Posts Tagged ‘analytics’

article-2614966-1D6DC26500000578-127_634x776In the 8th post on this blog (‘Theory, Research and Practice’), I referred to the lack of solid research into learning analytics. Whilst adaptive learning enthusiasts might disagree with much, or even most, of what I have written on this subject, here, at least, was an area of agreement. May of this year, however, saw the launch of the inaugural issue of the Journal of Learning Analytics, the first journal ‘dedicated to research into the challenges of collecting, analysing and reporting data with the specific intent to improve learning’. It is a peer-reviewed, open-access journal, available here , which is published by the Society for Learning Analytics Research (SoLAR), a consortium of academics from 9 universities in the US, Canada, Britain and Australia.

I decided to take a closer look. In this and my next two posts, I will focus on one article from this inaugural issue. It’s called Early Alert of Academically At‐Risk Students: An Open Source Analytics Initiative and it is co-authored by Sandeep M. Jayaprakash, Erik W. Moody, Eitel J.M. Lauría, James R. Regan, and Joshua D. Baron of Marist College in the US. Bear with me, please – it’s more interesting than it might sound!

The background to this paper is the often referred to problem of college drop-outs in the US, and the potential of learning analytics to address what is seen as a ‘national challenge’. The most influential work that has been done in this area to date was carried out at Purdue University. Purdue developed an analytical system, called Course Signals, which identified students at risk of course failure and offered a range of interventions (more about these in the next post) which were designed to improve student outcomes. I will have more to say about the work at Purdue in my third post, but, for the time being, it is enough to say that, in the field, it has been considered very successful, and that the authors of the paper I looked at have based their approach on the work done at Purdue.

Jayaprakash et al developed their own analytical system, based on Purdue’s Course Signals, and used it at their own institution, Marist College. Basically, they wanted to know if they could replicate the good results that had been achieved at Purdue. They then took the same analytical system to four different institutions, of very different kinds (public, as opposed to private; community colleges offering 2-year programmes rather than universities) to see if the results could be replicated there, too. They also wanted to find out if the interventions with students who had been signalled as at-risk would be as effective as they had been at Purdue. So far, so good: it is clearly very important to know if one particular piece of research has any significance beyond its immediate local context.

So, what did Jayaprakash et al find out? Basically, they learnt that their software worked as well at Marist as Course Signals had done at Purdue. They collected data on student demographics and aptitude, course grades and course related data, data on students’ interactions with the LMS they were using and performance data captured by the LMS. Oh, yes, and absenteeism. At the other institutions where they trialled their software, the system was 10% less accurate in predicting drop-outs, but the authors of the research still felt that ‘predictive models developed based on data from one institution may be scalable to other institutions’.

But more interesting than the question of whether or not the predictive analytics worked is the question of which specific features of the data were the most powerful predictors. What they discovered was that absenteeism was highly significant. No surprises there. They also learnt that the other most powerful predictors were (1) the students’ cumulative grade point average (GPA), an average of a student’s academic scores over their entire academic career, and (2) the scores recorded by the LMS of the work that students had done during the course which would contribute to their final grade. No surprises there, either. As the authors point out, ‘given that these two attributes are such fundamental aspects of academic success, it is not surprising that the predictive model has fared so well across these different institutions’.

Agreed, it is not surprising at all that students with lower scores and a history of lower scores are more likely to drop out of college than students with higher scores. But, I couldn’t help wondering, do we really need sophisticated learning analytics to tell us this? Wouldn’t any teacher know this already? They would, of course, if they knew their students, but if the teacher: student ratio is in the order of 1: 100 (not unheard of in lower-funded courses delivered primarily through an LMS), many teachers (and their students) might benefit from automated alert systems.

But back to the differences between the results at Purdue and Marist and at the other institutions. Why were the predictive analytics less successful at the latter? The answer is in the nature of the institutions. Essentially, it boils down to this. In institutions with low drop-out rates, the analytics are more reliable than in institutions with high drop-out rates, because the more at-risk students there are, the harder it is to predict the particular individuals who will actually drop out. Jayaprakash et al provide the key information in a useful table. Students at Marist College are relatively well-off (only 16% receive Pell Grants, which are awarded to students in financial need), and only a small number (12%) are from ‘ethnic minorities’. The rate of course non-completion in normal time is relatively low (at 20%). In contrast, at one of the other institutions, the College of the Redwoods in California, 44% of the students receive Pell Grants and 22% of them are from ‘ethnic minorities’. The non-completion rate is a staggering 96%. At Savannah State University, 78% of the students receive Pell Grants, and the non-completion rate is 70%. The table also shows the strong correlation between student poverty and high student: faculty ratios.

