Posts Tagged ‘Bliu Bliu’

I’m a sucker for meta-analyses, those aggregates of multiple studies that generate an effect size, and I am even fonder of meta-meta analyses. I skip over the boring stuff about inclusion criteria and statistical procedures and zoom in on the results and discussion. I’ve pored over Hattie (2009) and, more recently, Dunlosky et al (2013), and quoted both more often than is probably healthy. Hardly surprising, then, that I was eager to read Luke Plonsky and Nicole Ziegler’s ‘The CALL–SLA interface: insights from a second-order synthesis’ (Plonsky & Ziegler, 2016), an analysis of nearly 30 meta-analyses (later whittled down to 14) looking at the impact of technology on L2 learning. The big question they were looking to find an answer to? How effective is computer-assisted language learning compared to face-to-face contexts?

Plonsky & Ziegler

Plonsky and Ziegler found that there are unequivocally ‘positive effects of technology on language learning’. In itself, this doesn’t really tell us anything, simply because there are too many variables. It’s a statistical soundbite, ripe for plucking by anyone with an edtech product to sell. Much more useful is to understand which technologies used in which ways are likely to have a positive effect on learning. It appears from Plonsky and Ziegler’s work that the use of CALL glosses (to develop reading comprehension and vocabulary development) provides the strongest evidence of technology’s positive impact on learning. The finding is reinforced by the fact that this particular technology was the most well-represented research area in the meta-analyses under review.

What we know about glosses

gloss_gloss_WordA gloss is ‘a brief definition or synonym, either in L1 or L2, which is provided with [a] text’ (Nation, 2013: 238). They can take many forms (e.g. annotations in the margin or at the foot a printed page), but electronic or CALL glossing is ‘an instant look-up capability – dictionary or linked’ (Taylor, 2006; 2009) which is becoming increasingly standard in on-screen reading. One of the most widely used is probably the translation function in Microsoft Word: here’s the French gloss for the word ‘gloss’.

Language learning tools and programs are making increasing use of glosses. Here are two examples. The first is Lingro , a dictionary tool that learners can have running alongside any webpage: clicking on a word brings up a dictionary entry, and the word can then be exported into a wordlist which can be practised with spaced repetition software. The example here is using the English-English dictionary, but a number of bilingual pairings are available. The second is from Bliu Bliu , a language learning app that I unkindly reviewed here .Lingro_example


So, what did Plonsky and Ziegler discover about glosses? There were two key takeways:

  • both L1 and L2 CALL glossing can be beneficial to learners’ vocabulary development (Taylor, 2006, 2009, 2013)
  • CALL / electronic glosses lead to more learning gains than paper-based glosses (p.22)

On the surface, this might seem uncontroversial, but if you took a good look at the three examples (above) of online glosses, you’ll be thinking that something is not quite right here. Lingro’s gloss is a fairly full dictionary entry: it contains too much information for the purpose of a gloss. Cognitive Load Theory suggests that ‘new information be provided concisely so as not to overwhelm the learner’ (Khezrlou et al, 2017: 106): working out which definition is relevant here (the appropriate definition is actually the sixth in this list) will overwhelm many learners and interfere with the process of reading … which the gloss is intended to facilitate. In addition, the language of the definitions is more difficult than the defined item. Cognitive load is, therefore, further increased. Lingro needs to use a decent learner’s dictionary (with a limited defining vocabulary), rather than relying on the free Wiktionary.

Nation (2013: 240) cites research which suggests that a gloss is most effective when it provides a ‘core meaning’ which users will have to adapt to what is in the text. This is relatively unproblematic, from a technological perspective, but few glossing tools actually do this. The alternative is to use NLP tools to identify the context-specific meaning: our ability to do this is improving all the time but remains some way short of total accuracy. At the very least, NLP tools are needed to identify part of speech (which will increase the probability of hitting the right meaning). Bliu Bliu gets things completely wrong, confusing the verb and the adjective ‘own’.

Both Lingro and Bliu Bliu fail to meet the first requirement of a gloss: ‘that it should be understood’ (Nation, 2013: 239). Neither is likely to contribute much to the vocabulary development of learners. We will need to modify Plonsky and Ziegler’s conclusions somewhat: they are contingent on the quality of the glosses. This is not, however, something that can be assumed …. as will be clear from even the most cursory look at the language learning tools that are available.

Nation (2013: 447) also cites research that ‘learning is generally better if the meaning is written in the learner’s first language. This is probably because the meaning can be easily understood and the first language meaning already has many rich associations for the learner. Laufer and Shmueli (1997) found that L1 glosses are superior to L2 glosses in both short-term and long-term (five weeks) retention and irrespective of whether the words are learned in lists, sentences or texts’. Not everyone agrees, and a firm conclusion either way is probably not possible: learner variables (especially learner preferences) preclude anything conclusive, which is why I’ve highlighted Nation’s use of the word ‘generally’. If we have a look at Lingro’s bilingual gloss, I think you’ll agree that the monolingual and bilingual glosses are equally unhelpful, equally unlikely to lead to better learning, whether it’s vocabulary acquisition or reading comprehension.bilingual lingro


The issues I’ve just discussed illustrate the complexity of the ‘glossing’ question, but they only scratch the surface. I’ll dig a little deeper.

