Archive for the ‘Online learning’ Category

Online teaching is big business. Very big business. Online language teaching is a significant part of it, expected to be worth over $5 billion by 2025. Within this market, the biggest demand is for English and the lion’s share of the demand comes from individual learners. And a sizable number of them are Chinese kids.

There are a number of service providers, and the competition between them is hot. To give you an idea of the scale of this business, here are a few details taken from a report in USA Today. VIPKid, is valued at over $3 billion, attracts celebrity investors, and has around 70,000 tutors who live in the US and Canada. 51Talk has 14,800 English teachers from a variety of English-speaking countries. BlingABC gets over 1,000 American applicants a month for its online tutoring jobs. There are many, many others.

Demand for English teachers in China is huge. The Pie News, citing a Chinese state media announcement, reported in September of last year that there were approximately 400,000 foreign citizens working in China as English language teachers, two-thirds of whom were working illegally. Recruitment problems, exacerbated by quotas and more stringent official requirements for qualifications, along with a very restricted desired teacher profile (white, native-speakers from a few countries like the US and the UK), have led more providers to look towards online solutions. Eric Yang, founder of the Shanghai-based iTutorGroup, which operates under a number of different brands and claims to be the ‘largest English-language learning institution in the world’, said that he had been expecting online tutoring to surpass F2F classes within a few years. With coronavirus, he now thinks it will come ‘much earlier’.

Typically, the work does not require much, if anything, in the way of training (besides familiarity with the platform), although a 40-hour TEFL course is usually preferred. Teachers deliver pre-packaged lessons. According to the USA Today report, Chinese students pay between $49 and $80 dollars an hour for the classes.

It’s a highly profitable business and the biggest cost to the platform providers is the rates they pay the tutors. If you google “Teaching TEFL jobs online”, you’ll quickly find claims that teachers can earn $40 / hour and up. Such claims are invariably found on the sites of recruitment agencies, who are competing for attention. However, although it’s possible that a small number of people might make this kind of money, the reality is that most will get nowhere near it. Scroll down the pages a little and you’ll discover that a more generally quoted and accepted figure is between $14 and $20 / hour. These tutors are, of course, freelancers, so the wages are before tax, and there is no health coverage or pension plan.

Reed job advertVIPKid, for example, considered to be one of the better companies, offers payment in the $14 – $22 / hour range. Others offer considerably less, especially if you are not a white, graduate US citizen. Current rates advertised on OETJobs include work for Ziktalk ($10 – 15 / hour), NiceTalk ($10 – 11 / hour), 247MyTutor ($5 – 8 / hour) and Weblio ($5 – 6 / hour). The number of hours that you get is rarely fixed and tutors need to build up a client base by getting good reviews. They will often need to upload short introductory videos, selling their skills. They are in direct competition with other tutors.

They also need to make themselves available when demand for their services is highest. Peak hours for VIPKid, for example, are between 2 and 8 in the morning, depending on where you live in the US. Weekends, too, are popular. With VIPKid, classes are scheduled in advance, but this is not always the case with other companies, where you log on to show that you are available and hope someone wants you. This is the case with, for example, Cambly (which pays $10.20 / hour … or rather $0.17 / minute) and NiceTalk. According to one review, Cambly has a ‘priority hours system [which] allows teachers who book their teaching slots in advance to feature higher on the teacher list than those who have just logged in, meaning that they will receive more calls’. Teachers have to commit to a set schedule and any changes are heavily penalised. The review states that ‘new tutors on the platform should expect to receive calls for about 50% of the time they’re logged on’.

 

Taking the gig economy to its logical conclusion, there are other companies where tutors can fix their own rates. SkimaTalk, for example, offers a deal where tutors first teach three unpaid lessons (‘to understand how the system works and build up their initial reputation on the platform’), then the system sets $16 / hour as a default rate, but tutors can change this to anything they wish. With another, Palfish, where tutors set their own rate, the typical rate is $10 – 18 / hour, and the company takes a 20% commission. With Preply, here is the deal on offer:

Your earnings depend on the hourly rate you set in your profile and how often you can provide lessons. Preply takes a 100% commission fee of your first lesson payment with every new student. For all subsequent lessons, the commission varies from 33 to 18% and depends on the number of completed lesson hours with students. The more tutoring you do through Preply, the less commission you pay.