In other words, the poorer you are, the less likely you are to complete your course of study, and the less likely you are to know your tutors (these two factors also correlate). In other other words, the whiter you are, the more likely you are to complete your course of study (because of the strong correlations between race and poverty). While we are playing the game of statistical correlations, let’s take it a little further. As the authors point out, ‘there is considerable evidence that students with lower socio-economic status have lower GPAs and graduation rates’. If, therefore, GPAs are one of the most significant predictors of academic success, we can say that socio-economic status (and therefore race) is one of the most significant predictors of academic success … even if the learning analytics do not capture this directly.

Actually, we have known this for a long time. The socio-economic divide in education is frequently cited as one of the big reasons for moving towards digitally delivered courses. This particular piece of research was funded (more about this in the next posts) with the stipulation that it ‘investigated and demonstrated effective techniques to improve student retention in socio-economically disadvantaged populations’. We have also known for some time that digitally delivered education increases the academic divide between socio-economic groups. So what we now have is a situation where a digital technology (learning analytics) is being used as a partial solution to a problem that has always been around, but which has been exacerbated by the increasing use of another digital technology (LMSs) in education. We could say, then, that if we weren’t using LMSs, learning analytics would not be possible … but we would need them less, anyway.

My next post will look at the results of the interventions with students that were prompted by the alerts generated by the learning analytics. Advance warning: it will make what I have written so far seem positively rosy.

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.

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

 

Let’s take a look at the business of adaptive learning from a publisher’s perspective. Not an ELT publisher, but someone a few rungs higher on the ladder with strategic responsibilities. You might not know a great deal about ELT. It is, after all, only one of a number of divisions you are responsible for, and not an especially profitable one at that. You will, however, know a lot about creative destruction, the process by which one industry is replaced by another. The decline and demise of printed magazines, newspapers and books, of book reviewers and traditional booksellers, and their replacement by digital products will be such a part of your professional knowledge that they hardly need to be mentioned. Graphs such as the one below from PricewaterhouseCoopers (PwC) will be terribly familiar. You will also be aware that the gales of creative destruction in publishing are blowing harder than ever before.

2014-03-31_1020

In fact, you probably owe your job to your ability to talk convincingly about creative destruction and how to profit from it. Whatever your particular strategy for the future might be, you will have noted the actions of others. You will have evaluated advice, such as the following, from Publishing Perspectives

  • Do not delay in taking action when there are clear signals of a decline in market value.
  • Trade your low-profit print assets (even though some may have limited digital value) for core product that has a higher likelihood of success and can be exploited digitally.
  • Look for an orderly transition from print to digital product which enables a company to reinvent itself.

You will be looking to invest in technology, and prioritizing the acquisition of technological expertise (through partnerships or the purchase of start-ups) over the development of traditional ELT products. Your company will be restructured, and possibly renamed, to facilitate the necessary changes.

You will also know that big data and analytics have already transformed other industries. And you will know that educational publishing is moving towards a winner-take-all business market, where ‘the best performers are able to capture a very large share of the rewards, and the remaining competitors are left with very little’ (Investopedia). Erik Brynjolfsson and Andrew McAfee’s new book, The Second Machine Age (New York: Norton, 2014), argues that ‘each time a market becomes more digital, winner-take-all economics become a little more compelling …Digitization creates winner-take-all markets because [there are] enormous economies of scale, giving the market leader a huge cost advantage and room to beat the price of any competitor while still making a good profit’ (pp.153-155).

the second machine age

It is in this light that we need to understand the way that companies like Pearson and Macmillan are banking everything on a digital future. Laurie Harrison’s excellent blog post at eltjam  summarises the Pearson position: ‘the world’s biggest education publisher is spending £150m on a total restructure which involves an immediate move to digital learning, a focus on emerging markets, and a transformation from publisher to education services provider. If the English language learning market is worth $4billion a year, then Pearson still only have a very small chunk of it. And if you’re a company as successful and ambitious as Pearson, that just isn’t good enough – so a change of direction is needed. In order to deliver this change, the company have recently announced their new senior management team.’

