Perspectives on language teaching research

I’ve written about the relationship (or, rather, the lack of one) between language teachers and language teaching research before. I’m talking about the kind of research that is primarily of the ‘what-works’ variety, since that is likely to be of most relevance to teachers. It’s the kind of research that asks questions like: can correction be beneficial to language learners? Or: can spaced repetition be helpful in vocabulary acquisition? Whether teachers find this relevant or not, there is ample evidence that the vast majority rarely look at it (Borg, 2009).

See here, for example, for a discussion of calls from academic researchers for more dialogue between researchers and teachers. The desire, on the part of researchers, for teachers to engage more (or even a little) with research, continues to grow, as shown by two examples. The first is the development of TESOLgraphics, which aims to make research ‘easy to read and understand to ESL, EFL, EAP, ESP, ESOL, EAL, TEFL teachers’ by producing infographic summaries. The second is a proposed special issue of the journal ‘System’ devoted to ‘the nexus of research and practice in and for language teacher education’ and hopes to find ways of promoting more teacher engagement with research. Will either of these initiatives have much impact? I doubt it, and to explain why, I need to take you on a little detour.

The map and the territory

Riffing off an ultra-short story by Jorge Luis Borges (‘On Exactitude in Science’, 1946), the corpus linguist Michael Stubbs (2013) wrote a piece entitled ‘Of Exactitude in Linguistics’, which marked his professional retirement. In it, he described a world where

the craft of Descriptive Linguistics attained such Perfection that the Transcription of a single short Conversation covered the floor of an entire University seminar room, and the Transcription of a Representative Sample of a single Text-Type covered the floor area of a small department to a depth of several feet. In the course of time, especially after the development of narrow phonetic transcription with intonational and proxemic annotation, even these extensive Records were found somehow wanting, and with the advent of fully automatic voice-to-orthography transcription, the weight of the resulting Text Collections threatened severe structural damage to University buildings.

As with all humour, there’s more than a grain of truth behind this Borgesian fantasy. These jokes pick up on what is known as the Richardson Effect, named after a British mathematician who noted that the length of the coastline of Great Britain varies according to the size of the units that are used to measure it – the smaller the unit, the longer the coastline. But at what point does increasing exactitude cease to tell us anything of value?

Both Borges and Lewis Fry Richardson almost certainly knew Lewis Carroll’s novel ‘Sylvie and Bruno Concluded’ (1893) which features a map that has the scale of a mile to a mile. This extraordinarily accurate map is, however, never used, since it is too large to spread out. The cost of increasing exactitude is practical usefulness.

The map of language

Language is rather like a coastline when it comes to drilling down in order to capture its features with smaller and smaller units of measurement. Before very long, you are forced into making decisions about the variety of the language and the contexts of use that you are studying. Precisely what kind of English are you measuring? At some point, you get down to the level of idiolect, but idiolects can be broken down further as they vary depending on the contexts of use. The trouble, of course, is that idiolects tell us little that is of value about the much broader ‘language’ that you set out to measure in the first place. The linguistic map obscures the linguistic terrain.

In ultra close-up, we can no longer distinguish one named language from another just by using linguistic criteria (Makoni & Pennycook, 20077:1). Extending this logic further, it makes little sense to even talk about named languages like English, to talk about first or second languages, about native speakers or about language errors. The close-up view requires us to redefine the thing – language – that we set out to define and describe. English is no longer a fixed and largely territorial system owned by native-speakers, but a dynamic, complex, social, deterritorialized practice owned by its users (May, 2013; Meier, 2017; Li Wei, 2018). In this view, both the purpose and the consequence of describing language in this way is to get away from the social injustice of native-speaker norms, of accentism, and linguistic prejudice.

A load of Ballungs

Language is a fuzzy and context-dependent concept. It is ‘too multifaceted to be measured on a single metric without loss of meaning, and must be represented by a matrix of indices or by several different measures depending on which goals and values are at play’ (Tal, 2020). In the philosophy of measurement, concepts like these are known as ‘Ballung’ concepts (Cartwright & Bradburn, 2011). Much of what is studied by researchers into language learning are also ‘Ballung’ concepts. Language proficiency and language acquisition are ‘Ballung’ concepts, too. As are reading and listening skills, mediation, metacognition and motivation. Critical thinking and digital literacies … the list goes on. Research into all these areas is characterised by multiple and ever-more detailed taxonomies, as researchers struggle to define precisely what it is that they are studying. It is in the nature of most academic study that it strives towards exactitude by becoming more and more specialised in its analysis of ‘ever more particular fractions of our world’ (Pardo-Guerra, 2022: 17).

But the perspective on language of Makoni, Pennycook, Li Wei et al is not what we might call the ‘canonical view’, the preferred viewpoint of the majority of people in apprehending the reality of the outside world (Palmer, 1981). Canonical views of language are much less close-up and allow for the unproblematic differentiation of one language from another. Canonical views – whether of social constructs like language or everyday objects like teacups or birds – become canonical because they are more functional for many people for everyday purposes than less familiar perspectives. If you want to know how far it is to walk from A to B along a coastal footpath, the more approximate measure of metres is more useful than one that counts every nook and cranny in microns. Canonical views can, of course, change over time – if the purpose to which they are put changes, too.

Language teaching research

There is a clear preference in academia for quantitative, empirical research where as many variables as possible are controlled. Research into language teaching is no different. It’s not enough to ask, in general terms, about the impact on learning of correction or spaced repetition. ‘What works’ is entirely context-dependent (Al-Hoorie, et al., 2023: 278). Since all languages, language learners and language learning contexts are ‘ultimately different’ (Widdowson, 2023: 397), there’s never any end to the avenues that researchers can explore: it is a ‘self-generating academic area of inquiry’ (ibid.). So we can investigate the impact of correction on the writing (as opposed to the speaking) of a group of Spanish (as opposed to another nationality) university students (as opposed to another age group) in an online setting (as opposed to face-to-face) where the correction is delayed (as opposed to immediate) and delivered by WhatsApp (as opposed to another medium) (see, for example, Murphy et al., 2023). We could carry on playing around with the variables for as long as we like – this kind of research has already been going on for decades.

When it comes to spaced repetition, researchers need to consider the impact of different algorithms (e.g. the length of the spaces) on different kinds of learners (age, level, motivation, self-regulation, etc.) in their acquisition of different kinds of lexical items (frequency, multi-word units, etc.) and how these items are selected and grouped, the nature of this acquisition (e.g. is it for productive use or is it purely recognition?). And so on (see the work of Tatsuya Nakata, for example).

