

discovery learning

A myth with a long-gone expiry date.

disruptive (as in disruptive innovation in education)

A word used in utter seriousness by people who dream of getting rich from the privatisation of education.

drones

Handy for speaking and writing exercises, according to elearningindustry.com. They open up a new set of opportunities to make classes more relevant and engaging for students. They can in fact enrich students’ imagination and get them more involved into the learning process.

ecosystem (as in learning ecosystem)

All the different ways that data about learners can be captured, sold or hacked.

EdSurge

The go-to site for ‘news’ about edtech. The company’s goal is ‘to promote the smart adoption of education technology through impartial reporting’ … much of which is paid for by investors in edtech start-ups.

edutainment

PowerPoint, for example.

efficacy

A fancy word for efficiency that nobody bothers with much any more.

empowerment

Not connected to power in any way at all.

engagement

Sticking with something.

flipped (as in flipped classrooms)

Watching educational videos at home.

formative assessment

A critically important tool in the iterative process of maximizing the learning environment and customizing instruction to meet students’ needs. Also known as testing.

gamification

Persuading people to push buttons.

global citizens

Nice people.

immersive

Used to describe a learning activity that is less boring than other learning activities.

inclusive (as in inclusive practices)

Not to be confused with virtue-signalling.

innovative

A meaningless word that sounds good to some people. Interchangeable with cutting-edge and state-of-the-art

interactive

With buttons that can be pushed.

interactive whiteboard

A term you won’t hear this year, except when accompanied with a scoff, because everyone has forgotten it and wants to move on. Cf. 60% of the other terms in this glossary by 2025

(the) knowledge economy

Platform capitalism.

leadership

A smokescreen for poor pay and conditions. Cf. 21st century skills

literacy (as in critical literacy, digital literacy, emotional literacy, media literacy, visual literacy)

A jargon word used to mean that someone can do something.

MALL (Mobile assisted language learning)

Chatting or playing games with your phone in class.

markets

Another contemporary way of referring to students. Cf. customer

mediation

Translating, interpreting and things like that.

mindfulness

An ever-growing industry.

motivation

U.S. education technology companies raised $1.45 billion from venture capitalists and private-equity investors in 2018 (according to EdSurge).

outcomes (as in learning outcomes)

‘Learning’, or whatever, that can be measured.

personalized

A meaningless word useful for selling edtech stuff. Interchangeable with differentiated and individualized.

providers

A euphemism for sellers. Cf. solutions

publisher

An obsolete word for providers of educational learning solutions. Cf. solutions

quality

A bit of management jargon from the last century. It doesn’t really matter if you don’t know exactly what it means – you can define it yourself.

research

A slippery word that is meant to elicit a positive response.

resilience

Also known as grit, the ability to suspend your better judgment and plough on.

scaffolding

Something to do with Vygotsky, but it probably doesn’t matter what exactly. It’s a ‘good thing’.

SEL (Social-Emotional Learning)

A VA (value-added) experience needed by students who spend too long in CAL in a VLE with poor UX.

skills (as in 21st century skills)

The abilities that young people will need for an imagined future workplace. These are to be paid for by the state, rather than the companies that might employ a small number of them on zero-hour contracts.

soft skills

Everything you need to be a compliant employee.

solutions (as in learning solutions)

A euphemism for stuff that someone is trying to sell to schools.

teacherpreneur

A teacher in need of a reality check.

thought leaders (as in educational thought leaders)

Effective self-promoters, usually with no background in education.

transformative

Nothing to do with Transformative Learning Theory (Mezirow) … just another buzz word.

VR

Technology that makes you dizzy.

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.

 

It’s international ELT conference season again, with TESOL Chicago having just come to a close and IATEFL Brighton soon to start. I decided to take a look at how the subject of personalized learning will be covered at the second of these. Taking the conference programme , I trawled through looking for references to my topic.

Jing_word_cloudMy first question was: how do conference presenters feel about personalised learning? One way of finding out is by looking at the adjectives that are found in close proximity. This is what you get.

The overall enthusiasm is even clearer when the contexts are looked at more closely. Here are a few examples:

  • inspiring assessment, personalising learning
  • personalised training can contribute to professionalism and […] spark ideas for teacher trainers
  • a personalised educational experience that ultimately improves learner outcomes
  • personalised teacher development: is it achievable?

Particularly striking is the complete absence of anything that suggests that personalized learning might not be a ‘good thing’. The assumption throughout is that personalized learning is desirable and the only question that is asked is how it can be achieved. Unfortunately (and however much we might like to believe that it is a ‘good thing’), there is a serious lack of research evidence which demonstrates that this is the case. I have written about this here and here and here . For a useful summary of the current situation, see Benjamin Riley’s article where he writes that ‘it seems wise to ask what evidence we presently have that personalized learning works. Answer: Virtually none. One remarkable aspect of the personalized-learning craze is how quickly the concept has spread despite the almost total absence of rigorous research in support of it, at least thus far.’

Given that personalized learning can mean so many things and given the fact that people do not have space to define their terms in their conference abstracts, it is interesting to see what other aspects of language learning / teaching it is associated with. The four main areas are as follows (in alphabetical order):

  • assessment (especially formative assessment) / learning outcomes
  • continuous professional development
  • learner autonomy
  • technology / blended learning

The IATEFL TD SIG would appear to be one of the main promoters of personalized learning (or personalized teacher development) with a one-day pre-conference event entitled ‘Personalised teacher development – is it achievable?’ and a ‘showcase’ forum entitled ‘Forum on Effective & personalised: the holy grail of CPD’. Amusingly (but coincidentally, I suppose), the forum takes place in the ‘Cambridge room’ (see below).

I can understand why the SIG organisers may have chosen this focus. It’s something of a hot topic, and getting hotter. For example:

  • Cambridge University Press has identified personalization as one of the ‘six key principles of effective teacher development programmes’ and is offering tailor-made teacher development programmes for institutions.
  • NILE and Macmillan recently launched a partnership whose brief is to ‘curate personalised professional development with an appropriate mix of ‘formal’ and ‘informal’ learning delivered online, blended and face to face’.
  • Pearson has developed the Pearson’s Teacher Development Interactive (TDI) – ‘an interactive online course to train and certify teachers to deliver effective instruction in English as a foreign language […] You can complete each module on your own time, at your own pace from anywhere you have access to the internet.’

