Posts Tagged ‘efficacy’

440px-HydraOrganization_HeadLike the mythical monster, the ancient Hydra organisation of Marvel Comics grows two more heads if one is cut off, becoming more powerful in the process. With the most advanced technology on the planet and with a particular focus on data gathering, Hydra operates through international corporations and highly-placed individuals in national governments.
Personalized learning has also been around for centuries. Its present incarnation can be traced to the individualized instructional programmes of the late 19th century which ‘focused on delivering specific subject matter […] based on the principles of scientific management. The intent was to solve the practical problems of the classroom by reducing waste and increasing efficiency, effectiveness, and cost containment in education (Januszewski, 2001: 58). Since then, personalized learning has adopted many different names, including differentiated instruction, individualized instruction, individually guided education, programmed instruction, personalized learning, personalized instruction, and individually prescribed instruction.
Disambiguating the terms has never been easy. In the world of language learning / teaching, it was observed back in the early 1970s ‘that there is little agreement on the description and definition of individualized foreign language instruction’ (Garfinkel, 1971: 379). The point was echoed a few years later by Grittner (1975: 323): it ‘means so many things to so many different people’. A UNESCO document (Chaix & O’Neil, 1978: 6) complained that ‘the term ‘individualization’ and the many expressions using the same root, such as ‘individualized learning’, are much too ambiguous’. Zoom forward to the present day and nothing has changed. Critiquing the British government’s focus on personalized learning, the Institute for Public Policy Research (Johnson, 2004: 17) wrote that it ‘remains difficult to be certain what the Government means by personalised learning’. In the U.S. context, a piece by Sean Cavanagh (2014) in Education Week (which is financially supported by the Gates Foundation) noted that although ‘the term “personalized learning” seems to be everywhere, there is not yet a shared understanding of what it means’. In short, as Arthur Levine  has put it, the words personalized learning ‘generate more heat than light’.
Despite the lack of clarity about what precisely personalized learning actually is, it has been in the limelight of language teaching and learning since before the 1930s when Pendleton (1930: 195) described the idea as being more widespread than ever before. Zoom forward to the 1970s and we find it described as ‘one of the major movements in second-language education at the present time’ (Chastain, 1975: 334). In 1971, it was described as ‘a bandwagon onto which foreign language teachers at all levels are jumping’ (Altman & Politzer, 1971: 6). A little later, in the 1980s, ‘words or phrases such as ‘learner-centered’, ‘student-centered’, ‘personalized’, ‘individualized’, and ‘humanized’ appear as the most frequent modifiers of ‘instruction’ in journals and conferences of foreign language education (Altman & James, 1980). Continue to the present day, and we find that personalized learning is at the centre of the educational policies of governments across the world. Between 2012 and 2015, the U.S. Department of Education threw over half a billion dollars at personalized learning initiatives (Bulger, 2016: 22). At the same time, there is massive sponsorship of personalized learning from the biggest international corporations (the William and Flora Hewlett Foundation, Rogers Family Foundation, Susan and Michael Dell Foundation, and the Eli and Edythe Broad Foundation) (Bulger, 2016: 22). The Bill & Melinda Gates Foundation has invested nearly $175 million in personalized learning development and Facebook’s Mark Zuckerberg is ploughing billions of dollars into it.
There has, however, been one constant: the belief that technology can facilitate the process of personalization (whatever that might be). Technology appears to offer the potential to realise the goal of personalized learning. We have come a long way from Sydney Pressey’s attempts in the 1920s to use teaching machines to individualize instruction. At that time, the machines were just one part of the programme (and not the most important). But each new technology has offered a new range of possibilities to be exploited and each new technology, its advocates argue, ‘will solve the problems better than previous efforts’ (Ferster, 2014: xii). With the advent of data-capturing learning technologies, it has now become virtually impossible to separate advocacy of personalized instruction from advocacy of digitalization in education. As the British Department for Education has put it ‘central to personalised learning is schools’ use of data (DfES (2005) White Paper: Higher Standards, Better Schools for All. London, Department for Education and Skills, para 4.50). When the U.S. Department of Education threw half a billion dollars at personalized learning initiatives, the condition was that these projects ‘use collaborative, data-based strategies and 21st century tools to deliver instruction’ (Bulger, 2016: 22).
Is it just a coincidence that the primary advocates of personalized learning are either vendors of technology or are very close to them in the higher echelons of Hydra (World Economic Forum, World Bank, IMF, etc.)? ‘Personalized learning’ has ‘almost no descriptive value’: it is ‘a term that sounds good without the inconvenience of having any obviously specific pedagogical meaning’ (Feldstein & Hill, 2016: 30). It evokes positive responses, with its ‘nod towards more student-centered learning […], a move that honors the person learning not just the learning institution’ (Watters, 2014). As such, it is ‘a natural for marketing purposes’ since nobody in their right mind would want unpersonalized or depersonalized learning (Feldstein & Hill, 2016: 25). It’s ‘a slogan that nobody’s going to be against, and everybody’s going to be for. Nobody knows what it means, because it doesn’t mean anything. Its crucial value is that it diverts your attention from a question that does mean something: Do you support our policy?’ (Chomsky, 1997).
None of the above is intended to suggest that there might not be goals that come under the ‘personalized learning’ umbrella that are worth working towards. But that’s another story – one I will return to in another post. For the moment, it’s just worth remembering that, in one of the Marvel Comics stories, Captain America, who appeared to be fighting the depersonalized evils of the world, was actually a deep sleeper agent for Hydra.

