Posts Tagged ‘vocabulary’

subtitlesAs both a language learner and a teacher, I have a number of questions about the value of watching subtitled videos for language learning. My interest is in watching extended videos, rather than short clips for classroom use, so I am concerned with incidental, rather than intentional, learning, mostly of vocabulary. My questions include:

  • Is it better to watch a video that is subtitled or unsubtitled?
  • Is it better to watch a video with L1 or L2 subtitles?
  • If a video is watched more than once, what is the best way to start and proceed? In which order (no subtitles, L1 subtitles and L2 subtitles) is it best to watch?

For help, I turned to three recent books about video and language learning: Ben Goldstein and Paul Driver’s Language Learning with Digital Video (CUP, 2015), Kieran Donaghy’s Film in Action (Delta, 2015) and Jamie Keddie’s Bringing Online Video into the Classroom (OUP, 2014). I was surprised to find no advice, but, as I explored more, I discovered that there may be a good reason for these authors’ silence.

There is now a huge literature out there on subtitles and language learning, and I cannot claim to have read it all. But I think I have read enough to understand that I am not going to find clear-cut answers to my questions.

The learning value of subtitles

It has been known for some time that the use of subtitles during extensive viewing of video in another language can help in the acquisition of that language. The main gains are in vocabulary acquisition and the development of listening skills (Montero Perez et al., 2013). This is true of both L1 subtitles (with an L2 audio track), sometimes called interlingual subtitles, (Incalcaterra McLoughlin et al, 2011) and L2 subtitles (with an L2 audio track), sometimes called intralingual subtitles or captions (Vanderplank, 1988). Somewhat more surprisingly, vocabulary gains may also come from what are called reversed subtitles (L2 subtitles and an L1 audio track) (Burczyńska, 2015). Of course, certain conditions apply for subtitled video to be beneficial, and I’ll come on to these. But there is general research agreement (an exception is Karakaş & Sariçoban, 2012) that more learning is likely to take place from watching a subtitled video in a target language than an unsubtitled one.

Opposition to the use of subtitles as a tool for language learning has mostly come from three angles. The first of these, which concerns L1 subtitles, is an antipathy to any use at all of L1. Although such an attitude remains entrenched in some quarters, there is no evidence to support it (Hall & Cook, 2012; Kerr, 2016). Researchers and, increasingly, teachers have moved on.

The second reservation that is sometimes expressed is that learners may not attend to either the audio track or the subtitles if they do not need to. They may, for example, ignore the subtitles in the case of reversed subtitles or ignore the L2 audio track when there are L1 subtitles. This can, of course, happen, but it seems that, on the whole, this is not the case. In an eye-tracking study by Bisson et al (2012), for example, it was found that most people followed the subtitles, irrespective of what kind they were. Unsurprisingly, they followed the subtitles more closely when the audio track was in a language that was less familiar. When conditions are right (see below), reading subtitles becomes a very efficient and partly automatized cognitive activity, which does not prevent people from processing the audio track at the same time (d’Ydewalle & Pavakanun, 1997).

Related to the second reservation is the concern that the two sources of information (audio and subtitles), combined with other information (images and music or sound effects), may be in competition and lead to cognitive overload, impacting negatively on both comprehension and learning. Recent research suggests that this concern is ungrounded (Kruger et al, 2014). L1 subtitles generate less cognitive load than L2 subtitles, but overload is not normally reached and mental resources are still available for learning (Baranowska, 2020). The absence of subtitles generates more cognitive load.

Conditions for learning

Before looking at the differences between L1 and L2 subtitles, it’s a good idea to look at the conditions under which learning is more likely to take place with subtitles. Some of these are obvious, others less so.

First of all, the video material must be of sufficient intrinsic interest to the learner. Secondly, the subtitles must be of a sufficiently high quality. This is not always the case with automatically generated captions, especially if the speech-to-text software struggles with the audio accent. It is also not always the case with professionally produced L1 subtitles, especially when the ‘translations are non-literal and made at the phrase level, making it hard to find connections between the subtitle text and the words in the video’ (Kovacs, 2013, cited by Zabalbeascoa et al., 2015: 112). As a minimum, standard subtitling guidelines, such as those produced for the British Channel 4, should be followed. These limit, for example, the number of characters per line to about 40 and a maximum of two lines.

For reasons that I’ll come on to, learners should be able to switch easily between L1 and L2 subtitles. They are also likely to benefit if reliably accurate glosses or hyperlinks are ‘embedded in the subtitles, making it possible for a learner to simply click for additional verbal, auditory or even pictorial glosses’ (Danan, 2015: 49).

At least as important as considerations of the materials or tools, is a consideration of what the learner brings to the activity (Frumuselu, 2019: 104). Vanderplank (2015) describes these different kinds of considerations as the ‘effects of’ subtitles on a learner and the ‘effects with’ subtitles on learner behaviour.

In order to learn from subtitles, you need to be able to read fast enough to process them. Anyone with a slow reading speed (e.g. some dyslexics) in their own language is going to struggle. Even with L1 subtitles, Vanderplank (2015: 24) estimates that it is only around the age of 10 that children can do this with confidence. Familarity with both the subject matter and with subtitle use will impact on this ability to read subtitles fast enough.