Such attempts to control the variables are a necessary part of scientific enquiry, they are part of the ‘disciplinary agenda’, but they are unlikely to be of much relevance to most teachers. Researchers need precision, but the more they attempt to ‘approximate the complexities of real life, the more unwieldy [their] theories inevitably become’ (Al-Hoorie et al., 2023). Teachers, on the other hand, are typically more interested in canonical views that can lead to general take-aways that can be easily applied in their lessons. It is only secondary research in the form of meta-analyses or literature reviews (of the kind that TESOLgraphics) that can avoid the Richardson Effect and might offer something of help to the average classroom practitioner. But this secondary research, stripped of the contextual variables, can only be fairly vague. It can only really tell us, for example, that some form of written correction or spaced repetition may be helpful to some learners in some contexts some of the time. In need of ‘substantial localization’, it has been argued that the broad-stroke generalisations are often closer to ‘pseudo-applications’ (Al-Hoorie et al., 2023) than anything that is reliably actionable. That is not to say, however, that broad-stroke generalisations are of no value at all.

Finding the right map

Henry Widdowson (e.g. 2023) has declared himself sceptical about the practical relevance of SLA research. Reading journals like ‘Studies in Second Language Acquisition’ or ‘System’, it’s hard not to agree. Attempts to increase the accessibility of research (e.g. open-access or simple summaries) may not have the desired impact since they do not do anything about ‘the tenuous link between research and practice’ (Hwang, 2023). They cannot bridge the ‘gap between two sharply contrasting kinds of knowledge’ (McIntyre, 2006).

There is an alternative: classroom-based action research carried out by teachers. One of the central ideas behind it is that teachers may benefit more from carrying out their own research than from reading someone else’s. Enthusiasm for action research has been around for a long time: it was very fashionable in the 1980s when I trained as a teacher. In the 1990s, there was a series of conferences for English language teachers called ‘Teachers Develop Teachers Research’ (see, for example, Field et al., 1997). Tirelessly promoted by people like Richard Smith, Paula Rebolledo (Smith et al., 2014) and Anne Burns, action research seems to be gaining traction. A recent British Council publication (Burns, 2023) is a fine example of what insights teachers may gain and act on with an exploratory action research approach.

References

Al-Hoorie A. H., Hiver, P., Larsen-Freeman, D. & Lowie, W. (2023) From replication to substantiation: A complexity theory perspective. Language Teaching, 56 (2): pp. 276 – 291

Borg, S. (2009) English language teachers’ conceptions of research. Applied Linguistics, 30 (3): 358 – 88

Burns, A. (Ed.) (2023) Exploratory Action Research in Thai Schools: English teachers identifying problems, taking action and assessing results. Bangkok, Thailand: British Council

Cartwright, N., Bradburn, N. M., & Fuller, J. (2016) A theory of measurement. Working Paper. Centre for Humanities Engaging Science and Society (CHESS), Durham.

Field, J., Graham, A., Griffiths, E. & Head. K. (Eds.) (1997) Teachers Develop Teachers Research 2. Whitstable, Kent: IATEFl

Hwang, H.-B. (2023) Is evidence-based L2 pedagogy achievable? The research–practice dialogue in grammar instruction. The Modern Language Journal, 2023: 1 – 22 https://onlinelibrary.wiley.com/doi/full/10.1111/modl.12864

Li Wei. (2018) Translanguaging as a Practical Theory of Language. Applied Linguistics, 39 (1): 9 – 30

Makoni, S. & Pennycook, A. (Eds.) (2007) Disinventing and Reconstituting Languages. Clevedon: Multilingual Matters

May. S. (Ed.) (2013) The multilingual turn: Implications for SLA, TESOL and Bilingual education. New York: Routledge

McIntyre, D. (2006) Bridging the gap between research and practice. Cambridge Journal of Education 35 (3): 357 – 382

Meier, G. S. (2017) The multilingual turn as a critical movement in education: assumptions, challenges and a need for reflection. Applied Linguistics Review, 8 (1): 131-161

Murphy, B., Mackay J. & Tragant, E. (2023) ‘(Ok I think I was totally wrong: new try!)’: language learning in WhatsApp through the provision of delayed corrective feedback provided during and after task performance’, The Language Learning Journal, DOI: 10.1080/09571736.2023.2223217

Palmer, S.E. et al. (1981) Canonical perspective and the perception of objects. In Longand, J. & Baddeley. A. (Eds.) Attention and Performance IX. Hillsdale, NJ: Erlbaum. pp. 135 – 151

Pardo-Guerra, J. P. (2022) The Quantified Scholar. New York: Columbia University Press

Smith, R., Connelly, T. & Rebolledo, P. (2014). Teacher research as CPD: A project with Chilean secondary school teachers. In D. Hayes (Ed.), Innovations in the continuing professional development of English language teachers (pp. 111–128). The British Council.

Tal, E. “Measurement in Science”, In The Stanford Encyclopedia of Philosophy (Fall 2020 Edition), Edward N. Zalta (Ed.), https://plato.stanford.edu/archives/fall2020/entries/measurement-science/

Widdowson, H. (2023) Webinar on the subject of English and applied linguistics. Language Teaching, 56 (3): 393 – 401

Vocab Victor: a review

Vocab Victor is a very curious vocab app. It’s not a flashcard system, designed to extend vocabulary breadth. Rather it tests the depth of a user’s vocabulary knowledge.

The app’s website refers to the work of Paul Meara (see, for example, Meara, P. 2009. Connected Words. Amsterdam: John Benjamins). Meara explored the ways in which an analysis of the words that we associate with other words can shed light on the organisation of our mental lexicon. Described as ‘gigantic multidimensional cobwebs’ (Aitchison, J. 1987. Words in the Mind. Oxford: Blackwell, p.86), our mental lexicons do not appear to store lexical items in individual slots, but rather they are distributed across networks of associations.

The size of the web (i.e. the number of words, or the level of vocabulary breadth) is important, but equally important is the strength of the connections within the web (or vocabulary depth), as this determines the robustness of vocabulary knowledge. These connections or associations are between different words and concepts and experiences, and they are developed by repeated, meaningful, contextualised exposure to a word. In other words, the connections are firmed up through extensive opportunities to use language.

In word association research, a person is given a prompt word and asked to say the first other word that comes to their mind. For an entertaining example of this process at work, you might enjoy this clip from the comedy show ‘Help’. The research has implications for a wide range of questions, not least second language acquisition. For example, given a particular prompt, native speakers produce a relatively small number of associative responses, and these are reasonably predictable. Learners, on the other hand, typically produce a much greater variety of responses (which might seem surprising, given that they have a smaller vocabulary store to select from).

One way of classifying the different kinds of response is to divide them into two categories: syntagmatic (words that are discoursally connected to the prompt, such as collocations) and paradigmatic (words that are semantically close to the prompt and are the same part of speech). Linguists have noted that learners (both L1 children and L2 learners) show a shift from predominantly syntagmatic responses to more paradigmatic responses as their mental lexicon develops.