References
Altman, H.B. & James, C.V. (eds.) 1980. Foreign Language Teaching: Meeting Individual Needs. Oxford: Pergamon Press
Altman, H.B. & Politzer, R.L. (eds.) 1971. Individualizing Foreign Language Instruction: Proceedings of the Stanford Conference, May 6 – 8, 1971. Washington, D.C.: Office of Education, U.S. Department of Health, Education, and Welfare
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. New York: Data and Society Research Institute.
Cavanagh, S. 2014. ‘What Is ‘Personalized Learning’? Educators Seek Clarity’ Education Week
Chaix, P., & O’Neil, C. 1978. A Critical Analysis of Forms of Autonomous Learning (Autodidaxy and Semi-autonomy in the Field of Foreign Language Learning. Final Report. UNESCO Doc Ed 78/WS/58
Chastain, K. 1975. ‘An Examination of the Basic Assumptions of “Individualized” Instruction’ The Modern Language Journal 59 / 7: 334 – 344
Chomsky, N. 1997. Media Control: The Spectacular Achievements of Propaganda. New York: Seven Stories Press
Feldstein, M. & Hill, P. 2016. ‘Personalized Learning: What it Really is and why it Really Matters’ EduCause Review March / April 2016: 25 – 35
Ferster, B. 2014. Teaching Machines. Baltimore: John Hopkins University Press
Garfinkel, A. 1971. ‘Stanford University Conference on Individualizing Foreign Language Instruction, May 6-8, 1971.’ The Modern Language Journal Vol. 55, No. 6 (Oct., 1971), pp. 378-381
Grittner, F. M. 1975. ‘Individualized Instruction: An Historical Perspective’ The Modern Language Journal 59 / 7: 323 – 333
Januszewski, A. 2001. Educational Technology: The Development of a Concept. Englewood, Colorado: Libraries Unlimited
Johnson, M. 2004. Personalised Learning – an Emperor’s Outfit? London: Institute for Public Policy Research
Pendleton, C. S. 1930. ‘Personalizing English Teaching’ Peabody Journal of Education 7 / 4: 195 – 200
Watters, A. 2014. The problem with ‘personalization’ Hack Education

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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

 

 

51Fgn6C4sWL__SY344_BO1,204,203,200_Decent research into adaptive learning remains very thin on the ground. Disappointingly, the Journal of Learning Analytics has only managed one issue so far in 2015, compared to three in 2014. But I recently came across an article in Vol. 18 (pp. 111 – 125) of  Informing Science: the International Journal of an Emerging Transdiscipline entitled Informing and performing: A study comparing adaptive learning to traditional learning by Murray, M. C., & Pérez, J. of Kennesaw State University.

The article is worth reading, not least because of the authors’ digestible review of  adaptive learning theory and their discussion of levels of adaptation, including a handy diagram (see below) which they have reproduced from a white paper by Tyton Partners ‘Learning to Adapt: Understanding the Adaptive Learning Supplier Landscape’. Murray and Pérez make clear that adaptive learning theory is closely connected to the belief that learning is improved when instruction is personalized — adapted to individual learning styles, but their approach is surprisingly uncritical. They write, for example, that the general acceptance of learning styles is evidenced in recommended teaching strategies in nearly every discipline, and learning styles continue to inform the evolution of adaptive learning systems, and quote from the much-quoted Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008) Learning styles: concepts and evidence, Psychological Science in the Public Interest, 9, 105–119. But Pashler et al concluded that the current evidence supporting the use of learning style-matched approaches is virtually non-existent (see here for a review of Pashler et al). And, in the world of ELT, an article in the latest edition of ELTJ by Carol Lethaby and Patricia Harries disses learning styles and other neuromyths. Given the close connection between adaptive learning theory and learning styles, one might reasonably predict that a comparative study of adaptive learning and traditional learning would not come out with much evidence in support of the former.

adaptive_taxonomyMurray and Pérez set out, anyway, to explore the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Their study compared adaptive and traditional methods in a university-level digital literacy course. Their conclusion? This study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes.

I was, however, more interested in the comments which followed this general conclusion. They point out that learning outcomes are only one measure of quality. Others, such as student persistence and engagement, they claim, can be positively affected by the employment of adaptive systems. I am not convinced. I think it’s simply far too soon to be able to judge this, and we need to wait quite some time for novelty effects to wear off. Murray and Pérez provide two references in support of their claim. One is an article by Josh Jarrett, Bigfoot, Goldilocks, and Moonshots: A Report from the Frontiers of Personalized Learning in Educause. Jarrett is Deputy Director for Postsecondary Success at the Bill & Melinda Gates Foundation and Educause is significantly funded by the Gates Foundation. Not, therefore, an entirely unbiased and trustworthy source. The other is a journalistic piece in Forbes. It’s by Tim Zimmer, entitled Rethinking higher ed: A case for adaptive learning and it reads like an advert. Zimmer is a ‘CCAP contributor’. CCAP is the Centre for College Affordability and Productivity, a libertarian, conservative foundation with a strong privatization agenda. Not, therefore, a particularly reliable source, either.

