Posts Tagged ‘learning outcomes’

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

 

 

Ok, let’s be honest here. This post is about teacher training, but ‘development’ sounds more respectful, more humane, more modern. Teacher development (self-initiated, self-evaluated, collaborative and holistic) could be adaptive, but it’s unlikely that anyone will want to spend the money on developing an adaptive teacher development platform any time soon. Teacher training (top-down, pre-determined syllabus and externally evaluated) is another matter. If you’re not too clear about this distinction, see Penny Ur’s article in The Language Teacher.

decoding_adaptive jpgThe main point of adaptive learning tools is to facilitate differentiated instruction. They are, as Pearson’s latest infomercial booklet describes them, ‘educational technologies that can respond to a student’s interactions in real-time by automatically providing the student with individual support’. Differentiation or personalization (or whatever you call it) is, as I’ve written before  , the declared goal of almost everyone in educational power these days. What exactly it is may be open to question (see Michael Feldstein’s excellent article), as may be the question of whether or not it is actually such a desideratum (see, for example, this article ). But, for the sake of argument, let’s agree that it’s mostly better than one-size-fits-all.

Teachers around the world are being encouraged to adopt a differentiated approach with their students, and they are being encouraged to use technology to do so. It is technology that can help create ‘robust personalized learning environments’ (says the White House)  . Differentiation for language learners could be facilitated by ‘social networking systems, podcasts, wikis, blogs, encyclopedias, online dictionaries, webinars, online English courses,’ etc. (see Alexandra Chistyakova’s post on eltdiary ).

But here’s the crux. If we want teachers to adopt a differentiated approach, they really need to have experienced it themselves in their training. An interesting post on edweek  sums this up: If professional development is supposed to lead to better pedagogy that will improve student learning AND we are all in agreement that modeling behaviors is the best way to show people how to do something, THEN why not ensure all professional learning opportunities exhibit the qualities we want classroom teachers to have?

Differentiated teacher development / training is rare. According to the Center for Public Education’s Teaching the Teachers report , almost all teachers participate in ‘professional development’ (PD) throughout the year. However, a majority of those teachers find the PD in which they participate ineffective. Typically, the development is characterised by ‘drive-by’ workshops, one-size-fits-all presentations, ‘been there, done that’ topics, little or no modelling of what is being taught, a focus on rotating fads and a lack of follow-up. This report is not specifically about English language teachers, but it will resonate with many who are working in English language teaching around the world.cindy strickland

The promotion of differentiated teacher development is gaining traction: see here or here , for example, or read Cindy A. Strickland’s ‘Professional Development for Differentiating Instruction’.

Remember, though, that it’s really training, rather than development, that we’re talking about. After all, if one of the objectives is to equip teachers with a skills set that will enable them to become more effective instructors of differentiated learning, this is most definitely ‘training’ (notice the transitivity of the verbs ‘enable’ and ‘equip’!). In this context, a necessary starting point will be some sort of ‘knowledge graph’ (which I’ve written about here ). For language teachers, these already exist, including the European Profiling Grid , the Eaquals Framework for Language Teacher Training and Development, the Cambridge English Teaching Framework and the British Council’s Continuing Professional Development Framework (CPD) for Teachers  . We can expect these to become more refined and more granularised, and a partial move in this direction is the Cambridge English Digital Framework for Teachers  . Once a knowledge graph is in place, the next step will be to tag particular pieces of teacher training content (e.g. webinars, tasks, readings, etc.) to locations in the framework that is being used. It would not be too complicated to engineer dynamic frameworks which could be adapted to individual or institutional needs.cambridge_english_teaching_framework jpg

This process will be facilitated by the fact that teacher training content is already being increasingly granularised. Whether it’s an MA in TESOL or a shorter, more practically oriented course, things are getting more and more bite-sized, with credits being awarded to these short bites, as course providers face stiffer competition and respond to market demands.