The developers of Vocab Victor have set out to build ‘more and stronger associations for the words your students already know, and teaches new words by associating them with existing, known words, helping students acquire native-like word networks. Furthermore, Victor teaches different types of knowledge, including synonyms, “type-of” relationships, collocations, derivations, multiple meanings and form-focused knowledge’. Since we know how important vocabulary depth is, this seems like a pretty sensible learning target.

The app attempts to develop this breadth in two main ways (see below). The ‘core game’ is called ‘Word Strike’ where learners have to pick the word on the arrow which most closely matches the word on the target. The second is called ‘Word Drop’ where a bird holds a word card and the user has to decide if it relates more to one of two other words below. Significantly, they carry out these tasks before any kind of association between form and meaning has been established. The meaning of unknown items can be checked in a monolingual dictionary later. There are a couple of other, less important games that I won’t describe now. The graphics are attractive, if a little juvenile. The whole thing is gamified with levels, leaderboards and so on. It’s free and, presumably, still under development.

The app claims to be for ‘English language learners of all ages [to] develop a more native-like vocabulary’. It also says that it is appropriate for ‘native speaking primary students [to] build and strengthen vocabulary for better test performance and stronger reading skills’, as well as ‘secondary students [to] prepare for the PSAT and SAT’. It was the scope of these claims that first set my alarm bells ringing. How could one app be appropriate for such diverse users? (Spoiler: it can’t, and attempts to make an edtech product suitable for everyone inevitably end up with a product that is suitable for no one.)

Rich, associative lexical networks are the result of successful vocabulary acquisition, but neither Paul Meara nor anyone else in the word association field has, to the best of my knowledge, ever suggested that deliberate study is the way to develop the networks. It is uncontentious to say that vocabulary depth (as shown by associative networks) is best developed through extensive exposure to input – reading and listening.

It is also reasonably uncontentious to say that deliberate study of vocabulary pays greatest dividends in developing vocabulary breadth (not depth), especially at lower levels, with a focus on the top three to eight thousand words in terms of frequency. It may also be useful at higher levels when a learner needs to acquire a limited number of new words for a particular purpose. An example of this would be someone who is going to study in an EMI context and would benefit from rapid learning of the words of the Academic Word List.

The Vocab Victor website says that the app ‘is uniquely focused on intermediate-level vocabulary. The app helps get students beyond this plateau by selecting intermediate-level vocabulary words for your students’. At B1 and B2 levels, learners typically know words that fall between #2500 and #3750 in the frequency tables. At level C2, they know most of the most frequent 5000 items. The less frequent a word is, the less point there is in studying it deliberately.

For deliberate study of vocabulary to serve any useful function, the target language needs to be carefully selected, with a focus on high-frequency items. It makes little sense to study words that will already be very familiar. And it makes no sense to deliberately study apparently random words that are so infrequent (i.e. outside the top 10,000) that it is unlikely they will be encountered again before the deliberate study has been forgotten. Take a look at the examples below and judge for yourself how well chosen the items are.

Vocab Victor appears to focus primarily on semantic fields, as in the example above with ‘smashed’ as a key word. ‘Smashed’, ‘fractured’, ‘shattered’ and ‘cracked’ are all very close in meaning. In order to disambiguate them, it would help learners to see which nouns typically collocate with these words. But they don’t get this with the app – all they get are English-language definitions from Merriam-Webster. What this means is that learners are (1) unlikely to develop a sufficient understanding of target items to allow them to incorporate them into their productive lexicon, and (2) likely to get completely confused with a huge number of similar, low-frequency words (that weren’t really appropriate for deliberate study in the first place). What’s more, lexical sets of this kind may not be a terribly good idea, anyway (see my blog post on the topic).

Vocab Victor takes words, as opposed to lexical items, as the target learning objects. Users may be tested on the associations of any of the meanings of polysemantic items. In the example below (not perhaps the most appropriate choice for primary students!), there are two main meanings, but with other items, things get decidedly more complex (see the example with ‘toss’). Learners are also asked to do the associative tasks ‘Word Strike’ and ‘Word Drop’ before they have had a chance to check the possible meanings of either the prompt item or the associative options.

How anyone could learn from any of this is quite beyond me. I often struggled to choose the correct answer myself; there were also a small number of items whose meaning I wasn’t sure of. I could see no clear way in which items were being recycled (there’s no spaced repetition here). The website claims that ‘adaptating [sic] to your student’s level happens automatically from the very first game’, but I could not see this happening. In fact, it’s very hard to adapt target item selection to an individual learner, since right / wrong or multiple choice answers tell us so little. Does a correct answer tell us that someone knows an item or just that they made a lucky guess? Does an incorrect answer tell us that an item is unknown or just that, under game pressure, someone tapped the wrong button? And how do you evaluate a learner’s lexical level (as a starting point),  even with very rough approximation,  without testing knowledge of at least thirty items first? All in all, then, a very curious app.

One of the most powerful associative responses to a word (especially with younger learners) is what is called a ‘klang’ response: another word which rhymes with or sounds like the prompt word. So, if someone says the word ‘app’ to you, what’s the first klang response that comes to mind?

How often do you need to encounter a word before you learn it?

There has been wide agreement for a long time that one of the most important ways of building the mental lexicon is by having extended exposure to language input through reading and listening. Some researchers (e.g. Krashen, 2008) have gone as far as to say that direct vocabulary instruction serves little purpose, as there is no interface between explicit and implicit knowledge. This remains, however, a minority position, with a majority of researchers agreeing with Barcroft (2015) that deliberate learning plays an important role, even if it is only ‘one step towards knowing the word’ (Nation, 2013: 46).

There is even more agreement when it comes to the differences between deliberate study and extended exposure to language input, in terms of the kinds of learning that takes place. Whilst basic knowledge of lexical items (the pairings of meaning and form) may be developed through deliberate learning (e.g. flash cards), it is suggested that ‘the more ‘contextualized’ aspects of vocabulary (e.g. collocation) cannot be easily taught explicitly and are best learned implicitly through extensive exposure to the use of words in context’ (Schmitt, 2008: 333). In other words, deliberate study may develop lexical breadth, but, for lexical depth, reading and listening are the way to go.

This raises the question of how many times a learner would need to encounter a word (in reading or listening) in order to learn its meaning. Learners may well be developing other aspects of word knowledge at the same time, of course, but a precondition for this is probably that the form-meaning relationship is sorted out. Laufer and Nation (2012: 167) report that ‘researchers seem to agree that with ten exposures, there is some chance of recognizing the meaning of a new word later on’. I’ve always found this figure interesting, but strangely unsatisfactory, unsure of what, precisely, it was actually telling me. Now, with the recent publication of a meta-analysis looking at the effects of repetition on incidental vocabulary learning (Uchihara, Webb & Yanagisawa, 2019), things are becoming a little clearer.