With L2 subtitles, the language proficiency of the learner related to the level of difficulty (especially lexical difficulty) of the subtitles will clearly be of some significance. It is unlikely that L2 subtitles will be of much benefit to beginners (Taylor, 2005). It also suggests that, at lower levels, materials need to be chosen carefully. On the whole, researchers have found that higher proficiency levels correlate with greater learning gains (Pujadas & Muñoz, 2019; Suárez & Gesa, 2019), but one earlier meta-analysis (Montero Perez et al., 2013) did not find that proficiency levels were significant.

Measures of general language proficiency may be too blunt an instrument to help us all of the time. I can learn more from Portuguese than from Arabic subtitles, even though I am a beginner in both languages. The degree of proximity between two languages, especially the script (Winke et al., 2010), is also likely to be significant.

But a wide range of other individual learner differences will also impact on the learning from subtitles. It is known that learners approach subtitles in varied and idiosyncratic ways (Pujolá, 2002), with some using L2 subtitles only as a ‘back-up’ and others relying on them more. Vanderplank (2019) grouped learners into three broad categories: minimal users who were focused throughout on enjoying films as they would in their L1, evolving users who showed marked changes in their viewing behaviour over time, and maximal users who tended to be experienced at using films to enhance their language learning.

Categories like these are only the tip of the iceberg. Sensory preferences, personality types, types of motivation, the impact of subtitles on anxiety levels and metacognitive strategy awareness are all likely to be important. For the last of these, Danan (2015: 47) asks whether learners should be taught ‘techniques to make better use of subtitles and compensate for weaknesses: techniques such as a quick reading of subtitles before listening, confirmation of word recognition or meaning after listening, as well as focus on form for spelling or grammatical accuracy?’

In short, it is, in practice, virtually impossible to determine optimal conditions for learning from subtitles, because we cannot ‘take into account all the psycho-social, cultural and pedagogic parameters’ (Gambier, 2015). With that said, it’s time to take a closer look at the different potential of L1 and L2 subtitles.

L1 vs L2 subtitles

Since all other things are almost never equal, it is not possible to say that one kind of subtitles offers greater potential for learning than another. As regards gains in vocabulary acquisition and listening comprehension, there is no research consensus (Baranowska, 2020: 107). Research does, however, offer us a number of pointers.

Extensive viewing of subtitled video (both L1 and L2) can offer ‘massive quantities of authentic and comprehensible input’ (Vanderplank, 1988: 273). With lower level learners, the input is likely to be more comprehensible with L1 subtitles, and, therefore, more enjoyable and motivating. This makes them often more suitable for what Caimi (2015: 11) calls ‘leisure viewing’. Vocabulary acquisition may be better served with L2 subtitles, because they can help viewers to recognize the words that are being spoken, increase their interaction with the target language, provide further language context, and increase the redundancy of information, thereby enhancing the possibility of this input being stored in long-term memory (Frumuselu et al., 2015). These effects are much more likely with Vanderplank’s (2019) motivated, ‘maximal’ users than with ‘minimal’ users.

There is one further area where L2 subtitles may have the edge over L1. One of the values of extended listening in a target language is the improvement in phonetic retuning (see, for example, Reinisch & Holt, 2013), the ability to adjust the phonetic boundaries in your own language to the boundaries that exist in the target language. Learning how to interpret unusual speech-sounds, learning how to deal with unusual mappings between sounds and words and learning how to deal with the acoustic variations of different speakers of the target language are all important parts of acquiring another language. Research by Mitterer and McQueen (2009) suggests that L2 subtitles help in this process, but L1 subtitles hinder it.

Classroom implications?

The literature on subtitles and language learning echoes with the refrain of ‘more research needed’, but I’m not sure that further research will lead to less ambiguous, practical conclusions. One of my initial questions concerned the optimal order of use of different kinds of subtitles. In most extensive viewing contexts, learners are unlikely to watch something more than twice. If they do (watching a recorded academic lecture, for example), they are likely to be more motivated by a desire to learn from the content than to learn language from the content. L1 subtitles will probably be preferred, and will have the added bonus of facilitating note-taking in the L1. For learners who are more motivated to learn the target language (Vanderplank’s ‘maximal’ users), a sequence of subtitle use, starting with the least cognitively challenging and moving to greater challenge, probably makes sense. Danan (2015: 46) suggests starting with an L1 soundtrack and reversed (L2) subtitles, then moving on to an L2 soundtrack and L2 subtitles, and ending with an L2 soundtrack and no subtitles. I would replace her first stage with an L2 soundtrack and L1 subtitles, but this is based on hunch rather than research.

This sequencing of subtitle use is common practice in language classrooms, but, here, (1) the video clips are usually short, and (2) the aim is often not incidental learning of vocabulary. Typically, the video clip has been selected as a tool for deliberate teaching of language items, so different conditions apply. At least one study has confirmed the value of the common teaching practice of pre-teaching target vocabulary items before viewing (Pujadas & Muñoz, 2019). The drawback is that, by getting learners to focus on particular items, less incidental learning of other language features is likely to take place. Perhaps this doesn’t matter too much. In a short clip of a few minutes, the opportunities for incidental learning are limited, anyway. With short clips and a deliberate learning aim, it seems reasonable to use L2 subtitles for a first viewing, and no subtitles thereafter.