Not one to miss a trick, Ziktalk (‘currently focusing on language learning and building global audience’) encourages teachers ‘to upload educational videos in order to attract more students’. Or, to put it another way, teachers provide free content in order to have more chance of earning $10 – 15 / hour. Ah, the joys of digital labour!

And, then, coronavirus came along. With schools shutting down, first in China and then elsewhere, tens of millions of students are migrating online. In Hong Kong, for example, the South China Morning Post reports that schools will remain closed until April 20, at the earliest, but university entrance exams will be going ahead as planned in late March. CNBC reported yesterday that classes are being cancelled across the US, and the same is happening, or is likely to happen, in many other countries.

Shares in the big online providers soared in February, with Forbes reporting that $3.2 billion had been added to the share value of China’s e-Learning leaders. Stock in New Oriental (owners of BlingABC, mentioned above) ‘rose 7.3% last month, adding $190 million to the wealth of its founder Yu Minhong [whose] current net worth is estimated at $3.4 billion’.

DingTalk, a communication and management app owned by Alibaba (and the most downloaded free app in China’s iOS App Store), has been adapted to offer online services for schools, reports Xinhua, the official state-run Chinese news agency. The scale of operations is enormous: more than 10,000 new cloud servers were deployed within just two hours.

Current impacts are likely to be dwarfed by what happens in the future. According to Terry Weng, a Shenzhen-based analyst, ‘The gradual exit of smaller education firms means there are more opportunities for TAL and New Oriental. […] Investors are more keen for their future performance.’ Zhu Hong, CTO of DingTalk, observes ‘the epidemic is like a catalyst for many enterprises and schools to adopt digital technology platforms and products’.

For edtech investors, things look rosy. Smaller, F2F providers are in danger of going under. In an attempt to mop up this market and gain overall market share, many elearning providers are offering weighty discounts and free services. Profits can come later.

For the hundreds of thousands of illegal or semi-legal English language teachers in China, things look doubly bleak. Their situation is likely to become even more precarious, with the online gig economy their obvious fall-back path. But English language teachers everywhere are likely to be affected one way or another, as will the whole world of TEFL.

Now seems like a pretty good time to find out more about precarity (see the Teachers as Workers website) and native-speakerism (see TEFL Equity Advocates).

Book_coverIn my last post, I looked at shortcomings in edtech research, mostly from outside the world of ELT. I made a series of recommendations of ways in which such research could become more useful. In this post, I look at two very recent collections of ELT edtech research. The first of these is Digital Innovations and Research in Language Learning, edited by Mavridi and Saumell, and published this February by the Learning Technologies SIG of IATEFL. I’ll refer to it here as DIRLL. It’s available free to IATEFL LT SIG members, and can be bought for $10.97 as an ebook on Amazon (US). The second is the most recent edition (February 2020) of the Language Learning & Technology journal, which is open access and available here. I’ll refer to it here as LLTJ.

In both of these collections, the focus is not on ‘technology per se, but rather issues related to language learning and language teaching, and how they are affected or enhanced by the use of digital technologies’. However, they are very different kinds of publication. Nobody involved in the production of DIRLL got paid in any way (to the best of my knowledge) and, in keeping with its provenance from a teachers’ association, has ‘a focus on the practitioner as teacher-researcher’. Almost all of the contributing authors are university-based, but they are typically involved more in language teaching than in research. With one exception (a grant from the EU), their work was unfunded.

The triannual LLTJ is funded by two American universities and published by the University of Hawaii Press. The editors and associate editors are well-known scholars in their fields. The journal’s impact factor is high, close to the impact factor of the paywalled reCALL (published by the University of Cambridge), which is the highest-ranking journal in the field of CALL. The contributing authors are all university-based, many with a string of published articles (in prestige journals), chapters or books behind them. At least six of the studies were funded by national grant-awarding bodies.