These examples do not, of course, provide any explanation for why personalized learning is a hot topic, but the answer to that is simple. Money. Billions and billions, and if you want a breakdown, have a look at the appendix of Monica Bulger’s report, ‘Personalized Learning: The Conversations We’re Not Having’ . Starting with Microsoft and the Gates Foundation plus Facebook and the Chan / Zuckerberg Foundation, dozens of venture philanthropists have thrown unimaginable sums of money at the idea of personalized learning. They have backed up their cash with powerful lobbying and their message has got through. Consent has been successfully manufactured.

PearsonOne of the most significant players in this field is Pearson, who have long been one of the most visible promoters of personalized learning (see the screen capture). At IATEFL, two of the ten conference abstracts which include the word ‘personalized’ are directly sponsored by Pearson. Pearson actually have ten presentations they have directly sponsored or are very closely associated with. Many of these do not refer to personalized learning in the abstract, but would presumably do so in the presentations themselves. There is, for example, a report on a professional development programme in Brazil using TDI (see above). There are two talks about the GSE, described as a tool ‘used to provide a personalised view of students’ language’. The marketing intent is clear: Pearson is to be associated with personalized learning (which is, in turn, associated with a variety of tech tools) – they even have a VP of data analytics, data science and personalized learning.

But the direct funding of the message is probably less important these days than the reinforcement, by those with no vested interests, of the set of beliefs, the ideology, which underpin the selling of personalized learning products. According to this script, personalized learning can promote creativity, empowerment, inclusiveness and preparedness for the real world of work. It sets itself up in opposition to lockstep and factory models of education, and sets learners free as consumers in a world of educational choice. It is a message with which it is hard for many of us to disagree.

manufacturing consentIt is also a marvellous example of propaganda, of the way that consent is manufactured. (If you haven’t read it yet, it’s probably time to read Herman and Chomsky’s ‘Manufacturing Consent: The Political Economy of the Mass Media’.) An excellent account of the way that consent for personalized learning has been manufactured can be found at Benjamin Doxtdator’s blog .

So, a hot topic it is, and its multiple inclusion in the conference programme will no doubt be welcomed by those who are selling ‘personalized’ products. It must be very satisfying to see how normalised the term has become, how it’s no longer necessary to spend too much on promoting the idea, how it’s so associated with technology, (formative) assessment, autonomy and teacher development … since others are doing it for you.

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

Introduction

In the last post, I looked at issues concerning self-pacing in personalized language learning programmes. This time, I turn to personalized goal-setting. Most definitions of personalized learning, such as that offered by Next Generation Learning Challenges http://nextgenlearning.org/ (a non-profit supported by Educause, the Gates Foundation, the Broad Foundation, the Hewlett Foundation, among others), argue that ‘the default perspective [should be] the student’s—not the curriculum, or the teacher, and that schools need to adjust to accommodate not only students’ academic strengths and weaknesses, but also their interests, and what motivates them to succeed.’ It’s a perspective shared by the United States National Education Technology Plan 2017 https://tech.ed.gov/netp/ , which promotes the idea that learning objectives should vary based on learner needs, and should often be self-initiated. It’s shared by the massively funded Facebook initiative that is developing software that ‘puts students in charge of their lesson plans’, as the New York Times https://www.nytimes.com/2016/08/10/technology/facebook-helps-develop-software-that-puts-students-in-charge-of-their-lesson-plans.html?_r=0 put it. How, precisely, personalized goal-setting can be squared with standardized, high-stakes testing is less than clear. Are they incompatible by any chance?

In language learning, the idea that learners should have some say in what they are learning is not new, going back, at least, to the humanistic turn in the 1970s. Wilga Rivers advocated ‘giving the students opportunity to choose what they want to learn’ (Rivers, 1971: 165). A few years later, Renee Disick argued that the extent to which a learning programme can be called personalized (although she used the term ‘individualized’) depends on the extent to which learners have a say in the choice of learning objectives and the content of learning (Disick, 1975). Coming more up to date, Penny Ur advocated giving learners ‘a measure of freedom to choose how and what to learn’ (Ur, 1996: 233).

The benefits of personalized goal-setting

Personalized goal-setting is closely related to learner autonomy and learner agency. Indeed, it is hard to imagine any meaningful sense of learner autonomy or agency without some control of learning objectives. Without this control, it will be harder for learners to develop an L2 self. This matters because ‘ultimate attainment in second-language learning relies on one’s agency … [it] is crucial at the point where the individuals must not just start memorizing a dozen new words and expressions but have to decide on whether to initiate a long, painful, inexhaustive, and, for some, never-ending process of self-translation. (Pavlenko & Lantolf, 2000: 169 – 170). Put bluntly, if learners ‘have some responsibility for their own learning, they are more likely to be engaged than if they are just doing what the teacher tells them to’ (Harmer, 2012: 90). A degree of autonomy should lead to increased motivation which, in turn, should lead to increased achievement (Dickinson, 1987: 32; Cordova & Lepper, 1996: 726).

Strong evidence for these claims is not easy to provide, not least since autonomy and agency cannot be measured. However, ‘negative evidence clearly shows that a lack of agency can stifle learning by denying learners control over aspects of the language-learning process’ (Vandergriff, 2016: 91). Most language teachers (especially in compulsory education) have witnessed the negative effects that a lack of agency can generate in some students. Irrespective of the extent to which students are allowed to influence learning objectives, the desirability of agency / autonomy appears to be ‘deeply embedded in the professional consciousness of the ELT community’ (Borg and Al-Busaidi, 2012; Benson, 2016: 341). Personalized goal-setting may not, for a host of reasons, be possible in a particular learning / teaching context, but in principle it would seem to be a ‘good thing’.

