Oxford Test of English – adaptive and personalized

I was intrigued to learn earlier this year that Oxford University Press had launched a new online test of English language proficiency, called the Oxford Test of English (OTE). At the conference where I first heard about it, I was struck by the fact that the presentation of the OUP sponsored plenary speaker was entitled ‘The Power of Assessment’ and dealt with formative assessment / assessment for learning. Oxford clearly want to position themselves as serious competitors to Pearson and Cambridge English in the testing business.

The brochure for the exam kicks off with a gem of a marketing slogan, ‘Smart. Smarter. SmarTest’ (geddit?), and the next few pages give us all the key information.

‘Traditional language proficiency tests’ is presumably intended to refer to the main competition (Pearson and Cambridge English). Cambridge First takes, in total, 3½ hours; the Pearson Test of English Academic takes 3 hours. The OTE takes, in total, 2 hours and 5 minutes. It can be taken, in theory, on any day of the year, although this depends on the individual Approved Test Centres, and, again, in theory, it can be booked as little as 14 days in advance. Results should take only two weeks to arrive. Further flexibility is offered in the way that candidates can pick ’n’ choose which of the four skills they want to have tests, just one or all four, although, as an incentive to go the whole hog, they will only get a ‘Certificate of Proficiency’ if they do all four.

A further incentive to do all four skills at the same time can be found in the price structure. One centre in Spain is currently offering the test for one single skill at Ꞓ41.50, but do the whole lot, and it will only set you back Ꞓ89. For a high-stakes test, this is cheap. In the UK right now, both Cambridge First and Pearson Academic cost in the region of £150, and IELTS a bit more than that. So, faster, more flexible and cheaper … Oxford means business.

The ‘individual experience’ on the next page of the brochure is pure marketing guff. This is, after all, a high-stakes, standardised test. It may be true that ‘the Speaking and Writing modules provide randomly generated tasks, making the overall test different each time’, but there can only be a certain number of permutations. What’s more, in ‘traditional tests’, like Cambridge First, where there is a live examiner or two, an individualised experience is unavoidable.

More interesting to me is the reference to adaptive technology. According to the brochure, ‘The Listening and Reading modules are adaptive, which means the test difficulty adjusts in response to your answers, quickly finding the right level for each test taker. This means that the questions are at just the right level of challenge, making the test shorter and less stressful than traditional proficiency tests’.

My curiosity piqued, I decided to look more closely at the Reading module. I found one practice test online which is the same as the demo that is available at the OTE website . Unfortunately, this example is not adaptive: it is at B1 level. The actual test records scores between 51 and 140, corresponding to levels A2, B1 and B2.

The tasks in the Reading module are familiar from coursebooks and other exams: multiple choice, multiple matching and gapped texts.

According to the exam specifications, these tasks are designed to measure the following skills:

• Reading to identify main message, purpose, detail
• Expeditious reading to identify specific information, opinion and attitude
• Reading to identify text structure, organizational features of a text
• Reading to identify attitude / opinion, purpose, reference, the meanings of words in context, global meaning

The ability to perform these skills depends, ultimately, on the candidate’s knowledge of vocabulary and grammar, as can be seen in the examples below.

How exactly, I wonder, does the test difficulty adjust in response to the candidate’s answers? The algorithm that is used depends on measures of the difficulty of the test items. If these items are to be made harder or easier, the only significant way that I can see of doing this is by making the key vocabulary lower- or higher-frequency. This, in turn, is only possible if vocabulary and grammar has been tagged as being at a particular level. The most well-known tools for doing this have been developed by Pearson (with the GSE Teacher Toolkit ) and Cambridge English Profile . To the best of my knowledge, Oxford does not yet have a tool of this kind (at least, none that is publicly available). However, the data that OUP will accumulate from OTE scripts and recordings will be invaluable in building a database which their lexicographers can use in developing such a tool.

Even when a data-driven (and numerically precise) tool is available for modifying the difficulty of test items, I still find it hard to understand how the adaptivity will impact on the length or the stress of the reading test. The Reading module is only 35 minutes long and contains only 22 items. Anything that is significantly shorter must surely impact on the reliability of the test.

My conclusion from this is that the adaptive element of the Reading and Listening modules in the OTE is less important to the test itself than it is to building a sophisticated database (not dissimilar to the GSE Teacher Toolkit or Cambridge English Profile). The value of this will be found, in due course, in calibrating all OUP materials. The OTE has already been aligned to the Oxford Online Placement Test (OOPT) and, presumably, coursebooks will soon follow. This, in turn, will facilitate a vertically integrated business model, like Pearson and CUP, where everything from placement test, to coursework, to formative assessment, to final proficiency testing can be on offer.

The fun they had

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Jenny Holzer, Protect me from what I want

AI and language teaching

It’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. They’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.

 

Personalized learning at IATEFL

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Automated generation of vocabulary learning materials

A personalized language learning programme that is worth its name needs to offer a wide variety of paths to accommodate the varying interests, priorities, levels and preferred approaches to learning of the users of the programme. For this to be possible, a huge quantity of learning material is needed (Iwata et al., 2011: 1): the preparation and curation of this material is extremely time-consuming and expensive (despite the pittance that is paid to writers and editors). It’s not surprising, then, that a growing amount of research is being devoted to the exploration of ways of automatically generating language learning material. One area that has attracted a lot of attention is the learning of vocabulary.