I should begin by making clear that there was much in both collections that I found interesting. However, it was not usually the research itself that I found informative, but the literature review that preceded it. Two of the chapters in DIRLL were not really research, anyway. One was the development of a template for evaluating ICT-mediated tasks in CLIL, another was an advocacy of comics as a resource for language teaching. Both of these were new, useful and interesting to me. LLTJ included a valuable literature review of research into VR in FL learning (but no actual new research). With some exceptions in both collections, though, I felt that I would have been better off curtailing my reading after the reviews. Admittedly, there wouldn’t be much in the way of literature reviews if there were no previous research to report …

It was no surprise to see the learners who were the subjects of this research were overwhelmingly university students. In fact, only one article (about a high-school project in Israel, reported in DIRLL) was not about university students. The research areas focused on reflected this bias towards tertiary contexts: online academic reading skills, academic writing, online reflective practices in teacher training programmes, etc.

In a couple of cases, the selection of experimental subjects seemed plain bizarre. Why, if you want to find out about the extent to which Moodle use can help EAP students become better academic readers (in DIRLL), would you investigate this with a small volunteer cohort of postgraduate students of linguistics, with previous experience of using Moodle and experience of teaching? Is a less representative sample imaginable? Why, if you want to investigate the learning potential of the English File Pronunciation app (reported in LLTJ), which is clearly most appropriate for A1 – B1 levels, would you do this with a group of C1-level undergraduates following a course in phonetics as part of an English Studies programme?

More problematic, in my view, was the small sample size in many of the research projects. The Israeli virtual high school project (DIRLL), previously referred to, started out with only 11 students, but 7 dropped out, primarily, it seems, because of institutional incompetence: ‘the project was probably doomed […] to failure from the start’, according to the author. Interesting as this was as an account of how not to set up a project of this kind, it is simply impossible to draw any conclusions from 4 students about the potential of a VLE for ‘interaction, focus and self-paced learning’. The questionnaire investigating experience of and attitudes towards VR (in DIRLL) was completed by only 7 (out of 36 possible) students and 7 (out of 70+ possible) teachers. As the author acknowledges, ‘no great claims can be made’, but then goes on to note the generally ‘positive attitudes to VR’. Perhaps those who did not volunteer had different attitudes? We will never know. The study of motivational videos in tertiary education (DIRLL) started off with 15 subjects, but 5 did not complete the necessary tasks. The research into L1 use in videoconferencing (LLTJ) started off with 10 experimental subjects, all with the same L1 and similar cultural backgrounds, but there was no data available from 4 of them (because they never switched into L1). The author claims that the paper demonstrates ‘how L1 is used by language learners in videoconferencing as a social semiotic resource to support social presence’ – something which, after reading the literature review, we already knew. But the paper also demonstrates quite clearly how L1 is not used by language learners in videoconferencing as a social semiotic resource to support social presence. In all these cases, it is the participants who did not complete or the potential participants who did not want to take part that have the greatest interest for me.

Unsurprisingly, the LLTJ articles had larger sample sizes than those in DIRLL, but in both collections the length of the research was limited. The production of one motivational video (DIRLL) does not really allow us to draw any conclusions about the development of students’ critical thinking skills. Two four-week interventions do not really seem long enough to me to discover anything about learner autonomy and Moodle (DIRLL). An experiment looking at different feedback modes needs more than two written assignments to reach any conclusions about student preferences (LLTJ).

More research might well be needed to compensate for the short-term projects with small sample sizes, but I’m not convinced that this is always the case. Lacking sufficient information about the content of the technologically-mediated tools being used, I was often unable to reach any conclusions. A gamified Twitter environment was developed in one project (DIRLL), using principles derived from contemporary literature on gamification. The authors concluded that the game design ‘failed to generate interaction among students’, but without knowing a lot more about the specific details of the activity, it is impossible to say whether the problem was the principles or the particular instantiation of those principles. Another project, looking at the development of pronunciation materials for online learning (LLTJ), came to the conclusion that online pronunciation training was helpful – better than none at all. Claims are then made about the value of the method used (called ‘innovative Cued Pronunciation Readings’), but this is not compared to any other method / materials, and only a very small selection of these materials are illustrated. Basically, the reader of this research has no choice but to take things on trust. The study looking at the use of Alexa to help listening comprehension and speaking fluency (LLTJ) cannot really tell us anything about IPAs unless we know more about the particular way that Alexa is being used. Here, it seems that the students were using Alexa in an interactive storytelling exercise, but so little information is given about the exercise itself that I didn’t actually learn anything at all. The author’s own conclusion is that the results, such as they are, need to be treated with caution. Nevertheless, he adds ‘the current study illustrates that IPAs may have some value to foreign language learners’.