Visible classroom home_page_screenshotClassroom practice could also form part of such an adaptive system. One tool that could be deployed would be Visible Classroom , an automated system for providing real-time evaluative feedback for teachers. There is an ‘online dashboard providing teachers with visual information about their teaching for each lesson in real-time. This includes proportion of teacher talk to student talk, number and type of questions, and their talking speed.’ John Hattie, who is behind this project, says that teachers ‘account for about 30% of the variance in student achievement and [are] the largest influence outside of individual student effort.’ Teacher development with a tool like Visible Classroom is ultimately all about measuring teacher performance (against a set of best-practice benchmarks identified by Hattie’s research) in order to improve the learning outcomes of the students.Visible_classroom_panel_image jpg

You may have noticed the direction in which this part of this blog post is going. I began by talking about social networking systems, podcasts, wikis, blogs and so on, and just now I’ve mentioned the summative, credit-bearing possibilities of an adaptive teacher development training programme. It’s a tension that is difficult to resolve. There’s always a paradox in telling anyone that they are going to embark on a self-directed course of professional development. Whoever pays the piper calls the tune and, if an institution decides that it is worth investing significant amounts of money in teacher development, they will want a return for their money. The need for truly personalised teacher development is likely to be overridden by the more pressing need for accountability, which, in turn, typically presupposes pre-determined course outcomes, which can be measured in some way … so that quality (and cost-effectiveness and so on) can be evaluated.

Finally, it’s worth asking if language teaching (any more than language learning) can be broken down into small parts that can be synthesized later into a meaningful and valuable whole. Certainly, there are some aspects of language teaching (such as the ability to use a dashboard on an LMS) which lend themselves to granularisation. But there’s a real danger of losing sight of the forest of teaching if we focus on the individual trees that can be studied and measured.

If you’re going to teach vocabulary, you need to organise it in some way. Almost invariably, this organisation is topical, with words grouped into what are called semantic sets. In coursebooks, the example below (from Rogers, M., Taylore-Knowles, J. & S. Taylor-Knowles. 2010. Open Mind Level 1. London: Macmillan, p.68) is fairly typical.

open mind

Coursebooks are almost always organised in a topical way. The example above comes in a unit (of 10 pages), entitled ‘You have talent!’, which contains two main vocabulary sections. It’s unsurprising to find a section called ‘personality adjectives’ in such a unit. What’s more, such an approach lends itself to the requisite, but largely, spurious ‘can-do’ statement in the self-evaluation section: I can talk about people’s positive qualities. We must have clearly identifiable learning outcomes, after all.

There is, undeniably, a certain intuitive logic in this approach. An alternative might entail a radical overhaul of coursebook architecture – this might not be such a bad thing, but might not go down too well in the markets. How else, after all, could the vocabulary strand of the syllabus be organised?

Well, there are a number of ways in which a vocabulary syllabus could be organised. Including the standard approach described above, here are four possibilities:

1 semantic sets (e.g. bee, butterfly, fly, mosquito, etc.)

2 thematic sets (e.g. ‘pets’: cat, hate, flea, feed, scratch, etc.)

3 unrelated sets

4 sets determined by a group of words’ occurrence in a particular text

Before reading further, you might like to guess what research has to say about the relative effectiveness of these four approaches.

The answer depends, to some extent, on the level of the learner. For advanced learners, it appears to make no, or little, difference (Al-Jabri, 2005, cited by Ellis & Shintani, 2014: 106). But, for the vast majority of English language learners (i.e. those at or below B2 level), the research is clear: the most effective way of organising vocabulary items to be learnt is by grouping them into thematic sets (2) or by mixing words together in a semantically unrelated way (3) – not by teaching sets like ‘personality adjectives’. It is surprising how surprising this finding is to so many teachers and materials writers. It goes back at least to 1988 and West’s article on ‘Catenizing’ in ELTJ, which argued that semantic grouping made little sense from a psycho-linguistic perspective. Since then, a large amount of research has taken place. This is succinctly summarised by Paul Nation (2013: 128) in the following terms: Avoid interference from related words. Words which are similar in form (Laufer, 1989) or meaning (Higa, 1963; Nation, 2000; Tinkham, 1993; Tinkham, 1997; Waring, 1997) are more difficult to learn together than they are to learn separately. For anyone who is interested, the most up-to-date review of this research that I can find is in chapter 11 of Barcroft (2105).

