Archive for the ‘Online learning’ Category

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

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

Lava flow Hawaii

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

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

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

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

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

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

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

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

2 Technological innovation is good and necessary

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

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

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

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

3 Technological innovations are best driven by the private sector

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

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

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

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

4 Accountability is crucial

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

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

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

 

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

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

Big_data_Google_Trend

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

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

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

Macmillan_catalogue_2015

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

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

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

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

 

 

 

 

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

The use of big data and analytics in education continues to grow.

A vast apparatus of measurement is being developed to underpin national education systems, institutions and the actions of the individuals who occupy them. […] The presence of digital data and software in education is being amplified through massive financial and political investment in educational technologies, as well as huge growth in data collection and analysis in policymaking practices, extension of performance measurement technologies in the management of educational institutions, and rapid expansion of digital methodologies in educational research. To a significant extent, many of the ways in which classrooms function, educational policy departments and leaders make decisions, and researchers make sense of data, simply would not happen as currently intended without the presence of software code and the digital data processing programs it enacts. (Williamson, 2017: 4)

The most common and successful use of this technology so far has been in the identification of students at risk of dropping out of their courses (Jørno & Gynther, 2018: 204). The kind of analytics used in this context may be called ‘academic analytics’ and focuses on educational processes at the institutional level or higher (Gelan et al, 2018: 3). However, ‘learning analytics’, the capture and analysis of learner and learning data in order to personalize learning ‘(1) through real-time feedback on online courses and e-textbooks that can ‘learn’ from how they are used and ‘talk back’ to the teacher, and (2) individualization and personalization of the educational experience through adaptive learning systems that enable materials to be tailored to each student’s individual needs through automated real-time analysis’ (Mayer-Schönberger & Cukier, 2014) has become ‘the main keyword of data-driven education’ (Williamson, 2017: 10). See my earlier posts on this topic here and here and here.

Learning with big dataNear the start of Mayer-Schönberger and Cukier’s enthusiastic sales pitch (Learning with Big Data: The Future of Education) for the use of big data in education, there is a discussion of Duolingo. They quote Luis von Ahn, the founder of Duolingo, as saying ‘there has been little empirical work on what is the best way to teach a foreign language’. This is so far from the truth as to be laughable. Von Ahn’s comment, along with the Duolingo product itself, is merely indicative of a lack of awareness of the enormous amount of research that has been carried out. But what could the data gleaned from the interactions of millions of users with Duolingo tell us of value? The example that is given is the following. Apparently, ‘in the case of Spanish speakers learning English, it’s common to teach pronouns early on: words like “he,” “she,” and “it”.’ But, Duolingo discovered, ‘the term “it” tends to confuse and create anxiety for Spanish speakers, since the word doesn’t easily translate into their language […] Delaying the introduction of “it” until a few weeks later dramatically improves the number of people who stick with learning English rather than drop out.’ Was von Ahn unaware of the decades of research into language transfer effects? Did von Ahn (who grew up speaking Spanish in Guatemala) need all this data to tell him that English personal pronouns can cause problems for Spanish learners of English? Was von Ahn unaware of the debates concerning the value of teaching isolated words (especially grammar words!)?

The area where little empirical research has been done is not in different ways of learning another language: it is in the use of big data and learning analytics to assist language learning. Claims about the value of these technologies in language learning are almost always speculative – they are based on comparison to other school subjects (especially, mathematics). Gelan et al (2018: 2), who note this lack of research, suggest that ‘understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways’ (my italics). Reinders (2018: 81) writes ‘that analysis of prior experiences with certain groups or certain courses may help to identify key moments at which students need to receive more or different support. Analysis of student engagement and performance throughout a course may help with early identification of learning problems and may prompt early intervention’ (italics added). But there is some research out there, and it’s worth having a look at. Most studies that have collected learner-tracking data concern glossary use for reading comprehension and vocabulary retention (Gelan et al, 2018: 5), but a few have attempted to go further in scope.