Many simple vocabulary learning tasks are relatively simple to generate automatically. These include matching tasks of various kinds, such as the matching of words or phrases to meanings (either in English or the L1), pictures or collocations, as in many flashcard apps. Doing it well is rather harder: the definitions or translations have to be good and appropriate for learners of the level, the pictures need to be appropriate. If, as is often the case, the lexical items have come from a text or form part of a group of some kind, sense disambiguation software will be needed to ensure that the right meaning is being practised. Anyone who has used flashcard apps knows that the major problem is usually the quality of the content (whether it has been automatically generated or written by someone).

A further challenge is the generation of distractors. In the example here (from Memrise), the distractors have been so badly generated as to render the task more or less a complete waste of time. Distractors must, in some way, be viable alternatives (Smith et al., 2010) but still clearly wrong. That means they should normally be the same part of speech and true cognates should be avoided. Research into the automatic generation of distractors is well-advanced (see, for instance, Kumar at al., 2015) with Smith et al (2010), for example, using a very large corpus and various functions of Sketch Engine (the most well-known corpus query tool) to find collocates and other distractors. Their TEDDCLOG (Testing English with Data-Driven CLOze Generation) system produced distractors that were deemed acceptable 91% of the time. Whilst impressive, there is still a long way to go before human editing / rewriting is no longer needed.

One area that has attracted attention is, of course, tests, and some tasks, such as those in TOEFL (see image). Susanti et al (2015, 2017) were able, given a target word, to automatically generate a reading passage from web sources along with questions of the TOEFL kind. However, only about half of them were considered good enough to be used in actual tests. Again, that is some way off avoiding human intervention altogether, but the automatically generated texts and questions can greatly facilitate the work of human item writers.

 

Other tools that might be useful include the University of Nottingham AWL (Academic Word List) Gapmaker . This allows users to type or paste in a text, from which items from the AWL are extracted and replaced as a gap. See the example below. It would, presumably, not be too difficult, to combine this approach with automatic distractor generation and to create multiple choice tasks.

There are a number of applications that offer the possibility of generating cloze tasks from texts selected by the user (learner or teacher). These have not always been designed with the language learner in mind but one that was is the Android app, WordGap (Knoop & Wilske, 2013). Described by its developers as a tool that ‘provides highly individualized exercises to support contextualized mobile vocabulary learning …. It matches the interests of the learner and increases the motivation to learn’. It may well do all that, but then again, perhaps not. As Knoop & Wilske acknowledge, it is only appropriate for adult, advanced learners and its value as a learning task is questionable. The target item that has been automatically selected is ‘novel’, a word that features in the list Oxford 2000 Keywords (as do all three distractors), and therefore ought to be well below the level of the users. Some people might find this fun, but, in terms of learning, they would probably be better off using an app that made instant look-up of words in the text possible.

More interesting, in my view, is TEDDCLOG (Smith et al., 2010), a system that, given a target learning item (here the focus is on collocations), trawls a large corpus to find the best sentence that illustrates it. ‘Good sentences’ were defined as those which were short (but not too short, or there is not enough useful context, begins with a capital letter and ends with a full stop, has a maximum of two commas; and otherwise contains only the 26 lowercase letters. It must be at a lexical and grammatical level that an intermediate level learner of English could be expected to understand. It must be well-formed and without too much superfluous material. All others were rejected. TEDDCLOG uses Sketch Engine’s GDEX function (Good Dictionary Example Extractor, Kilgarriff et al 2008) to do this.

My own interest in this area came about as a result of my work in the development of the Oxford Vocabulary Trainer . The app offers the possibility of studying both pre-determined lexical items (e.g. the vocabulary list of a coursebook that the learner is using) and free choice (any item could be activated and sent to a learning queue). In both cases, practice takes the form of sentences with the target item gapped. There are a range of hints and help options available to the learner, and feedback is both automatic and formative (i.e. if the supplied answer is not correct, hints are given to push the learner to do better on a second attempt). Leveraging some fairly heavy technology, we were able to achieve a fair amount of success in the automation of intelligent feedback, but what had, at first sight, seemed a lesser challenge – the generation of suitable ‘carrier sentences’, proved more difficult.

The sentences which ‘carry’ the gap should, ideally, be authentic: invented examples often ‘do not replicate the phraseology and collocational preferences of naturally-occurring text’ (Smith et al., 2010). The technology of corpus search tools should allow us to do a better job than human item writers. For that to be the case, we need not only good search tools but a good corpus … and some are better than others for the purposes of language learning. As Fenogenova & Kuzmenko (2016) discovered when using different corpora to automatically generate multiple choice vocabulary exercises, the British Academic Written English corpus (BAWE) was almost 50% more useful than the British National Corpus (BNC). In the development of the Oxford Vocabulary Trainer, we thought we had the best corpus we could get our hands on – the tagged corpus used for the production of the Oxford suite of dictionaries. We could, in addition and when necessary, turn to other corpora, including the BAWE and the BNC. Our requirements for acceptable carrier sentences were similar to those of Smith et al (2010), but were considerably more stringent.