The message is clear. So clear that you have to wonder how it is not getting through to materials designers. Perhaps, coursebooks are different. They regularly eschew research findings for commercial reasons. But vocabulary apps? There is rarely, if ever, any pressure on the content-creation side of vocabulary apps (except those that are tied to coursebooks) to follow the popular misconceptions that characterise so many coursebooks. It wouldn’t be too hard to organise vocabulary into thematic sets (like, for example, the approach in the A2 level of Memrise German that I’m currently using). Is it simply because the developers of so many vocabulary apps just don’t know much about language learning?

References

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

Nation, I. S. P. 2013. Learning Vocabulary in Another Language 2nd edition. Cambridge: Cambridge University Press

Ellis, R. & N. Shintani, N. 2014. Exploring Language Pedagogy through Second Language Acquisition Research. Abingdon, Oxon: Routledge

West, M. 1988. ‘Catenizing’ English Language Teaching Journal 6: 147 – 151

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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2014-09-30_2216Jose Ferreira, the fast-talking sales rep-in-chief of Knewton, likes to dazzle with numbers. In a 2012 talk hosted by the US Department of Education, Ferreira rattles off the stats: So Knewton students today, we have about 125,000, 180,000 right now, by December it’ll be 650,000, early next year it’ll be in the millions, and next year it’ll be close to 10 million. And that’s just through our Pearson partnership. For each of these students, Knewton gathers millions of data points every day. That, brags Ferreira, is five orders of magnitude more data about you than Google has. … We literally have more data about our students than any company has about anybody else about anything, and it’s not even close. With just a touch of breathless exaggeration, Ferreira goes on: We literally know everything about what you know and how you learn best, everything.

The data is mined to find correlations between learning outcomes and learning behaviours, and, once correlations have been established, learning programmes can be tailored to individual students. Ferreira explains: We take the combined data problem all hundred million to figure out exactly how to teach every concept to each kid. So the 100 million first shows up to learn the rules of exponents, great let’s go find a group of people who are psychometrically equivalent to that kid. They learn the same ways, they have the same learning style, they know the same stuff, because Knewton can figure out things like you learn math best in the morning between 8:40 and 9:13 am. You learn science best in 42 minute bite sizes the 44 minute mark you click right, you start missing questions you would normally get right.

The basic premise here is that the more data you have, the more accurately you can predict what will work best for any individual learner. But how accurate is it? In the absence of any decent, independent research (or, for that matter, any verifiable claims from Knewton), how should we respond to Ferreira’s contribution to the White House Education Datapalooza?

A 51Oy5J3o0yL._AA258_PIkin4,BottomRight,-46,22_AA280_SH20_OU35_new book by Stephen Finlay, Predictive Analytics, Data Mining and Big Data (Palgrave Macmillan, 2014) suggests that predictive analytics are typically about 20 – 30% more accurate than humans attempting to make the same judgements. That’s pretty impressive and perhaps Knewton does better than that, but the key thing to remember is that, however much data Knewton is playing with, and however good their algorithms are, we are still talking about predictions and not certainties. If an adaptive system could predict with 90% accuracy (and the actual figure is typically much lower than that) what learning content and what learning approach would be effective for an individual learner, it would still mean that it was wrong 10% of the time. When this is scaled up to the numbers of students that use Knewton software, it means that millions of students are getting faulty recommendations. Beyond a certain point, further expansion of the data that is mined is unlikely to make any difference to the accuracy of predictions.

A further problem identified by Stephen Finlay is the tendency of people in predictive analytics to confuse correlation and causation. Certain students may have learnt maths best between 8.40 and 9.13, but it does not follow that they learnt it best because they studied at that time. If strong correlations do not involve causality, then actionable insights (such as individualised course design) can be no more than an informed gamble.

Knewton’s claim that they know how every student learns best is marketing hyperbole and should set alarm bells ringing. When it comes to language learning, we simply do not know how students learn (we do not have any generally accepted theory of second language acquisition), let alone how they learn best. More data won’t help our theories of learning! Ferreira’s claim that, with Knewton, every kid gets a perfectly optimized textbook, except it’s also video and other rich media dynamically generated in real time is equally preposterous, not least since the content of the textbook will be at least as significant as the way in which it is ‘optimized’. And, as we all know, textbooks have their faults.