Volk et al (2015) looked at the behaviour of the 20,000 students per day using the platform which accompanies ‘More!’ (Gerngross et al. 2008) to do their English homework for Austrian lower secondary schools. They discovered that

  • the exercises used least frequently were those that are located further back in the course book
  • usage is highest from Monday to Wednesday, declining from Thursday, with a rise again on Sunday
  • most interaction took place between 3:00 and 5:00 pm.
  • repetition of exercises led to a strong improvement in success rate
  • students performed better on multiple choice and matching exercises than they did where they had to produce some language

The authors of this paper conclude by saying that ‘the results of this study suggest a number of new avenues for research. In general, the authors plan to extend their analysis of exercise results and applied exercises to the population of all schools using the online learning platform more-online.at. This step enables a deeper insight into student’s learning behaviour and allows making more generalizing statements.’ When I shared these research findings with the Austrian lower secondary teachers that I work with, their reaction was one of utter disbelief. People get paid to do this research? Why not just ask us?

More useful, more actionable insights may yet come from other sources. For example, Gu Yueguo, Pro-Vice-Chancellor of the Beijing Foreign Studies University has announced the intention to set up a national Big Data research center, specializing in big data-related research topics in foreign language education (Yu, 2015). Meanwhile, I’m aware of only one big research project that has published its results. The EC Erasmus+ VITAL project (Visualisation Tools and Analytics to monitor Online Language Learning & Teaching) was carried out between 2015 and 2017 and looked at the learning trails of students from universities in Belgium, Britain and the Netherlands. It was discovered (Gelan et al, 2015) that:

  • students who did online exercises when they were supposed to do them were slightly more successful than those who were late carrying out the tasks
  • successful students logged on more often, spent more time online, attempted and completed more tasks, revisited both exercises and theory pages more frequently, did the work in the order in which it was supposed to be done and did more work in the holidays
  • most students preferred to go straight into the assessed exercises and only used the theory pages when they felt they needed to; successful students referred back to the theory pages more often than unsuccessful students
  • students made little use of the voice recording functionality
  • most online activity took place the day before a class and the day of the class itself

EU funding for this VITAL project amounted to 274,840 Euros[1]. The technology for capturing the data has been around for a long time. In my opinion, nothing of value, or at least nothing new, has been learnt. Publishers like Pearson and Cambridge University Press who have large numbers of learners using their platforms have been capturing learning data for many years. They do not publish their findings and, intriguingly, do not even claim that they have learnt anything useful / actionable from the data they have collected. Sure, an exercise here or there may need to be amended. Both teachers and students may need more support in using the more open-ended functionalities of the platforms (e.g. discussion forums). But are they getting ‘unprecedented insights into what works and what doesn’t’ (Mayer-Schönberger & Cukier, 2014)? Are they any closer to building better pedagogies? On the basis of what we know so far, you wouldn’t want to bet on it.

It may be the case that all the learning / learner data that is captured could be used in some way that has nothing to do with language learning. Show me a language-learning app developer who does not dream of monetizing the ‘behavioural surplus’ (Zuboff, 2018) that they collect! But, for the data and analytics to be of any value in guiding language learning, it must lead to actionable insights. Unfortunately, as Jørno & Gynther (2018: 198) point out, there is very little clarity about what is meant by ‘actionable insights’. There is a danger that data and analytics ‘simply gravitates towards insights that confirm longstanding good practice and insights, such as “students tend to ignore optional learning activities … [and] focus on activities that are assessed” (Jørno & Gynther, 2018: 211). While this is happening, the focus on data inevitably shapes the way we look at the object of study (i.e. language learning), ‘thereby systematically excluding other perspectives’ (Mau, 2019: 15; see also Beer, 2019). The belief that tech is always the solution, that all we need is more data and better analytics, remains very powerful: it’s called techno-chauvinism (Broussard, 2018: 7-8).