Despite their own findings, Murray and Pérez follow up their claim about student persistence and engagement with what they describe as a more compelling still argument for adaptive learning. This, they say, is the intuitively appealing case for adaptive learning systems as engines with which institutions can increase access and reduce costs. Ah, now we’re getting to the point!

 

 

 

 

 

 

 

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Back in December 2013, in an interview with eltjam , David Liu, COO of the adaptive learning company, Knewton, described how his company’s data analysis could help ELT publishers ‘create more effective learning materials’. He focused on what he calls ‘content efficacy[i]’ (he uses the word ‘efficacy’ five times in the interview), a term which he explains below:

A good example is when we look at the knowledge graph of our partners, which is a map of how concepts relate to other concepts and prerequisites within their product. There may be two or three prerequisites identified in a knowledge graph that a student needs to learn in order to understand a next concept. And when we have hundreds of thousands of students progressing through a course, we begin to understand the efficacy of those said prerequisites, which quite frankly were made by an author or set of authors. In most cases they’re quite good because these authors are actually good in what they do. But in a lot of cases we may find that one of those prerequisites actually is not necessary, and not proven to be useful in achieving true learning or understanding of the current concept that you’re trying to learn. This is interesting information that can be brought back to the publisher as they do revisions, as they actually begin to look at the content as a whole.

One commenter on the post, Tom Ewens, found the idea interesting. It could, potentially, he wrote, give us new insights into how languages are learned much in the same way as how corpora have given us new insights into how language is used. Did Knewton have any plans to disseminate the information publicly, he asked. His question remains unanswered.

At the time, Knewton had just raised $51 million (bringing their total venture capital funding to over $105 million). Now, 16 months later, Knewton have launched their new product, which they are calling Knewton Content Insights. They describe it as the world’s first and only web-based engine to automatically extract statistics comparing the relative quality of content items — enabling us to infer more information about student proficiency and content performance than ever before possible.

The software analyses particular exercises within the learning content (and particular items within them). It measures the relative difficulty of individual items by, for example, analysing how often a question is answered incorrectly and how many tries it takes each student to answer correctly. It also looks at what they call ‘exhaustion’ – how much content students are using in a particular area – and whether they run out of content. The software can correlate difficulty with exhaustion. Lastly, it analyses what they call ‘assessment quality’ – how well  individual questions assess a student’s understanding of a topic.

Knewton’s approach is premised on the idea that learning (in this case language learning) can be broken down into knowledge graphs, in which the information that needs to be learned can be arranged and presented hierarchically. The ‘granular’ concepts are then ‘delivered’ to the learner, and Knewton’s software can optimise the delivery. The first problem, as I explored in a previous post, is that language is a messy, complex system: it doesn’t lend itself terribly well to granularisation. The second problem is that language learning does not proceed in a linear, hierarchical way: it is also messy and complex. The third is that ‘language learning content’ cannot simply be delivered: a process of mediation is unavoidable. Are the people at Knewton unaware of the extensive literature devoted to the differences between synthetic and analytic syllabuses, of the differences between product-oriented and process-oriented approaches? It would seem so.

Knewton’s ‘Content Insights’ can only, at best, provide some sort of insight into the ‘language knowledge’ part of any learning content. It can say nothing about the work that learners do to practise language skills, since these are not susceptible to granularisation: you simply can’t take a piece of material that focuses on reading or listening and analyse its ‘content efficacy at the concept level’. Because of this, I predicted (in the post about Knowledge Graphs) that the likely focus of Knewton’s analytics would be discrete item, sentence-level grammar (typically tenses). It turns out that I was right.

Knewton illustrate their new product with screen shots such as those below.

Content-Insight-Assessment-1

 

 

 

 

 

Content-Insight-Exhaustion-1

 

 

 

 

 

 

 

They give a specific example of the sort of questions their software can answer. It is: do students generally find the present simple tense easier to understand than the present perfect tense? Doh!

It may be the case that Knewton Content Insights might optimise the presentation of this kind of grammar, but optimisation of this presentation and practice is highly unlikely to have any impact on the rate of language acquisition. Students are typically required to study the present perfect at every level from ‘elementary’ upwards. They have to do this, not because the presentation in, say, Headway, is not optimised. What they need is to spend a significantly greater proportion of their time on ‘language use’ and less on ‘language knowledge’. This is not just my personal view: it has been extensively researched, and I am unaware of any dissenting voices.

The number-crunching in Knewton Content Insights is unlikely, therefore, to lead to any actionable insights. It is, however, very likely to lead (as writer colleagues at Pearson and other publishers are finding out) to an obsession with measuring the ‘efficacy’ of material which, quite simply, cannot meaningfully be measured in this way. It is likely to distract from much more pressing issues, notably the question of how we can move further and faster away from peddling sentence-level, discrete-item grammar.