An alternative frequent use of short video clips in classrooms is to use them as a springboard for speaking. In these cases, Baranowska (2020: 113) suggests that teachers may opt for L1 subtitles first, and follow up with L2 subtitles. Of course, with personal viewing devices or in online classes, teachers may want to exploit the possibilities of differentiating the subtitle condition for different learners.

REFERENCES

Baranowska, K. (2020). Learning most with least effort: subtitles and cognitive load. ELT Journal 74 (2): pp.105 – 115

Bisson, M.-J., Van Heuven, W.J.B., Conklin, K. and Tunney, R.J. (2012). Processing of native and foreign language subtitles in films: An eye tracking study. Applied Psycholingistics, 35 (2): pp. 399 – 418

Burczyńska, P. (2015). Reversed Subtitles as a Powerful Didactic Tool in SLA. In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences. Bern: Peter Lang (pp. 221 – 244)

Caimi, A. (2015). Introduction. In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences. Bern: Peter Lang (pp. 9 – 18)

Danan, M. (2015). Subtitling as a Language Learning Tool: Past Findings, Current Applications, and Future Paths. In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences. Bern: Peter Lang (pp. 41 – 61)

d’Ydewalle, G. & Pavakanun, U. (1997). Could Enjoying a Movie Lead to Language Acquisition?. In: Winterhoff-Spurk, P., van der Voort, T.H.A. (Eds.) New Horizons in Media Psychology. VS Verlag für Sozialwissenschaften, Wiesbaden. https://doi.org/10.1007/978-3-663-10899-3_10

Frumuselu, A.D., de Maeyer, S., Donche, V. & Gutierrez Colon Plana, M. (2015). Television series inside the EFL classroom: bridging the gap between teaching and learning informal language through subtitles. Linguistics and Education, 32: pp. 107 – 17

Frumuselu, A. D. (2019). ‘A Friend in Need is a Film Indeed’: Teaching Colloquial Expressions with Subtitled Television Series. In Herrero, C. & Vanderschelden, I. (Eds.) Using Film and Media in the Language Classroom. Bristol: Multimedia Matters. pp.92 – 107

Gambier, Y. (2015). Subtitles and Language Learning (SLL): Theoretical background. In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences. Bern: Peter Lang (pp. 63 – 82)

Hall, G. & Cook, G. (2012). Own-language Use in Language Teaching and Learning. Language Learning, 45 (3): pp. 271 – 308

Incalcaterra McLoughlin, L., Biscio, M. & Ní Mhainnín, M. A. (Eds.) (2011). Audiovisual Translation, Subtitles and Subtitling. Theory and Practice. Bern: Peter Lang

Karakaş, A. & Sariçoban, A. (2012). The impact of watching subtitled animated cartoons on incidental vocabulary learning of ELT students. Teaching English with Technology, 12 (4): pp. 3 – 15

Kerr, P. (2016). Questioning ‘English-only’ Classrooms: Own-language Use in ELT. In Hall, G. (Ed.) The Routledge Handbook of English Language Teaching (pp. 513 – 526)

Kruger, J. L., Hefer, E. & Matthew, G. (2014). Attention distribution and cognitive load in a subtitled academic lecture: L1 vs. L2. Journal of Eye Movement Research, 7: pp. 1 – 15

Mitterer, H. & McQueen, J. M. (2009). Foreign Subtitles Help but Native-Language Subtitles Harm Foreign Speech Perception. PLoS ONE 4 (11): e7785.doi:10.1371/journal.pone.0007785

Montero Perez, M., Van Den Noortgate, W., & Desmet, P. (2013). Captioned video for L2 listening and vocabulary learning: A meta-analysis. System, 41, pp. 720–739 doi:10.1016/j.system.2013.07.013

Pujadas, G. & Muñoz, C. (2019). Extensive viewing of captioned and subtitled TV series: a study of L2 vocabulary learning by adolescents, The Language Learning Journal, 47:4, 479-496, DOI: 10.1080/09571736.2019.1616806

Pujolá, J.- T. (2002). CALLing for help: Researching language learning strategies using help facilities in a web-based multimedia program. ReCALL, 14 (2): pp. 235 – 262

Reinisch, E. & Holt, L. L. (2013). Lexically Guided Phonetic Retuning of Foreign-Accented Speech and Its Generalization. Journal of Experimental Psychology: Human Perception and Performance. Advance online publication. doi: 10.1037/a0034409

Suárez, M. & Gesa, F. (2019) Learning vocabulary with the support of sustained exposure to captioned video: do proficiency and aptitude make a difference? The Language Learning Journal, 47:4, 497-517, DOI: 10.1080/09571736.2019.1617768

Taylor, G. (2005). Perceived processing strategies of students watching captioned video. Foreign Language Annals, 38(3), pp. 422-427

Vanderplank, R. (1988). The value of teletext subtitles in language learning. ELT Journal, 42 (4): pp. 272 – 281

Vanderplank, R. (2015). Thirty Years of Research into Captions / Same Language Subtitles and Second / Foreign Language Learning: Distinguishing between ‘Effects of’ Subtitles and ‘Effects with’ Subtitles for Future Research. In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences. Bern: Peter Lang (pp. 19 – 40)

Vanderplank, R. (2019). ‘Gist watching can only take you so far’: attitudes, strategies and changes in behaviour in watching films with captions, The Language Learning Journal, 47:4, 407-423, DOI: 10.1080/09571736.2019.1610033

Winke, P., Gass, S. M., & Sydorenko, T. (2010). The Effects of Captioning Videos Used for Foreign Language Listening Activities. Language Learning & Technology, 1 (1): pp. 66 – 87

Zabalbeascoa, P., González-Casillas, S. & Pascual-Herce, R. (2015). In Gambier, Y., Caimi, A. & Mariotti, C. (Eds.), Subtitles and Language Learning. Principles, strategies and practical experiences Bern: Peter Lang (pp. 105–126)

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.