This brings me onto my final gripe. To be told that IPAs like Alexa may have some value to foreign language learners is to be told something that I already know. This wasn’t the only time this happened during my reading of these collections. I appreciate that research cannot always tell us something new and interesting, but a little more often would be nice. I ‘learnt’ that goal-setting plays an important role in motivation and that gamification can boost short-term motivation. I ‘learnt’ that reflective journals can take a long time for teachers to look at, and that reflective video journals are also very time-consuming. I ‘learnt’ that peer feedback can be very useful. I ‘learnt’ from two papers that intercultural difficulties may be exacerbated by online communication. I ‘learnt’ that text-to-speech software is pretty good these days. I ‘learnt’ that multimodal literacy can, most frequently, be divided up into visual and auditory forms.

With the exception of a piece about online safety issues (DIRLL), I did not once encounter anything which hinted that there may be problems in using technology. No mention of the use to which student data might be put. No mention of the costs involved (except for the observation that many students would not be happy to spend money on the English File Pronunciation app) or the cost-effectiveness of digital ‘solutions’. No consideration of the institutional (or other) pressures (or the reasons behind them) that may be applied to encourage teachers to ‘leverage’ edtech. No suggestion that a zero-tech option might actually be preferable. In both collections, the language used is invariably positive, or, at least, technology is associated with positive things: uncovering the possibilities, promoting autonomy, etc. Even if the focus of these publications is not on technology per se (although I think this claim doesn’t really stand up to close examination), it’s a little disingenuous to claim (as LLTJ does) that the interest is in how language learning and language teaching is ‘affected or enhanced by the use of digital technologies’. The reality is that the overwhelming interest is in potential enhancements, not potential negative effects.

I have deliberately not mentioned any names in referring to the articles I have discussed. I would, though, like to take my hat off to the editors of DIRLL, Sophia Mavridi and Vicky Saumell, for attempting to do something a little different. I think that Alicia Artusi and Graham Stanley’s article (DIRLL) about CPD for ‘remote’ teachers was very good and should interest the huge number of teachers working online. Chryssa Themelis and Julie-Ann Sime have kindled my interest in the potential of comics as a learning resource (DIRLL). Yu-Ju Lan’s article about VR (LLTJ) is surely the most up-to-date, go-to article on this topic. There were other pieces, or parts of pieces, that I liked, too. But, to me, it’s clear that ‘more research is needed’ … much less than (1) better and more critical research, and (2) more digestible summaries of research.

Colloquium

At the beginning of March, I’ll be going to Cambridge to take part in a Digital Learning Colloquium (for more information about the event, see here ). One of the questions that will be explored is how research might contribute to the development of digital language learning. In this, the first of two posts on the subject, I’ll be taking a broad overview of the current state of play in edtech research.

I try my best to keep up to date with research. Of the main journals, there are Language Learning and Technology, which is open access; CALICO, which offers quite a lot of open access material; and reCALL, which is the most restricted in terms of access of the three. But there is something deeply frustrating about most of this research, and this is what I want to explore in these posts. More often than not, research articles end with a call for more research. And more often than not, I find myself saying ‘Please, no, not more research like this!’

First, though, I would like to turn to a more reader-friendly source of research findings. Systematic reviews are, basically literature reviews which can save people like me from having to plough through endless papers on similar subjects, all of which contain the same (or similar) literature review in the opening sections. If only there were more of them. Others agree with me: the conclusion of one systematic review of learning and teaching with technology in higher education (Lillejord et al., 2018) was that more systematic reviews were needed.

Last year saw the publication of a systematic review of research on artificial intelligence applications in higher education (Zawacki-Richter, et al., 2019) which caught my eye. The first thing that struck me about this review was that ‘out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis’. In other words, only just over 5% of the research was considered worthy of inclusion.