References

Beer, D. 2019. The Data Gaze. London: Sage

Broussard, M. 2018. Artificial Unintelligence. Cambridge, Mass.: MIT Press

Gelan, A., Fastre, G., Verjans, M., Martin, N., Jansenswillen, G., Creemers, M., Lieben, J., Depaire, B. & Thomas, M. 2018. ‘Affordances and limitations of learning analytics for computer­assisted language learning: a case study of the VITAL project’. Computer Assisted Language Learning. pp. 1­26. http://clok.uclan.ac.uk/21289/

Gerngross, G., Puchta, H., Holzmann, C., Stranks, J., Lewis-Jones, P. & Finnie, R. 2008. More! 1 Cyber Homework. Innsbruck, Austria: Helbling

Jørno, R. L. & Gynther, K. 2018. ‘What Constitutes an “Actionable Insight” in Learning Analytics?’ Journal of Learning Analytics 5 (3): 198 – 221

Mau, S. 2019. The Metric Society. Cambridge: Polity Press

Mayer-Schönberger, V. & Cukier, K. 2014. Learning with Big Data: The Future of Education. New York: Houghton Mifflin Harcourt

Reinders, H. 2018. ‘Learning analytics for language learning and teaching’. JALT CALL Journal 14 / 1: 77 – 86 https://files.eric.ed.gov/fulltext/EJ1177327.pdf

Volk, H., Kellner, K. & Wohlhart, D. 2015. ‘Learning Analytics for English Language Teaching.’ Journal of Universal Computer Science, Vol. 21 / 1: 156-174 http://www.jucs.org/jucs_21_1/learning_analytics_for_english/jucs_21_01_0156_0174_volk.pdf

Williamson, B. 2017. Big Data in Education. London: Sage

Yu, Q. 2015. ‘Learning Analytics: The next frontier for computer assisted language learning in big data age’ SHS Web of Conferences, 17 https://www.shs-conferences.org/articles/shsconf/pdf/2015/04/shsconf_icmetm2015_02013.pdf

Zuboff, S. 2019. The Age of Surveillance Capitalism. London: Profile Books

 

[1] See https://ec.europa.eu/programmes/erasmus-plus/sites/erasmusplus2/files/ka2-2015-he_en.pdf

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.

 

9781316629178More and more language learning is taking place, fully or partially, on online platforms and the affordances of these platforms for communicative interaction are exciting. Unfortunately, most platform-based language learning experiences are a relentless diet of drag-and-drop, drag-till-you-drop grammar or vocabulary gap-filling. The chat rooms and discussion forums that the platforms incorporate are underused or ignored. Lindsay Clandfield and Jill Hadfield’s new book is intended to promote online interaction between and among learners and the instructor, rather than between learners and software.

Interaction Online is a recipe book, containing about 80 different activities (many more if you consider the suggested variations). Subtitled ‘Creative activities for blended learning’, the authors have selected and designed the activities so that any teacher using any degree of blend (from platform-based instruction to occasional online homework) will be able to use them. The activities do not depend on any particular piece of software, as they are all designed for basic tools like Facebook, Skype and chat rooms. Indeed, almost every single activity could be used, sometimes with some slight modification, for teachers in face-to-face settings.

A recipe book must be judged on the quality of the activities it contains, and the standard here is high. They range from relatively simple, short activities to much longer tasks which will need an hour or more to complete. An example of the former is a sentence-completion activity (‘Don’t you hate / love it when ….?’ – activity 2.5). As an example of the latter, there is a complex problem-solving information-gap where students have to work out the solution to a mystery (activity 6.13), an activity which reminds me of some of the material in Jill Hadfield’s much-loved Communication Games books.

In common with many recipe books, Interaction Online is not an easy book to use, in the sense that it is hard to navigate. The authors have divided up the tasks into five kinds of interaction (personal, factual, creative, critical and fanciful), but it is not always clear precisely why one activity has been assigned to one category rather than another. In any case, the kind of interaction is likely to be less important to many teachers than the kind and amount of language that will be generated (among other considerations), and the table of contents is less than helpful. The index at the back of the book helps to some extent, but a clearer tabulation of activities by interaction type, level, time required, topic and language focus (if any) would be very welcome. Teachers will need to devise their own system of referencing so that they can easily find activities they want to try out.

