Archive for the ‘adaptive’ Category

There are a number of reasons why we sometimes need to describe a person’s language competence using a single number. Most of these are connected to the need for a shorthand to differentiate people, in summative testing or in job selection, for example. Numerical (or grade) allocation of this kind is so common (and especially in times when accountability is greatly valued) that it is easy to believe that this number is an objective description of a concrete entity, rather than a shorthand description of an abstract concept. In the process, the abstract concept (language competence) becomes reified and there is a tendency to stop thinking about what it actually is.

Language is messy. It’s a complex, adaptive system of communication which has a fundamentally social function. As Diane Larsen-Freeman and others have argued patterns of use strongly affect how language is acquired, is used, and changes. These processes are not independent of one another but are facets of the same complex adaptive system. […] The system consists of multiple agents (the speakers in the speech community) interacting with one another [and] the structures of language emerge from interrelated patterns of experience, social interaction, and cognitive mechanisms.

As such, competence in language use is difficult to measure. There are ways of capturing some of it. Think of the pages and pages of competency statements in the Common European Framework, but there has always been something deeply unsatisfactory about documents of this kind. How, for example, are we supposed to differentiate, exactly and objectively, between, say, can participate fully in an interview (C1) and can carry out an effective, fluent interview (B2)? The short answer is that we can’t. There are too many of these descriptors anyway and, even if we did attempt to use such a detailed tool to describe language competence, we would still be left with a very incomplete picture. There is at least one whole book devoted to attempts to test the untestable in language education (edited by Amos Paran and Lies Sercu, Multilingual Matters, 2010).

So, here is another reason why we are tempted to use shorthand numerical descriptors (such as A1, A2, B1, etc.) to describe something which is very complex and abstract (‘overall language competence’) and to reify this abstraction in the process. From there, it is a very short step to making things even more numerical, more scientific-sounding. Number-creep in recent years has brought us the Pearson Global Scale of English which can place you at a precise point on a scale from 10 to 90. Not to be outdone, Cambridge English Language Assessment now has a scale that runs from 80 points to 230, although Cambridge does, at least, allocate individual scores for four language skills.

As the title of this post suggests (in its reference to Stephen Jay Gould’s The Mismeasure of Man), I am suggesting that there are parallels between attempts to measure language competence and the sad history of attempts to measure ‘general intelligence’. Both are guilty of the twin fallacies of reification and ranking – the ordering of complex information as a gradual ascending scale. These conceptual fallacies then lead us, through the way that they push us to think about language, into making further conceptual errors about language learning. We start to confuse language testing with the ways that language learning can be structured.

We begin to granularise language. We move inexorably away from difficult-to-measure hazy notions of language skills towards what, on the surface at least, seem more readily measurable entities: words and structures. We allocate to them numerical values on our testing scales, so that an individual word can be deemed to be higher or lower on the scale than another word. And then we have a syllabus, a synthetic syllabus, that lends itself to digital delivery and adaptive manipulation. We find ourselves in a situation where materials writers for Pearson, writing for a particular ‘level’, are only allowed to use vocabulary items and grammatical structures that correspond to that ‘level’. We find ourselves, in short, in a situation where the acquisition of a complex and messy system is described as a linear, additive process. Here’s an example from the Pearson website: If you score 29 on the scale, you should be able to identify and order common food and drink from a menu; at 62, you should be able to write a structured review of a film, book or play. And because the GSE is so granular in nature, you can conquer smaller steps more often; and you are more likely to stay motivated as you work towards your goal. It’s a nonsense, a nonsense that is dictated by the needs of testing and adaptive software, but the sciency-sounding numbers help to hide the conceptual fallacies that lie beneath.

Perhaps, though, this doesn’t matter too much for most language learners. In the early stages of language learning (where most language learners are to be found), there are countless millions of people who don’t seem to mind the granularised programmes of Duolingo or Rosetta Stone, or the Grammar McNuggets of coursebooks. In these early stages, anything seems to be better than nothing, and the testing is relatively low-stakes. But as a learner’s interlanguage becomes more complex, and as the language she needs to acquire becomes more complex, attempts to granularise it and to present it in a linearly additive way become more problematic. It is for this reason, I suspect, that the appeal of granularised syllabuses declines so rapidly the more progress a learner makes. It comes as no surprise that, the further up the scale you get, the more that both teachers and learners want to get away from pre-determined syllabuses in coursebooks and software.

Adaptive language learning software is continuing to gain traction in the early stages of learning, in the initial acquisition of basic vocabulary and structures and in coming to grips with a new phonological system. It will almost certainly gain even more. But the challenge for the developers and publishers will be to find ways of making adaptive learning work for more advanced learners. Can it be done? Or will the mismeasure of language make it impossible?

‘Adaptive’ is a buzzword in the marketing of educational products. Chris Dragon, President of Pearson Digital Learning, complained on the Pearson Research blog. that there are so many EdTech providers claiming to be ‘adaptive’ that you have to wonder if they are not using the term too loosely. He talks about semantic satiation, the process whereby ‘temporary loss of meaning [is] experienced when one is exposed to the uninterrupted repetition of a word or phrase’. He then goes on to claim that Pearson’s SuccessMaker (‘educational software that differentiates and personalizes K-8 reading and math instruction’) is the real adaptive McCoy.