To cut quite a long story short, we learnt fairly quickly that we simply couldn’t automate the generation of carrier sentences with sufficient consistency or reliability. As with some of the other examples discussed in this post, we were able to use the technology to help the writers in their work. We also learnt (rather belatedly, it has to be admitted) that we were trying to find technological solutions to problems that we hadn’t adequately analysed at the start. We hadn’t, for example, given sufficient thought to learner differences, especially the role of L1 (and other languages) in learning English. We hadn’t thought enough about the ‘messiness’ of either language or language learning. It’s possible, given enough resources, that we could have found ways of improving the algorithms, of leveraging other tools, or of deploying additional databases (especially learner corpora) in our quest for a personalised vocabulary learning system. But, in the end, it became clear to me that we were only nibbling at the problem of vocabulary learning. Deliberate learning of vocabulary may be an important part of acquiring a language, but it remains only a relatively small part. Technology may be able to help us in a variety of ways (and much more so in testing than learning), but the dreams of the data scientists (who wrote much of the research cited here) are likely to be short-lived. Experienced writers and editors of learning materials will be needed for the foreseeable future. And truly personalized vocabulary learning, fully supported by technology, will not be happening any time soon.

 

References

Fenogenova, A. & Kuzmenko, E. 2016. Automatic Generation of Lexical Exercises Available online at http://www.dialog-21.ru/media/3477/fenogenova.pdf

Iwata, T., Goto, T., Kojiri, T., Watanabe, T. & T. Yamada. 2011. ‘Automatic Generation of English Cloze Questions Based on Machine Learning’. NTT Technical Review Vol. 9 No. 10 Oct. 2011

Kilgarriff, A. et al. 2008. ‘GDEX: Automatically Finding Good Dictionary Examples in a Corpus.’ In E. Bernal and J. DeCesaris (eds.), Proceedings of the XIII EURALEX International Congress: Barcelona, 15-19 July 2008. Barcelona: l’Institut Universitari de Lingüística Aplicada (IULA) dela Universitat Pompeu Fabra, 425–432.

Knoop, S. & Wilske, S. 2013. ‘WordGap – Automatic generation of gap-filling vocabulary exercises for mobile learning’. Proceedings of the second workshop on NLP for computer-assisted language learning at NODALIDA 2013. NEALT Proceedings Series 17 / Linköping Electronic Conference Proceedings 86: 39–47. Available online at http://www.ep.liu.se/ecp/086/004/ecp13086004.pdf

Kumar, G., Banchs, R.E. & D’Haro, L.F. 2015. ‘RevUP: Automatic Gap-Fill Question Generation from Educational Texts’. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 154–161, Denver, Colorado, June 4, Association for Computational Linguistics

Smith, S., Avinesh, P.V.S. & Kilgariff, A. 2010. ‘Gap-fill tests for Language Learners: Corpus-Driven Item Generation’. Proceedings of ICON-2010: 8th International Conference on Natural Language Processing, Macmillan Publishers, India. Available online at https://curve.coventry.ac.uk/open/file/2b755b39-a0fa-4171-b5ae-5d39568874e5/1/smithcomb2.pdf

Susanti, Y., Iida, R. & Tokunaga, T. 2015. ‘Automatic Generation of English Vocabulary Tests’. Proceedings of 7th International Conference on Computer Supported Education. Available online https://pdfs.semanticscholar.org/aead/415c1e07803756902b859e8b6e47ce312d96.pdf

Susanti, Y., Tokunaga, T., Nishikawa, H. & H. Obari 2017. ‘Evaluation of automatically generated English vocabulary questions’ Research and Practice in Technology Enhanced Learning 12 / 11

 

Personalization and goal-setting

Introduction

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

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

The benefits of personalized goal-setting

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

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

Goal-setting and technology

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

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

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

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

The problems with personalized goal-setting

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Rivers, W. M. 1971. ‘Techniques for Developing Proficiency in the Spoken Language in an Individualized Foreign Language program’ in Altman, H.B. & Politzer, R.L. (eds.) 1971. Individualizing Foreign Language Instruction: Proceedings of the Stanford Conference, May 6 – 8, 1971. Washington, D.C.: Office of Education, U.S. Department of Health, Education, and Welfare. pp. 165 – 169

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

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

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

Self-paced language learning

Introduction

Allowing learners to determine the amount of time they spend studying, and, therefore (in theory at least) the speed of their progress is a key feature of most personalized learning programs. In cases where learners follow a linear path of pre-determined learning items, it is often the only element of personalization that the programs offer. In the Duolingo program that I am using, there are basically only two things that can be personalized: the amount of time I spend studying each day, and the possibility of jumping a number of learning items by ‘testing out’.

Self-regulated learning or self-pacing, as this is commonly referred to, has enormous intuitive appeal. It is clear that different people learn different things at different rates. We’ve known for a long time that ‘the developmental stages of child growth and the individual differences among learners make it impossible to impose a single and ‘correct’ sequence on all curricula’ (Stern, 1983: 439). It therefore follows that it makes even less sense for a group of students (typically determined by age) to be obliged to follow the same curriculum at the same pace in a one-size-fits-all approach. We have probably all experienced, as students, the frustration of being behind, or ahead of, the rest of our colleagues in a class. One student who suffered from the lockstep approach was Sal Khan, founder of the Khan Academy. He has described how he was fed up with having to follow an educational path dictated by his age and how, as a result, individual pacing became an important element in his educational approach (Ferster, 2014: 132-133). As teachers, we have all experienced the challenges of teaching a piece of material that is too hard or too easy for many of the students in the class.