1 Glosses are only likely to be of value to learning if they are used selectively. Nation (2013: 242) suggests that ‘it is best to assume that the highest density of glossing should be no more than 5% and preferably around 3% of the running words’. Online glosses make the process of look-up extremely easy. This is an obvious advantage over look-ups in a paper dictionary, but there is a real risk, too, that the ease of online look-up encourages unnecessary look-ups. More clicks do not always lead to more learning. The value of glosses cannot therefore be considered independently of a consideration of the level (i.e. appropriacy) of the text that they are being used with.

2 A further advantage of online glosses is that they can offer a wide range of information, e.g. pronunciation, L1 translation, L2 definition, visuals, example sentences. The review of literature by Khezrlou et al (2017: 107) suggests that ‘multimedia glosses can promote vocabulary learning but uncertainty remains as to whether they also facilitate reading comprehension’. Barcroft (2015), however, warns that pictures may help learners with meaning, but at the cost of retention of word form, and the research of Boers et al did not find evidence to support the use of pictures. Even if we were to accept the proposition that pictures might be helpful, we would need to hold two caveats. First, the amount of multimodal support should not lead to cognitive overload. Second, pictures need to be clear and appropriate: a condition that is rarely met in online learning programs. The quality of multimodal glosses is more important than their inclusion / exclusion.

3 It’s a commonplace to state that learners will learn more if they are actively engaged or involved in the learning, rather than simply (receptively) looking up a gloss. So, it has been suggested that cognitive engagement can be stimulated by turning the glosses into a multiple-choice task, and a fair amount of research has investigated this possibility. Barcroft (2015: 143) reports research that suggests that ‘multiple-choice glosses [are] more effective than single glosses’, but Nation (2013: 246) argues that ‘multiple choice glosses are not strongly supported by research’. Basically, we don’t know and even if we have replication studies to re-assess the benefits of multimodal glosses (as advocated by Boers et al, 2017), it is again likely that learner variables will make it impossible to reach a firm conclusion.

Learning from meta-analyses

Discussion of glosses is not new. Back in the late 19th century, ‘most of the Reform Movement teachers, took the view that glossing was a sensible technique’ (Howatt, 2004: 191). Sensible, but probably not all that important in the broader scheme of language learning and teaching. Online glosses offer a number of potential advantages, but there is a huge number of variables that need to be considered if the potential is to be realised. In essence, I have been arguing that asking whether online glosses are more effective than print glosses is the wrong question. It’s not a question that can provide us with a useful answer. When you look at the details of the research that has been brought together in the meta-analysis, you simply cannot conclude that there are unequivocally positive effects of technology on language learning, if the most positive effects are to be found in the digital variation of an old sensible technique.

Interesting and useful as Plonsky and Ziegler’s study is, I think it needs to be treated with caution. More generally, we need to be cautious about using meta-analyses and effect sizes. Mura Nava has a useful summary of an article by Adrian Simpson (Simpson, 2017), that looks at inclusion criteria and statistical procedures and warns us that we cannot necessarily assume that the findings of meta-meta-analyses are educationally significant. More directly related to technology and language learning, Boulton’s paper (Boulton, 2016) makes a similar point: ‘Meta-analyses need interpreting with caution: in particular, it is tempting to seize on a single figure as the ultimate answer to the question: Does it work? […] More realistically, we need to look at variation in what works’.

For me, the greatest value in Plonsky and Ziegler’s paper was nothing to do with effect sizes and big answers to big questions. It was the bibliography … and the way it forced me to be rather more critical about meta-analyses.


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

Boers, F., Warren, P., He, L. & Deconinck, J. 2017. ‘Does adding pictures to glosses enhance vocabulary uptake from reading?’ System 66: 113 – 129

Boulton, A. 2016. ‘Quantifying CALL: significance, effect size and variation’ in S. Papadima-Sophocleus, L. Bradley & S. Thouësny (eds.) CALL Communities and Culture – short papers from Eurocall 2016 pp.55 – 60

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J. & Willingham, D.T. 2013. ‘Improving Students’ Learning With Effective Learning Techniques’ Psychological Science in the Public Interest 14 / 1: 4 – 58

Hattie, J.A.C. 2009. Visible Learning. Abingdon, Oxon.: Routledge

Howatt, A.P.R. 2004. A History of English Language Teaching 2nd edition. Oxford: Oxford University Press