Word strike backsideBird drop certain

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.

Year etcsmashed etc

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.

Stripper definitionStripper taskToss definition

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?

In my last post , I looked at the use of digital dictionaries. This post is a sort of companion piece to that one.

I noted in that post that teachers are typically less keen on bilingual dictionaries (preferring monolingual versions) than their students. More generally, it seems that teachers are less keen on any kind of dictionary, preferring their students to attempt to work out the meaning of unknown words from context. Coursebooks invariably promote the skill of guessing meaning from context (also known as ‘lexical inferencing’) and some suggest that dictionary work should be banned from the classroom (Haynes & Baker, 1993, cited in Folse, 2004: 112). Teacher educators usually follow suit. Scott Thornbury, for example, has described guessing from context as ‘probably one of the most useful skills learners can acquire and apply both inside and outside the classroom’ (Thornbury, 2002: 148) and offers a series of steps to train learners in this skill before adding ‘when all else fails, consult a dictionary’. Dictionary use, then, is a last resort.

These steps are fairly well known and a typical example (from Clarke & Nation, 1980, cited in Webb & Nation, 2017: 169) is

1 Determine the part of speech of the unknown word

2 Analyse the immediate context to try to determine the meaning of the unknown word

3 Analyse the wider context to try to determine the meaning of the unknown word

4 Guess the meaning of the unknown word

5 Check the guess against the information that was found in the first four steps

It has been suggested that training in the use of this skill should be started at low levels, so that learners have a general strategy for dealing with unknown words. As proficiency develops, more specific instruction in the recognition and interpretation of context clues can be provided (Walters, 2006: 188). Training may include a demonstration by the teacher using a marked-up text, perhaps followed by ‘think-aloud’ sessions, where learners say out loud the step-by-step process they are going through when inferring meaning. It may also include a progression from, first, cloze exercises to, second, texts where highlighted words are provided with multiple choice definitions to, finally, texts with no support.

Although research has not established what kind of training is likely to be most effective, or whether specific training is more valuable than the provision of lots of opportunities to practise the skill, it would seem that this kind of work is likely to lead to gains in reading comprehension.

Besides the obvious value of this skill in helping learners to decode the meaning of unknown items in a text, it has been hypothesized that learners are ‘more likely to remember the form and meaning of a word when they have inferred its meaning by themselves than when the meaning has been given to them’ (Hulstijn, 1992). This is because memorisation is likely to be enhanced when mental effort has been exercised. The hypothesis was confirmed by Hulstijn in his 1992 study.

Unfortunately, Hulstijn’s study is not, in itself, sufficient evidence to prove the hypothesis. Other studies have shown the opposite. Keith Folse (2004: 112) cites a study by Knight (1994) which ‘found that subjects who used a bilingual dictionary while reading a passage not only learned more words but also achieved higher reading comprehension scores than subjects who did not have a dictionary and therefore had to rely on guessing from context clues’. More recently, Mokhtar & Rawian (2012) entitled their paper ‘Guessing Word Meaning from Context Has Its Limit: Why?’ They argue that ‘though it is not impossible for ESL learners to derive vocabulary meanings from context, guessing strategy by itself does not foster retention of meanings’.

What, then, are the issues here?

  • First of all, Liu and Nation (1985) have estimated that learners ought to know at least 95 per cent of the context words in order to be able to infer meaning from context. Whilst this figure may not be totally accurate, it is clear that because ‘the more words you know, the more you are able to acquire new words’ (Prince, 1996), guessing from context is likely to work better with students at higher levels of proficiency than those with a lower level.
  • Although exercises in coursebooks which require students to guess meaning from context have usually been written in such a way that it is actually possible to do so, ‘such a nicely packaged contextual environment is rare’ in the real world (Folse, 2004: 115). The skill of guessing from context may not be as useful as was previously assumed.
  • There is clearly a risk that learners will guess wrong and, therefore, learn the wrong meaning. Nassaji (2003: 664) found in one study that learners guessed wrong more than half the time.
  • Lastly, it appears that many learners do not like to employ this strategy, believing that using a dictionary is more useful to them and, possibly as a result of this attitude, fail to devote sufficient mental effort to it (Prince, 1996: 480).

Perhaps the most forceful critique of the promotion of guessing meaning from context has come from Catherine Walter and Michael Swan (2009), who referred to it as ‘an alleged ‘skill’’ and considered it, along with skimming and scanning, to be ‘mostly a waste of time’. Scott Thornbury (2006), in a marked departure from his comments (from a number of years earlier) quoted at the start of this post, also questioned the relevance of ‘guessing from context’ activities, arguing that, if students can employ a strategy such as inferring when reading their own language, they can transfer it to another language … so teachers are at risk of teaching their students what they already know.