Historical attempts to facilitate self-paced learning

An interest in self-paced learning can be traced back to the growth of mass schooling and age-graded classes in the 19^th century. In fact, the ‘factory model’ of education has never existed without critics who saw the inherent problems of imposing uniformity on groups of individuals. These critics were not marginal characters. Charles Eliot (president of Harvard from 1869 – 1909), for example, described uniformity as ‘the curse of American schools’ and argued that ‘the process of instructing students in large groups is a quite sufficient school evil without clinging to its twin evil, an inflexible program of studies’ (Grittner, 1975: 324).

Attempts to develop practical solutions were not uncommon and these are reasonably well-documented. One of the earliest, which ran from 1884 to 1894, was launched in Pueblo, Colorado and was ‘a self-paced plan that required each student to complete a sequence of lessons on an individual basis’ (Januszewski, 2001: 58-59). More ambitious was the Burk Plan (at its peak between 1912 and 1915), named after Frederick Burk of the San Francisco State Normal School, which aimed to allow students to progress through materials (including language instruction materials) at their own pace with only a limited amount of teacher presentations (Januszewski, ibid.). Then, there was the Winnetka Plan (1920s), developed by Carlton Washburne, an associate of Frederick Burk and the superintendent of public schools in Winnetka, Illinois, which also ‘allowed learners to proceed at different rates, but also recognised that learners proceed at different rates in different subjects’ (Saettler, 1990: 65). The Winnetka Plan is especially interesting in the way it presaged contemporary attempts to facilitate individualized, self-paced learning. It was described by its developers in the following terms:

A general technique [consisting] of (a) breaking up the common essentials curriculum into very definite units of achievement, (b) using complete diagnostic tests to determine whether a child has mastered each of these units, and, if not, just where his difficulties lie and, (c) the full use of self-instructive, self corrective practice materials. (Washburne, C., Vogel, M. & W.S. Gray. 1926. A Survey of the Winnetka Public Schools. Bloomington, IL: Public School Press)

Not dissimilar was the Dalton (Massachusetts) Plan in the 1920s which also used a self-paced program to accommodate the different ability levels of the children and deployed contractual agreements between students and teachers (something that remains common educational practice around the world). There were many others, both in the U.S. and other parts of the world.

The personalization of learning through self-pacing was not, therefore, a minor interest. Between 1910 and 1924, nearly 500 articles can be documented on the subject of individualization (Grittner, 1975: 328). In just three years (1929 – 1932) of one publication, The Education Digest, there were fifty-one articles dealing with individual instruction and sixty-three entries treating individual differences (Chastain, 1975: 334). Foreign language teaching did not feature significantly in these early attempts to facilitate self-pacing, but see the Burk Plan described above. Only a handful of references to language learning and self-pacing appeared in articles between 1916 and 1924 (Grittner, 1975: 328).

Disappointingly, none of these initiatives lasted long. Both costs and management issues had been significantly underestimated. Plans such as those described above were seen as progress, but not the hoped-for solution. Problems included the fact that the materials themselves were not individualized and instructional methods were too rigid (Pendleton, 1930: 199). However, concomitant with the interest in individualization (mostly, self-pacing), came the advent of educational technology.

Sidney L. Pressey, the inventor of what was arguably the first teaching machine, was inspired by his experiences with schoolchildren in rural Indiana in the 1920s where he ‘was struck by the tremendous variation in their academic abilities and how they were forced to progress together at a slow, lockstep pace that did not serve all students well’ (Ferster, 2014: 52). Although Pressey failed in his attempts to promote his teaching machines, he laid the foundation stones in the synthesizing of individualization and technology.

Pressey may be seen as the direct precursor of programmed instruction, now closely associated with B. F. Skinner (see my post on Behaviourism and Adaptive Learning). It is a quintessentially self-paced approach and is described by John Hattie as follows:

Programmed instruction is a teaching method of presenting new subject matter to students in graded sequence of controlled steps. A book version, for example, presents a problem or issue, then, depending on the student’s answer to a question about the material, the student chooses from optional answers which refers them to particular pages of the book to find out why they were correct or incorrect – and then proceed to the next part of the problem or issue. (Hattie, 2009: 231)

Programmed instruction was mostly used for the teaching of mathematics, but it is estimated that 4% of programmed instruction programs were for foreign languages (Saettler, 1990: 297). It flourished in the 1960s and 1970s, but even by 1968 foreign language instructors were sceptical (Valdman, 1968). A survey carried out by the Center for Applied Linguistics revealed then that only about 10% of foreign language teachers at college and university reported the use of programmed materials in their departments. (Valdman, 1968: 1).

Research studies had failed to demonstrate the effectiveness of programmed instruction (Saettler, 1990: 303). Teachers were often resistant and students were often bored, finding ‘ingenious ways to circumvent the program, including the destruction of their teaching machines!’ (Saettler, ibid.).

In the case of language learning, there were other problems. For programmed instruction to have any chance of working, it was necessary to specify rigorously the initial and terminal behaviours of the learner so that the intermediate steps leading from the former to the latter could be programmed. As Valdman (1968: 4) pointed out, this is highly problematic when it comes to languages (a point that I have made repeatedly in this blog). In addition, students missed the personal interaction that conventional instruction offered, got bored and lacked motivation (Valdman, 1968: 10).