Adaptive learning fits the new business paradigm perfectly. If the hype is to be believed, adaptive learning will be a game-changer. ‘The shifting of education from analog to digital is a one-time event in the history of the human race. At scale, it will have as big an effect on the world as indoor plumbing or electricity,’ writes Jose Ferreira of Knewton. ‘True disruption,’ he says elsewhere, ‘happens when entrepreneurs aim big and go after a giant problem, a problem that, if solved, would usher in an era of large-scale transformation across industries and nations. … Education is the last of the information industries to move online,’ he goes on. ‘When it breaks, it breaks fast. And that’s going to happen in the next five years. All the education content will go online in the next 10 years. And textbooks will go away. … Ultimately, all learning materials will be digital and they will all be adaptive.’

Ferreira clearly knows all about creative disruption. He also knows about winner-take-all markets. ‘The question is who is going to power [the] platform,’ he writes. ‘It’s probably going to be one or two companies’. He states his ambition for Knewton very clearly: ‘Knewton’s goal is to be like Amazon Web Services for education’. ‘It’s pretty clear to us,’ he writes, ‘that there’s going to be one dominant data platform for education, the way there’s one dominant data platform for search, social media, etailing. But in education, it’s going to be even more winner-take-all; there will be a number of companies that make up the platform, like Wintel. People might make a perverse choice to use Bing for search because they don’t like Google. But no one’s going to make the choice to subject their kid to the second-best adaptive learning platform, if that means there’s a 23% structural disadvantage. 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.’

What is at stake in this winner-take-all market? Over to Jose Ferreira one more time: ‘The industry is massive. It’s so massive that virtually nobody I’ve met truly grasps how big it is. It’s beyond their frame of reference. The total amount of money (both public and private) spent annually exceeds all spending, both online and offline, of every other information industry combined: that is, all media, entertainment, games, news, software, Internet and mobile media, e-tailing, etc.’

But, still, a few questions continue to nag away at me. If all of this is so certain, why does Jose Ferreira feel the need to talk about it so much? If all of this is so certain, why don’t all the ELT publishers jump on the bandwagon? What sort of track record does economic forecasting have, anyway?

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

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

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

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

core5-ss-small

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

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

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

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

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

 

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

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

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

I mentioned the issue of privacy very briefly in Part 9 of the ‘Guide’, and it seems appropriate to take a more detailed look.

Adaptive learning needs big data. Without the big data, there is nothing for the algorithms to work on, and the bigger the data set, the better the software can work. Adaptive language learning will be delivered via a platform, and the data that is generated by the language learner’s interaction with the English language program on the platform is likely to be only one, very small, part of the data that the system will store and analyse. Full adaptivity requires a psychometric profile for each student.

It would make sense, then, to aggregate as much data as possible in one place. Besides the practical value in massively combining different data sources (in order to enhance the usefulness of the personalized learning pathways), such a move would possibly save educational authorities substantial amounts of money and allow educational technology companies to mine the rich goldmine of student data, along with the standardised platform specifications, to design their products.

And so it has come to pass. The Gates Foundation (yes, them again) provided most of the $100 million funding. A division of Murdoch’s News Corp built the infrastructure. Once everything was ready, a non-profit organization called inBloom was set up to run the thing. The inBloom platform is open source and the database was initially free, although this will change. Preliminary agreements were made with 7 US districts and involved millions of children. The data includes ‘students’ names, birthdates, addresses, social security numbers, grades, test scores, disability status, attendance, and other confidential information’ (Ravitch, D. ‘Reign of Error’ NY: Knopf, 2013, p. 235-236). Under federal law, this information can be ‘shared’ with private companies selling educational technology and services.

The edtech world rejoiced. ‘This is going to be a huge win for us’, said one educational software provider; ‘it’s a godsend for us,’ said another. Others are not so happy. If the technology actually works, if it can radically transform education and ‘produce game-changing outcomes’ (as its proponents claim so often), the price to be paid might just conceivably be worth paying. But the price is high and the research is not there yet. The price is privacy.