In the long run, it is reasonable to predict that the attempt to optimise the delivery of language knowledge will come to be seen as an attempt to tackle the wrong question. It will make no significant difference to language learners and language learning. In the short term, how much time and money will be wasted?

[i] ‘Efficacy’ is the buzzword around which Pearson has built its materials creation strategy, a strategy which was launched around the same time as this interview. Pearson is a major investor in Knewton.

Pearson’s ‘Efficacy’ initiative is a series of ‘commitments designed to measure and increase the company’s impact on learning outcomes around the world’. The company’s dedicated website  offers two glossy brochures with a wide range of interesting articles, a good questionnaire tool that can be used by anyone to measure the efficacy of their own educational products or services, as well as an excellent selection of links to other articles, some of which are critical of the initiative. These include Michael Feldstein’s long blog post  ‘Can Pearson Solve the Rubric’s Cube?’ which should be a first port of call for anyone wanting to understand better what is going on.

What does it all boil down to? The preface to Pearson’s ‘Asking More: the Path to Efficacy’ by CEO John Fallon provides a succinct introduction. Efficacy in education, says Fallon, is ‘making a measurable impact on someone’s life through learning’. ‘Measurable’ is the key word, because, as Fallon continues, ‘it is increasingly possible to determine what works and what doesn’t in education, just as in healthcare.’ We need ‘a relentless focus’ on ‘the learning outcomes we deliver’ because it is these outcomes that can be measured in ‘a systematic, evidence-based fashion’. Measurement, of course, is all the easier when education is delivered online, ‘real-time learner data’ can be captured, and the power of analytics can be deployed.

Pearson are very clearly aligning themselves with recent moves towards a more evidence-based education. In the US, Obama’s Race to the Top is one manifestation of this shift. Britain (with, for example, the Education Endowment Foundation) and France (with its Fonds d’Expérimentation pour la Jeunesse ) are both going in the same direction. Efficacy is all about evidence-based practice.

Both the terms ‘efficacy’ and ‘evidence-based practice’ come originally from healthcare. Fallon references this connection in the quote two paragraphs above. In the UK last year, Ben Goldacre (medical doctor, author of ‘Bad Science’ and a relentless campaigner against pseudo-science) was commissioned by the UK government to write a paper entitled ‘Building Evidence into Education’ . In this, he argued for the need to introduce randomized controlled trials into education in a similar way to their use in medicine.

As Fallon observed in the preface to the Pearson ‘Efficacy’ brochure, this all sounds like ‘common sense’. But, as Ben Goldacre discovered, things are not so straightforward in education. An excellent article in The Guardian outlined some of the problems in Goldacre’s paper.

With regard to ELT, Pearson’s ‘Efficacy’ initiative will stand or fall with the validity of their Global Scale of English, discussed in my March post ‘Knowledge Graphs’ . However, there are a number of other considerations that make the whole evidence-based / efficacy business rather less common-sensical than might appear at first glance.

  • The purpose of English language teaching and learning (at least, in compulsory education) is rather more than simply the mastery of grammatical and lexical systems, or the development of particular language skills. Some of these other purposes (e.g. the development of intercultural competence or the acquisition of certain 21st century skills, such as creativity) continue to be debated. There is very little consensus about the details of what these purposes (or outcomes) might be, or how they can be defined. Without consensus about these purposes / outcomes, it is not possible to measure them.
  • Even if we were able to reach a clear consensus, many of these outcomes do not easily lend themselves to measurement, and even less to low-cost measurement.
  • Although we clearly need to know what ‘works’ and what ‘doesn’t work’ in language teaching, there is a problem in assigning numerical values. As the EduThink blog observes, ‘the assignation of numerical values is contestable, problematic and complex. As teachers and researchers we should be engaging with the complexity [of education] rather than the reductive simplicities of [assigning numerical values]’.
  • Evidence-based medicine has resulted in unquestionable progress, but it is not without its fierce critics. A short summary of the criticisms can be found here .  It would be extremely risky to assume that a contested research procedure from one discipline can be uncritically applied to another.
  • Kathleen Graves, in her plenary at IATEFL 2014, ‘The Efficiency of Inefficiency’, explicitly linked health care and language teaching. She described a hospital where patient care was as much about human relationships as it was about medical treatment, an aspect of the hospital that went unnoticed by efficiency experts, since this could not be measured. See this blog for a summary of her talk.

These issues need to be discussed much further before we get swept away by the evidence-based bandwagon. If they are not, the real danger is that, as John Fallon cautions, we end up counting things that don’t really count, and we don’t count the things that really do count. Somehow, I doubt that an instrument like the Global Scale of English will do the trick.

‘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.

Talk the big data talk

Posted: March 7, 2014 in big data
Tags: ,

Pearson’s Efficacy document has a chapter called ‘A New Era of Learning Efficacy on a Planet of Smarter Systems’ by Jon Iwata. It’s basically a paean of praise to the potential of big data.  I threw the text into a word cloud program to see what would come up. And what we get is a handy little guide for anyone who wants to bluff their way in a discussion about adaptive learning. Alternatively, you could use it for buzz word bingo.