Again, like many recipe books, Interaction Online is a mix of generic task-types and activities that will only work with the supporting materials that are provided. Teachers will enjoy the latter, but will want to experiment with the former and it is these generic task-types that they are most likely to add to their repertoire. In activity 2.7 (‘Foodies’ – personal interaction), for example, students post pictures of items of food and drink, to which other students must respond with questions. The procedure is clear and effective, but, as the authors note, the pictures could be of practically anything. ‘From pictures to questions’ might be a better title for the activity than ‘Foodies’. Similarly, activity 3.4 (‘Find a festival’ –factual interaction) uses a topic (‘festivals’), rather than a picture, to generate questions and responses. The procedure is slightly different from activity 2.7, but the interactional procedures of the two activities could be swapped around as easily as the topics could be changed.

Perhaps the greatest strength of this book is the variety of interactional procedures that is suggested. The majority of activities contain (1) suggestions for a stimulus, (2) suggestions for managing initial responses to this stimulus, and (3) suggestions for further interaction. As readers work their way through the book, they will be struck by similarities between the activities. The final chapter (chapter 8: ‘Task design’) provides an excellent summary of the possibilities of communicative online interaction, and more experienced teachers may want to read this chapter first.

Chapter 7 provides a useful, but necessarily fairly brief, overview of considerations regarding feedback and assessment

Overall, Interaction Online is a very rich resource, and one that will be best mined in multiple visits. For most readers, I would suggest an initial flick through and a cherry-picking of a small number of activities to try out. For materials writers and course designers, a better starting point may be the final two chapters, followed by a sampling of activities. For everyone, though, Online Interaction is a powerful reminder that technology-assisted language learning could and should be far more than what is usually is.

(This review first appeared in the International House Journal of Education and Development.)

 

I’m a sucker for meta-analyses, those aggregates of multiple studies that generate an effect size, and I am even fonder of meta-meta analyses. I skip over the boring stuff about inclusion criteria and statistical procedures and zoom in on the results and discussion. I’ve pored over Hattie (2009) and, more recently, Dunlosky et al (2013), and quoted both more often than is probably healthy. Hardly surprising, then, that I was eager to read Luke Plonsky and Nicole Ziegler’s ‘The CALL–SLA interface: insights from a second-order synthesis’ (Plonsky & Ziegler, 2016), an analysis of nearly 30 meta-analyses (later whittled down to 14) looking at the impact of technology on L2 learning. The big question they were looking to find an answer to? How effective is computer-assisted language learning compared to face-to-face contexts?

Plonsky & Ziegler

Plonsky and Ziegler found that there are unequivocally ‘positive effects of technology on language learning’. In itself, this doesn’t really tell us anything, simply because there are too many variables. It’s a statistical soundbite, ripe for plucking by anyone with an edtech product to sell. Much more useful is to understand which technologies used in which ways are likely to have a positive effect on learning. It appears from Plonsky and Ziegler’s work that the use of CALL glosses (to develop reading comprehension and vocabulary development) provides the strongest evidence of technology’s positive impact on learning. The finding is reinforced by the fact that this particular technology was the most well-represented research area in the meta-analyses under review.

What we know about glosses

gloss_gloss_WordA gloss is ‘a brief definition or synonym, either in L1 or L2, which is provided with [a] text’ (Nation, 2013: 238). They can take many forms (e.g. annotations in the margin or at the foot a printed page), but electronic or CALL glossing is ‘an instant look-up capability – dictionary or linked’ (Taylor, 2006; 2009) which is becoming increasingly standard in on-screen reading. One of the most widely used is probably the translation function in Microsoft Word: here’s the French gloss for the word ‘gloss’.