Goal-setting and technology

The idea that learners might learn more and better if allowed to set their own learning objectives is hardly new, dating back at least one hundred years to the establishment of Montessori’s first Casa dei Bambini. In language teaching, the interest in personalized learning that developed in the 1970s (see my previous post) led to numerous classroom experiments in personalized goal-setting. These did not result in lasting changes, not least because the workload of teachers became ‘overwhelming’ (Disick, 1975: 128).

Closely related was the establishment of ‘self-access centres’. It was clear to anyone, like myself, who was involved in the setting-up and maintenance of a self-access centre, that they cost a lot, in terms of both money and work (Ur, 2012: 236). But there were also nagging questions about how effective they were (Morrison, 2005). Even more problematic was a bigger question: did they actually promote the learner autonomy that was their main goal?

Post-2000, online technology rendered self-access centres redundant: who needs the ‘walled garden’ of a self-access centre when ‘learners are able to connect with multiple resources and communities via the World Wide Web in entirely individual ways’ (Reinders, 2012)? The cost problem of self-access centres was solved by the web. Readily available now were ‘myriad digital devices, software, and learning platforms offering educators a once-unimaginable array of options for tailoring lessons to students’ needs’ (Cavanagh, 2014). Not only that … online technology promised to grant agency, to ‘empower language learners to take charge of their own learning’ and ‘to provide opportunities for learners to develop their L2 voice’ (Vandergriff, 2016: 32). The dream of personalized learning has become inseparable from the affordances of educational technologies.

It is, however, striking just how few online modes of language learning offer any degree of personalized goal-setting. Take a look at some of the big providers – Voxy, Busuu, Duolingo, Rosetta Stone or Babbel, for example – and you will find only the most token nods to personalized learning objectives. Course providers appear to be more interested in claiming their products are personalized (‘You decide what you want to learn and when!’) than in developing a sufficient amount of content to permit personalized goal-setting. We are left with the ELT equivalent of personalized cans of Coke: a marketing tool.

coke_cans

The problems with personalized goal-setting

Would language learning products, such as those mentioned above, be measurably any better if they did facilitate the personalization of learning objectives in a significant way? Would they be able to promote learner autonomy and agency in a way that self-access centres apparently failed to achieve? It’s time to consider the square quotes that I put around ‘good thing’.

Researchers have identified a number of potential problems with goal-setting. I have already mentioned the problem of reconciling personalized goals and standardized testing. In most learning contexts, educational authorities (usually the state) regulate the curriculum and determine assessment practices. It is difficult to see, as Campbell et al. (Campbell et al., 2007: 138) point out, how such regulation ‘could allow individual interpretations of the goals and values of education’. Most assessment systems ‘aim at convergent outcomes and homogeneity’ (Benson, 2016: 345) and this is especially true of online platforms, irrespective of their claims to ‘personalization’. In weak (typically internal) assessment systems, the potential for autonomy is strongest, but these are rare.

In all contexts, it is likely that personalized goal-setting will only lead to learning gains when a number of conditions are met. The goals that are chosen need to be both specific, measurable, challenging and non-conflicting (Ordóñez et al. 2009: 2-3). They need to be realistic: if not, it is unlikely that self-efficacy (a person’s belief about their own capability to achieve or perform to a certain level) will be promoted (Koda-Dallow & Hobbs, 2005), and without self-efficacy, improved performance is also unlikely (Bandura, 1997). The problem is that many learners lack self-efficacy and are poor self-regulators. These things are teachable / learnable, but require time and support. Many learners need help in ‘becoming aware of themselves and their own understandings’ (McMahon & Oliver, 2001: 1304). If they do not get it, the potential advantages of personalized goal-setting will be negated. As learners become better self-regulators, they will want and need to redefine their learning goals: goal-setting should be an iterative process (Hussey & Smith, 2003: 358). Again, support will be needed. In online learning, such support is not common.

A further problem that has been identified is that goal-setting can discourage a focus on non-goal areas (Ordóñez et al. 2009: 2) and can lead to ‘a focus on reaching the goal rather than on acquiring the skills required to reach it’ (Locke & Latham, 2006: 266). We know that much language learning is messy and incidental. Students do not only learn the particular thing that they are studying at the time (the belief that they do was described by Dewey as ‘the greatest of all pedagogical fallacies’). Goal-setting, even when personalized, runs the risk of promoting tunnel-vision.

The incorporation of personalized goal-setting in online language learning programmes is, in so many ways, a far from straightforward matter. Simply tacking it onto existing programmes is unlikely to result in anything positive: it is not an ‘over-the-counter treatment for motivation’ (Ordóñez et al.:2). Course developers will need to look at ‘the complex interplay between goal-setting and organizational contexts’ (Ordóñez et al. 2009: 16). Motivating students is not simply ‘a matter of the teacher deploying the correct strategies […] it is an intensely interactive process’ (Lamb, M. 2017). More generally, developers need to move away from a positivist and linear view of learning as a technical process where teaching interventions (such as the incorporation of goal-setting, the deployment of gamification elements or the use of a particular algorithm) will lead to predictable student outcomes. As Larry Cuban reminds us, ‘no persuasive body of evidence exists yet to confirm that belief (Cuban, 1986: 88). The most recent research into personalized learning has failed to identify any single element of personalization that can be clearly correlated with improved outcomes (Pane et al., 2015: 28).

In previous posts, I considered learning styles and self-pacing, two aspects of personalized learning that are highly problematic. Personalized goal-setting is no less so.