First of all, the number ten is a ballpark figure, rather than a scientifically proven statistic. In their literature review, Uchihara et al. report that ‘the number of encounters necessary to learn words rang[es] from 6, 10, 12, to more than 20 times. That is to say, ‘the number of encounters necessary for learning of vocabulary to occur during meaning-focussed input remains unclear’. If you ask a question to which there is a great variety of answers, there is a strong probability that there is something wrong with the question. That, it would appear, is the case here.

Unsurprisingly, there is, at least, a correlation between repeated encounters of a word and learning, described by Uchihara et al as statistically significant (with a medium effect size). More interesting are the findings about the variables in the studies that were looked at. These included ‘learner variables’ (age and the current size of the learner’s lexicon), ‘treatment variables’ (the amount of spacing between the encounters, listening versus reading, the presence or absence of visual aids, the degree to which learners ‘engage’ with the words they encounter) and ‘methodological variables’ in the design of the research (the kinds of words that are being looked at, word characteristics, the use of non-words, the test format and whether or not learners were told that they were going to be tested).

Here is a selection of the findings:

• Older learners tend to benefit more from repeated encounters than younger learners.
• Learners with a smaller vocabulary size tend to benefit more from repeated encounters with L2 words, but this correlation was not statistically significant. ‘Beyond a certain point in vocabulary growth, learners may be able to acquire L2 words in fewer encounters and need not receive as many encounters as learners with smaller vocabulary size’.
• Learners made greater gains when the repeated exposure took place under massed conditions (e.g. on the same day), rather than under ‘spaced conditions’ (spread out over a longer period of time).
• Repeated exposure during reading and, to a slightly lesser extent, listening resulted in more gains than reading while listening and viewing.
• ‘Learners presented with visual information during meaning-focused tasks benefited less from repeated encounters than those who had no access to the information’. This does not mean that visual support is counter-productive: only that the positive effect of repeated encounters is not enhanced by visual support.
• ‘A significantly larger effect was found for treatments involving no engagement compared to treatment involving engagement’. Again, this does not mean that ‘no engagement’ is better than ‘engagement’: only that the positive effect of repeated encounters is not enhanced by ‘engagement’.
• ‘The frequency-learning correlation does not seem to increase beyond a range of around 20 encounters with a word’.
• Experiments using non-words may exaggerate the effect of frequent encounters (i.e. in the real world, with real words, the learning potential of repeated encounters may be less than indicated by some research).
• Forewarning learners of an upcoming comprehension test had a positive impact on gains in vocabulary learning. Again, this does not mean that teachers should systematically test their students’ comprehension of what they have read.

For me, the most interesting finding was that ‘about 11% of the variance in word learning through meaning-focused input was explained by frequency of encounters’. This means, quite simply, that a wide range of other factors, beyond repeated encounters, will determine the likelihood of learners acquiring vocabulary items from extensive reading and listening. The frequency of word encounters is just one factor among many.

I’m still not sure what the takeaways from this meta-analysis should be, besides the fact that it’s all rather complex. The research does not, in any way, undermine the importance of massive exposure to meaning-focussed input in learning a language. But I will be much more circumspect in my teacher training work about making specific claims concerning the number of times that words need to be encountered before they are ‘learnt’. And I will be even more sceptical about claims for the effectiveness of certain online language learning programs which use algorithms to ensure that words reappear a certain number of times in written, audio and video texts that are presented to learners.

References

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

Laufer, B. & Nation, I.S.P. 2012. Vocabulary. In Gass, S.M. & Mackey, A. (Eds.) The Routledge Handbook of Second Language Acquisition (pp.163 – 176). Abingdon, Oxon.: Routledge

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

Krashen, S. 2008. The comprehension hypothesis extended. In T. Piske & M. Young-Scholten (Eds.), Input Matters in SLA (pp.81 – 94). Bristol, UK: Multilingual Matters

Schmitt, N. 2008. Review article: instructed second language vocabulary learning. Language Teaching Research 12 (3): 329 – 363

Uchihara, T., Webb, S. & Yanagisawa, A. 2019. The Effects of Repetition on Incidental Vocabulary Learning: A Meta-Analysis of Correlational Studies. Language Learning, 69 (3): 559 – 599) Available online: https://www.researchgate.net/publication/330774796_The_Effects_of_Repetition_on_Incidental_Vocabulary_Learning_A_Meta-Analysis_of_Correlational_Studies

Lexical sets

Digital flashcard systems like Memrise and Quizlet remain among the most popular language learning apps. Their focus is on the deliberate learning of vocabulary, an approach described by Paul Nation (Nation, 2005) as ‘one of the least efficient ways of developing learners’ vocabulary knowledge but nonetheless […] an important part of a well-balanced vocabulary programme’. The deliberate teaching of vocabulary also features prominently in most platform-based language courses.

For both vocabulary apps and bigger courses, the lexical items need to be organised into sets for the purposes of both presentation and practice. A common way of doing this, especially at lower levels, is to group the items into semantic clusters (sets with a classifying superordinate, like body part, and a collection of example hyponyms, like arm, leg, head, chest, etc.).

The problem, as Keith Folse puts it, is that such clusters ‘are not only unhelpful, they actually hinder vocabulary retention’ (Folse, 2004: 52). Evidence for this claim may be found in Higa (1963), Tinkham (1993, 1997), Waring (1997), Erten & Tekin (2008) and Barcroft (2015), to cite just some of the more well-known studies. The results, says Folse, ‘are clear and, I think, very conclusive’. The explanation that is usually given draws on interference theory: semantic similarity may lead to confusion (e.g. when learners mix up days of the week, colour words or adjectives to describe personality).

It appears, then, to be long past time to get rid of semantic clusters in language teaching. Well … not so fast. First of all, although most of the research sides with Folse, not all of it does. Nakata and Suzuki (2019) in their survey of more recent research found that results were more mixed. They found one study which suggested that there was no significant difference in learning outcomes between presenting words in semantic clusters and semantically unrelated groups (Ishii, 2015). And they found four studies (Hashemi & Gowdasiaei, 2005; Hoshino, 2010; Schneider, Healy, & Bourne, 1998, 2002) where semantic clusters had a positive effect on learning.

Nakata and Suzuki (2019) offer three reasons why semantic clustering might facilitate vocabulary learning: it (1) ‘reflects how vocabulary is stored in the mental lexicon, (2) introduces desirable difficulty, and (3) leads to extra attention, effort, or engagement from learners’. Finkbeiner and Nicol (2003) make a similar point: ‘although learning semantically related words appears to take longer, it is possible that words learned under these conditions are learned better for the purpose of actual language use (e.g., the retrieval of vocabulary during production and comprehension). That is, the very difficulty associated with learning the new labels may make them easier to process once they are learned’. Both pairs of researcher cited in this paragraph conclude that semantic clusters are best avoided, but their discussion of the possible benefits of this clustering is a recognition that the research (for reasons which I will come on to) cannot lead to categorical conclusions.