Cui bono? Perhaps huge data and predictive analytics will benefit students; perhaps not. We will need to wait and find out. But Stephen Finlay reminds us that in gold rushes (and internet booms and the exciting world of Big Data) the people who sell the tools make a lot of money. Far more strike it rich selling picks and shovels to prospectors than do the prospectors. Likewise, there is a lot of money to be made selling Big Data solutions. Whether the buyer actually gets any benefit from them is not the primary concern of the sales people. (p.16/17) Which is, perhaps, one of the reasons that some sales people talk so fast.

(This post was originally published at eltjam.)

learning_teaching_ngramWe now have young learners and very young learners, learner differences and learner profiles, learning styles, learner training, learner independence and autonomy, learning technologies, life-long learning, learning management systems, virtual learning environments, learning outcomes, learning analytics and adaptive learning. Much, but not perhaps all, of this is to the good, but it’s easy to forget that it wasn’t always like this.

The rise in the use of the terms ‘learner’ and ‘learning’ can be seen in policy documents, educational research and everyday speech, and it really got going in the mid 1980s[1]. Duncan Hunter and Richard Smith[2] have identified a similar trend in ELT after analysing a corpus of articles from the English Language Teaching Journal. They found that ‘learner’ had risen to near the top of the key-word pile in the mid 1980s, but had been practically invisible 15 years previously. Accompanying this rise has been a relative decline of words like ‘teacher’, ‘teaching’, ‘pupil’ and, even, ‘education’. Gert Biesta has described this shift in discourse as a ‘new language of learning’ and the ‘learnification of education’.

It’s not hard to see the positive side of this change in focus towards the ‘learner’ and away from the syllabus, the teachers and the institution in which the ‘learning’ takes place. We can, perhaps, be proud of our preference for learner-centred approaches over teacher-centred ones. We can see something liberating (for our students) in the change of language that we use. But, as Bingham and Biesta[3] have pointed out, this gain is also a loss.

The language of ‘learners’ and ‘learning’ focusses our attention on process – how something is learnt. This was a much-needed corrective after an uninterrupted history of focussing on end-products, but the corollary is that it has become very easy to forget not only about the content of language learning, but also its purposes and the social relationships through which it takes place.

There has been some recent debate about the content of language learning, most notably in the work of the English as a Lingua Franca scholars. But there has been much more attention paid to the measurement of the learners’ acquisition of that content (through the use of tools like the Pearson Global Scale of English). There is a growing focus on ‘granularized’ content – lists of words and structures, and to a lesser extent language skills, that can be easily measured. It looks as though other things that we might want our students to be learning – critical thinking skills and intercultural competence, for example – are being sidelined.

More significant is the neglect of the purposes of language learning. The discourse of ELT is massively dominated by the paying sector of private language schools and semi-privatised universities. In these contexts, questions of purpose are not, perhaps, terribly important, as the whole point of the enterprise can be assumed to be primarily instrumental. But the vast majority of English language learners around the world are studying in state-funded institutions as part of a broader educational programme, which is as much social and political as it is to do with ‘learning’. The ultimate point of English lessons in these contexts is usually stated in much broader terms. The Council of Europe’s Common European Framework of Reference, for example, states that the ultimate point of the document is to facilitate better intercultural understanding. It is very easy to forget this when we are caught up in the business of levels and scales and measuring learning outcomes.

Lastly, a focus on ‘learners’ and ‘learning’ distracts attention away from the social roles that are enacted in classrooms. 25 years ago, Henry Widdowson[4] pointed out that there are two quite different kinds of role. The first of these is concerned with occupation (student / pupil vs teacher / master / mistress) and is identifying. The second (the learning role) is actually incidental and cannot be guaranteed. He reminds us that the success of the language learning / teaching enterprise depends on ‘recognizing and resolving the difficulties inherent in the dual functioning of roles in the classroom encounter’[5]. Again, this may not matter too much in the private sector, but, elsewhere, any attempt to tackle the learning / teaching conundrum through an exclusive focus on learning processes is unlikely to succeed.