References

Bandura, A. 1997. Self-efficacy: The exercise of control. New York: W.H. Freeman and Company

Benson, P. 2016. ‘Learner Autonomy’ in Hall, G. (ed.) The Routledge Handbook of English Language Teaching. Abingdon: Routledge. pp.339 – 352

Borg, S. & Al-Busaidi, S. 2012. ‘Teachers’ beliefs and practices regarding learner autonomy’ ELT Journal 66 / 3: 283 – 292

Cavanagh, S. 2014. ‘What Is ‘Personalized Learning’? Educators Seek Clarity’ Education Week http://www.edweek.org/ew/articles/2014/10/22/09pl-overview.h34.html

Cordova, D. I. & Lepper, M. R. 1996. ‘Intrinsic Motivation and the Process of Learning: Beneficial Effects of Contextualization, Personalization, and Choice’ Journal of Educational Psychology 88 / 4: 715 -739

Cuban, L. 1986. Teachers and Machines. New York: Teachers College Press

Dickinson, L. 1987. Self-instruction in Language Learning. Cambridge: Cambridge University Press

Disick, R.S. 1975 Individualizing Language Instruction: Strategies and Methods. New York: Harcourt Brace Jovanovich

Harmer, J. 2012. Essential Teacher Knowledge. Harlow: Pearson Education

Hussey, T. & Smith, P. 2003. ‘The Uses of Learning Outcomes’ Teaching in Higher Education 8 / 3: 357 – 368

Lamb, M. 2017 (in press) ‘The motivational dimension of language teaching’ Language Teaching 50 / 3

Locke, E. A. & Latham, G. P. 2006. ‘New Directions in Goal-Setting Theory’ Current Directions in Psychological Science 15 / 5: 265 – 268

McMahon, M. & Oliver, R. (2001). Promoting self-regulated learning in an on-line environment. In C. Montgomerie & J. Viteli (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2001 (pp. 1299-1305). Chesapeake, VA: AACE

Morrison, B. 2005. ‘Evaluating learning gain in a self-access learning centre’ Language Teaching Research 9 / 3: 267 – 293

Ordóñez, L. D., Schweitzer, M. E., Galinsky, A. D. & Bazerman, M. H. 2009. Goals Gone Wild: The Systematic Side Effects of Over-Prescribing Goal Setting. Harvard Business School Working Paper 09-083

Pane, J. F., Steiner, E. D., Baird, M. D. & Hamilton, L. S. 2015. Continued Progress: Promising Evidence on Personalized Learning. Seattle: Rand Corporation

Pavlenko, A. & Lantolf, J. P. 2000. ‘Second language learning as participation and the (re)construction of selves’ In J.P. Lantolf (ed.), Sociocultural Theory and Second Language Learning. Oxford: Oxford University Press, pp. 155 – 177

Reinders, H. 2012. ‘The end of self-access? From walled garden to public park’ ELT World Online 4: 1 – 5

Rivers, W. M. 1971. ‘Techniques for Developing Proficiency in the Spoken Language in an Individualized Foreign Language program’ in 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. pp. 165 – 169

Ur, P. 1996. A Course in Language Teaching: Practice and Theory. Cambridge: Cambridge University Press

Ur, P. 2012. A Course in English Language Teaching. Cambridge: Cambridge University Press

Vandergriff, I. Second-language Discourse in the Digital World. 2016. Amsterdam: John Benjamins

All aboard …

The point of adaptive learning is that it can personalize learning. When we talk about personalization, mention of learning styles is rarely far away. Jose Ferreira of Knewton (but now ex-CEO Knewton) made his case for learning styles in a blog post that generated a superb and, for Ferreira, embarrassing  discussion in the comments that were subsequently deleted by Knewton. fluentu_learning_stylesFluentU (which I reviewed here) clearly approves of learning styles, or at least sees them as a useful way to market their product, even though it is unclear how their product caters to different styles. Busuu claims to be ‘personalised to fit your style of learning’. Voxy, Inc. (according to their company overview) ‘operates a language learning platform that creates custom curricula for English language learners based on their interests, routines, goals, and learning styles’. Bliu Bliu (which I reviewed here) recommended, in a recent blog post, that learners should ‘find out their language learner type and use it to their advantage’ and suggests, as a starter, trying out ‘Bliu Bliu, where pretty much any learner can find what suits them best’. Memrise ‘uses clever science to adapt to your personal learning style’.  Duolingo’s learning tree ‘effectively rearranges itself to suit individual learning styles’ according to founder, Louis Von Ahn. This list could go on and on.

Learning styles are thriving in ELT coursebooks, too. Here are just three recent examples for learners of various ages. Today! by Todd, D. & Thompson, T. (Pearson, 2014) ‘shapes learning around individual students with graded difficulty practice for mixed-ability classes’ and ‘makes testing mixed-ability classes easier with tests that you can personalise to students’ abilities’.today

Move  it! by Barraclough, C., Beddall, F., Stannett, K., Wildman, J. (Pearson, 2015) offers ‘personalized pathways [which] allow students to optimize their learning outcomes’ and a ‘complete assessment package to monitor students’ learning process’. pearson_move_it

Open Mind Elementary (A2) 2nd edition by Rogers, M., Taylor-Knowles, J. & Taylor-Knowles, S. (Macmillan, 2014) has a whole page devoted to learning styles in the ‘Life Skills’ strand of the course. The scope and sequence describes it in the following terms: ‘Thinking about what you like to do to find your learning style and improve how you learn English’. Here’s the relevant section:macmillan_coursebook

rosenber-learning-stylesMethodology books offer more tips for ways that teachers can cater to different learning styles. Recent examples include Patrycja Kamińska’s  Learning Styles and Second Language Education (Cambridge Scholars, 2014), Tammy Gregersen & Peter D. MacIntyre’s Capitalizing on Language Learners’ Individuality (Multilingual Matters, 2014) and Marjorie Rosenberg’s Spotlight on Learning Styles (Delta Publishing, 2013). Teacher magazines show a continuing interest  in the topic. Humanising Language Teaching and English Teaching Professional are particularly keen. The British Council offers courses about learning styles and its Teaching English website has many articles and lesson plans on the subject (my favourite explains that your students will be more successful if you match your teaching style to their learning styles), as do the websites of all the major publishers. Most ELT conferences will also offer something on the topic.oup_learning_styles

How about language teaching qualifications and frameworks? The Cambridge English Teaching Framework contains a component entitled ‘Understanding learners’ and this specifies as the first part of the component a knowledge of concepts such as learning styles (e.g., visual, auditory, kinaesthetic), multiple intelligences, learning strategies, special needs, affect. Unsurprisingly, the Cambridge CELTA qualification requires successful candidates to demonstrate an awareness of the different learning styles and preferences that adults bring to learning English. The Cambridge DELTA requires successful candidates to accommodate learners according to their different abilities, motivations, and learning styles. The Eaquals Framework for Language Teacher Training and Development requires teachers at Development Phase 2 t0 have the skill of determining and anticipating learners’ language learning needs and learning styles at a range of levels, selecting appropriate ways of finding out about these.