Programmed instruction worked best when teachers were very enthusiastic, but perhaps the most significant lesson to be learned from the experiments was that it was ‘a difficult, time-consuming task to introduce programmed instruction’ (Saettler, 1990: 299). It entailed changes to well-established practices and attitudes, and for such changes to succeed there must be consideration of the social, political, and economic contexts. As Saettler (1990: 306), notes, ‘without the support of the community and the entire teaching staff, sustained innovation is unlikely’. In this light, Hattie’s research finding that ‘when comparisons are made between many methods, programmed instruction often comes near the bottom’ (Hattie, 2009: 231) comes as no great surprise.

Just as programmed instruction was in its death throes, the world of language teaching discovered individualization. Launched as a deliberate movement in the early 1970s at the Stanford Conference (Altman & Politzer, 1971), it was a ‘systematic attempt to allow for individual differences in language learning’ (Stern, 1983: 387). Inspired, in part, by the work of Carl Rogers, this ‘humanistic turn’ was a recognition that ‘each learner is unique in personality, abilities, and needs. Education must be personalized to fit the individual; the individual must not be dehumanized in order to meet the needs of an impersonal school system’ (Disick, 1975:38). In ELT, this movement found many adherents and remains extremely influential to this day.

In language teaching more generally, the movement lost impetus after a few years, ‘probably because its advocates had underestimated the magnitude of the task they had set themselves in trying to match individual learner characteristics with appropriate teaching techniques’ (Stern, 1983: 387). What precisely was meant by individualization was never adequately defined or agreed (a problem that remains to the present time). What was left was self-pacing. In 1975, it was reported that ‘to date the majority of the programs in second-language education have been characterized by a self-pacing format […]. Practice seems to indicate that ‘individualized’ instruction is being defined in the class room as students studying individually’ (Chastain, 1975: 344).

Lessons to be learned

This brief account shows that historical attempts to facilitate self-pacing have largely been characterised by failure. The starting point of all these attempts remains as valid as ever, but it is clear that practical solutions are less than simple. To avoid the insanity of doing the same thing over and over again and expecting different results, we should perhaps try to learn from the past.

One of the greatest challenges that teachers face is dealing with different levels of ability in their classes. In any blended scenario where the online component has an element of self-pacing, the challenge will be magnified as ability differentials are likely to grow rather than decrease as a result of the self-pacing. Bart Simpson hit the nail on the head in a memorable line: ‘Let me get this straight. We’re behind the rest of the class and we’re going to catch up to them by going slower than they are? Coo coo!’ Self-pacing runs into immediate difficulties when it comes up against standardised tests and national or state curriculum requirements. As Ferster observes, ‘the notion of individual pacing [remains] antithetical to […] a graded classroom system, which has been the model of schools for the past century. Schools are just not equipped to deal with students who do not learn in age-processed groups, even if this system is clearly one that consistently fails its students (Ferster, 2014: 90-91).

Ability differences are less problematic if the teacher focusses primarily on communicative tasks in F2F time (as opposed to more teaching of language items), but this is a big ‘if’. Many teachers are unsure of how to move towards a more communicative style of teaching, not least in large classes in compulsory schooling. Since there are strong arguments that students would benefit from a more communicative, less transmission-oriented approach anyway, it makes sense to focus institutional resources on equipping teachers with the necessary skills, as well as providing support, before a shift to a blended, more self-paced approach is implemented.

Such issues are less important in private institutions, which are not age-graded, and in self-study contexts. However, even here there may be reasons to proceed cautiously before buying into self-paced approaches. Self-pacing is closely tied to autonomous goal-setting (which I will look at in more detail in another post). Both require a degree of self-awareness at a cognitive and emotional level (McMahon & Oliver, 2001), but not all students have such self-awareness (Magill, 2008). If students do not have the appropriate self-regulatory strategies and are simply left to pace themselves, there is a chance that they will ‘misregulate their learning, exerting control in a misguided or counterproductive fashion and not achieving the desired result’ (Kirschner & van Merriënboer, 2013: 177). Before launching students on a path of self-paced language study, ‘thought needs to be given to the process involved in users becoming aware of themselves and their own understandings’ (McMahon & Oliver, 2001: 1304). Without training and support provided both before and during the self-paced study, the chances of dropping out are high (as we see from the very high attrition rate in language apps).

However well-intentioned, many past attempts to facilitate self-pacing have also suffered from the poor quality of the learning materials. The focus was more on the technology of delivery, and this remains the case today, as many posts on this blog illustrate. Contemporary companies offering language learning programmes show relatively little interest in the content of the learning (take Duolingo as an example). Few app developers show signs of investing in experienced curriculum specialists or materials writers. Glossy photos, contemporary videos, good UX and clever gamification, all of which become dull and repetitive after a while, do not compensate for poorly designed materials.

Over forty years ago, a review of self-paced learning concluded that the evidence on its benefits was inconclusive (Allison, 1975: 5). Nothing has changed since. For some people, in some contexts, for some of the time, self-paced learning may work. Claims that go beyond that cannot be substantiated.