Language learning tools and programs are making increasing use of glosses. Here are two examples. The first is Lingro , a dictionary tool that learners can have running alongside any webpage: clicking on a word brings up a dictionary entry, and the word can then be exported into a wordlist which can be practised with spaced repetition software. The example here is using the English-English dictionary, but a number of bilingual pairings are available. The second is from Bliu Bliu , a language learning app that I unkindly reviewed here .Lingro_example

Bliu_Bliu_example_2

So, what did Plonsky and Ziegler discover about glosses? There were two key takeways:

  • both L1 and L2 CALL glossing can be beneficial to learners’ vocabulary development (Taylor, 2006, 2009, 2013)
  • CALL / electronic glosses lead to more learning gains than paper-based glosses (p.22)

On the surface, this might seem uncontroversial, but if you took a good look at the three examples (above) of online glosses, you’ll be thinking that something is not quite right here. Lingro’s gloss is a fairly full dictionary entry: it contains too much information for the purpose of a gloss. Cognitive Load Theory suggests that ‘new information be provided concisely so as not to overwhelm the learner’ (Khezrlou et al, 2017: 106): working out which definition is relevant here (the appropriate definition is actually the sixth in this list) will overwhelm many learners and interfere with the process of reading … which the gloss is intended to facilitate. In addition, the language of the definitions is more difficult than the defined item. Cognitive load is, therefore, further increased. Lingro needs to use a decent learner’s dictionary (with a limited defining vocabulary), rather than relying on the free Wiktionary.

Nation (2013: 240) cites research which suggests that a gloss is most effective when it provides a ‘core meaning’ which users will have to adapt to what is in the text. This is relatively unproblematic, from a technological perspective, but few glossing tools actually do this. The alternative is to use NLP tools to identify the context-specific meaning: our ability to do this is improving all the time but remains some way short of total accuracy. At the very least, NLP tools are needed to identify part of speech (which will increase the probability of hitting the right meaning). Bliu Bliu gets things completely wrong, confusing the verb and the adjective ‘own’.

Both Lingro and Bliu Bliu fail to meet the first requirement of a gloss: ‘that it should be understood’ (Nation, 2013: 239). Neither is likely to contribute much to the vocabulary development of learners. We will need to modify Plonsky and Ziegler’s conclusions somewhat: they are contingent on the quality of the glosses. This is not, however, something that can be assumed …. as will be clear from even the most cursory look at the language learning tools that are available.

Nation (2013: 447) also cites research that ‘learning is generally better if the meaning is written in the learner’s first language. This is probably because the meaning can be easily understood and the first language meaning already has many rich associations for the learner. Laufer and Shmueli (1997) found that L1 glosses are superior to L2 glosses in both short-term and long-term (five weeks) retention and irrespective of whether the words are learned in lists, sentences or texts’. Not everyone agrees, and a firm conclusion either way is probably not possible: learner variables (especially learner preferences) preclude anything conclusive, which is why I’ve highlighted Nation’s use of the word ‘generally’. If we have a look at Lingro’s bilingual gloss, I think you’ll agree that the monolingual and bilingual glosses are equally unhelpful, equally unlikely to lead to better learning, whether it’s vocabulary acquisition or reading comprehension.bilingual lingro

 

The issues I’ve just discussed illustrate the complexity of the ‘glossing’ question, but they only scratch the surface. I’ll dig a little deeper.

1 Glosses are only likely to be of value to learning if they are used selectively. Nation (2013: 242) suggests that ‘it is best to assume that the highest density of glossing should be no more than 5% and preferably around 3% of the running words’. Online glosses make the process of look-up extremely easy. This is an obvious advantage over look-ups in a paper dictionary, but there is a real risk, too, that the ease of online look-up encourages unnecessary look-ups. More clicks do not always lead to more learning. The value of glosses cannot therefore be considered independently of a consideration of the level (i.e. appropriacy) of the text that they are being used with.

2 A further advantage of online glosses is that they can offer a wide range of information, e.g. pronunciation, L1 translation, L2 definition, visuals, example sentences. The review of literature by Khezrlou et al (2017: 107) suggests that ‘multimedia glosses can promote vocabulary learning but uncertainty remains as to whether they also facilitate reading comprehension’. Barcroft (2015), however, warns that pictures may help learners with meaning, but at the cost of retention of word form, and the research of Boers et al did not find evidence to support the use of pictures. Even if we were to accept the proposition that pictures might be helpful, we would need to hold two caveats. First, the amount of multimodal support should not lead to cognitive overload. Second, pictures need to be clear and appropriate: a condition that is rarely met in online learning programs. The quality of multimodal glosses is more important than their inclusion / exclusion.

