Knowble – a review

Knowble, claims its developers, is a browser extension that will improve English vocabulary and reading comprehension. It also describes itself as an ‘adaptive language learning solution for publishers’. It’s currently beta and free, and sounds right up my street so I decided to give it a run.

Users are asked to specify a first language (I chose French) and a level (A1 to C2): I chose B1, but this did not seem to impact on anything that subsequently happened. They are then offered a menu of about 30 up-to-date news items, grouped into 5 categories (world, science, business, sport, entertainment). Clicking on one article takes you to the article on the source website. There’s a good selection, including USA Today, CNN, Reuters, the Independent and the Torygraph from Britain, the Times of India, the Independent from Ireland and the Star from Canada. A large number of words are underlined: a single click brings up a translation in the extension box. Double-clicking on all other words will also bring up translations. Apart from that, there is one very short exercise (which has presumably been automatically generated) for each article.

For my trial run, I picked three articles: ‘Woman asks firefighters to help ‘stoned’ raccoon’ (from the BBC, 240 words), ‘Plastic straw and cotton bud ban proposed’ (also from the BBC, 823 words) and ‘London’s first housing market slump since 2009 weighs on UK price growth’ (from the Torygraph, 471 words).

Translations

Research suggests that the use of translations, rather than definitions, may lead to more learning gains, but the problem with Knowble is that it relies entirely on Google Translate. Google Translate is fast improving. Take the first sentence of the ‘plastic straw and cotton bud’ article, for example. It’s not a bad translation, but it gets the word ‘bid’ completely wrong, translating it as ‘offre’ (= offer), where ‘tentative’ (= attempt) is needed. So, we can still expect a few problems with Google Translate …

One of the reasons that Google Translate has improved is that it no longer treats individual words as individual lexical items. It analyses groups of words and translates chunks or phrases (see, for example, the way it translates ‘as part of’). It doesn’t do word-for-word translation. Knowble, however, have set their software to ask Google for translations of each word as individual items, so the phrase ‘as part of’ is translated ‘comme’ + ‘partie’ + ‘de’. Whilst this example is comprehensible, problems arise very quickly. ‘Cotton buds’ (‘cotons-tiges’) become ‘coton’ + ‘bourgeon’ (= botanical shoots of cotton). Phrases like ‘in time’, ‘run into’, ‘sleep it off’ ‘take its course’, ‘fire station’ or ‘going on’ (all from the stoned raccoon text) all cause problems. In addition, Knowble are not using any parsing tools, so the system does not identify parts of speech, and further translation errors inevitably appear. In the short article of 240 words, about 10% are wrongly translated. Knowble claim to be using NLP tools, but there’s no sign of it here. They’re just using Google Translate rather badly.

Highlighted items

NLP tools of some kind are presumably being used to select the words that get underlined. Exactly how this works is unclear. On the whole, it seems that very high frequency words are ignored and that lower frequency words are underlined. Here, for example, is the list of words that were underlined in the stoned raccoon text. I’ve compared them with (1) the CEFR levels for these words in the English Profile Text Inspector, and (2) the frequency information from the Macmillan dictionary (more stars = more frequent). In the other articles, some extremely high frequency words were underlined (e.g. price, cost, year) while much lower frequency items were not.

It is, of course, extremely difficult to predict which items of vocabulary a learner will know, even if we have a fairly accurate idea of their level. Personal interests play a significant part, so, for example, some people at even a low level will have no problem with ‘cannabis’, ‘stoned’ and ‘high’, even if these are low frequency. First language, however, is a reasonably reliable indicator as cognates can be expected to be easy. A French speaker will have no problem with ‘appreciate’, ‘unique’ and ‘symptom’. A recommendation engine that can meaningfully personalize vocabulary suggestions will, at the very least, need to consider cognates.

In short, the selection and underlining of vocabulary items, as it currently stands in Knowble, appears to serve no clear or useful function.

Vocabulary learning

Knowble offers a very short exercise for each article. They are of three types: word completion, dictation and drag and drop (see the example). The rationale for the selection of the target items is unclear, but, in any case, these exercises are tokenistic in the extreme and are unlikely to lead to any significant learning gains. More valuable would be the possibility of exporting items into a spaced repetition flash card system.

The claim that Knowble’s ‘learning effect is proven scientifically’ seems to me to be without any foundation. If there has been any proper research, it’s not signposted anywhere. Sure, reading lots of news articles (with a look-up function – if it works reliably) can only be beneficial for language learners, but they can do that with any decent dictionary running in the background.

Similar in many ways to en.news, which I reviewed in my last post, Knowble is another example of a technology-driven product that shows little understanding of language learning.