‘Adaptive’ is also a buzzword in marketing itself. Google the phrase ‘adaptive marketing’ and you’ll quickly come up with things like Adaptive Marketing Set to Become the Next Big Thing or Adaptive marketing changes the name of the game. Adaptive marketing is what you might expect: the use of big data to track customers and enable ‘marketers to truly tailor their activities in rapid and unparalleled ways to meet their customers’ interests and needs’ (Advertising Age, February 2012). It strikes me that this sets up an extraordinary potential loop: students using adaptive learning software that generates a huge amount of data which could then be used by adaptive marketers to sell other products.

I decided it might be interesting to look at the way one adaptive software company markets itself. Knewton, for example, which claims its products are more adaptive than anybody else’s.

Knewton clearly spend a lot of time and money on their marketing efforts. There is their blog and a magazine called ‘The Knerd’. There are very regular interviews by senior executives with newspapers, magazines and other blogs. There are very frequent conference presentations. All of these are easily accessible, so it is quite easy to trace Knewton’s marketing message. And even easier when they are so open about it. David Liu, Chief Operating Officer has given an interview  in which he outlines his company’s marketing strategy. Knewton, he says, focuses on driving organic interests and traffic. To that end, we have a digital marketing group that’s highly skilled and focused on creating content marketing so users, influencers and partners alike can understand our product, the value we bring and how to work with us. We also use a lot of advanced digital and online lead generation type of techniques to target potential partners and users to be able to get the right people in those discussions.

The message consists of four main strands, which I will call EdTech, EduCation, EduBusiness and EdUtopia. Depending on the audience, the marketing message will be adapted, with one or other of these strands given more prominence.

1 EdTech

Hardly surprisingly, Knewton focuses on what they call their ‘heavy duty infrastructure for an adaptive world’. They are very proud of their adaptive credentials, their ‘rigorous data science’. The basic message is that ‘only Knewton provides true personalization for any student, anywhere’. They are not shy of using technical jargon and providing technical details to prove their point.

2 EduCation

The key message here is effectiveness (Knewton also uses the term ‘efficacy’). Statistics about growth in pass rates and reduction in withdrawal rates at institutions are cited. At the same time, teachers are directly appealed to with statements like ‘as a teacher, you get tools you never had before’ and ‘teachers will be able to add their own content, upload it, tag it and seamlessly use it’. Accompanying this fairly direct approach is a focus on buzz words and phrases which can be expected to resonate with teachers. Recent blog posts include in their headlines: ‘supporting creativity’, ‘student-centred learning’, ‘peer mentoring’, ‘formative evaluation’, ‘continuous assessment’, ‘learning styles’, ‘scaffolding instruction’, ‘real-world examples’, ‘enrichment’ or ‘lifelong learning’.

There is an apparent openness in Knewton’s readiness to communicate with the rest of the world. The blog invites readers to start discussions and post comments. Almost no one does. But one blog post by Jose Ferreira called ‘Rebooting Learning Styles’  provoked a flurry of highly critical and well-informed responses. These remain unanswered. A similar thing happened when David Liu did a guest post at eltjam. A flurry of criticism, but no response. My interpretation of this is that Knewton are a little scared of engaging in debate and of having their marketing message hijacked.

3 EduBusiness

Here’s a sample of ways that Knewton speak to potential customers and investors:

an enormous new market of online courses that bring high margin revenue and rapid growth for institutions that start offering them early and declining numbers for those who do not.

Because Knewton is trying to disrupt the traditional industry, we have nothing to lose—we’re not cannibalising ourselves—by partnering.

Unlike other groups dabbling in adaptive learning, Knewton doesn’t force you to buy pre-fabricated products using our own content. Our platform makes it possible for anyone — publishers, instructors, app developers, and others — to build her own adaptive applications using any content she likes.

The data platform industries tend to have a winner-take-all dynamic. You take that and multiply it by a very, very high-stakes product and you get an even more winner-take-all dynamic.

4 EdUtopia

I personally find this fourth strand the most interesting. Knewton are not unique in adopting this line, but it is a sign of their ambition that they choose to do so. All of the quotes that follow are from Jose Ferreira:

We can’t improve education by curing poverty. We have to cure poverty by improving education.

Edtech is our best hope to narrow — at scale — the Achievement Gap between rich and poor. Yet, for a time, it will increase that gap. Society must push past that unfortunate moment and use tech-assisted outcome improvements as the rationale to drive spending in poor schools.

I started Knewton to do my bit to fix the world’s education system. Education is among the most important problems we face, because it’s the ultimate “gateway” problem. That is, it drives virtually every global problem that we face as a species. But there’s a flip-side: if we can fix education, then we’ll dramatically improve the other problems, too. So in fact, I started Knewton not just to help fix education but to try to fix just about everything.

What if the girl who invents the cure for ovarian cancer is growing up in a Cambodian fishing village and otherwise wouldn’t have a chance? As distribution of technology continues to improve, adaptive learning will give her and similar students countless opportunities that they otherwise wouldn’t have.