References

Allison, E. 1975. ‘Self-Paced Instruction: A Review’ The Journal of Economic Education 7 / 1: 5 – 12

Altman, H.B. & Politzer, R.L. (eds.) 1971. Individualizing Foreign Language Instruction: Proceedings of the Stanford Conference, May 6 – 8, 1971. Washington, D.C.: Office of Education, U.S. Department of Health, Education, and Welfare

Chastain, K. 1975. ‘An Examination of the Basic Assumptions of “Individualized” Instruction’ The Modern Language Journal 59 / 7: 334 – 344

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

Ferster, B. 2014. Teaching Machines. Baltimore: John Hopkins University Press

Grittner, F. M. 1975. ‘Individualized Instruction: An Historical Perspective’ The Modern Language Journal 59 / 7: 323 – 333

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

Januszewski, A. 2001. Educational Technology: The Development of a Concept. Englewood, Colorado: Libraries Unlimited

Kirschner, P. A. & van Merriënboer, J. J. G. 2013. ‘Do Learners Really Know Best? Urban Legends in Education’ Educational Psychologist, 48:3, 169-183

Magill, D. S. 2008. ‘What Part of Self-Paced Don’t You Understand?’ University of Wisconsin 24th Annual Conference on Distance Teaching & Learning Conference Proceedings.

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

Pendleton, C. S. 1930. ‘Personalizing English Teaching’ Peabody Journal of Education 7 / 4: 195 – 200

Saettler, P. 1990. The Evolution of American Educational Technology. Denver: Libraries Unlimited

Stern, H.H. 1983. Fundamental Concepts of Language Teaching. Oxford: Oxford University Press

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

 

ELT as an Industry

by Philip Kerr & Andrew Wickham

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

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

Education in a neoliberal world

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

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

Efficacy and learning outcomes

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

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

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

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

Digital technology

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

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

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

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

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

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

Teachers’ pay and conditions

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

Implications

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

References

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

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

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

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

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

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

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

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

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

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

 

 

Word lists

Every now and then, someone recommends me to take a look at a flashcard app. It’s often interesting to see what developers have done with design, gamification and UX features, but the content is almost invariably awful. Most recently, I was encouraged to look at Word Pash. The screenshots below are from their promotional video.

   

The content problems are immediately apparent: an apparently random selection of target items, an apparently random mix of high and low frequency items, unidiomatic language examples, along with definitions and distractors that are less frequent than the target item. I don’t know if these are representative of the rest of the content. The examples seem to come from ‘Stage 1 Level 3’, whatever that means. (My confidence in the product was also damaged by the fact that the Word Pash website includes one testimonial from a certain ‘Janet Reed – Proud Mom’, whose son ‘was able to increase his score and qualify for academic scholarships at major universities’ after using the app. The picture accompanying ‘Janet Reed’ is a free stock image from Pexels and ‘Janet Reed’ is presumably fictional.)

According to the website, ‘WordPash is a free-to-play mobile app game for everyone in the global audience whether you are a 3rd grader or PhD, wordbuff or a student studying for their SATs, foreign student or international business person, you will become addicted to this fast paced word game’. On the basis of the promotional video, the app couldn’t be less appropriate for English language learners. It seems unlikely that it would help anyone improve their ACT or SAT test scores. The suggestion that the vocabulary development needs of 9-year-olds and doctoral students are comparable is pure chutzpah.

The deliberate study of more or less random words may be entertaining, but it’s unlikely to lead to very much in practical terms. For general purposes, the deliberate learning of the highest frequency words, up to about a frequency ranking of #7500, makes sense, because there’s a reasonably high probability that you’ll come across these items again before you’ve forgotten them. Beyond that frequency level, the value of the acquisition of an additional 1000 words tails off very quickly. Adding 1000 words from frequency ranking #8000 to #9000 is likely to result in an increase in lexical understanding of general purpose texts of about 0.2%. When we get to frequency ranks #19,000 to #20,000, the gain in understanding decreases to 0.01%[1]. In other words, deliberate vocabulary learning needs to be targeted. The data is relatively recent, but the principle goes back to at least the middle of the last century when Michael West argued that a principled approach to vocabulary development should be driven by a comparison of the usefulness of a word and its ‘learning cost’[2]. Three hundred years before that, Comenius had articulated something very similar: ‘in compiling vocabularies, my […] concern was to select the words in most frequent use’[3].

I’ll return to ‘general purposes’ later in this post, but, for now, we should remember that very few language learners actually study a language for general purposes. Globally, the vast majority of English language learners study English in an academic (school) context and their immediate needs are usually exam-specific. For them, general purpose frequency lists are unlikely to be adequate. If they are studying with a coursebook and are going to be tested on the lexical content of that book, they will need to use the wordlist that matches the book. Increasingly, publishers make such lists available and content producers for vocabulary apps like Quizlet and Memrise often use them. Many examinations, both national and international, also have accompanying wordlists. Examples of such lists produced by examination boards include the Cambridge English young learners’ exams (Starters, Movers and Flyers) and Cambridge English Preliminary. Other exams do not have official word lists, but reasonably reliable lists have been produced by third parties. Examples include Cambridge First, IELTS and SAT. There are, in addition, well-researched wordlists for academic English, including the Academic Word List (AWL)  and the Academic Vocabulary List  (AVL). All of these make sensible starting points for deliberate vocabulary learning.