3 It’s a commonplace to state that learners will learn more if they are actively engaged or involved in the learning, rather than simply (receptively) looking up a gloss. So, it has been suggested that cognitive engagement can be stimulated by turning the glosses into a multiple-choice task, and a fair amount of research has investigated this possibility. Barcroft (2015: 143) reports research that suggests that ‘multiple-choice glosses [are] more effective than single glosses’, but Nation (2013: 246) argues that ‘multiple choice glosses are not strongly supported by research’. Basically, we don’t know and even if we have replication studies to re-assess the benefits of multimodal glosses (as advocated by Boers et al, 2017), it is again likely that learner variables will make it impossible to reach a firm conclusion.

Learning from meta-analyses

Discussion of glosses is not new. Back in the late 19th century, ‘most of the Reform Movement teachers, took the view that glossing was a sensible technique’ (Howatt, 2004: 191). Sensible, but probably not all that important in the broader scheme of language learning and teaching. Online glosses offer a number of potential advantages, but there is a huge number of variables that need to be considered if the potential is to be realised. In essence, I have been arguing that asking whether online glosses are more effective than print glosses is the wrong question. It’s not a question that can provide us with a useful answer. When you look at the details of the research that has been brought together in the meta-analysis, you simply cannot conclude that there are unequivocally positive effects of technology on language learning, if the most positive effects are to be found in the digital variation of an old sensible technique.

Interesting and useful as Plonsky and Ziegler’s study is, I think it needs to be treated with caution. More generally, we need to be cautious about using meta-analyses and effect sizes. Mura Nava has a useful summary of an article by Adrian Simpson (Simpson, 2017), that looks at inclusion criteria and statistical procedures and warns us that we cannot necessarily assume that the findings of meta-meta-analyses are educationally significant. More directly related to technology and language learning, Boulton’s paper (Boulton, 2016) makes a similar point: ‘Meta-analyses need interpreting with caution: in particular, it is tempting to seize on a single figure as the ultimate answer to the question: Does it work? […] More realistically, we need to look at variation in what works’.

For me, the greatest value in Plonsky and Ziegler’s paper was nothing to do with effect sizes and big answers to big questions. It was the bibliography … and the way it forced me to be rather more critical about meta-analyses.

References

Barcroft, J. 2015. Lexical Input Processing and Vocabulary Learning. Amsterdam: John Benjamins

Boers, F., Warren, P., He, L. & Deconinck, J. 2017. ‘Does adding pictures to glosses enhance vocabulary uptake from reading?’ System 66: 113 – 129

Boulton, A. 2016. ‘Quantifying CALL: significance, effect size and variation’ in S. Papadima-Sophocleus, L. Bradley & S. Thouësny (eds.) CALL Communities and Culture – short papers from Eurocall 2016 pp.55 – 60 http://files.eric.ed.gov/fulltext/ED572012.pdf

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J. & Willingham, D.T. 2013. ‘Improving Students’ Learning With Effective Learning Techniques’ Psychological Science in the Public Interest 14 / 1: 4 – 58

Hattie, J.A.C. 2009. Visible Learning. Abingdon, Oxon.: Routledge

Howatt, A.P.R. 2004. A History of English Language Teaching 2nd edition. Oxford: Oxford University Press

Khezrlou, S., Ellis, R. & K. Sadeghi 2017. ‘Effects of computer-assisted glosses on EFL learners’ vocabulary acquisition and reading comprehension in three learning conditions’ System 65: 104 – 116

Laufer, B. & Shmueli, K. 1997. ‘Memorizing new words: Does teaching have anything to do with it?’ RELC Journal 28 / 1: 89 – 108

Nation, I.S.P. 2013. Learning Vocabulary in Another Language. Cambridge: Cambridge University Press

Plonsky, L. & Ziegler, N. 2016. ‘The CALL–SLA interface:  insights from a second-order synthesis’ Language Learning & Technology 20 / 2: 17 – 37

Simpson, A. 2017. ‘The misdirection of public policy: Comparing and combining standardised effect sizes’ Journal of Education Policy, 32 / 4: 450-466

Taylor, A. M. 2006. ‘The effects of CALL versus traditional L1 glosses on L2 reading comprehension’. CALICO Journal, 23, 309–318.