Efficacy

Some of the words you won’t be needing at all are ‘teach’, ‘teachers’, ‘classrooms’ or ‘lessons’. Sorry about the blur in the image: it’s my laptop having an emotional response.

One could be forgiven for thinking that there are no problems associated with adaptive learning in ELT. Type the term into a search engine and you’ll mostly come up with enthusiasm or sales talk. There are, however, a number of reasons to be deeply skeptical about the whole business. In the post after this, I will be considering the political background.

1. Learning theory

Jose Fereira, the CEO of Knewton, spoke, in an interview with Digital Journal[1] in October 2009, about getting down to the ‘granular level’ of learning. He was referencing, in an original turn of phrase, the commonly held belief that learning is centrally concerned with ‘gaining knowledge’, knowledge that can be broken down into very small parts that can be put together again. In this sense, the adaptive learning machine is very similar to the ‘teaching machine’ of B.F. Skinner, the psychologist who believed that learning was a complex process of stimulus and response. But how many applied linguists would agree, firstly, that language can be broken down into atomised parts (rather than viewed as a complex, dynamic system), and, secondly, that these atomised parts can be synthesized in a learning program to reform a complex whole? Human cognitive and linguistic development simply does not work that way, despite the strongly-held contrary views of ‘folk’ theories of learning (Selwyn Education and Technology 2011, p.3).

machine

Furthermore, even if an adaptive system delivers language content in personalized and interesting ways, it is still premised on a view of learning where content is delivered and learners receive it. The actual learning program is not personalized in any meaningful way: it is only the way that it is delivered that responds to the algorithms. This is, again, a view of learning which few educationalists (as opposed to educational leaders) would share. Is language learning ‘simply a technical business of well managed information processing’ or is it ‘a continuing process of ‘participation’ (Selwyn, Education and Technology 2011, p.4)?

Finally, adaptive learning is also premised on the idea that learners have particular learning styles, that these can be identified by the analytics (even if they are not given labels), and that actionable insights can be gained from this analysis (i.e. the software can decide on the most appropriate style of content delivery for an individual learner). Although the idea that teaching programs can be modified to cater to individual learning styles continues to have some currency among language teachers (e.g. those who espouse Neuro-Linguistic Programming or Multiple Intelligences Theory), it is not an idea that has much currency in the research community.

It might be the case that adaptive learning programs will work with some, or even many, learners, but it would be wise to carry out more research (see the section on Research below) before making grand claims about its efficacy. If adaptive learning can be shown to be more effective than other forms of language learning, it will be either because our current theories of language learning are all wrong, or because the learning takes place despite the theory, (and not because of it).

2. Practical problems

However good technological innovations may sound, they can only be as good, in practice, as the way they are implemented. Language laboratories and interactive whiteboards both sounded like very good ideas at the time, but they both fell out of favour long before they were technologically superseded. The reasons are many, but one of the most important is that classroom teachers did not understand sufficiently the potential of these technologies or, more basically, how to use them. Given the much more radical changes that seem to be implied by the adoption of adaptive learning, we would be wise to be cautious. The following is a short, selected list of questions that have not yet been answered.

  • Language teachers often struggle with mixed ability classes. If adaptive programs (as part of a blended program) allow students to progress at their own speed, the range of abilities in face-to-face lessons may be even more marked. How will teachers cope with this? Teacher – student ratios are unlikely to improve!
  • Who will pay for the training that teachers will need to implement effective blended learning and when will this take place?
  • How will teachers respond to a technology that will be perceived by some as a threat to their jobs and their professionalism and as part of a growing trend towards the accommodation of commercial interests (see the next post)?
  • How will students respond to online (adaptive) learning when it becomes the norm, rather than something ‘different’?

3 Research

Technological innovations in education are rarely, if ever, driven by solidly grounded research, but they are invariably accompanied by grand claims about their potential. Motion pictures, radio, television and early computers were all seen, in their time, as wonder technologies that would revolutionize education (Cuban, Teachers and Machines: The Classroom Use of Technology since 1920 1986). Early research seemed to support the claims, but the passage of time has demonstrated all too clearly the precise opposite. The arrival on the scene of e-learning in general, and adaptive learning in particular, has also been accompanied by much cheer-leading and claims of research support.

Examples of such claims of research support for adaptive learning in higher education in the US and Australia include an increase in pass rates of between 7 and 18%, a decrease of between 14 and 47% in student drop-outs, and an acceleration of 25% in the time needed to complete courses[2]. However, research of this kind needs to be taken with a liberal pinch of salt. First of all, the research has usually been commissioned, and sometimes carried out, by those with vested commercial interests in positive results. Secondly, the design of the research study usually guarantees positive results. Finally, the results cannot be interpreted to have any significance beyond their immediate local context. There is no reason to expect that what happened in a particular study into adaptive learning in, say, the University of Arizona would be replicated in, say, the Universities of Amman, Astana or anywhere else. Very often, when this research is reported, the subject of the students’ study is not even mentioned, as if this were of no significance.