The problem, as so often with pedagogical research, is the gap between research conditions and real-world classrooms. Before looking at this in a little more detail, one relatively uncontentious observation can be made. Even those scholars who advise against semantic clustering (e.g. Papathanasiou, 2009), acknowledge that the situation is complicated by other factors, especially the level of proficiency of the learner and whether or not one or more of the hyponyms are known to the learner. At higher levels (when it is more likely that one or more of the hyponyms are already, even partially, known), semantic clustering is not a problem. I would add that, on the whole at higher levels, the deliberate learning of vocabulary is even less efficient than at lower levels and should be an increasingly small part of a well-balanced vocabulary programme.

So, why is there a problem drawing practical conclusions from the research? In order to have any scientific validity at all, researchers need to control a large number of variable. They need, for example, to be sure that learners do not already know any of the items that are being presented. The only practical way of doing this is to present sets of invented words, and this is what most of the research does (Sarioğlu, 2018). These artificial words solve one problem, but create others, the most significant of which is item difficulty. Many factors impact on item difficulty, and these include word frequency (obviously a problem with invented words), word length, pronounceability and the familiarity and length of the corresponding item in L1. None of the studies which support the abandonment of semantic clusters have controlled all of these variables (Nakata and Suzuki, 2019). Indeed, it would be practically impossible to do so. Learning pseudo-words is a very different proposition to learning real words, which a learner may subsequently encounter or want to use.

Take, for example, the days of the week. It’s quite common for learners to muddle up Tuesday and Thursday. The reason for this is not just semantic similarity (Tuesday and Monday are less frequently confused). They are also very similar in terms of both spelling and pronunciation. They are ‘synforms’ (see Laufer, 2009), which, like semantic clusters, can hinder learning of new items. But, now imagine a French-speaking learner of Spanish studying the days of the week. It is much less likely that martes and jueves will be muddled, because of their similarity to the French words mardi and jeudi. There would appear to be no good reason not to teach the complete set of days of the week to a learner like this. All other things being equal, it is probably a good idea to avoid semantic clusters, but all other things are very rarely equal.

Again, in an attempt to control for variables, researchers typically present the target items in isolation (in bilingual pairings). But, again, the real world does not normally conform to this condition. Leo Sellivan (2014) suggests that semantic clusters (e.g. colours) are taught as part of collocations. He gives the examples of red dress, green grass and black coffee, and points out that the alliterative patterns can serve as mnemonic devices which will facilitate learning. The suggestion is, I think, a very good one, but, more generally, it’s worth noting that the presentation of lexical items in both digital flashcards and platform courses is rarely context-free. Contexts will inevitably impact on learning and may well obviate the risks of semantic clustering.

Finally, this kind of research typically gives participants very restricted time to memorize the target words (Sarioğlu, 2018) and they are tested in very controlled recall tasks. In the case of language platform courses, practice of target items is usually spread out over a much longer period of time, with a variety of exposure opportunities (in controlled practice tasks, exposure in texts, personalisation tasks, revision exercises, etc.) both within and across learning units. In this light, it is not unreasonable to argue that laboratory-type research offers only limited insights into what should happen in the real world of language learning and teaching. The choice of learning items, the way they are presented and practised, and the variety of activities in the well-balanced vocabulary programme are probably all more significant than the question of whether items are organised into semantic clusters.

Although semantic clusters are quite common in language learning materials, much more common are thematic clusters (i.e. groups of words which are topically related, but include a variety of parts of speech (see below). Researchers, it seems, have no problem with this way of organising lexical sets. By way of conclusion, here’s an extract from a recent book:

‘Introducing new words together that are similar in meaning (synonyms), such as scared and frightened, or forms (synforms), like contain and maintain, can be confusing, and students are less likely to remember them. This problem is known as ‘interference’. One way to avoid this is to choose words that are around the same theme, but which include a mix of different parts of speech. For example, if you want to focus on vocabulary to talk about feelings, instead of picking lots of adjectives (happy, sad, angry, scared, frightened, nervous, etc.) include some verbs (feel, enjoy, complain) and some nouns (fun, feelings, nerves). This also encourages students to use a variety of structures with the vocabulary.’ (Hughes, et al., 2015: 25)

 

References

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

Erten, I.H., & Tekin, M. 2008. Effects on vocabulary acquisition of presenting new words in semantic sets versus semantically-unrelated sets. System, 36 (3), 407-422

Finkbeiner, M. & Nicol, J. 2003. Semantic category effects in second language word learning. Applied Psycholinguistics 24 (2003), 369–383

Folse, K. S. 2004. Vocabulary Myths. Ann Arbor: University of Michigan Press

Hashemi, M.R., & Gowdasiaei, F. 2005. An attribute-treatment interaction study: Lexical-set versus semantically-unrelated vocabulary instruction. RELC Journal, 36 (3), 341-361

Higa, M. 1963. Interference effects of intralist word relationships in verbal learning. Journal of Verbal Learning and Verbal Behavior, 2, 170-175

Hoshino, Y. 2010. The categorical facilitation effects on L2 vocabulary learning in a classroom setting. RELC Journal, 41, 301–312

Hughes, S. H., Mauchline, F. & Moore, J. 2019. ETpedia Vocabulary. Shoreham-by-Sea: Pavilion Publishing and Media

Ishii, T. 2015. Semantic connection or visual connection: Investigating the true source of confusion. Language Teaching Research, 19, 712–722

Laufer, B. 2009. The concept of ‘synforms’ (similar lexical forms) in vocabulary acquisition. Language and Education, 2 (2): 113 – 132

Nakata, T. & Suzuki, Y. 2019. Effects Of Massing And Spacing On The Learning Of Semantically Related And Unrelated Words. Studies in Second Language Acquisition 41 (2), 287 – 311

Nation, P. 2005. Teaching Vocabulary. Asian EFL Journal. http://www.asian-efl-journal.com/sept_05_pn.pdf

Papathanasiou, E. 2009. An investigation of two ways of presenting vocabulary. ELT Journal 63 (4), 313 – 322

Sarioğlu, M. 2018. A Matter of Controversy: Teaching New L2 Words in Semantic Sets or Unrelated Sets. Journal of Higher Education and Science Vol 8 / 1: 172 – 183

Schneider, V. I., Healy, A. F., & Bourne, L. E. 1998. Contextual interference effects in foreign language vocabulary acquisition and retention. In Healy, A. F. & Bourne, L. E. (Eds.), Foreign language learning: Psycholinguistic studies on training and retention (pp. 77–90). Mahwah, NJ: Erlbaum

Schneider, V. I., Healy, A. F., & Bourne, L. E. 2002. What is learned under difficult conditions is hard to forget: Contextual interference effects in foreign vocabulary acquisition, retention, and transfer. Journal of Memory and Language, 46, 419–440

Sellivan, L. 2014. Horizontal alternatives to vertical lists. Blog post: http://leoxicon.blogspot.com/2014/03/horizontal-alternatives-to-vertical.html

Tinkham, T. 1993. The effect of semantic clustering on the learning of second language vocabulary. System 21 (3), 371-380.