Khezrlou, S., Ellis, R. & K. Sadeghi 2017. ‘Effects of computer-assisted glosses on EFL learners’ vocabulary acquisition and reading comprehension in three learning conditions’ System 65: 104 – 116

Laufer, B. & Shmueli, K. 1997. ‘Memorizing new words: Does teaching have anything to do with it?’ RELC Journal 28 / 1: 89 – 108

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

Plonsky, L. & Ziegler, N. 2016. ‘The CALL–SLA interface:  insights from a second-order synthesis’ Language Learning & Technology 20 / 2: 17 – 37

Simpson, A. 2017. ‘The misdirection of public policy: Comparing and combining standardised effect sizes’ Journal of Education Policy, 32 / 4: 450-466

Taylor, A. M. 2006. ‘The effects of CALL versus traditional L1 glosses on L2 reading comprehension’. CALICO Journal, 23, 309–318.

Taylor, A. M. 2009. ‘CALL-based versus paper-based glosses: Is there a difference in reading comprehension?’ CALICO Journal, 23, 147–160.

Taylor, A. M. 2013. CALL versus paper: In which context are L1 glosses more effective? CALICO Journal, 30, 63-8

In the words of its founder and CEO, self-declared ‘visionary’ Claudio Santori, Bliu Bliu is ‘the only company in the world that teaches languages we don’t even know’. This claim, which was made during a pitch  for funding in October 2014, tells us a lot about the Bliu Bliu approach. It assumes that there exists a system by which all languages can be learnt / taught, and the particular features of any given language are not of any great importance. It’s questionable, to say the least, and Santori fails to inspire confidence when he says, in the same pitch, ‘you join Bliu Bliu, you use it, we make something magical, and after a few weeks you can understand the language’.

The basic idea behind Bliu Bliu is that a language is learnt by using it (e.g. by reading or listening to texts), but that the texts need to be selected so that you know the great majority of words within them. The technological challenge, therefore, is to find (online) texts that contain the vocabulary that is appropriate for you. After that, Santori explains , ‘you progress, you input more words and you will get more text that you can understand. Hours and hours of conversations you can fully understand and listen. Not just stupid exercise from stupid grammar book. Real conversation. And in all of them you know 100% of the words. […] So basically you will have the same opportunity that a kid has when learning his native language. Listen hours and hours of native language being naturally spoken at you…at a level he/she can understand plus some challenge, everyday some more challenge, until he can pick up words very very fast’ (sic).


On entering the site, you are invited to take a test. In this, you are shown a series of words and asked to say if you find them ‘easy’ or ‘difficult’. There were 12 words in total, and each time I clicked ‘easy’. The system then tells you how many words it thinks you know, and offers you one or more words to click on. Here are the words I was presented with and, to the right, the number of words that Bliu Blu thinks I know, after clicking ‘easy’ on the preceding word.

hello 4145
teenager 5960
soap, grape 7863
receipt, washing, skateboard 9638
motorway, tram, luggage, footballer, weekday 11061


Finally, I was asked about my knowledge of other languages. I said that my French was advanced and that my Spanish and German were intermediate. On the basis of this answer, I was now told that Bliu Bliu thinks that I know 11,073 words.

Eight of the words in the test are starred in the Macmillan dictionaries, meaning they are within the most frequent 7,500 words in English. Of the other four, skateboard, footballer and tram are very international words. The last, weekday, is a readily understandable compound made up of two extremely high frequency words. How could Bliu Bliu know, with such uncanny precision, that I know 11,073 words from a test like this? I decided to try the test for French. Again, I clicked ‘easy’ for each of the twelve words that was offered. This time, I was offered a very different set of words, with low frequency items like polynôme, toponymie, diaspora, vectoriel (all of which are cognate with English words), along with the rather surprising vichy (which should have had a capital letter, as it is a proper noun). Despite finding all these words easy, I was mortified to be told that I only knew 6546 words in French.

I needn’t have bothered with the test, anyway. Irrespective of level, you are offered vocabulary sets of high frequency words. Examples of sets I was offered included [the, be, of, and, to], [way, state, say, world, two], [may, man, hear, said, call] and [life, down, any, show, t]. Bliu Bliu then gives you a series of short texts that include the target words. You can click on any word you don’t know and you are given either a definition or a translation (I opted for French translations). There is no task beyond simply reading these texts. Putting aside for the moment the question of why I was being offered these particular words when my level is advanced, how does the software perform?

The vast majority of the texts are short quotes from, and here is the first problem. Quotes tend to be pithy and often play with words: their comprehensibility is not always a function of the frequency of the words they contain. For the word ‘say’, for example, the texts included the Shakespearean quote It will have blood, they say; blood will have blood. For the word ‘world’, I was offered this line from Alexander Pope: The world forgetting, by the world forgot. Not, perhaps, the best way of learning a couple of very simple, high-frequency words. But this was the least of the problems.