To summarize, then, we might say that (1) the skill of guessing from context may not be as helpful in the real world as previously imagined, (2) it may not be as useful in acquiring vocabulary items as previously imagined. When a teacher is asked by a student for the meaning of a word in a text, the reflex response of ‘try to work it out from the context’ may also not be as helpful as previously imagined. Translations and / or dictionary advice may well, at times, be more appropriate.

References

Clarke, D.F. & Nation, I.S.P. 1980. ‘Guessing the meanings of words from context: Strategy and techniques.’ System, 8 (3): 211 -220

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

Haynes, M. & Baker, I. 1993. ‘American and Chinese readers learning from lexical familiarization in English texts.’ In Huckin, T., Haynes, M. & Coady, J. (Eds.) Second Language Reading and Vocabulary Acquisition. Norwood, NJ.: Ablex. pp. 130 – 152

Hulstijn, J. 1992. ‘Retention of inferred and given word meanings: experiments in incidental vocabulary learning.’ In Arnaud, P. & Bejoint, H. (Eds.) Vocabulary and Applied Linguistics. London: Macmillan Academic and Professional Limited, pp. 113 – 125

Liu, N. & Nation, I. S. P. 1985. ‘Factors affecting guessing vocabulary in context.’ RELC Journal 16 (1): 33–42

Mokhtar, A. A. & Rawian, R. M. 2012. ‘Guessing Word Meaning from Context Has Its Limit: Why?’ International Journal of Linguistics 4 (2): 288 – 305

Nassaji, H. 2003. ‘L2 vocabulary learning from context: Strategies, knowledge sources, and their relationship with success in L2 lexical inferencing.’ TESOL Quarterly, 37(4): 645-670

Prince, P. 1996. ‘Second Language vocabulary Learning: The Role of Context versus Translations as a Function of Proficiency.’ The Modern Language Journal, 80(4): 478-493

Thornbury, S. 2002. How to Teach Vocabulary. Harlow: Pearson Education

Thornbury, S. 2006. The End of Reading? One Stop English,

Walter, C. & Swan, M. 2009. ‘Teaching reading skills: mostly a waste of time?’ In Beaven B. (Ed.) IATEFL 2008 Exeter Conference Selections. Canterbury: IATEFL, pp. 70-71

Walters, J.M. 2004. ‘Teaching the use of context to infer meaning: A longitudinal survey of L1 and L2 vocabulary research.’ Language Teaching, 37(4), pp. 243-252

Walters, J.D. 2006. ‘Methods of teaching inferring meaning from context.’ RELC Journal, 37(2), pp. 176-190

Webb, S. & Nation, P. 2017. How Vocabulary is Learned. Oxford: Oxford University Press

 

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 2nd 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

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

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.

Knowble reader

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 …

google_translateOne 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

word_listNLP 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.

taskVocabulary 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.

effectiveThe 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.

Last month, I wrote a post about the automated generation of vocabulary learning materials. Yesterday, I got an email from Mike Elchik, inviting me to take a look at the product that his company, WeSpeke, has developed in partnership with CNN. Called en.news, it’s a very regularly updated and wide selection of video clips and texts from CNN, which are then used to ‘automatically create a pedagogically structured, leveled and game-ified English lesson‘. Available at the AppStore and Google Play, as well as a desktop version, it’s free. Revenues will presumably be generated through advertising and later sales to corporate clients.

With 6.2 million dollars in funding so far, WeSpeke can leverage some state-of-the-art NLP and AI tools. Co-founder and chief technical adviser of the company is Jaime Carbonell, Director of the Language Technologies Institute at Carnegie Mellon University, described in Wikipedia as one of the gurus of machine learning. I decided to have a closer look.

home_page

Users are presented with a menu of CNN content (there were 38 items from yesterday alone), these are tagged with broad categories (Politics, Opinions, Money, Technology, Entertainment, etc.) and given a level, ranging from 1 to 5, although the vast majority of the material is at the two highest levels.

menu.jpg

I picked two lessons: a reading text about Mark Zuckerberg’s Congressional hearing (level 5) and a 9 minute news programme of mixed items (level 2 – illustrated above). In both cases, the lesson begins with the text. With the reading, you can click on words to bring up dictionary entries from the Collins dictionary. With the video, you can activate captions and again click on words for definitions. You can also slow down the speed. So far, so good.

There then follows a series of exercises which focus primarily on a set of words that have been automatically selected. This is where the problems began.

Level

It’s far from clear what the levels (1 – 5) refer to. The Zuckerberg text is 930 words long and is rated as B2 by one readability tool. But, using the English Profile Text Inspector, there are 19 types at C1 level, 14 at C2, and 98 which are unlisted. That suggests something substantially higher than B2. The CNN10 video is delivered at breakneck speed (as is often the case with US news shows). Yes, it can be slowed down, but that still won’t help with some passages, such as the one below:

A squirrel recently fell out of a tree in Western New York. Why would that make news?Because she bwoke her widdle leg and needed a widdle cast! Yes, there are casts for squirrels, as you can see in this video from the Orphaned Wildlife Center. A windstorm knocked the animal’s nest out of a tree, and when a woman saw that the baby squirrel was injured, she took her to a local vet. Doctors say she’s going to be just fine in a couple of weeks. Well, why ‘rodent’ she be? She’s been ‘whiskered’ away and cast in both a video and a plaster. And as long as she doesn’t get too ‘squirrelly’ before she heals, she’ll have quite a ‘tail’ to tell.