Tinkham, T. 1997. The effects of semantic and thematic clustering on the learning of a second language vocabulary. Second Language Research, 13 (2),138-163

Waring, R. 1997. The negative effects of learning words in semantic sets: a replication. System, 25 (2), 261 – 274

AI and language teaching

It’s hype time again. Spurred on, no doubt, by the current spate of books and articles  about AIED (artificial intelligence in education), the IATEFL Learning Technologies SIG is organising an online event on the topic in November of this year. Currently, the most visible online references to AI in language learning are related to Glossika , basically a language learning system that uses spaced repetition, whose marketing department has realised that references to AI might help sell the product. They’re not alone – see, for example, Knowble which I reviewed earlier this year .

In the wider world of education, where AI has made greater inroads than in language teaching, every day brings more stuff: How artificial intelligence is changing teaching , 32 Ways AI is Improving Education , How artificial intelligence could help teachers do a better job , etc., etc. There’s a full-length book by Anthony Seldon, The Fourth Education Revolution: will artificial intelligence liberate or infantilise humanity? (2018, University of Buckingham Press) – one of the most poorly researched and badly edited books on education I’ve ever read, although that won’t stop it selling – and, no surprises here, there’s a Pearson commissioned report called Intelligence Unleashed: An argument for AI in Education (2016) which is available free.

Common to all these publications is the claim that AI will radically change education. When it comes to language teaching, a similar claim has been made by Donald Clark (described by Anthony Seldon as an education guru but perhaps best-known to many in ELT for his demolition of Sugata Mitra). In 2017, Clark wrote a blog post for Cambridge English (now unavailable) entitled How AI will reboot language learning, and a more recent version of this post, called AI has and will change language learning forever (sic) is available on Clark’s own blog. Given the history of the failure of education predictions, Clark is making bold claims. Thomas Edison (1922) believed that movies would revolutionize education. Radios were similarly hyped in the 1940s and in the 1960s it was the turn of TV. In the 1980s, Seymour Papert predicted the end of schools – ‘the computer will blow up the school’, he wrote. Twenty years later, we had the interactive possibilities of Web 2.0. As each technology failed to deliver on the hype, a new generation of enthusiasts found something else to make predictions about.

But is Donald Clark onto something? Developments in AI and computational linguistics have recently resulted in enormous progress in machine translation. Impressive advances in automatic speech recognition and generation, coupled with the power that can be packed into a handheld device, mean that we can expect some re-evaluation of the value of learning another language. Stephen Heppell, a specialist at Bournemouth University in the use of ICT in Education, has said: ‘Simultaneous translation is coming, making language teachers redundant. Modern languages teaching in future may be more about navigating cultural differences’ (quoted by Seldon, p.263). Well, maybe, but this is not Clark’s main interest.

Less a matter of opinion and much closer to the present day is the issue of assessment. AI is becoming ubiquitous in language testing. Cambridge, Pearson, TELC, Babbel and Duolingo are all using or exploring AI in their testing software, and we can expect to see this increase. Current, paper-based systems of testing subject knowledge are, according to Rosemary Luckin and Kristen Weatherby, outdated, ineffective, time-consuming, the cause of great anxiety and can easily be automated (Luckin, R. & Weatherby, K. 2018. ‘Learning analytics, artificial intelligence and the process of assessment’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.253). By capturing data of various kinds throughout a language learner’s course of study and by using AI to analyse learning development, continuous formative assessment becomes possible in ways that were previously unimaginable. ‘Assessment for Learning (AfL)’ or ‘Learning Oriented Assessment (LOA)’ are two terms used by Cambridge English to refer to the potential that AI offers which is described by Luckin (who is also one of the authors of the Pearson paper mentioned earlier). In practical terms, albeit in a still very limited way, this can be seen in the CUP course ‘Empower’, which combines CUP course content with validated LOA from Cambridge Assessment English.

Will this reboot or revolutionise language teaching? Probably not and here’s why. AIED systems need to operate with what is called a ‘domain knowledge model’. This specifies what is to be learnt and includes an analysis of the steps that must be taken to reach that learning goal. Some subjects (especially STEM subjects) ‘lend themselves much more readily to having their domains represented in ways that can be automatically reasoned about’ (du Boulay, D. et al., 2018. ‘Artificial intelligences and big data technologies to close the achievement gap’ in Luckin, R. (ed.) Enhancing Learning and Teaching with Technology, 2018. UCL Institute of Education Press, p.258). This is why most AIED systems have been built to teach these areas. Language are rather different. We simply do not have a domain knowledge model, except perhaps for the very lowest levels of language learning (and even that is highly questionable). Language learning is probably not, or not primarily, about acquiring subject knowledge. Debate still rages about the relationship between explicit language knowledge and language competence. AI-driven formative assessment will likely focus most on explicit language knowledge, as does most current language teaching. This will not reboot or revolutionise anything. It will more likely reinforce what is already happening: a model of language learning that assumes there is a strong interface between explicit knowledge and language competence. It is not a model that is shared by most SLA researchers.

So, one thing that AI can do (and is doing) for language learning is to improve the algorithms that determine the way that grammar and vocabulary are presented to individual learners in online programs. AI-optimised delivery of ‘English Grammar in Use’ may lead to some learning gains, but they are unlikely to be significant. It is not, in any case, what language learners need.

AI, Donald Clark suggests, can offer personalised learning. Precisely what kind of personalised learning this might be, and whether or not this is a good thing, remains unclear. A 2015 report funded by the Gates Foundation found that we currently lack evidence about the effectiveness of personalised learning. We do not know which aspects of personalised learning (learner autonomy, individualised learning pathways and instructional approaches, etc.) or which combinations of these will lead to gains in language learning. The complexity of the issues means that we may never have a satisfactory explanation. You can read my own exploration of the problems of personalised learning starting here .