Outside of ELT, learning styles also continue to thrive. Phil Newton (2015 ‘The learning styles myth is thriving in higher education’ Frontiers in Psychology 6: 1908) carried out a survey of educational publications  (higher education) between 2013 and 2016, and found that an overwhelming majority (89%) implicitly or directly endorse the use of learning styles. He also cites research showing that 93% of UK schoolteachers believe that ‘individuals learn better when they receive information in their preferred Learning Style’, with similar figures in other countries. 72% of Higher Education institutions in the US teach ‘learning style theory’ as part of faculty development for online teachers. Advocates of learning styles in English language teaching are not alone.

But, unfortunately, …

In case you weren’t aware of it, there is a rather big problem with learning styles. There is a huge amount of research  which suggests that learning styles (and, in particular, teaching attempts to cater to learning styles) need to be approached with extreme scepticism. Much of this research was published long before the blog posts, advertising copy, books and teaching frameworks (listed above) were written.  What does this research have to tell us?

The first problem concerns learning styles taxonomies. There are three issues here: many people do not fit one particular style, the information used to assign people to styles is often inadequate, and there are so many different styles that it becomes cumbersome to link particular learners to particular styles (Kirschner, P. A. & van Merriënboer, J. J. G. 2013. ‘Do Learners Really Know Best? Urban Legends in Education’ Educational Psychologist, 48 / 3, 169-183). To summarise, given the lack of clarity as to which learning styles actually exist, it may be ‘neither viable nor justified’ for learning styles to form the basis of lesson planning (Hall, G. 2011. Exploring English Language Teaching. Abingdon, Oxon.: Routledge p.140). More detailed information about these issues can be found in the following sources:

Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. 2004. Learning styles and pedagogy in post-16 learning: a systematic and critical review. London: Learning and Skills Research Centre

Dembo, M. H. & Howard, K. 2007. Advice about the use of learning styles: a major myth in education. Journal of College Reading & Learning 37 / 2: 101 – 109

Kirschner, P. A. 2017. Stop propagating the learning styles myth. Computers & Education 106: 166 – 171

Pashler, H., McDaniel, M., Rohrer, D. & Bjork, E. 2008. Learning styles concepts and evidence. Psychological Science in the Public Interest 9 / 3: 105 – 119

Riener, C. & Willingham, D. 2010. The myth of learning styles. Change – The Magazine of Higher Learning

The second problem concerns what Pashler et al refer to as the ‘meshing hypothesis’: the idea that instructional interventions can be effectively tailored to match particular learning styles. Pashler et al concluded that the available taxonomies of student types do not offer any valid help in deciding what kind of instruction to offer each individual. Even in 2008, their finding was not new. Back in 1978, a review of 15 studies that looked at attempts to match learning styles to approaches to first language reading instruction, concluded that modality preference ‘has not been found to interact significantly with the method of teaching’ (Tarver, Sara & M. M. Dawson. 1978. Modality preference and the teaching of reading. Journal of Learning Disabilities 11: 17 – 29). The following year, two other researchers concluded that [the assumption that one can improve instruction by matching materials to children’s modality strengths] appears to lack even minimal empirical support. (Arter, J.A. & Joseph A. Jenkins 1979 ‘Differential diagnosis-prescriptive teaching: A critical appraisal’ Review of Educational Research 49: 517-555). Fast forward 20 years to 1999, and Stahl (Different strokes for different folks?’ American Educator Fall 1999 pp. 1 – 5) was writing the reason researchers roll their eyes at learning styles is the utter failure to find that assessing children’s learning styles and matching to instructional methods has any effect on learning. The area with the most research has been the global and analytic styles […]. Over the past 30 years, the names of these styles have changed – from ‘visual’ to ‘global’ and from ‘auditory’ to ‘analytic’ – but the research results have not changed. For a recent evaluation of the practical applications of learning styles, have a look at Rogowsky, B. A., Calhoun, B. M. & Tallal, P. 2015. ‘Matching Learning Style to Instructional Method: Effects on Comprehension’ Journal of Educational Psychology 107 / 1: 64 – 78. Even David Kolb, the Big Daddy of learning styles, now concedes that there is no strong evidence that teachers should tailor their instruction to their student’s particular learning styles (reported in Glenn, D. 2009. ‘Matching teaching style to learning style may not help students’ The Chronicle of Higher Education). To summarise, the meshing hypothesis is entirely unsupported in the scientific literature. It is a myth (Howard-Jones, P. A. 2014. ‘Neuroscience and education: myths and messages’ Nature Reviews Neuroscience).

This brings me back to the blog posts, advertising blurb, coursebooks, methodology books and so on that continue to tout learning styles. The writers of these texts typically do not acknowledge that there’s a problem of any kind. Are they unaware of the research? Or are they aware of it, but choose not to acknowledge it? I suspect that the former is often the case with the app developers. But if the latter is the case, what  might those reasons be? In the case of teacher training specifications, the reason is probably practical. Changing a syllabus is an expensive and time-consuming operation. But in the case of some of the ELT writers, I suspect that they hang on in there because they so much want to believe.

As Newton (2015: 2) notes, intuitively, there is much that is attractive about the concept of Learning Styles. People are obviously different and Learning Styles appear to offer educators a way to accommodate individual learner differences.  Pashler et al (2009:107) add that another related factor that may play a role in the popularity of the learning-styles approach has to do with responsibility. If a person or a person’s child is not succeeding or excelling in school, it may be more comfortable for the person to think that the educational system, not the person or the child himself or herself, is responsible. That is, rather than attribute one’s lack of success to any lack of ability or effort on one’s part, it may be more appealing to think that the fault lies with instruction being inadequately tailored to one’s learning style. In that respect, there may be linkages to the self-esteem movement that became so influential, internationally, starting in the 1970s. There is no reason to doubt that many of those who espouse learning styles have good intentions.