When we turn to other, out-of-school learners the number of reasons for studying English is huge. Different learners have different lexical needs, and working with a general purpose frequency list may be, at least in part, a waste of time. EFL and ESL learners are likely to have very different needs, as will EFL and ESP learners, as will older and younger learners, learners in different parts of the world, learners who will find themselves in English-speaking countries and those who won’t, etc., etc. For some of these demographics, specialised corpora (from which frequency-based wordlists can be drawn) exist. For most learners, though, the ideal list simply does not exist. Either it will have to be created (requiring a significant amount of time and expertise[4]) or an available best-fit will have to suffice. Paul Nation, in his recent ‘Making and Using Word Lists for Language Learning and Testing’ (John Benjamins, 2016) includes a useful chapter on critiquing wordlists. For anyone interested in better understanding the issues surrounding the development and use of wordlists, three good articles are freely available online. These are:

Lessard-Clouston, M. 2012 / 2013. ‘Word Lists for Vocabulary Learning and Teaching’ The CATESOL Journal 24.1: 287- 304

Lessard-Clouston, M. 2016. ‘Word lists and vocabulary teaching: options and suggestions’ Cornerstone ESL Conference 2016

Sorell, C. J. 2013. A study of issues and techniques for creating core vocabulary lists for English as an International Language. Doctoral thesis.

But, back to ‘general purposes’ …. Frequency lists are the obvious starting point for preparing a wordlist for deliberate learning, but they are very problematic. Frequency rankings depend on the corpus on which they are based and, since these are different, rankings vary from one list to another. Even drawing on just one corpus, rankings can be a little strange. In the British National Corpus, for example, ‘May’ (the month) is about twice as frequent as ‘August’[5], but we would be foolish to infer from this that the learning of ‘May’ should be prioritised over the learning of ‘August’. An even more striking example from the same corpus is the fact that ‘he’ is about twice as frequent as ‘she’[6]: should, therefore, ‘he’ be learnt before ‘she’?

List compilers have to make a number of judgement calls in their work. There is not space here to consider these in detail, but two particularly tricky questions concerning the way that words are chosen may be mentioned: Is a verb like ‘list’, with two different and unrelated meanings, one word or two? Should inflected forms be considered as separate words? The judgements are not usually informed by considerations of learners’ needs. Learners will probably best approach vocabulary development by building their store of word senses: attempting to learn all the meanings and related forms of any given word is unlikely to be either useful or successful.

Frequency lists, in other words, are not statements of scientific ‘fact’: they are interpretative documents. They have been compiled for descriptive purposes, not as ways of structuring vocabulary learning, and it cannot be assumed they will necessarily be appropriate for a purpose for which they were not designed.

A further major problem concerns the corpus on which the frequency list is based. Large databases, such as the British National Corpus or the Corpus of Contemporary American English, are collections of language used by native speakers in certain parts of the world, usually of a restricted social class. As such, they are of relatively little value to learners who will be using English in contexts that are not covered by the corpus. A context where English is a lingua franca is one such example.

A different kind of corpus is the Cambridge Learner Corpus (CLC), a collection of exam scripts produced by candidates in Cambridge exams. This has led to the development of the English Vocabulary Profile (EVP) , where word senses are tagged as corresponding to particular levels in the Common European Framework scale. At first glance, this looks like a good alternative to frequency lists based on native-speaker corpora. But closer consideration reveals many problems. The design of examination tasks inevitably results in the production of language of a very different kind from that produced in other contexts. Many high frequency words simply do not appear in the CLC because it is unlikely that a candidate would use them in an exam. Other items are very frequent in this corpus just because they are likely to be produced in examination tasks. Unsurprisingly, frequency rankings in EVP do not correlate very well with frequency rankings from other corpora. The EVP, then, like other frequency lists, can only serve, at best, as a rough guide for the drawing up of target item vocabulary lists in general purpose apps or coursebooks[7].

There is no easy solution to the problems involved in devising suitable lexical content for the ‘global audience’. Tagging words to levels (i.e. grouping them into frequency bands) will always be problematic, unless very specific user groups are identified. Writers, like myself, of general purpose English language teaching materials are justifiably irritated by some publishers’ insistence on allocating words to levels with numerical values. The policy, taken to extremes (as is increasingly the case), has little to recommend it in linguistic terms. But it’s still a whole lot better than the aleatory content of apps like Word Pash.

[1] See Nation, I.S.P. 2013. Learning Vocabulary in Another Language 2nd edition. (Cambridge: Cambridge University Press) p. 21 for statistical tables. See also Nation, P. & R. Waring 1997. ‘Vocabulary size, text coverage and word lists’ in Schmitt & McCarthy (eds.) 1997. Vocabulary: Description, Acquisition and Pedagogy. (Cambridge: Cambridge University Press) pp. 6 -19

[2] See Kelly, L.G. 1969. 25 Centuries of Language Teaching. (Rowley, Mass.: Rowley House) p.206 for a discussion of West’s ideas.

[3] Kelly, L.G. 1969. 25 Centuries of Language Teaching. (Rowley, Mass.: Rowley House) p. 184

[4] See Timmis, I. 2015. Corpus Linguistics for ELT (Abingdon: Routledge) for practical advice on doing this.

[5] Nation, I.S.P. 2016. Making and Using Word Lists for Language Learning and Testing. (Amsterdam: John Benjamins) p.58

[6] Taylor, J.R. 2012. The Mental Corpus. (Oxford: Oxford University Press) p.151

[7] For a detailed critique of the limitations of using the CLC as a guide to syllabus design and textbook development, see Swan, M. 2014. ‘A Review of English Profile Studies’ ELTJ 68/1: 89-96

Vocabulary apps – time and motivation

I have been putting in a lot of time studying German vocabulary with Memrise lately, but this is not a review of the Memrise app. For that, I recommend you read Marek Kiczkowiak’s second post on this app. Like me, he’s largely positive, although I am less enthusiastic about Memrise’s USP, the use of mnemonics. It’s not that mnemonics don’t work – there’s a lot of evidence that they do: it’s just that there is little or no evidence that they’re worth the investment of time.