The lack of serious research into the effectiveness of adaptive learning does not lead us to the conclusion that it is ineffective. It is simply too soon to say, and if the examples of motion pictures, radio and television are any guide, it will be a long time before we have any good evidence. By that time, it is reasonable to assume, adaptive learning will be a very different beast from what it is today. Given the recency of this kind of learning, the lack of research is not surprising. For online learning in general, a meta-analysis commissioned by the US Department of Education (Means et al, Evaluation of Evidence-Based Practice in Online Learning 2009, p.9) found that there were only a small number of rigorous published studies, and that it was not possible to attribute any gains in learning outcomes to online or blended learning modes. As the authors of this report were aware, there are too many variables (social, cultural and economic) to compare in any direct way the efficacy of one kind of learning with another. This is as true of attempts to compare adaptive online learning with face-to-face instruction as it is with comparisons of different methodological approaches in purely face-to-face teaching. There is, however, an irony in the fact that advocates of adaptive learning (whose interest in analytics leads them to prioritise correlational relationships over causal ones) should choose to make claims about the causal relationship between learning outcomes and adaptive learning.

Perhaps, as Selwyn (Education and Technology 2011, p.87) suggests, attempts to discover the relative learning advantages of adaptive learning are simply asking the wrong question, not least as there cannot be a single straightforward answer. Perhaps a more useful critique would be to look at the contexts in which the claims for adaptive learning are made, and by whom. Selwyn also suggests that useful insights may be gained from taking a historical perspective. It is worth noting that the technicist claims for adaptive learning (that ‘it works’ or that it is ‘effective’) are essentially the same as those that have been made for other education technologies. They take a universalising position and ignore local contexts, forgetting that ‘pedagogical approach is bound up with a web of cultural assumption’ (Wiske, ‘A new culture of teaching for the 21st century’ in Gordon, D.T. (ed.) The Digital Classroom: How Technology is Changing the Way we teach and Learn 2000, p.72). Adaptive learning might just possibly be different from other technologies, but history advises us to be cautious.


[2] These figures are quoted in Learning to Adapt: A Case for Accelerating Adaptive Learning in Higher Education, a booklet produced in March 2013 by Education Growth Advisors, an education consultancy firm. Their research is available at http://edgrowthadvisors.com/research/

Adaptive learning is a product to be sold. How?

1 Individualised learning

In the vast majority of contexts, language teaching is tied to a ‘one-size-fits-all’ model. This is manifested in institutional and national syllabuses which provide lists of structures and / or competences that all students must master within a given period of time. It is usually actualized in the use of coursebooks, often designed for ‘global markets’. Reaction against this model has been common currency for some time, and has led to a range of suggestions for alternative approaches (such as DOGME), none of which have really caught on. The advocates of adaptive learning programs have tapped into this zeitgeist and promise ‘truly personalized learning’. Atomico, a venture capital company that focuses on consumer technologies, and a major investor in Knewton, describes the promise of adaptive learning in the following terms: ‘Imagine lessons that adapt on-the-fly to the way in which an individual learns, and powerful predictive analytics that help teachers differentiate instruction and understand what each student needs to work on and why[1].’

This is a seductive message and is often framed in such a way that disagreement seems impossible. A post on one well-respected blog, eltjam, which focuses on educational technology in language learning, argued the case for adaptive learning very strongly in July 2013: ‘Adaptive Learning is a methodology that is geared towards creating a learning experience that is unique to each individual learner through the intervention of computer software. Rather than viewing learners as a homogenous collective with more or less identical preferences, abilities, contexts and objectives who are shepherded through a glossy textbook with static activities/topics, AL attempts to tap into the rich meta-data that is constantly being generated by learners (and disregarded by educators) during the learning process. Rather than pushing a course book at a class full of learners and hoping that it will (somehow) miraculously appeal to them all in a compelling, salubrious way, AL demonstrates that the content of a particular course would be more beneficial if it were dynamic and interactive. When there are as many responses, ideas, personalities and abilities as there are learners in the room, why wouldn’t you ensure that the content was able to map itself to them, rather than the other way around?[2]

Indeed. But it all depends on what, precisely, the content is – a point I will return to in a later post. For the time being, it is worth noting the prominence that this message is given in the promotional discourse. It is a message that is primarily directed at teachers. It is more than a little disingenuous, however, because teachers are not the primary targets of the promotional discourse, for the simple reason that they are not the ones with purchasing power. The slogan on the homepage of the Knewton website shows clearly who the real audience is: ‘Every education leader needs an adaptive learning infrastructure’[3].

2 Learning outcomes and testing

Education leaders, who are more likely these days to come from the world of business and finance than the world of education, are currently very focused on two closely interrelated topics: the need for greater productivity and accountability, and the role of technology. They generally share the assumption of other leaders in the World Economic Forum that ICT is the key to the former and ‘the key to a better tomorrow’ (Spring, Education Networks, 2012, p.52). ‘We’re at an important transition point,’ said Arne Duncan, the U.S. Secretary of Education in 2010, ‘we’re getting ready to move from a predominantly print-based classroom to a digital learning environment’ (quoted by Spring, 2012, p.58). Later in the speech, which was delivered at the time as the release of the new National Education Technology Plan, Duncan said ‘just as technology has increased productivity in the business world, it is an essential tool to help boost educational productivity’. The plan outlines how this increased productivity could be achieved: we must start ‘with being clear about the learning outcomes we expect from the investments we make’ (Office of Educational Technology, Transforming American Education: Learning Powered by Technology, U.S. Department of Education, 2010). The greater part of the plan is devoted to discussion of learning outcomes and assessment of them.