Taylor, A. M. 2009. ‘CALL-based versus paper-based glosses: Is there a difference in reading comprehension?’ CALICO Journal, 23, 147–160.

Taylor, A. M. 2013. CALL versus paper: In which context are L1 glosses more effective? CALICO Journal, 30, 63-8

by Philip Kerr & Andrew Wickham

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

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

Education in a neoliberal world

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

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

Efficacy and learning outcomes

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

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

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

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

Digital technology

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

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

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

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

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

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

Teachers’ pay and conditions

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

Implications

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

References

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

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

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

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

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

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

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

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

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

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

 

 

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.

 

 

About two and a half years ago when I started writing this blog, there was a lot of hype around adaptive learning and the big data which might drive it. Two and a half years are a long time in technology. A look at Google Trends suggests that interest in adaptive learning has been pretty static for the last couple of years. It’s interesting to note that 3 of the 7 lettered points on this graph are Knewton-related media events (including the most recent, A, which is Knewton’s latest deal with Hachette) and 2 of them concern McGraw-Hill. It would be interesting to know whether these companies follow both parts of Simon Cowell’s dictum of ‘Create the hype, but don’t ever believe it’.

Google_trends

A look at the Hype Cycle (see here for Wikipedia’s entry on the topic and for criticism of the hype of Hype Cycles) of the IT research and advisory firm, Gartner, indicates that both big data and adaptive learning have now slid into the ‘trough of disillusionment’, which means that the market has started to mature, becoming more realistic about how useful the technologies can be for organizations.

A few years ago, the Gates Foundation, one of the leading cheerleaders and financial promoters of adaptive learning, launched its Adaptive Learning Market Acceleration Program (ALMAP) to ‘advance evidence-based understanding of how adaptive learning technologies could improve opportunities for low-income adults to learn and to complete postsecondary credentials’. It’s striking that the program’s aims referred to how such technologies could lead to learning gains, not whether they would. Now, though, with the publication of a report commissioned by the Gates Foundation to analyze the data coming out of the ALMAP Program, things are looking less rosy. The report is inconclusive. There is no firm evidence that adaptive learning systems are leading to better course grades or course completion. ‘The ultimate goal – better student outcomes at lower cost – remains elusive’, the report concludes. Rahim Rajan, a senior program office for Gates, is clear: ‘There is no magical silver bullet here.’

The same conclusion is being reached elsewhere. A report for the National Education Policy Center (in Boulder, Colorado) concludes: Personalized Instruction, in all its many forms, does not seem to be the transformational technology that is needed, however. After more than 30 years, Personalized Instruction is still producing incremental change. The outcomes of large-scale studies and meta-analyses, to the extent they tell us anything useful at all, show mixed results ranging from modest impacts to no impact. Additionally, one must remember that the modest impacts we see in these meta-analyses are coming from blended instruction, which raises the cost of education rather than reducing it (Enyedy, 2014: 15 -see reference at the foot of this post). In the same vein, a recent academic study by Meg Coffin Murray and Jorge Pérez (2015, ‘Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning’) found that ‘adaptive learning systems have negligible impact on learning outcomes’.

future-ready-learning-reimagining-the-role-of-technology-in-education-1-638In the latest educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Reimagining the Role of Technology in Education’, 2016) the only mentions of the word ‘adaptive’ are in the context of testing. And the latest OECD report on ‘Students, Computers and Learning: Making the Connection’ (2015), finds, more generally, that information and communication technologies, when they are used in the classroom, have, at best, a mixed impact on student performance.

There is, however, too much money at stake for the earlier hype to disappear completely. Sponsored cheerleading for adaptive systems continues to find its way into blogs and national magazines and newspapers. EdSurge, for example, recently published a report called ‘Decoding Adaptive’ (2016), sponsored by Pearson, that continues to wave the flag. Enthusiastic anecdotes take the place of evidence, but, for all that, it’s a useful read.