But our ultimate vision – and what really motivated me to start the company – is to solve the access problem for the human race once and for all. Only 22% of the world finishes high school; only 55% finish sixth grade. This is a preventable tragedy. Adaptive learning can give students around the world access to high-quality education they wouldn’t otherwise have.

There is a lot that technology can do to help English language learners develop their reading skills. The internet makes it possible for learners to read an almost limitless number of texts that will interest them, and these texts can evaluated for readability and, therefore, suitability for level (see here for a useful article). RSS opens up exciting possibilities for narrow reading and the positive impact of multimedia-enhanced texts was researched many years ago. There are good online bilingual dictionaries and other translation tools. There are apps that go with graded readers (see this review in the Guardian) and there are apps that can force you to read at a certain speed. And there is more. All of this could very effectively be managed on a good learning platform.

Could adaptive software add another valuable element to reading skills development?

Adaptive reading programs are spreading in the US in primary education, and, with some modifications, could be used in ELT courses for younger learners and for those who do not have the Roman alphabet. One of the most well-known has been developed by Lexia Learning®, a company that won a $500,000 grant from the Gates Foundation last year. Lexia Learning® was bought by Rosetta Stone® for $22.5 million in June 2013.

One of their products, Lexia Reading Core5, ‘provides explicit, systematic, personalized learning in the six areas of reading instruction, and delivers norm-referenced performance data and analysis without interrupting the flow of instruction to administer a test. Designed specifically to meet the Common Core and the most rigorous state standards, this research-proven, technology-based approach accelerates reading skills development, predicts students’ year-end performance and provides teachers data-driven action plans to help differentiate instruction’.

core5-ss-small

The predictable claim that it is ‘research-proven’ has not convinced everyone. Richard Allington, a professor of literacy studies at the University of Tennessee and a past president of both the International Reading Association and the National Reading Association, has said that all the companies that have developed this kind of software ‘come up with evidence – albeit potential evidence — that kids could improve their abilities to read by using their product. It’s all marketing. They’re selling a product. Lexia is one of these programs. But there virtually are no commercial programs that have any solid, reliable evidence that they improve reading achievement.’[1] He has argued that the $12 million that has been spent on the Lexia programs would have been better spent on a national program, developed at Ohio State University, that matches specially trained reading instructors with students known to have trouble learning to read.

But what about ELT? For an adaptive program like Lexia’s to work, reading skills need to be broken down in a similar way to the diagram shown above. Let’s get some folk linguistics out of the way first. The sub-skills of reading are not skimming, scanning, inferring meaning from context, etc. These are strategies that readers adopt voluntarily in order to understand a text better. If a reader uses these strategies in their own language, they are likely to transfer these strategies to their English reading. It seems that ELT instruction in strategy use has only limited impact, although this kind of training may be relevant to preparation for exams. This insight is taking a long time to filter down to course and coursebook design, but there really isn’t much debate[2]. Any adaptive ELT reading program that confuses reading strategies with reading sub-skills is going to have big problems.

What, then, are the sub-skills of reading? In what ways could reading be broken down into a skill tree so that it is amenable to adaptive learning? Researchers have provided different answers. Munby (1978), for example, listed 19 reading microskills, Heaton (1988) listed 14. However, a bigger problem is that other researchers (e.g. Lunzer 1979, Rost 1993) have failed to find evidence that distinct sub-skills actually exist. While it is easier to identify sub-skills for very low level readers (especially for those whose own language is very different from English), it is simply not possible to do so for higher levels.

Reading in another language is a complex process which involves both top-down and bottom-up strategies, is intimately linked to vocabulary knowledge and requires the activation of background, cultural knowledge. Reading ability, in the eyes of some researchers, is unitary or holistic. Others prefer to separate things into two components: word recognition and comprehension[3]. Either way, a consensus is beginning to emerge that teachers and learners might do better to focus on vocabulary extension (and this would include extensive reading) than to attempt to develop reading programs that assume the multidivisible nature of reading.

All of which means that adaptive learning software and reading skills in ELT are unlikely bedfellows. To be sure, an increased use of technology (as described in the first paragraph of this post) in reading work will generate a lot of data about learner behaviours. Analysis of this data may lead to actionable insights, and it may not! It will be interesting to find out.

 

[1] http://www.khi.org/news/2013/jun/17/budget-proviso-reading-program-raises-questions/

[2] See, for example, Walter, C. & M. Swan. 2008. ‘Teaching reading skills: mostly a waste of time?’ in Beaven, B. (ed.) IATEFL 2008 Exeter Conference Selections. (Canterbury: IATEFL). Or go back further to Alderson, J. C. 1984 ‘Reading in a foreign language: a reading problem or a language problem?’ in J.C. Alderson & A. H. Urquhart (eds.) Reading in a Foreign Language (London: Longman)

[3] For a useful summary of these issues, see ‘Reading abilities and strategies: a short introduction’ by Feng Liu (International Education Studies 3 / 3 August 2010) www.ccsenet.org/journal/index.php/ies/article/viewFile/6790/5321