The problem is simple. InBloom itself acknowledges that it ‘cannot guarantee the security of the information stored… or that the information will not be intercepted when it is being transmitted.’ Experience has already shown us that organisations as diverse as the CIA or the British health service cannot protect their data. Hackers like a good challenge. So do businesses.

The anti-privatization (and, by extension, the anti-adaptivity) lobby in the US has found an issue which is resonating with electors (and parents). These dissenting voices are led by Class Size Matters, and their voice is being heard. Of the original partners of inBloom, only one is now left. The others have all pulled out, mostly because of concerns about privacy, although the remaining partner, New York, involves personal data on 2.7 million students, which can be shared without any parental notification or consent.

inbloom-student-data-bill-gates

This might seem like a victory for the anti-privatization / anti-adaptivity lobby, but it is likely to be only temporary. There are plenty of other companies that have their eyes on the data-mining opportunities that will be coming their way, and Obama’s ‘Race to the Top’ program means that the inBloom controversy will be only a temporary setback. ‘The reality is that it’s going to be done. It’s not going to be a little part. It’s going to be a big part. And it’s going to be put in place partly because it’s going to be less expensive than doing professional development,’ says Eva Baker of the Center for the Study of Evaluation at UCLA.

It is in this light that the debate about adaptive learning becomes hugely significant. Class Size Matters, the odd academic like Neil Selwyn or the occasional blogger like myself will not be able to reverse a trend with seemingly unstoppable momentum. But we are, collectively, in a position to influence the way these changes will take place.

If you want to find out more, check out the inBloom and Class Size Matters links. And you might like to read more from the news reports which I have used for information in this post. Of these, the second was originally published by Scientific American (owned by Macmillan, one of the leading players in ELT adaptive learning). The third and fourth are from Education Week, which is funded in part by the Gates Foundation.

http://www.reuters.com/article/2013/03/03/us-education-database-idUSBRE92204W20130303

http://www.salon.com/2013/08/01/big_data_puts_teachers_out_of_work_partner/

http://www.edweek.org/ew/articles/2014/01/08/15inbloom_ep.h33.html

http://blogs.edweek.org/edweek/marketplacek12/2013/12/new_york_battle_over_inBloom_data_privacy_heading_to_court.html

busuu is an online language learning service. I did not refer to it in the ‘guide’ because it does not seem to use any adaptive learning software yet, but this is set to change. According to founder Bernhard Niesner, the company is already working on incorporation of adaptive software.

A few statistics will show the significance of busuu. The site currently has over 40 million users (El Pais, 8 February 2014) and is growing by 40,000 a day. The basic service is free, but the premium service costs Euro 69.99 a year. The company will not give detailed user statistics, but say that ‘hundreds of thousands’ are paying for the premium service, that turnover was a 7-figure number last year and will rise to 8 figures this year.

It is easy to understand why traditional publishers might be worried about competition like busuu and why they are turning away from print-based courses.

Busuu offers 12 languages, but, as a translation-based service, any one of these languages can only be studied if you speak one of the other languages on offer. The levels of the different courses are tagged to the CEFR.

busuuframe

In some ways, busuu is not so different from competitors like duolingo. Students are presented with bilingual vocabulary sets, accompanied by pictures, which are tested in a variety of ways. As with duolingo, some of this is a little strange. For German at level A1, I did a vocabulary set on ‘pets’ which presented the German words for a ferret, a tortoise and a guinea-pig, among others. There are dialogues, which are both written and recorded, that are sometimes surreal.

Child: Mum, look over there, there’s a dog without a collar, can we take it?

Mother: No, darling, our house is too small to have a dog.

Child: Mum your bedroom is very big, it can sleep with dad and you.

Mother: Come on, I’ll buy you a toy dog.

The dialogues are followed up by multiple choice questions which test your memory of the dialogue. There are also writing exercises where you are given a picture from National Geographic and asked to write about it. It’s not always clear what one is supposed to write. What would you say about a photo that showed a large number of parachutes in the sky, beyond ‘I can see a lot of parachutes’?