What’s left? Clark suggests that chatbots are one area with ‘huge potential’. I beg to differ and I explained my reasons eighteen months ago . Chatbots work fine in very specific domains. As Clark says, they can be used for ‘controlled practice’, but ‘controlled practice’ means practice of specific language knowledge, the practice of limited conversational routines, for example. It could certainly be useful, but more than that? Taking things a stage further, Clark then suggests more holistic speaking and listening practice with Amazon Echo, Alexa or Google Home. If and when the day comes that we have general, as opposed to domain-specific, AI, chatting with one of these tools would open up vast new possibilities. Unfortunately, general AI does not exist, and until then Alexa and co will remain a poor substitute for human-human interaction (which is readily available online, anyway). Incidentally, AI could be used to form groups of online language learners to carry out communicative tasks – ‘the aim might be to design a grouping of students all at a similar cognitive level and of similar interests, or one where the participants bring different but complementary knowledge and skills’ (Luckin, R., Holmes, W., Griffiths, M. & Forceir, L.B. 2016. Intelligence Unleashed: An argument for AI in Education. London: Pearson, p.26).

Predictions about the impact of technology on education have a tendency to be made by people with a vested interest in the technologies. Edison was a businessman who had invested heavily in motion pictures. Donald Clark is an edtech entrepreneur whose company, Wildfire, uses AI in online learning programs. Stephen Heppell is executive chairman of LP+ who are currently developing a Chinese language learning community for 20 million Chinese school students. The reporting of AIED is almost invariably in websites that are paid for, in one way or another, by edtech companies. Predictions need, therefore, to be treated sceptically. Indeed, the safest prediction we can make about hyped educational technologies is that inflated expectations will be followed by disillusionment, before the technology finds a smaller niche.

 

MosaLingua: a review

MosaLingua  (with the obligatory capital letter in the middle) is a vocabulary app, available for iOS and Android. There are packages for a number of languages and English variations include general English, business English, vocabulary for TOEFL and vocabulary for TOEIC. The company follows the freemium model, with free ‘Lite’ versions and fuller content selling for €4.99. I tried the ‘Lite’ general English app, opting for French as my first language. Since the app is translation-based, you need to have one of the language pairings that are on offer (the other languages are currently Italian, Spanish, Portuguese and German).

The app I looked at is basically a phrase book with spaced repetition. Even though this particular app was general English, it appeared to be geared towards the casual business traveller. It uses the same algorithm as Anki, and users are taken through a sequence of (1) listening to an audio recording of the target item (word or phrase) along with the possibility of comparing a recording of yourself with the recording provided, (2) standard bilingual flashcard practice, (3) a practice stage where you are given the word or phrase in your own language and you have to unscramble words or letters to form the equivalent in English, and (4) a self-evaluation stage where users select from one of four options (“review”, “hard”, “good”, “perfect”) where the choice made will influence the re-presentation of the item within the spaced repetition.

In addition to these words and phrases, there are a number of dialogues where you (1) listen to the dialogue (‘without worrying about understanding everything’), (2) are re-exposed to the dialogue with English subtitles, (3) see it again with subtitles in your own language, (4) practise it with standard flashcards.

The developers seem to be proud of their Mosa Learning Method®: they’ve registered this as a trademark. At its heart is spaced repetition. This is supplemented by what they refer to as ‘Active Recall’, the notion that things are better memorised if the learner has to make some sort of cognitive effort, however minimal, in recalling the target items. The principle is, at least to me, unquestionable, but the realisation (unjumbling words or letters) becomes rather repetitive and, ultimately, tedious. Then, there is what they call ‘metacognition’. Again, this is informed by research, even if the realisation (self-evaluation of learning difficulty into four levels) is extremely limited. Then there is the Pareto principle  – the 80-20 rule. I couldn’t understand the explanation of what this has to do with the trademarked method. Here’s the MosaLingua explanation  – figure it out for yourself:

Did you know that the 100 most common words in English account for half of the written corpus?

Evidently, you shouldn’t quit after learning only 100 words. Instead, you should concentrate on the most frequently used words and you’ll make spectacular progress. What’s more, globish (global English) has shown that it’s possible to express yourself using only 1500 well-chosen words (which would take less than 3 months with only 10 minutes per day with MosaLingua). Once you’ve acquired this base, MosaLingua proposes specialized vocabulary suited to your needs (the application has over 3000 words).

Finally, there’s some stuff about motivation and learner psychology. This boils down to That’s why we offer free learning help via email, presenting the Web’s best resources, as well as tips through bonus material or the learning community on the MosaLingua blog. We’ll give you all the tools you need to develop your own personalized learning method that is adapted to your needs. Some of these tips are not at all bad, but there’s precious little in the way of gamification or other forms of easy motivation.

In short, it’s all reasonably respectable, despite the predilection for sciency language in the marketing blurb. But what really differentiates this product from Anki, as the founder, Samuel Michelot, points out is the content. Mosalingua has lists of vocabulary and phrases that were created by professors. The word ‘professors’ set my alarm bells ringing, and I wasn’t overly reassured when all I could find out about these ‘professors’ was the information about the MosaLingua team .

Despite what some people  claim, content is, actually, rather important when it comes to language learning. I’ll leave you with some examples of MosaLingua content (one dialogue and a selection of words / phrases organised by level) and you can make up your own mind.

Dialogue

Hi there, have a seat. What seems to be the problem?

I haven’t been feeling well since this morning. I have a very bad headache and I feel sick.

Do you feel tired? Have you had cold sweats?

Yes, I’m very tired and have had cold sweats. I have been feeling like that since this morning.

Have you been out in the sun?

Yes, this morning I was at the beach with my friends for a couple hours.

OK, it’s nothing serious. It’s just a bad case of sunstroke. You must drink lots of water and rest. I’ll prescribe you something for the headache and some after sun lotion.

Great, thank you, doctor. Bye.

You’re welcome. Bye.

Level 1: could you help me, I would like a …, I need to …, I don’t know, it’s okay, I (don’t) agree, do you speak English, to drink, to sleep, bank, I’m going to call the police

Level 2: I’m French, cheers, can you please repeat that, excuse me how can I get to …, map, turn left, corner, far (from), distance, thief, can you tell me where I can find …

Level 3: what does … mean, I’m learning English, excuse my English, famous, there, here, until, block, from, to turn, street corner, bar, nightclub, I have to be at the airport tomorrow morning

Level 4: OK, I’m thirty (years old), I love this country, how do you say …, what is it, it’s a bit like …, it’s a sort of …, it’s as small / big as …, is it far, where are we, where are we going, welcome, thanks but I can’t, how long have you been here, is this your first trip to England, take care, district / neighbourhood, in front (of)

Level 5: of course, can I ask you a question, you speak very well, I can’t find the way, David this is Julia, we meet at last, I would love to, where do you want to go, maybe another day, I’ll miss you, leave me alone, don’t touch me, what’s you email

Level 6: I’m here on a business trip, I came with some friends, where are the nightclubs, I feel like going to a bar, I can pick you up at your house, let’s go to see a movie, we had a lot of fun, come again, thanks for the invitation

Heaving bosoms: the problems with Lingua.ly

Lingua.ly is an Israeli start-up which, in its own words, ‘is an innovative new learning solution that helps you learn a language from the open web’. Its platform ‘uses big-data paired with spaced repetition to help users bootstrap their way to fluency’. You can read more of this kind of adspeak at the Lingua.ly blog  or the Wikipedia entry  which seems to have been written by someone from the company.