The ‘learnification’ of education has been accompanied by two related developments: the casting of language learners as consumers of a ‘learning experience’ and the rise of digital technologies in education. For reasons of space, I will limit myself to commenting on the second of these[6]. Research by Geir Haugsbakk and Yngve Nordkvelle[7] has documented a clear and critical link between the new ‘language of learning’ and the rhetoric of edtech advocacy. These researchers suggest that these discourses are mutually reinforcing, that both contribute to the casting of the ‘learner’ as a consumer, and that the coupling of learning and digital tools is often purely rhetorical.

One of the net results of ‘learnification’ is the transformation of education into a technical or technological problem to be solved. It suggests, wrongly, that approaches to education can be derived purely from theories of learning. By adopting an ahistorical and apolitical standpoint, it hides ‘the complex nexus of political and economic power and resources that lies behind a considerable amount of curriculum organization and selection’[8]. The very real danger, as Biesta[9] has observed, is that ‘if we fail to engage with the question of good education head-on – there is a real risk that data, statistics and league tables will do the decision-making for us’.

[1] 2004 Biesta, G.J.J. ‘Against learning. Reclaiming a language for education in an age of learning’ Nordisk Pedagogik 24 (1), 70-82 & 2010 Biesta, G.J.J. Good Education in an Age of Measurement (Boulder, Colorado: Paradigm Publishers)

[2] 2012 Hunter, D. & R. Smith ‘Unpackaging the past: ‘CLT’ through ELTJ keywords’ ELTJ 66/4 430-439

[3] 2010 Bingham, C. & Biesta, G.J.J. Jacques Rancière: Education, Truth, Emancipation (London: Continuum) 134

[4] 1990 Widdowson, H.G. Aspects of Language Teaching (Oxford: OUP) 182 ff

[5] 1987 Widdowson, H.G. ‘The roles of teacher and learner’ ELTJ 41/2

[6] A compelling account of the way that students have become ‘consumers’ can be found in 2013 Williams, J. Consuming Higher Education (London: Bloomsbury)

[7] 2007 Haugsbakk, G. & Nordkvelle, Y. ‘The Rhetoric of ICT and the New Language of Learning: a critical analysis of the use of ICT in the curricular field’ European Educational Research Journal 6/1 1 – 12

[8] 2004 Apple, M. W. Ideology and Curriculum 3rd edition (New York: Routledge) 28

[9] 2010 Biesta, G.J.J. Good Education in an Age of Measurement (Boulder, Colorado: Paradigm Publishers) 27

 

 

(This post won’t make a lot of sense unless you read the previous one – Researching research: part 1!)

dropoutsI suggested in the previous post that the research of Jayaprakash et al had confirmed something that we already knew concerning the reasons why some students drop out of college. However, predictive analytics are only part of the story. As the authors of this paper point out, they ‘do not influence course completion and retention rates without being combined with effective intervention strategies aimed at helping at-risk students succeed’. The point of predictive analytics is to facilitate the deployment of effective and appropriate interventions strategies, and to do this sooner than would be possible without the use of the analytics. So, it is to these intervention strategies that I now turn.

Interventions to help at-risk students included the following:

  • Sending students messages to inform them that they are at risk of not completing the course (‘awareness messaging’)
  • Making students more aware of the available academic support services (which could, for example, direct them to a variety of campus-based or online resources)
  • Promoting peer-to-peer engagement (e.g. with an online ‘student lounge’ discussion forum)
  • Providing access to self-assessment tools

The design of these interventions was based on the work that had been done at Purdue, which was, in turn, inspired by the work of Vince Tinto, one of the world’s leading experts on student retention issues.

The work done at Purdue had shown that simple notifications to students that they were at risk could have a significant, and positive, effect on student behaviour. Jayaprakash and the research team took the students who had been identified as at-risk by the analytics and divided them into three groups: the first were issued with ‘awareness messages’, the second were offered a combination of the other three interventions in the bullet point list above, and the third, a control group, had no interventions at all. The results showed that the students who were in treatment groups (of either kind of intervention) showed a statistically significant improvement compared to those who received no treatment at all. However, there seemed to be no difference in the effectiveness of the different kinds of intervention.

So far, so good, but, once again, I was left thinking that I hadn’t really learned very much from all this. But then, in the last five pages, the article suddenly got very interesting. Remember that the primary purpose of this whole research project was to find ways of helping not just at-risk students, but specifically socioeconomically disadvantaged at-risk students (such as those receiving Pell Grants). Accordingly, the researchers then focussed on this group. What did they find?