No one, I think, seriously questions whether learners might not benefit from a wide variety of input styles and learning tasks. People are obviously different. MacIntyre et al (MacIntyre, P.D., Gregersen, T. & Clément, R. 2016. ‘Individual Differences’ in Hall, G. (ed.) The Routledge Handbook of English Language Teaching. Abingdon, Oxon: Routledge, pp.310 – 323, p.319) suggest that teachers might consider instructional methods that allow them to capitalise on both variety and choice and also help learners find ways to do this for themselves inside and outside the classroom. Jill Hadfield (2006. ‘Teacher Education and Trainee Learning Style’ RELC Journal 37 / 3: 369 – 388) recommends that we design our learning tasks across the range of learning styles so that our trainees can move across the spectrum, experiencing both the comfort of matching and the challenge produced by mismatching. But this is not the same thing as claiming that identification of a particular learning style can lead to instructional decisions. The value of books like Rosenberg’s Spotlight on Learning Styles lies in the wide range of practical suggestions for varying teaching styles and tasks. They contain ideas of educational value: it is unfortunate that the theoretical background is so thin.

In ELT things are, perhaps, beginning to change. Russ Mayne’s blog post Learning styles: facts and fictions in 2012 got a few heads nodding, and he followed this up 2 years later with a presentation at IATEFL looking at various aspects of ELT, including learning styles, which have little or no scientific credibility. Carol Lethaby and Patricia Harries gave a talk at IATEFL 2016, Changing the way we approach learning styles in teacher education, which was also much discussed and shared online. They also had an article in ELT Journal called Learning styles and teacher training: are we perpetuating neuromyths? (2016 ELTJ 70 / 1: 16 – 27). Even Pearson, in a blog post of November 2016, (Mythbusters: A review of research on learning styles) acknowledges that there is a shocking lack of evidence to support the core learning styles claim that customizing instruction based on students’ preferred learning styles produces better learning than effective universal instruction, concluding that  it is impossible to recommend learning styles as an effective strategy for improving learning outcomes.

 

 

Chatbots

Posted: December 1, 2016 in ed tech
Tags: , , , , , , , ,

 

Here’s a conversation I had recently with Cleverbot.

cleverbot

User (me): Hi Cleverbot. What are your views on the future of chatbots in language learning?

Cleverbot: Are you talking to yourself?

User: No, I’m asking you a question.

Cleverbot: Then what’s the question again? I forgot.

User: What are your views on the future of chatbots in language learning?

Cleverbot: Don’t you want me to finish my story?

User: No, I want you to answer my question.

Cleverbot: What is your question?

User: Do you think there is a future for chatbots in language education?

Cleverbot: Sure, if you are talking about the moon.

aliceI also tried to have a conversation with Alice, the avatar from EFL Classroom Bot, listed by Larry Ferlazzo as one of ‘the best online chatbots for practicing English’. I didn’t get any more sense out of her than out of Cleverbot.

Chatbots, apparently, are the next big thing. Again. David Mattin, head of trends and insights at trendwatching.com, writes (in the September 2016 issue of ‘Business Life’) that ‘the chatbot revolution is coming’ and that chatbots are a step towards the dream of an interface between user and technology that is so intuitive that the interface ‘simply fades away’. Chatbots have been around for some time. Remember Clippy – the Microsoft Office bot in the late 1990s – which you had to disable in order to stop yourself punching your computer screen? Since then, bots have become ubiquitous. There have been problems, such as Microsoft’s Tay bot that had to be taken down after sixteen hours earlier this year, when, after interacting with other Twitter users, it developed into an abusive Nazi. But chatbots aren’t going away and you’ve probably interacted with one to book a taxi, order food or attempt to talk to your bank. In September this year, the Guardian described them as ‘the talk of the town’ and ‘hot property in Silicon Valley’.

The real interest in chatbots is not, however, in the ‘exciting interface’ possibilities (both user interface and user experience remain pretty crude), but in the way that they are leaner, sit comfortably with the things we actually do on a phone and the fact that they offer a way of cutting out the high fees that developers have to pay to app stores . After so many start-up failures, chatbots offer a glimmer of financial hope to developers.

It’s no surprise, of course, to find the world of English language teaching beginning to sit up and take notice of this technology. A 2012 article by Ben Lehtinen in PeerSpectives enthuses about the possibilities in English language learning and reports the positive feedback of the author’s own students. ELTJam, so often so quick off the mark, developed an ELT Bot over the course of a hackathon weekend in March this year. Disappointingly, it wasn’t really a bot – more a case of humans pretending to be a bot pretending to be humans – but it probably served its exploratory purpose. duolingoAnd a few months ago Duolingo began incorporating bots. These are currently only available for French, Spanish and German learners in the iPhone app, so I haven’t been able to try it out and evaluate it. According to an infomercial in TechCrunch, ‘to make talking to the bots a bit more compelling, the company tried to give its different bots a bit of personality. There’s Chef Robert, Renee the Driver and Officer Ada, for example. They will react differently to your answers (and correct you as necessary), but for the most part, the idea here is to mimic a real conversation. These bots also allow for a degree of flexibility in your answers that most language-learning software simply isn’t designed for. There are plenty of ways to greet somebody, for example, but most services will often only accept a single answer. When you’re totally stumped for words, though, Duolingo offers a ‘help my reply’ button with a few suggested answers.’ In the last twelve months or so, Duolingo has considerably improved its ability to recognize multiple correct ways of expressing a particular idea, and its ability to recognise alternative answers to its translation tasks. However, I’m highly sceptical about its ability to mimic a real conversation any better than Cleverbot or Alice the EFL Bot, or its ability to provide systematically useful corrections.

My reasons lie in the current limitations of AI and NLP (Natural Language Processing). In a nutshell, we simply don’t know how to build a machine that can truly understand human language. Limited exchanges in restricted domains can be done pretty well (such as the early chatbot that did a good job of simulating an encounter with an evasive therapist, or, more recently ordering a taco and having a meaningless, but flirty conversation with a bot), but despite recent advances in semantic computing, we’re a long way from anything that can mimic a real conversation. As Audrey Watters puts it, we’re not even close.