In the world of ELT, there are plenty of sales people who want new products which they can call ‘adaptive’ (and gamified, too, please). But it’s striking that three years after I started following the hype, such products are rather thin on the ground. Pearson was the first of the big names in ELT to do a deal with Knewton, and invested heavily in the company. Their relationship remains close. But, to the best of my knowledge, the only truly adaptive ELT product that Pearson offers is the PTE test.

Macmillan signed a contract with Knewton in May 2013 ‘to provide personalized grammar and vocabulary lessons, exam reviews, and supplementary materials for each student’. In December of that year, they talked up their new ‘big tree online learning platform’: ‘Look out for the Big Tree logo over the coming year for more information as to how we are using our partnership with Knewton to move forward in the Language Learning division and create content that is tailored to students’ needs and reactive to their progress.’ I’ve been looking out, but it’s all gone rather quiet on the adaptive / platform front.

In September 2013, it was the turn of Cambridge to sign a deal with Knewton ‘to create personalized learning experiences in its industry-leading ELT digital products for students worldwide’. This year saw the launch of a major new CUP series, ‘Empower’. It has an online workbook with personalized extra practice, but there’s nothing (yet) that anyone would call adaptive. More recently, Cambridge has launched the online version of the 2nd edition of Touchstone. Nothing adaptive there, either.

Earlier this year, Cambridge published The Cambridge Guide to Blended Learning for Language Teaching, edited by Mike McCarthy. It contains a chapter by M.O.Z. San Pedro and R. Baker on ‘Adaptive Learning’. It’s an enthusiastic account of the potential of adaptive learning, but it doesn’t contain a single reference to language learning or ELT!

So, what’s going on? Skepticism is becoming the order of the day. The early hype of people like Knewton’s Jose Ferreira is now understood for what it was. Companies like Macmillan got their fingers badly burnt when they barked up the wrong tree with their ‘Big Tree’ platform.

Noel Enyedy captures a more contemporary understanding when he writes: Personalized Instruction is based on the metaphor of personal desktop computers—the technology of the 80s and 90s. Today’s technology is not just personal but mobile, social, and networked. The flexibility and social nature of how technology infuses other aspects of our lives is not captured by the model of Personalized Instruction, which focuses on the isolated individual’s personal path to a fixed end-point. To truly harness the power of modern technology, we need a new vision for educational technology (Enyedy, 2014: 16).

Adaptive solutions aren’t going away, but there is now a much better understanding of what sorts of problems might have adaptive solutions. Testing is certainly one. As the educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Re-imagining the Role of Technology in Education’, 2016) puts it: Computer adaptive testing, which uses algorithms to adjust the difficulty of questions throughout an assessment on the basis of a student’s responses, has facilitated the ability of assessments to estimate accurately what students know and can do across the curriculum in a shorter testing session than would otherwise be necessary. In ELT, Pearson and EF have adaptive tests that have been well researched and designed.

Vocabulary apps which deploy adaptive technology continue to become more sophisticated, although empirical research is lacking. Automated writing tutors with adaptive corrective feedback are also developing fast, and I’ll be writing a post about these soon. Similarly, as speech recognition software improves, we can expect to see better and better automated adaptive pronunciation tutors. But going beyond such applications, there are bigger questions to ask, and answers to these will impact on whatever direction adaptive technologies take. Large platforms (LMSs), with or without adaptive software, are already beginning to look rather dated. Will they be replaced by integrated apps, or are apps themselves going to be replaced by bots (currently riding high in the Hype Cycle)? In language learning and teaching, the future of bots is likely to be shaped by developments in natural language processing (another topic about which I’ll be blogging soon). Nobody really has a clue where the next two and a half years will take us (if anywhere), but it’s becoming increasingly likely that adaptive learning will be only one very small part of it.

 

Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning. Boulder, CO: National Education Policy Center. Retrieved 17.07.16 from http://nepc.colorado.edu/publication/personalized-instruction