Learning outcomes (and their assessment) are also at the heart of ‘Asking More: the Path to Efficacy’ (Barber and Rizvi (eds), Asking More: the Path to Efficacy Pearson, 2013), Pearson’s blueprint for the future of education. According to John Fallon, the CEO of Pearson, ‘our focus should unfalteringly be on honing and improving the learning outcomes we deliver’ (Barber and Rizvi, 2013, p.3). ‘High quality learning’ is associated with ‘a relentless focus on outcomes’ (ibid, p.3) and words like ‘measuring / measurable’, ‘data’ and ‘investment’ are almost as salient as ‘outcomes’. A ‘sister’ publication, edited by the same team, is entitled ‘The Incomplete Guide to Delivering Learning Outcomes’ (Barber and Rizvi (eds), Pearson, 2013) and explores further Pearson’s ambition to ‘become the world’s leading education company’ and to ‘deliver learning outcomes’.

It is no surprise that words like ‘outcomes’, ‘data’ and ‘measure’ feature equally prominently in the language of adaptive software companies like Knewton (see, for example, the quotation from Jose Ferreira, CEO of Knewton, in an earlier post). Adaptive software is premised on the establishment and measurement of clearly defined learning outcomes. If measurable learning outcomes are what you’re after, it’s hard to imagine a better path to follow than adaptive software. If your priorities include standards and assessment, it is again hard to imagine an easier path to follow than adaptive software, which was used in testing long before its introduction into instruction. As David Kuntz, VP of research at Knewton and, before that, a pioneer of algorithms in the design of tests, points out, ‘when a student takes a course powered by Knewton, we are continuously evaluating their performance, what others have done with that material before, and what [they] know’[4]. Knewton’s claim that every education leader needs an adaptive learning infrastructure has a powerful internal logic.

3 New business models

‘Adapt or die’ (a phrase originally coined by the last prime minister of apartheid South Africa) is a piece of advice that is often given these days to both educational institutions and publishers. British universities must adapt or die, according to Michael Barber, author of ‘An Avalanche is Coming[5]’ (a report commissioned by the British Institute for Public Policy Research), Chief Education Advisor to Pearson, and editor of the Pearson ‘Efficacy’ document (see above). ELT publishers ‘must change or die’, reported the eltjam blog[6], and it is a message that is frequently repeated elsewhere. The move towards adaptive learning is seen increasingly often as one of the necessary adaptations for both these sectors.

The problems facing universities in countries like the U.K. are acute. Basically, as the introduction to ‘An Avalanche is Coming’ puts it, ‘the traditional university is being unbundled’. There are a number of reasons for this including the rising cost of higher education provision, greater global competition for the same students, funding squeezes from central governments, and competition from new educational providers (such as MOOCs). Unsurprisingly, universities (supported by national governments) have turned to technology, especially online course delivery, as an answer to their problems. There are two main reasons for this. Firstly, universities have attempted to reduce operating costs by looking for increases in scale (through mergers, transnational partnerships, international branch campuses and so on). Mega-universities are growing, and there are thirty-three in Asia alone (Selwyn Education in a Digital World New York: Routledge 2013, p.6). Universities like the Turkish Anadolu University, with over one million students, are no longer exceptional in terms of scale. In this world, online educational provision is a key element. Secondly, and not to put too fine a point on it, online instruction is cheaper (Spring, Education Networks 2012, p.2).

All other things being equal, why would any language department of an institute of higher education not choose an online environment with an adaptive element? Adaptive learning, for the time being at any rate, may be seen as ‘the much needed key to the “Iron Triangle” that poses a conundrum to HE providers; cost, access and quality. Any attempt to improve any one of those conditions impacts negatively on the others. If you want to increase access to a course you run the risk of escalating costs and jeopardising quality, and so on.[7]

Meanwhile, ELT publishers have been hit by rampant pirating of their materials, spiraling development costs of their flagship products and the growth of open educational resources. An excellent blog post by David Wiley[8] explains why adaptive learning services are a heaven-sent opportunity for publishers to modify their business model. ‘While the broad availability of free content and open educational resources have trained internet users to expect content to be free, many people are still willing to pay for services. Adaptive learning systems exploit this willingness by deeply intermingling content and services so that you cannot access one with using the other. Naturally, because an adaptive learning service is comprised of content plus adaptive services, it will be more expensive than static content used to be. And because it is a service, you cannot simply purchase it like you used to buy a textbook. An adaptive learning service is something you subscribe to, like Netflix. […] In short, why is it in a content company’s interest to enable you to own anything? Put simply, it is not. When you own a copy, the publisher completely loses control over it. When you subscribe to content through a digital service (like an adaptive learning service), the publisher achieves complete and perfect control over you and your use of their content.’