When it comes to identifying language errors made by language learners, we’re not really much better off. Apps like Grammarly are not bad at identifying grammatical errors (but not good enough to be reliable), but pretty hopeless at dealing with lexical appropriacy. Much more reliable feedback to learners can be offered when the software is trained on particular topics and text types. Write & Improve does this with a relatively small selection of Cambridge English examination tasks, but a free conversation ….? Forget it.

So, how might chatbots be incorporated into language teaching / learning? A blog post from December 2015 entitled AI-powered chatbots and the future of language learning suggests one plausible possibility. Using an existing messenger service, such as WhatsApp or Telegram, an adaptive chatbot would send tasks (such as participation in a conversation thread with a predetermined topic, register, etc., or pronunciation practice or translation exercises) to a learner, provide feedback and record the work for later recycling. At the same time, the bot could send out reminders of work that needs to be done or administrative tasks that must be completed.

Kat Robb has written a very practical article about using instant messaging in English language classrooms. Her ideas are interesting (although I find the idea of students in a F2F classroom messaging each other slightly bizarre) and it’s easy to imagine ways in which her activities might be augmented with chatbot interventions. The Write & Improve app, mentioned above, could deploy a chatbot interface to give feedback instead of the flat (and, in my opinion, perfectly adequate) pop-up boxes currently in use. Come to think of it, more or less any digital language learning tool could be pimped up with a bot. Countless revisions can be envisioned.

But the overwhelming question is: would it be worth it? Bots are not likely, any time soon, to revolutionise language learning. What they might just do, however, is help to further reduce language teaching to a series of ‘mechanical and scripted gestures’. More certain is that a lot of money will be thrown down the post-truth edtech drain. Then, in the not too distant future, this latest piece of edtech will fall into the trough of disillusionment, to be replaced by the latest latest thing.

 

 

Screenshot_2016-04-29-09-48-05I call Lern Deutsch a vocabulary app, although it’s more of a game than anything else. Developed by the Goethe Institute, the free app was probably designed primarily as a marketing tool rather than a serious attempt to develop an educational language app. It’s available for speakers of Arabic, English, Spanish, Italian, French, Italian, Portuguese and Russian. It’s aimed at A1 learners.

Users of the app create an avatar and roam around a virtual city, learning new vocabulary and practising situational language. They can interact in language challenges with other players. As they explore, they earn Goethe coins, collect accessories for their avatars and progress up a leader board.Screenshot_2016-04-29-09-50-12

As they explore the virtual city, populated by other avatars, they find objects that can be clicked on to add to their vocabulary list. They hear a recording of an example sentence containing the target word, with the word gapped and three multiple choice possibilities. They are then required to type the missing word (see the image below). After collecting a certain number of words, they complete exercises which include the following task types:

  • Jumbled sentences
  • Audio recording of individual words and multiple choice selection
  • Gapped sentences with multiple choice answers
  • Dictation
  • Example sentences containing target item and multiple choice pictures
  • Typing sentences which are buried in a string of random letters

Screenshot_2016-05-02-14-23-07Screenshot_2016-05-02-14-26-13

Screenshot_2016-05-02-14-27-21Screenshot_2016-05-02-14-31-49

 

 

 

 

 

 

 

 

 

The developers have focused their attention on providing variety: engagement and ‘fun’ override other considerations. But how does the app stand up as a language learning tool? Surprisingly, for something developed by the Goethe Institute, it’s less than impressive.

The words that you collect as you navigate the virtual city are all nouns (Hotel, Auto, Mann, Banane, etc), but some (e.g. Sehenswurdigkeit) seem out of level. Any app that uses illustrations as the basic means of conveying meaning runs into problems when it moves away from concrete nouns, but a diet of nouns only (as here) is of necessarily limited value. Other parts of speech are introduced via the example sentences, but no help with meaning is provided so when you come across the word for ‘egg’, for example, your example sentence is ‘Ich möchte das Frühstück mit Ei.’ It’s all very well embedding the target vocabulary in example sentences that have a functional value, but example sentences are only of value if they are understandable: the app badly needs a look-up function for the surrounding language.

The practice exercises are varied, too, but they also vary in their level of difficulty. It makes sense to do receptive / recognition tasks before productive ones, but there is no evidence that I could see of pedagogical considerations of this kind. Neither does there seem to be any spaced repetition at work: the app is driven by the needs of the game design rather than any learning principles.

It’s unclear to me who the app is for. The functional language that is presented is adult: the situations are adult situations (buying a bed, booking a hotel room, ordering a beer). However, the graphic design and the gamification features are juvenile (adding a pirate patch to your avatar, for example).

The lack of attention to the business of learning is especially striking in the English of the English language version that I used. The number of examples of dodgy English that I came across do not inspire confidence.

  • Quite alright! You win your first Goethe coin.
  • What sightseeings do you spot in the city center and the train station?
  • Have a picknick in the park. You now have a picnic in the park with the musician.
  • You still search for your teacher. Whom do you meet in the park? What do they work?

 

All in all, it’s an interesting example of a gamified approach to language, and other app developers may find ideas here that they could do something with. It’s of less interest, though, to anyone who wants to learn a bit of German.

In ELT circles, ‘behaviourism’ is a boo word. In the standard history of approaches to language teaching (characterised as a ‘procession of methods’ by Hunter & Smith 2012: 432[1]), there were the bad old days of behaviourism until Chomsky came along, savaged the theory in his review of Skinner’s ‘Verbal Behavior’, and we were all able to see the light. In reality, of course, things weren’t quite like that. The debate between Chomsky and the behaviourists is far from over, behaviourism was not the driving force behind the development of audiolingual approaches to language teaching, and audiolingualism is far from dead. For an entertaining and eye-opening account of something much closer to reality, I would thoroughly recommend a post on Russ Mayne’s Evidence Based ELT blog, along with the discussion which follows it. For anyone who would like to understand what behaviourism is, was, and is not (before they throw the term around as an insult), I’d recommend John A. Mills’ ‘Control: A History of Behavioral Psychology’ (New York University Press, 1998) and John Staddon’s ‘The New Behaviorism 2nd edition’ (Psychology Press, 2014).