The review did not paint a very pretty picture of the current state of AIEd research. As the second part of the title of this review (‘Where are the educators?’) makes clear, the research, taken as a whole, showed a ‘weak connection to theoretical pedagogical perspectives’. This is not entirely surprising. As Bates (2019) has noted: ‘since AI tends to be developed by computer scientists, they tend to use models of learning based on how computers or computer networks work (since of course it will be a computer that has to operate the AI). As a result, such AI applications tend to adopt a very behaviourist model of learning: present / test / feedback.’ More generally, it is clear that technology adoption (and research) is being driven by technology enthusiasts, with insufficient expertise in education. The danger is that edtech developers ‘will simply ‘discover’ new ways to teach poorly and perpetuate erroneous ideas about teaching and learning’ (Lynch, 2017).

This, then, is the first of my checklist of things that, collectively, researchers need to do to improve the value of their work. The rest of this list is drawn from observations mostly, but not exclusively, from the authors of systematic reviews, and mostly come from reviews of general edtech research. In the next blog post, I’ll look more closely at a recent collection of ELT edtech research (Mavridi & Saumell, 2020) to see how it measures up.

1 Make sure your research is adequately informed by educational research outside the field of edtech

Unproblematised behaviourist assumptions about the nature of learning are all too frequent. References to learning styles are still fairly common. The most frequently investigated skill that is considered in the context of edtech is critical thinking (Sosa Neira, et al., 2017), but this is rarely defined and almost never problematized, despite a broad literature that questions the construct.

2 Adopt a sceptical attitude from the outset

Know your history. Decades of technological innovation in education have shown precious little in the way of educational gains and, more than anything else, have taught us that we need to be sceptical from the outset. ‘Enthusiasm and praise that are directed towards ‘virtual education, ‘school 2.0’, ‘e-learning and the like’ (Selwyn, 2014: vii) are indications that the lessons of the past have not been sufficiently absorbed (Levy, 2016: 102). The phrase ‘exciting potential’, for example, should be banned from all edtech research. See, for example, a ‘state-of-the-art analysis of chatbots in education’ (Winkler & Söllner, 2018), which has nothing to conclude but ‘exciting potential’. Potential is fine (indeed, it is perhaps the only thing that research can unambiguously demonstrate – see section 3 below), but can we try to be a little more grown-up about things?

3 Know what you are measuring

Measuring learning outcomes is tricky, to say the least, but it’s understandable that researchers should try to focus on them. Unfortunately, ‘the vast array of literature involving learning technology evaluation makes it challenging to acquire an accurate sense of the different aspects of learning that are evaluated, and the possible approaches that can be used to evaluate them’ (Lai & Bower, 2019). Metrics such as student grades are hard to interpret, not least because of the large number of variables and the danger of many things being conflated in one score. Equally, or possibly even more, problematic, are self-reporting measures which are rarely robust. It seems that surveys are the most widely used instrument in qualitative research (Sosa Neira, et al., 2017), but these will tell us little or nothing when used for short-term interventions (see point 5 below).

4 Ensure that the sample size is big enough to mean something

In most of the research into digital technology in education that was analysed in a literature review carried out for the Scottish government (ICF Consulting Services Ltd, 2015), there were only ‘small numbers of learners or teachers or schools’.

5 Privilege longitudinal studies over short-term projects

The Scottish government literature review (ICF Consulting Services Ltd, 2015), also noted that ‘most studies that attempt to measure any outcomes focus on short and medium term outcomes’. The fact that the use of a particular technology has some sort of impact over the short or medium term tells us very little of value. Unless there is very good reason to suspect the contrary, we should assume that it is a novelty effect that has been captured (Levy, 2016: 102).

6 Don’t forget the content

The starting point of much edtech research is the technology, but most edtech, whether it’s a flashcard app or a full-blown Moodle course, has content. Research reports rarely give details of this content, assuming perhaps that it’s just fine, and all that’s needed is a little tech to ‘present learners with the ‘right’ content at the ‘right’ time’ (Lynch, 2017). It’s a foolish assumption. Take a random educational app from the Play Store, a random MOOC or whatever, and the chances are you’ll find it’s crap.