How does it work? First of all, state the language you want to study (currently there are 10 available) and the language you already speak (currently there are 18 available). Then, there are three possible starting points: insert a word which you want to study, click on a word in any web text or click on a word in one of the suggested reading texts. This then brings up a bilingual dictionary entry which, depending on the word, will offer a number of parts of speech and a number of translated word senses. Click on the appropriate part of speech and the appropriate word sense, and the item will be added to your personal word list. Once you have a handful of words in your word list, you can begin practising these words. Here there are two options. The first is a spaced repetition flashcard system. It presents the target word and 8 different translations in your own language, and you have to click on the correct option. Like most flashcard apps, spaced repetition software determines when and how often you will be re-presented with the item.

The second option is to read an authentic web text which contains one or more of your target items. The company calls this ‘digital language immersion, a method of employing a virtual learning environment to simulate the language learning environment’. The app ‘relies on a number of applied linguistics principles, including the Natural Approach and Krashen’s Input Hypothesis’, according to the Wikipedia entry. Apparently, the more you use the app, the more it knows about you as a learner, and the better able it is to select texts that are appropriate for you. As you read these texts, of course, you can click on more words and add them to your word list.

I tried out Lingua.ly, logging on as a French speaker wanting to learn English, and clicking on words as the fancy took me. I soon had a selection of texts to read. Users are offered a topic menu which consisted of the following: arts, business, education, entertainment, food, weird, beginners, green, health, living, news, politics, psychology, religion, science, sports, style. The sources are varied and not at all bad – Christian Science Monitor, The Grauniad, Huffington Post, Time, for example –and there are many very recent articles. Some texts were interesting; others seemed very niche. I began clicking on more words that I thought would be interesting to explore and here my problems began.

I quickly discovered that the system could only deal with single words, so phrasal verbs were off limits. One text I looked at had the phrasal verb ‘ripping off’, and although I could get translations for ‘ripping’ and ‘off’, this was obviously not terribly helpful. Learners who don’t know the phrasal verb ‘ripped off’ do not necessarily know that it is a phrasal verb, so the translations offered for the two parts of the verb are worse than unhelpful; they are actually misleading. Proper nouns were also a problem, although some of the more common ones were recognised. But the system failed to recognise many proper nouns for what they were, and offered me translations of homonymous nouns. With some words (e.g. ‘stablemate’), the dictionary offered only one translation (in this case, the literal translation), but not the translation (the much more common idiomatic one) that was needed in the context in which I came across the word. With others (e.g. ‘pertain’), I was offered a list of translations which included the one that was appropriate in the context, but, unfortunately, this is the French word ‘porter’, which has so many possible meanings that, if you genuinely didn’t know the word, you would be none the wiser.

Once you’ve clicked on an appropriate part of speech and translation (if you can find one), the dictionary look-up function offers both photos and example sentences. Here again there were problems. I’d clicked on the verb ‘pan’ which I’d encountered in the context of a critic panning a book they’d read. I was able to select an appropriate translation, but when I got to the photos, I was offered only multiple pictures of frying pans. There were no example sentences for my meaning of ‘pan’: instead, I was offered multiple sentences about cooking pans, and one about Peter Pan. In other cases, the example sentences were either unhelpful (e.g. the example for ‘deal’ was ‘I deal with that’) or bizarre (e.g. the example sentence for ‘deemed’ was ‘The boy deemed that he cheated in the examination’). For some words, there were no example sentences at all.

Primed in this way, I was intrigued to see how the system would deal with the phrase ‘heaving bosoms’ which came up in one text. ‘Heaving bosoms’ is an interesting case. It’s a strong collocation, and, statistically, ‘heaving bosoms’ plural are much more frequent than ‘a heaving bosom’ singular. ‘Heaving’, as an adjective, only really collocates with ‘bosoms’. You don’t find ‘heaving’ collocating with any of the synonyms for ‘bosoms’. The phrase is also heavily connoted, strongly associated with romance novels, and often used with humorous intent. Finally, there is also a problem of usage with ‘bosom’ / ‘bosoms’: men or women, one or two – all in all, it’s a tricky word.

Lingua.ly was no help at all. There was no dictionary entry for an adjectival ‘heaving’, and the translations for the verb ‘heave’ were amusing, but less than appropriate. As for ‘bosom’, there were appropriate translations (‘sein’ and ‘poitrine’), but absolutely no help with how the word is actually used. Example sentences, which are clearly not tagged to the translation which has been chosen, included ‘Or whether he shall die in the bosom of his family or neglected and despised in a foreign land’ and ‘Can a man take fire in his bosom, and his clothes not be burned?’

Lingua.ly has a number of problems. First off, its software hinges on a dictionary (it’s a Babylon dictionary) which can only deal with single words, is incomplete, and does not deal with collocation, connotation, style or register. As such, it can only be of limited value for receptive use, and of no value whatsoever for productive use. Secondly, the web corpus that it is using simply isn’t big enough. Thirdly, it doesn’t seem to have any Natural Language Processing tool which could enable it to deal with meanings in context. It can’t disambiguate words automatically. Such software does now exist, and Lingua.ly desperately needs it.

Unfortunately, there are other problems, too. The flashcard practice is very repetitive and soon becomes boring. With eight translations to choose from, you have to scroll down the page to see them all. But there’s a timer mechanism, and I frequently timed out before being able to select the correct translation (partly because words are presented with no context, so you have to remember the meaning which you clicked in an earlier study session). The texts do not seem to be graded for level. There is no indication of word frequency or word sense frequency. There is just one gamification element (a score card), but there is no indication of how scores are achieved. Last, but certainly not least, the system is buggy. My word list disappeared into the cloud earlier today, and has not been seen since.

I think it’s a pity that Lingua.ly is not better. The idea behind it is good – even if the references to Krashen are a little unfortunate. The company says that they have raised $800,000 in funding, but with their freemium model they’ll be desperately needing more, and they’ve gone to market too soon. One reviewer, Language Surfer,  wrote a withering review of Lingua.ly’s Arabic program (‘it will do more harm than good to the Arabic student’), and Brendan Wightman, commenting at eltjam,  called it ‘dull as dish water, […] still very crude, limited and replete with multiple flaws’. But, at least, it’s free.