There is a close connection between behaviourism and adaptive learning. Audrey Watters, no fan of adaptive technology, suggests that ‘any company touting adaptive learning software’ has been influenced by Skinner. In a more extended piece, ‘Education Technology and Skinner’s Box, Watters explores further her problems with Skinner and the educational technology that has been inspired by behaviourism. But writers much more sympathetic to adaptive learning, also see close connections to behaviourism. ‘The development of adaptive learning systems can be considered as a transformation of teaching machines,’ write Kara & Sevim[2] (2013: 114 – 117), although they go on to point out the differences between the two. Vendors of adaptive learning products, like DreamBox Learning©, are not shy of associating themselves with behaviourism: ‘Adaptive learning has been with us for a while, with its history of adaptive learning rooted in cognitive psychology, beginning with the work of behaviorist B.F. Skinner in the 1950s, and continuing through the artificial intelligence movement of the 1970s.’

That there is a strong connection between adaptive learning and behaviourism is indisputable, but I am not interested in attempting to establish the strength of that connection. This would, in any case, be an impossible task without some reductionist definition of both terms. Instead, my interest here is to explore some of the parallels between the two, and, in the spirit of the topic, I’d like to do this by comparing the behaviours of behaviourists and adaptive learning scientists.

Data and theory

Both behaviourism and adaptive learning (in its big data form) are centrally concerned with behaviour – capturing and measuring it in an objective manner. In both, experimental observation and the collection of ‘facts’ (physical, measurable, behavioural occurrences) precede any formulation of theory. John Mills’ description of behaviourists could apply equally well to adaptive learning scientists: theory construction was a seesaw process whereby one began with crude outgrowths from observations and slowly created one’s theory in such a way that one could make more and more precise observations, building those observations into the theory at each stage. No behaviourist ever considered the possibility of taking existing comprehensive theories of mind and testing or refining them.[3]

Positivism and the panopticon

Both behaviourism and adaptive learning are pragmatically positivist, believing that truth can be established by the study of facts. J. B. Watson, the founding father of behaviourism whose article ‘Psychology as the Behaviorist Views Itset the behaviourist ball rolling, believed that experimental observation could ‘reveal everything that can be known about human beings’[4]. Jose Ferreira of Knewton has made similar claims: We get five orders of magnitude more data per user than Google does. We get more data about people than any other data company gets about people, about anything — and it’s not even close. We’re looking at what you know, what you don’t know, how you learn best. […] We know everything about what you know and how you learn best because we get so much data. Digital data analytics offer something that Watson couldn’t have imagined in his wildest dreams, but he would have approved.

happiness industryThe revolutionary science

Big data (and the adaptive learning which is a part of it) is presented as a game-changer: The era of big data challenges the way we live and interact with the world. […] Society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality[5]. But the reverence for technology and the ability to reach understandings of human beings by capturing huge amounts of behavioural data was adumbrated by Watson a century before big data became a widely used term. Watson’s 1913 lecture at Columbia University was ‘a clear pitch’[6] for the supremacy of behaviourism, and its potential as a revolutionary science.

Prediction and controlnudge

The fundamental point of both behaviourism and adaptive learning is the same. The research practices and the theorizing of American behaviourists until the mid-1950s, writes Mills[7] were driven by the intellectual imperative to create theories that could be used to make socially useful predictions. Predictions are only useful to the extent that they can be used to manipulate behaviour. Watson states this very baldly: the theoretical goal of psychology is the prediction and control of behaviour[8]. Contemporary iterations of behaviourism, such as behavioural economics or nudge theory (see, for example, Thaler & Sunstein’s best-selling ‘Nudge’, Penguin Books, 2008), or the British government’s Behavioural Insights Unit, share the same desire to divert individual activity towards goals (selected by those with power), ‘without either naked coercion or democratic deliberation’[9]. Jose Ferreira of Knewton has an identical approach: We can predict failure in advance, which means we can pre-remediate it in advance. We can say, “Oh, she’ll struggle with this, let’s go find the concept from last year’s materials that will help her not struggle with it.” Like the behaviourists, Ferreira makes grand claims about the social usefulness of his predict-and-control technology: The end is a really simple mission. Only 22% of the world finishes high school, and only 55% finish sixth grade. Those are just appalling numbers. As a species, we’re wasting almost four-fifths of the talent we produce. […] I want to solve the access problem for the human race once and for all.

Ethics

Because they rely on capturing large amounts of personal data, both behaviourism and adaptive learning quickly run into ethical problems. Even where informed consent is used, the subjects must remain partly ignorant of exactly what is being tested, or else there is the fear that they might adjust their behaviour accordingly. The goal is to minimise conscious understanding of what is going on[10]. For adaptive learning, the ethical problem is much greater because of the impossibility of ensuring the security of this data. Everything is hackable.

Marketing

Behaviourism was seen as a god-send by the world of advertising. J. B. Watson, after a front-page scandal about his affair with a student, and losing his job at John Hopkins University, quickly found employment on Madison Avenue. ‘Scientific advertising’, as practised by the Mad Men from the 1920s onwards, was based on behaviourism. The use of data analytics by Google, Amazon, et al is a direct descendant of scientific advertising, so it is richly appropriate that adaptive learning is the child of data analytics.

[1] Hunter, D. and Smith, R. (2012) ‘Unpacking the past: “CLT” through ELTJ keywords’. ELT Journal, 66/4: 430-439.

[2] Kara, N. & Sevim, N. 2013. ‘Adaptive learning systems: beyond teaching machines’, Contemporary Educational Technology, 4(2), 108-120

[3] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.5

[4] Davies, W. (2015) The Happiness Industry. London: Verso. p.91

[5] Mayer-Schönberger, V. & Cukier, K. (2013) Big Data. London: John Murray, p.7

[6] Davies, W. (2015) The Happiness Industry. London: Verso. p.87

[7] Mills, J. A. (1998) Control: A History of Behavioral Psychology. New York: New York University Press, p.2

[8] Watson, J. B. (1913) ‘Behaviorism as the Psychologist Views it’ Psychological Review 20: 158

[9] Davies, W. (2015) The Happiness Industry. London: Verso. p.88

[10] Davies, W. (2015) The Happiness Industry. London: Verso. p.92