Although the initial development costs of building a suitable learning platform with adaptive capabilities are high, publishers will subsequently be able to produce and modify content (i.e. learning materials) much more efficiently. Since content will be mashed up and delivered in many different ways, author royalties will be cut or eliminated. Production and distribution costs will be much lower, and sales and marketing efforts can be directed more efficiently towards the most significant customers. The days of ELT sales reps trying unsuccessfully to get an interview with the director of studies of a small language school or university department are becoming a thing of the past. As with the universities, scale will be everything.


[2]http://www.eltjam.com/adaptive-learning/ (last accessed 13 January 2014)

[3] http://www.knewton.com/ (last accessed 13 January 2014)

[4] MIT Technology Review, November 26, 2012 http://www.technologyreview.com/news/506366/questions-surround-software-that-adapts-to-students/ (last accessed 13 January 2014)

[7] Tim Gifford Taking it Personally: Adaptive Learning July 9, 2013 http://www.eltjam.com/adaptive-learning/ (last accessed January 13, 2014)

[8] David Wiley, Buying our Way into Bondage: the risks of adaptive learning services March 20,2013 http://opencontent.org/blog/archives/2754 (last accessed January 13, 2014)

In order to understand more complex models of adaptive learning, it is necessary to take a temporary step sideways away from the world of language learning. Businesses have long used analytics – the analysis of data to find meaningful patterns – in insurance, banking and marketing. With the exponential growth in computer processing power and memory capacity, businesses now have access to volumes of data of almost unimaginable size. This is known as ‘big data’ and has been described as ‘a revolution that will transform how we live, work and think’ (Mayer-Schönberger & Cukier, ‘Big Data’, 2013). Frequently cited examples of the potential of big data are the success of Amazon to analyze and predict buying patterns and the use of big data analysis in Barack Obama’s 2012 presidential re-election. Business commentators are all singing the same song on the subject. This will be looked at again in later posts. For the time being, it is enough to be aware of the main message. ‘The high-performing organisation of the future will be one that places great value on data and analytical exploration’ (The Economist Intelligence Unit, ‘In Search of Insight and Foresight: Getting more out of big data’ 2013, p.15). ‘Almost no sphere of business activity will remain untouched by this movement,’ (McAfee & Brynjolfsson, ‘Big Data: The Management Revolution’, Harvard Business Review (October 2012), p. 65).

The Economist cover

With the growing bonds between business and education (another topic which will be explored later), it is unsurprising that language learning / teaching materials are rapidly going down the big data route. In comparison to what is now being developed for ELT, the data that is analyzed in the adaptive learning models I have described in an earlier post is very limited, and the algorithms used to shape the content are very simple.

The volume and variety of data and the speed of processing are now of an altogether different order. Jose Ferreira, CEO of Knewton, one of the biggest players in adaptive learning in ELT, spells out the kind of data that can be tapped[1]:

At Knewton, we divide educational data into five types: one pertaining to student identity and onboarding, and four student activity-based data sets that have the potential to improve learning outcomes. They’re listed below in order of how difficult they are to attain:

1) Identity Data: Who are you? Are you allowed to use this application? What admin rights do you have? What district are you in? How about demographic info?

2) User Interaction Data: User interaction data includes engagement metrics, click rate, page views, bounce rate, etc. These metrics have long been the cornerstone of internet optimization for consumer web companies, which use them to improve user experience and retention. This is the easiest to collect of the data sets that affect student outcomes. Everyone who creates an online app can and should get this for themselves.

3) Inferred Content Data: How well does a piece of content “perform” across a group, or for any one subgroup, of students? What measurable student proficiency gains result when a certain type of student interacts with a certain piece of content? How well does a question actually assess what it intends to? Efficacy data on instructional materials isn’t easy to generate — it requires algorithmically normed assessment items. However it’s possible now for even small companies to “norm” small quantities of items. (Years ago, before we developed more sophisticated methods of norming items at scale, Knewton did so using Amazon’s “Mechanical Turk” service.)

4) System-Wide Data: Rosters, grades, disciplinary records, and attendance information are all examples of system-wide data. Assuming you have permission (e.g. you’re a teacher or principal), this information is easy to acquire locally for a class or school. But it isn’t very helpful at small scale because there is so little of it on a per-student basis. At very large scale it becomes more useful, and inferences that may help inform system-wide recommendations can be teased out.

5) Inferred Student Data: Exactly what concepts does a student know, at exactly what percentile of proficiency? Was an incorrect answer due to a lack of proficiency, or forgetfulness, or distraction, or a poorly worded question, or something else altogether? What is the probability that a student will pass next week’s quiz, and what can she do right this moment to increase it?

Software of this kind keeps complex personal profiles, with millions of variables per student, on as many students as necessary. The more student profiles (and therefore students) that can be compared, the more useful the data is. Big players in this field, such as Knewton, are aiming for student numbers in the tens to hundreds of millions. Once data volume of this order is achieved, the ‘analytics’, or the algorithms that convert data into ‘actionable insights’ (J. Spring, ‘Education Networks’ (New York: Routledge, 2012), p.55) become much more reliable.