It’s hard to understand how a text like this got through the algorithms. But, as materials writers know, it is extremely hard to find authentic text that lends itself to language learning at anything below C1. On the evidence here, there is still some way to go before the process of selection can be automated. It may well be the case that CNN simply isn’t a particularly appropriate source.

Target learning items

The primary focus of these lessons is vocabulary learning, and it’s vocabulary learning of a very deliberate kind. Applied linguists are in general agreement that it makes sense for learners to approach the building of their L2 lexicon in a deliberate way (i.e. by studying individual words) for high-frequency items or items that can be identified as having a high surrender value (e.g. items from the AWL for students studying in an EMI context). Once you get to items that are less frequent than, say, the top 8,000 most frequent words, the effort expended in studying new words needs to be offset against their usefulness. Why spend a lot of time studying low frequency words when you’re unlikely to come across them again for some time … and will probably forget them before you do? Vocabulary development at higher levels is better served by extensive reading (and listening), possibly accompanied by glosses.

The target items in the Zuckerberg text were: advocacy, grilled, handicapping, sparked, diagnose, testified, hefty, imminent, deliberative and hesitant. One of these ‘grilled‘ is listed as A2 by English Vocabulary Profile, but that is with its literal, not metaphorical, meaning. Four of them are listed as C2 and the remaining five are off-list. In the CNN10 video, the target items were: strive, humble (verb), amplify, trafficked, enslaved, enacted, algae, trafficking, ink and squirrels. Of these, one is B1, two are C2 and the rest are unlisted. What is the point of studying these essentially random words? Why spend time going through a series of exercises that practise these items? Wouldn’t your time be better spent just doing some more reading? I have no idea how the automated selection of these items takes place, but it’s clear that it’s not working very well.

Practice exercises

There is plenty of variety of task-type but there are,  I think, two reasons to query the claim that these lessons are ‘pedagogically structured’. The first is the nature of the practice exercises; the second is the sequencing of the exercises. I’ll restrict my observations to a selection of the tasks.

1. Users are presented with a dictionary definition and an anagrammed target item which they must unscramble. For example:

existing for the purpose of discussing or planning something     VLREDBETEIIA

If you can’t solve the problem, you can always scroll through the text to find the answer. Burt the problem is in the task design. Dictionary definitions have been written to help language users decode a word. They simply don’t work very well when they are used for another purpose (as prompts for encoding).

2. Users are presented with a dictionary definition for which they must choose one of four words. There are many potential problems here, not the least of which is that definitions are often more complex than the word they are defining, or they present other challenges. As an example: cause to be unpretentious for to humble. On top of that, lexicographers often need or choose to embed the target item in the definition. For example:

a hefty amount of something, especially money, is very large

an event that is imminent, especially an unpleasant one, will happen very soon

When this is the case, it makes no sense to present these definitions and ask learners to find the target item from a list of four.

The two key pieces of content in this product – the CNN texts and the Collins dictionaries – are both less than ideal for their purposes.

3. Users are presented with a box of jumbled words which they must unscramble to form sentences that appeared in the text.

Rearrange_words_to_make_sentences

The sentences are usually long and hard to reconstruct. You can scroll through the text to find the answer, but I’m unclear what the point of this would be. The example above contains a mistake (vie instead of vice), but this was one of only two glitches I encountered.

4. Users are asked to select the word that they hear on an audio recording. For example:

squirreling     squirrel     squirreled     squirrels

Given the high level of challenge of both the text and the target items, this was a rather strange exercise to kick off the practice. The meaning has not yet been presented (in a matching / definition task), so what exactly is the point of this exercise?

5. Users are presented with gapped sentences from the text and asked to choose the correct grammatical form of the missing word. Some of these were hard (e.g. adjective order), others were very easy (e.g. some vs any). The example below struck me as plain weird for a lesson at this level.

________ have zero expectation that this Congress is going to make adequate changes. (I or Me ?)

6. At the end of both lessons, there were a small number of questions that tested your memory of the text. If, like me, you couldn’t remember all that much about the text after twenty minutes of vocabulary activities, you can scroll through the text to find the answers. This is not a task type that will develop reading skills: I am unclear what it could possibly develop.

Overall?

Using the lessons on offer here wouldn’t do a learner (as long as they already had a high level of proficiency) any harm, but it wouldn’t be the most productive use of their time, either. If a learner is motivated to read the text about Zuckerberg, rather than do lots of ‘busy’ work on a very odd set of words with gap-fills and matching tasks, they’d be better advised just to read the text again once or twice. They could use a look-up for words they want to understand and import them into a flashcard system with spaced repetition (en.news does have flashcards, but there’s no sign of spaced practice yet). More, they could check out another news website and read / watch other articles on the same subject (perhaps choosing websites with a different slant to CNN) and get valuable narrow-reading practice in this way.

My guess is that the technology has driven the product here, but without answering the fundamental questions about which words it’s appropriate for individual learners to study in a deliberate way and how this is best tackled, it doesn’t take learners very far.