7 Avoid anecdotal accounts of technology use in quasi-experiments as the basis of a ‘research article’

Control (i.e technology-free) groups may not always be possible but without them, we’re unlikely to learn much from a single study. What would, however, be extremely useful would be a large, collated collection of such action-research projects, using the same or similar technology, in a variety of settings. There is a marked absence of this kind of work.

8 Enough already of higher education contexts

Researchers typically work in universities where they have captive students who they can carry out research on. But we have a problem here. The systematic review of Lundin et al (2018), for example, found that ‘studies on flipped classrooms are dominated by studies in the higher education sector’ (besides lacking anchors in learning theory or instructional design). With some urgency, primary and secondary contexts need to be investigated in more detail, not just regarding flipped learning.

9 Be critical

Very little edtech research considers the downsides of edtech adoption. Online safety, privacy and data security are hardly peripheral issues, especially with younger learners. Ignoring them won’t make them go away.

More research?

So do we need more research? For me, two things stand out. We might benefit more from, firstly, a different kind of research, and, secondly, more syntheses of the work that has already been done. Although I will probably continue to dip into the pot-pourri of articles published in the main CALL journals, I’m looking forward to a change at the CALICO journal. From September of this year, one issue a year will be thematic, with a lead article written by established researchers which will ‘first discuss in broad terms what has been accomplished in the relevant subfield of CALL. It should then outline which questions have been answered to our satisfaction and what evidence there is to support these conclusions. Finally, this article should pose a “soft” research agenda that can guide researchers interested in pursuing empirical work in this area’. This will be followed by two or three empirical pieces that ‘specifically reflect the research agenda, methodologies, and other suggestions laid out in the lead article’.

But I think I’ll still have a soft spot for some of the other journals that are coyer about their impact factor and that can be freely accessed. How else would I discover (it would be too mean to give the references here) that ‘the effective use of new technologies improves learners’ language learning skills’? Presumably, the ineffective use of new technologies has the opposite effect? Or that ‘the application of modern technology represents a significant advance in contemporary English language teaching methods’?

References

Bates, A. W. (2019). Teaching in a Digital Age Second Edition. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from https://pressbooks.bccampus.ca/teachinginadigitalagev2/

ICF Consulting Services Ltd (2015). Literature Review on the Impact of Digital Technology on Learning and Teaching. Edinburgh: The Scottish Government. https://dera.ioe.ac.uk/24843/1/00489224.pdf

Lai, J.W.M. & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133(1), 27-42. Elsevier Ltd. Retrieved January 14, 2020 from https://www.learntechlib.org/p/207137/

Levy, M. 2016. Researching in language learning and technology. In Farr, F. & Murray, L. (Eds.) The Routledge Handbook of Language Learning and Technology. Abingdon, Oxon.: Routledge. pp.101 – 114

Lillejord S., Børte K., Nesje K. & Ruud E. (2018). Learning and teaching with technology in higher education – a systematic review. Oslo: Knowledge Centre for Education https://www.forskningsradet.no/siteassets/publikasjoner/1254035532334.pdf

Lundin, M., Bergviken Rensfeldt, A., Hillman, T. et al. (2018). Higher education dominance and siloed knowledge: a systematic review of flipped classroom research. International Journal of Educational Technology in Higher Education 15, 20 (2018) doi:10.1186/s41239-018-0101-6

Lynch, J. (2017). How AI Will Destroy Education. Medium, November 13, 2017. https://buzzrobot.com/how-ai-will-destroy-education-20053b7b88a6

Mavridi, S. & Saumell, V. (Eds.) (2020). Digital Innovations and Research in Language Learning. Faversham, Kent: IATEFL

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

Sosa Neira, E. A., Salinas, J. and de Benito Crosetti, B. (2017). Emerging Technologies (ETs) in Education: A Systematic Review of the Literature Published between 2006 and 2016. International Journal of Emerging Technologies in Education, 12 (5). https://online-journals.org/index.php/i-jet/article/view/6939

Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA. https://www.alexandria.unisg.ch/254848/1/JML_699.pdf

Zawacki-Richter, O., Bond, M., Marin, V. I. And Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education 2019

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