Once again, interventions proved more effective at raising student scores than no intervention at all. However, the averages of final scores are inevitably affected by drop-out rates (since students who drop out do not have final scores which can be included in the averages). At Purdue, the effect of interventions on drop-out rates had not been found to be significant. Remember that Purdue has a relatively well-off student demographic. However, in this research, which focussed on colleges with a much higher proportion of students on Pell Grants, the picture was very different. Of the Pell Grant students who were identified as at-risk and who were given some kind of treatment, 25.6% withdrew from the course. Of the Pell Grant students who were identified as at-risk but who were not ‘treated’ in any way (i.e. those in the control group), only 14.1% withdrew from the course. I recommend that you read those numbers again!

The research programme had resulted in substantially higher drop-out rates for socioeconomically disadvantaged students – the precise opposite of what it had set out to achieve. Jayaprakash et al devote one page of their article to the ethical issues this raises. They suggest that early intervention, resulting in withdrawal, might actually be to the benefit of some students who were going to fail whatever happened. It is better to get a ‘W’ (withdrawal) grade on your transcript than an ‘F’ (fail), and you may avoid wasting your money at the same time. This may be true, but it would be equally true that not allowing at-risk students (who, of course, are disproportionately from socioeconomically disadvantaged backgrounds) into college at all might also be to their ‘benefit’. The question, though, is: who has the right to make these decisions on behalf of other people?

The authors also acknowledge another ethical problem. The predictive analytics which will prompt the interventions are not 100% accurate. 85% accuracy could be considered a pretty good figure. This means that some students who are not at-risk are labelled as at-risk, and other who are at-risk are not identified. Of these two possibilities, I find the first far more worrying. We are talking about the very real possibility of individual students being pushed into making potentially life-changing decisions on the basis of dodgy analytics. How ethical is that? The authors’ conclusion is that the situation forces them ‘to develop the most accurate predictive models possible, as well as to take steps to reduce the likelihood that any intervention would result in the necessary withdrawal of a student’.

I find this extraordinary. It is premised on the assumption that predictive models can be made much, much more accurate. They seem to be confusing prediction and predeterminism. A predictive model is, by definition, only predictive. There will always be error. How many errors are ethically justifiable? And, the desire to reduce the likelihood of unnecessary withdrawals is a long way from the need to completely eliminate the likelihood of unnecessary withdrawals, which seems to me to be the ethical position. More than anything else in the article, this sentence illustrates that the a priori assumption is that predictive analytics can be a force for good, and that the only real problem is getting the science right. If a number of young lives are screwed up along the way, we can at least say that science is getting better.

In the authors’ final conclusion, they describe the results of their research as ‘promising’. They do not elaborate on who it is promising for. They say that relatively simple intervention strategies can positively impact student learning outcomes, but they could equally well have said that relatively simple intervention strategies can negatively impact learning outcomes. They could have said that predictive analytics and intervention programmes are fine for the well-off, but more problematic for the poor. Remembering once more that the point of the study was to look at the situation of socioeconomically disadvantaged at-risk students, it is striking that there is no mention of this group in the researchers’ eight concluding points. The vast bulk of the paper is devoted to technical descriptions of the design and training of the software; the majority of the conclusions are about the validity of that design and training. The ostensibly intended beneficiaries have got lost somewhere along the way.

How and why is it that a piece of research such as this can so positively slant its results? In the third and final part of this mini-series, I will turn my attention to answering that question.

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.

Personalization is one of the key leitmotifs in current educational discourse. The message is clear: personalization is good, one-size-fits-all is bad. ‘How to personalize learning and how to differentiate instruction for diverse classrooms are two of the great educational challenges of the 21st century,’ write Trilling and Fadel, leading lights in the Partnership for 21st Century Skills (P21)[1]. Barack Obama has repeatedly sung the praises of, and the need for, personalized learning and his policies are fleshed out by his Secretary of State, Arne Duncan, in speeches and on the White House blog: ‘President Obama described the promise of personalized learning when he launched the ConnectED initiative last June. Technology is a powerful tool that helps create robust personalized learning environments.’ In the UK, personalized learning has been government mantra for over 10 years. The EU, UNESCO, OECD, the Gates Foundation – everyone, it seems, is singing the same tune.