There are also many gamification elements. There is a learning carrot where you can set your own learning targets and users can earn ‘busuuberries’ which can then be traded in for animations in a ‘language garden’.

2014-02-25_0911

But in one significant respect, busuu differs from its competitors. It combines the usual vocabulary, grammar and dialogue work with social networking. Users can interact with text or video, and feedback on written work comes from other users. My own experience with this was mixed, but the potential is clear. Feedback on other learners’ work is encouraged by the awarding of ‘busuuberries’.

We will have to wait and see what busuu does with adaptive software and what it will do with the big data it is generating. For the moment, its interest lies in illustrating what could be done with a learning platform and adaptive software. The big ELT publishers know they have a new kind of competition and, with a lot more money to invest than busuu, we have to assume that what they will launch a few years from now will do everything that busuu does, and more. Meanwhile, busuu are working on site redesign and adaptivity. They would do well, too, to sort out their syllabus!

Adaptive learning is likely to impact on the lives of language teachers very soon. In my work as a writer of education materials, it has already dramatically impacted on mine. This impact has affected the kinds of things I am asked to write, the way in which I write them and my relationship with the editors and publishers I am writing for. I am as dismissive as Steve Jobs[1] was of the idea that technology can radically transform education, but in the short term it can radically disrupt it. Change is not necessarily progress.

Teachers and teacher trainers need to be very alert to what is going on if they don’t want to wake up one morning and find themselves out of work, or in a very different kind of job. The claims for adaptive language learning need to be considered in the bright light of particular, local contexts. Teachers and teacher trainers can even take a lesson from the proponents of adaptive learning who rail against the educational approach of one-size-fits-all. One size, whether it’s face-to-face with a print coursebook or whether it’s a blended adaptive program, will never fit all. We need to be very skeptical of the publishers and software providers who claim in a TED-style, almost evangelical way that they are doing the right thing for students, our society, or our world. There is a real risk that adaptive learning may be leading simply to ‘a more standardised, minimalist product targeted for a mass market, [that] will further ‘box in’ and ‘dumb down’ education’ (Selwyn, Education and Technology 2011, p.101).

There is nothing wrong, per se, with adaptive learning. It could be put to some good uses, but how likely is this? In order to understand how it may impact on our working lives, we need to be better informed. A historical perspective is often a good place to start and Larry Cuban’s Teachers and Machines: The Classroom Use of Technology since 1920 (New York: Teachers College Press, 1986) is still well worth reading.

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To get a good picture of where big data and analytics are now and where they are heading, Mayer-Schonberger & Cukier’s Big Data (London: John Murray, 2013) is informative and entertaining reading. If you are ‘an executive looking to integrate analytics in your decision making or a manager seeking to generate better conversations with the quants in your organisation’, I’d recommend Keeping up with the Quants by Thomas H. Davenport and Jinho Kim (Harvard Business School, 2013). Or you could just read ‘The Economist’ for this kind of thing.

If you want to follow up the connections between educational technology and neo-liberalism, the books by Stephen Ball (Global Education Inc., Abingdon, Oxon: Routledge, 2012), Neil Selwyn (Education and Technology, London: Continuum, 2011; Education in a Digital World, New York: Routledge, 2013; Distrusting Educational Technology, New York: Routledge, 2013), Diane Ravitch (Reign of Error, New York: Knopf, 2013) and Joel Spring (Education Networks, New York: Routledge, 2012; The Great American Education-Industrial Complex with Anthony G. Picciano, Routledge, 2013) are all good reads. And keep a look out for anything new from these writers.

Finally, to keep up to date with recent developments, the eltjam blog http://www.eltjam.com/ is a good one to follow, as is Richard Whiteside’s Scoop.it! page http://www.scoop.it/t/elt-publishing-by-richard-whiteside

I’ll be continuing to post things here from time to time! Thanks for following me so far.


[1] Jobs, however, did set his sights ‘on the $8 billion a year textbook industry, which he saw as ‘ripe for digital destruction’. His first instinct seems to have been to relieve kids from having to carry around heavy backpacks crammed with textbooks: ‘The iPad would solve that,’ he said, ever practical’ (Fullan, Stratosphere 2013, p.61).

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/