 

 

 

 

A personalized language learning programme that is worth its name needs to offer a wide variety of paths to accommodate the varying interests, priorities, levels and preferred approaches to learning of the users of the programme. For this to be possible, a huge quantity of learning material is needed (Iwata et al., 2011: 1): the preparation and curation of this material is extremely time-consuming and expensive (despite the pittance that is paid to writers and editors). It’s not surprising, then, that a growing amount of research is being devoted to the exploration of ways of automatically generating language learning material. One area that has attracted a lot of attention is the learning of vocabulary.

Memrise screenshot 2Many simple vocabulary learning tasks are relatively simple to generate automatically. These include matching tasks of various kinds, such as the matching of words or phrases to meanings (either in English or the L1), pictures or collocations, as in many flashcard apps. Doing it well is rather harder: the definitions or translations have to be good and appropriate for learners of the level, the pictures need to be appropriate. If, as is often the case, the lexical items have come from a text or form part of a group of some kind, sense disambiguation software will be needed to ensure that the right meaning is being practised. Anyone who has used flashcard apps knows that the major problem is usually the quality of the content (whether it has been automatically generated or written by someone).

A further challenge is the generation of distractors. In the example here (from Memrise), the distractors have been so badly generated as to render the task more or less a complete waste of time. Distractors must, in some way, be viable alternatives (Smith et al., 2010) but still clearly wrong. That means they should normally be the same part of speech and true cognates should be avoided. Research into the automatic generation of distractors is well-advanced (see, for instance, Kumar at al., 2015) with Smith et al (2010), for example, using a very large corpus and various functions of Sketch Engine (the most well-known corpus query tool) to find collocates and other distractors. Their TEDDCLOG (Testing English with Data-Driven CLOze Generation) system produced distractors that were deemed acceptable 91% of the time. Whilst impressive, there is still a long way to go before human editing / rewriting is no longer needed.

One area that has attracted attention is, of course, tests, and some tasks, such as those in TOEFL (see image). Susanti et al (2015, 2017) were able, given a target word, to automatically generate a reading passage from web sources along with questions of the TOEFL kind. However, only about half of them were considered good enough to be used in actual tests. Again, that is some way off avoiding human intervention altogether, but the automatically generated texts and questions can greatly facilitate the work of human item writers.

toefl task

 

Other tools that might be useful include the University of Nottingham AWL (Academic Word List) Gapmaker . This allows users to type or paste in a text, from which items from the AWL are extracted and replaced as a gap. See the example below. It would, presumably, not be too difficult, to combine this approach with automatic distractor generation and to create multiple choice tasks.

Nottingham_AWL_Gapmaster

WordGapThere are a number of applications that offer the possibility of generating cloze tasks from texts selected by the user (learner or teacher). These have not always been designed with the language learner in mind but one that was is the Android app, WordGap (Knoop & Wilske, 2013). Described by its developers as a tool that ‘provides highly individualized exercises to support contextualized mobile vocabulary learning …. It matches the interests of the learner and increases the motivation to learn’. It may well do all that, but then again, perhaps not. As Knoop & Wilske acknowledge, it is only appropriate for adult, advanced learners and its value as a learning task is questionable. The target item that has been automatically selected is ‘novel’, a word that features in the list Oxford 2000 Keywords (as do all three distractors), and therefore ought to be well below the level of the users. Some people might find this fun, but, in terms of learning, they would probably be better off using an app that made instant look-up of words in the text possible.

More interesting, in my view, is TEDDCLOG (Smith et al., 2010), a system that, given a target learning item (here the focus is on collocations), trawls a large corpus to find the best sentence that illustrates it. ‘Good sentences’ were defined as those which were short (but not too short, or there is not enough useful context, begins with a capital letter and ends with a full stop, has a maximum of two commas; and otherwise contains only the 26 lowercase letters. It must be at a lexical and grammatical level that an intermediate level learner of English could be expected to understand. It must be well-formed and without too much superfluous material. All others were rejected. TEDDCLOG uses Sketch Engine’s GDEX function (Good Dictionary Example Extractor, Kilgarriff et al 2008) to do this.

My own interest in this area came about as a result of my work in the development of the Oxford Vocabulary Trainer . The app offers the possibility of studying both pre-determined lexical items (e.g. the vocabulary list of a coursebook that the learner is using) and free choice (any item could be activated and sent to a learning queue). In both cases, practice takes the form of sentences with the target item gapped. There are a range of hints and help options available to the learner, and feedback is both automatic and formative (i.e. if the supplied answer is not correct, hints are given to push the learner to do better on a second attempt). Leveraging some fairly heavy technology, we were able to achieve a fair amount of success in the automation of intelligent feedback, but what had, at first sight, seemed a lesser challenge – the generation of suitable ‘carrier sentences’, proved more difficult.

The sentences which ‘carry’ the gap should, ideally, be authentic: invented examples often ‘do not replicate the phraseology and collocational preferences of naturally-occurring text’ (Smith et al., 2010). The technology of corpus search tools should allow us to do a better job than human item writers. For that to be the case, we need not only good search tools but a good corpus … and some are better than others for the purposes of language learning. As Fenogenova & Kuzmenko (2016) discovered when using different corpora to automatically generate multiple choice vocabulary exercises, the British Academic Written English corpus (BAWE) was almost 50% more useful than the British National Corpus (BNC). In the development of the Oxford Vocabulary Trainer, we thought we had the best corpus we could get our hands on – the tagged corpus used for the production of the Oxford suite of dictionaries. We could, in addition and when necessary, turn to other corpora, including the BAWE and the BNC. Our requirements for acceptable carrier sentences were similar to those of Smith et al (2010), but were considerably more stringent.