The system operates on a word level. It doesn’t recognise phrases or chunks, or even phrasal verbs. So, a word like ‘down’ (in one of the lists above) is presented without consideration of its multiple senses. The first set of sentences I was asked to read for ‘down’ included: I never regretted what I turned down, You get old, you slow down, I’m Creole, and I’m down to earth, I never fall down. I always fight, I like seeing girls throw down and I don’t take criticism lying down. Not exactly the best way of getting to grips with the word ‘down’ if you don’t know it!

bliubliu2You may have noticed the inclusion of the word ‘t’ in one of the lists above. Here are the example sentences for practising this word: (1) Knock the ‘t’ off the ‘can’t’, (2) Sometimes reality T.V. can be stressful, (3) Argentina Debt Swap Won’t Avoid Default, (4) OK, I just don’t understand Nethanyahu, (5) Venezuela: Hell on Earth by Walter T Molano and (6) Work will win when wishy washy wishing won t. I paid €7.99 for one month of this!

The translation function is equally awful. With high frequency words with multiple meanings, you get a long list of possible translations, but no indication of which one is appropriate for the context you are looking at. With other words, it is sometimes, simply, wrong. For example, in the sentence, Heaven lent you a soul, Earth will lend a grave, the translation for ‘grave’ was only for the homonymous adjective. In the sentence There’s a bright spot in every dark cloud, the translation for ‘spot’ was only for verbs. And the translation for ‘but’ in We love but once, for once only are we perfectly equipped for loving was ‘mais’ (not at all what it means here!). The translation tool couldn’t handle the first ‘for’ in this sentence, either.

Bliu Bliu’s claim that Bliu Bliu knows you very well, every single word you know or don’t know is manifest nonsense and reveals a serious lack of understanding about what it means to know a word. However, as you spend more time on the system, a picture of your vocabulary knowledge is certainly built up. The texts that are offered begin to move away from the one-liners from As reading (or listening to recorded texts) is the only learning task that is offered, the intrinsic interest of the texts is crucial. Here, again, I was disappointed. Texts that I was offered were sourced from IEEE Spectrum (The World’s Largest Professional Association for the Advancement of Technology), (the home of the #1 Internet News Show in the World), Latin America News and Analysis, the Google official blog (Meet 15 Finalists and Science in Action Winner for the 2013 GoogleScience Fair) MLB Trade Rumors (a clearinghouse for relevant, legitimate baseball rumors), and a long text entitled Robert Waldmann: Policy-Relevant Macro Is All in Samuelson and Solow (1960) from a blog called Brad DeLong’s Grasping Reality……with the Neural Network of a Moderately-Intelligent Cephalopod.

There is more curated content (selected from a menu which includes sections entitled ‘18+’ and ‘Controversial Jokes’). In these texts, words that the system thinks you won’t know (most of the proper nouns for example) are highlighted. And there is a small library of novels, again, where predicted unknown words are highlighted in pink. These include Dostoyevsky, Kafka, Oscar Wilde, Gogol, Conan Doyle, Joseph Conrad, Oblomov, H.P. Lovecraft, Joyce, and Poe. You can also upload your own texts if you wish.

But, by this stage, I’d had enough and I clicked on the button to cancel my subscription. I shouldn’t have been surprised when the system crashed and a message popped up saying the system had encountered an error.

Like so many ‘language learning’ start-ups, Bliu Bliu seems to know a little, but not a lot about language learning. The Bliu Bliu blog has a video of Stephen Krashen talking about comprehensible input (it is misleadingly captioned ‘Stephen Krashen on Bliu Bliu’) in which he says that we all learn languages the same way, and that is when we get comprehensible input in a low anxiety environment. Influential though it has been, Krashen’s hypothesis remains a hypothesis, and it is generally accepted now that comprehensible input may be necessary, but it is not sufficient for language learning to take place.

The hypothesis hinges, anyway, on a definition of what is meant by ‘comprehensible’ and no one has come close to defining what precisely this means. Bliu Bliu has falsely assumed that comprehensibility can be determined by self-reporting of word knowledge, and this assumption is made even more problematic by the confusion of words (as sequences of letters) with lexical items. Bliu Bliu takes no account of lexical grammar or collocation (fundamental to any real word knowledge).

The name ‘Bliu Bliu’ was inspired by an episode from ‘Friends’ where Joey tries and fails to speak French. In the episode, according to the ‘Friends’ wiki, ‘Phoebe helps Joey prepare for an audition by teaching him how to speak French. Joey does not progress well and just speaks gibberish, thinking he’s doing a great job. Phoebe explains to the director in French that Joey is her mentally disabled younger brother so he’ll take pity on Joey.’ Bliu Bliu was an unfortunately apt choice of name.