Personalization, we might all agree, is a good thing. How could it be otherwise? No one these days is going to promote depersonalization or impersonalization in education. What exactly it means, however, is less clear. According to a UNESCO Policy Brief[2], the term was first used in the context of education in the 1970s by Victor Garcìa Hoz, a senior Spanish educationalist and member of Opus Dei at the University of Madrid. This UNESCO document then points out that ‘unfortunately, up to this date there is no single definition of this concept’.

In ELT, the term has been used in a very wide variety of ways. These range from the far-reaching ideas of people like Gertrude Moskowitz, who advocated a fundamentally learner-centred form of instruction, to the much more banal practice of getting students to produce a few personalized examples of an item of grammar they have just studied. See Scott Thornbury’s A-Z blog for an interesting discussion of personalization in ELT.

As with education in general, and ELT in particular, ‘personalization’ is also bandied around the adaptive learning table. Duolingo advertises itself as the opposite of one-size-fits-all, and as an online equivalent of the ‘personalized education you can get from a small classroom teacher or private tutor’. Babbel offers a ‘personalized review manager’ and Rosetta Stone’s Classroom online solution allows educational institutions ‘to shift their language program away from a ‘one-size-fits-all-curriculum’ to a more individualized approach’. As far as I can tell, the personalization in these examples is extremely restricted. The language syllabus is fixed and although users can take different routes up the ‘skills tree’ or ‘knowledge graph’, they are totally confined by the pre-determination of those trees and graphs. This is no more personalized learning than asking students to make five true sentences using the present perfect. Arguably, it is even less!

This is not, in any case, the kind of personalization that Obama, the Gates Foundation, Knewton, et al have in mind when they conflate adaptive learning with personalization. Their definition is much broader and summarised in the US National Education Technology Plan of 2010: ‘Personalized learning means instruction is paced to learning needs, tailored to learning preferences, and tailored to the specific interests of different learners. In an environment that is fully personalized, the learning objectives and content as well as the method and pace may all vary (so personalization encompasses differentiation and individualization).’ What drives this is the big data generated by the students’ interactions with the technology (see ‘Part 4: big data and analytics’ of ‘The Guide’ on this blog).

What remains unclear is exactly how this might work in English language learning. Adaptive software can only personalize to the extent that the content of an English language learning programme allows it to do so. It may be true that each student using adaptive software ‘gets a more personalised experience no matter whose content the student is consuming’, as Knewton’s David Liu puts it. But the potential for any really meaningful personalization depends crucially on the nature and extent of this content, along with the possibility of variable learning outcomes. For this reason, we are not likely to see any truly personalized large-scale adaptive learning programs for English any time soon.

Nevertheless, technology is now central to personalized language learning. A good learning platform, which allows learners to connect to ‘social networking systems, podcasts, wikis, blogs, encyclopedias, online dictionaries, webinars, online English courses, various apps’, etc (see Alexandra Chistyakova’s eltdiary), means that personalization could be more easily achieved.

For the time being, at least, adaptive learning systems would seem to work best for ‘those things that can be easily digitized and tested like math problems and reading passages’ writes Barbara Bray . Or low level vocabulary and grammar McNuggets, we might add. Ideal for, say, ‘English Grammar in Use’. But meaningfully personalized language learning?

student-data-and-personalization

‘Personalized learning’ sounds very progressive, a utopian educational horizon, and it sounds like it ought to be the future of ELT (as Cleve Miller argues). It also sounds like a pretty good slogan on which to hitch the adaptive bandwagon. But somehow, just somehow, I suspect that when it comes to adaptive learning we’re more likely to see more testing, more data collection and more depersonalization.

[1] Trilling, B. & Fadel, C. 2009 21st Century Skills (San Francisco: Wiley) p.33

[2] Personalized learning: a new ICT­enabled education approach, UNESCO Institute for Information Technologies in Education, Policy Brief March 2012 iite.unesco.org/pics/publications/en/files/3214716.pdf

 

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/