Time … as I say, I have been putting in the hours. Every day, for over a month, averaging a couple of hours a day, it’s enough to get me very near the top of the leader board (which I keep a very close eye on) and it means that I am doing more work than 99% of other users. And, yes, my German is improving.

Putting in the time is the sine qua non of any language learning and a well-designed app must motivate users to do this. Relevant content will be crucial, as will satisfactory design, both visual and interactive. But here I’d like to focus on the two other key elements: task design / variety and gamification.

Memrise offers a limited range of task types: presentation cards (with word, phrase or sentence with translation and audio recording), multiple choice (target item with four choices), unscrambling letters or words, and dictation (see below).

As Marek writes, it does get a bit repetitive after a while (although less so than thumbing through a pack of cardboard flashcards). The real problem, though, is that there are only so many things an app designer can do with standard flashcards, if they are to contribute to learning. True, there could be a few more game-like tasks (as with Quizlet), races against the clock as you pop word balloons or something of the sort, but, while these might, just might, help with motivation, these games rarely, if ever, contribute much to learning.

What’s more, you’ll get fed up with the games sooner or later if you’re putting in serious study hours. Even if Memrise were to double the number of activity types, I’d have got bored with them by now, in the same way I got bored with the Quizlet games. Bear in mind, too, that I’ve only done a month: I have at least another two months to go before I finish the level I’m working on. There’s another issue with ‘fun’ activities / games which I’ll come on to later.

The options for task variety in vocabulary / memory apps are therefore limited. Let’s look at gamification. Memrise has leader boards (weekly, monthly, ‘all time’), streak badges, daily goals, email reminders and (in the laptop and premium versions) a variety of graphs that allow you to analyse your study patterns. Your degree of mastery of learning items is represented by a growing flower that grows leaves, flowers and withers. None of this is especially original or different from similar apps.

The trouble with all of this is that it can only work for a certain time and, for some people, never. There’s always going to be someone like me who can put in a couple of hours a day more than you can. Or someone, in my case, like ‘Nguyenduyha’, who must be doing about four hours a day, and who, I know, is out of my league. I can’t compete and the realisation slowly dawns that my life would be immeasurably sadder if I tried to.

Having said that, I have tried to compete and the way to do so is by putting in the time on the ‘speed review’. This is the closest that Memrise comes to a game. One hundred items are flashed up with four multiple choices and these are against the clock. The quicker you are, the more points you get, and if you’re too slow, or you make a mistake, you lose a life. That’s how you gain lots of points with Memrise. The problem is that, at best, this task only promotes receptive knowledge of the items, which is not what I need by this stage. At worst, it serves no useful learning function at all because I have learnt ways of doing this well which do not really involve me processing meaning at all. As Marek says in his post (in reference to Quizlet), ‘I had the feeling that sometimes I was paying more attention to ‘winning’ the game and scoring points, rather than to the words on the screen.’ In my case, it is not just a feeling: it’s an absolute certainty.

Sadly, the gamification is working against me. The more time I spend on the U-Bahn doing Memrise, the less time I spend reading the free German-language newspapers, the less time I spend eavesdropping on conversations. Two hours a day is all I have time for for my German study, and Memrise is eating it all up. I know that there are other, and better, ways of learning. In order to do what I know I should be doing, I need to ignore the gamification. For those, more reasonable, students, who can regularly do their fifteen minutes a day, day in – day out, the points and leader boards serve no real function at all.

Cheating at gamification, or gaming the system, is common in app-land. A few years ago, Memrise had to take down their leader board when they realised that cheating was taking place. There’s an inexorable logic to this: gamification is an attempt to motivate by rewarding through points, rather than the reward coming from the learning experience. The logic of the game overtakes itself. Is ‘Nguyenduyha’ cheating, or do they simply have nothing else to do all day? Am I cheating by finding time to do pointless ‘speed reviews’ that earn me lots of points?

For users like myself, then, gamification design needs to be a delicate balancing act. For others, it may be largely an irrelevance. I’ve been working recently on a general model of vocabulary app design that looks at two very different kinds of user. On the one hand, there are the self-motivated learners like myself or the millions of other who have chosen to use self-study apps. On the other, there are the millions of students in schools and colleges, studying English among other subjects, some of whom are now being told to use the vocabulary apps that are beginning to appear packaged with their coursebooks (or other learning material). We’ve never found entirely satisfactory ways of making these students do their homework, and the fact that this homework is now digital will change nothing (except, perhaps, in the very, very short term). The incorporation of games and gamification is unlikely to change much either: there will always be something more interesting and motivating (and unconnected with language learning) elsewhere.

Teachers and college principals may like the idea of gamification (without having really experienced it themselves) for their students. But more important for most of them is likely to be the teacher dashboard: the means by which they can check that their students are putting the time in. Likewise, they will see the utility of automated email reminders that a student is not working hard enough to meet their learning objectives, more and more regular tests that contribute to overall course evaluation, comparisons with college, regional or national benchmarks. Technology won’t solve the motivation issue, but it does offer efficient means of control.