Analytics and ELT course materials

Posted: May 2, 2015 in analytics, coursebooks
Tags: , , , , , , , , , ,

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

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

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

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

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

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

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

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















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

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

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

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

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

  1. Matthew says:

    More wasted money…yes. And because of the recent buzz about teaching conditions and pay (the lack thereof) where it really makes my mind go is: more money NOT for professional teachers, more money and waste AT THE TOP, more corporate, less communal, more information, less communication. Less human. Not good.

  2. […] Note: Coincidentally, Philip Kerr has just blogged on this same topic, i.e. Knewton’s ‘Content insights’, here: Adaptive Learning in ELT […]

  3. […] post I totally recommend is Phillip Kerr’s Anaylitics and Elt Courses Materials. At the end he makes a point that is hard to contest. And if you take the time to watch […]

  4. […] education – cannot be algo-ised, at least not without massively oversimplifying them. Philip Kerr and Scott Thornbury have both written fairly recently about adaptive learning company Knewton and […]

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