To cut quite a long story short, we learnt fairly quickly that we simply couldn’t automate the generation of carrier sentences with sufficient consistency or reliability. As with some of the other examples discussed in this post, we were able to use the technology to help the writers in their work. We also learnt (rather belatedly, it has to be admitted) that we were trying to find technological solutions to problems that we hadn’t adequately analysed at the start. We hadn’t, for example, given sufficient thought to learner differences, especially the role of L1 (and other languages) in learning English. We hadn’t thought enough about the ‘messiness’ of either language or language learning. It’s possible, given enough resources, that we could have found ways of improving the algorithms, of leveraging other tools, or of deploying additional databases (especially learner corpora) in our quest for a personalised vocabulary learning system. But, in the end, it became clear to me that we were only nibbling at the problem of vocabulary learning. Deliberate learning of vocabulary may be an important part of acquiring a language, but it remains only a relatively small part. Technology may be able to help us in a variety of ways (and much more so in testing than learning), but the dreams of the data scientists (who wrote much of the research cited here) are likely to be short-lived. Experienced writers and editors of learning materials will be needed for the foreseeable future. And truly personalized vocabulary learning, fully supported by technology, will not be happening any time soon.

 

References

Fenogenova, A. & Kuzmenko, E. 2016. Automatic Generation of Lexical Exercises Available online at http://www.dialog-21.ru/media/3477/fenogenova.pdf

Iwata, T., Goto, T., Kojiri, T., Watanabe, T. & T. Yamada. 2011. ‘Automatic Generation of English Cloze Questions Based on Machine Learning’. NTT Technical Review Vol. 9 No. 10 Oct. 2011

Kilgarriff, A. et al. 2008. ‘GDEX: Automatically Finding Good Dictionary Examples in a Corpus.’ In E. Bernal and J. DeCesaris (eds.), Proceedings of the XIII EURALEX International Congress: Barcelona, 15-19 July 2008. Barcelona: l’Institut Universitari de Lingüística Aplicada (IULA) dela Universitat Pompeu Fabra, 425–432.

Knoop, S. & Wilske, S. 2013. ‘WordGap – Automatic generation of gap-filling vocabulary exercises for mobile learning’. Proceedings of the second workshop on NLP for computer-assisted language learning at NODALIDA 2013. NEALT Proceedings Series 17 / Linköping Electronic Conference Proceedings 86: 39–47. Available online at http://www.ep.liu.se/ecp/086/004/ecp13086004.pdf

Kumar, G., Banchs, R.E. & D’Haro, L.F. 2015. ‘RevUP: Automatic Gap-Fill Question Generation from Educational Texts’. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 154–161, Denver, Colorado, June 4, Association for Computational Linguistics

Smith, S., Avinesh, P.V.S. & Kilgariff, A. 2010. ‘Gap-fill tests for Language Learners: Corpus-Driven Item Generation’. Proceedings of ICON-2010: 8th International Conference on Natural Language Processing, Macmillan Publishers, India. Available online at https://curve.coventry.ac.uk/open/file/2b755b39-a0fa-4171-b5ae-5d39568874e5/1/smithcomb2.pdf

Susanti, Y., Iida, R. & Tokunaga, T. 2015. ‘Automatic Generation of English Vocabulary Tests’. Proceedings of 7th International Conference on Computer Supported Education. Available online https://pdfs.semanticscholar.org/aead/415c1e07803756902b859e8b6e47ce312d96.pdf

Susanti, Y., Tokunaga, T., Nishikawa, H. & H. Obari 2017. ‘Evaluation of automatically generated English vocabulary questions’ Research and Practice in Technology Enhanced Learning 12 / 11

 

9781316629178More and more language learning is taking place, fully or partially, on online platforms and the affordances of these platforms for communicative interaction are exciting. Unfortunately, most platform-based language learning experiences are a relentless diet of drag-and-drop, drag-till-you-drop grammar or vocabulary gap-filling. The chat rooms and discussion forums that the platforms incorporate are underused or ignored. Lindsay Clandfield and Jill Hadfield’s new book is intended to promote online interaction between and among learners and the instructor, rather than between learners and software.

Interaction Online is a recipe book, containing about 80 different activities (many more if you consider the suggested variations). Subtitled ‘Creative activities for blended learning’, the authors have selected and designed the activities so that any teacher using any degree of blend (from platform-based instruction to occasional online homework) will be able to use them. The activities do not depend on any particular piece of software, as they are all designed for basic tools like Facebook, Skype and chat rooms. Indeed, almost every single activity could be used, sometimes with some slight modification, for teachers in face-to-face settings.

A recipe book must be judged on the quality of the activities it contains, and the standard here is high. They range from relatively simple, short activities to much longer tasks which will need an hour or more to complete. An example of the former is a sentence-completion activity (‘Don’t you hate / love it when ….?’ – activity 2.5). As an example of the latter, there is a complex problem-solving information-gap where students have to work out the solution to a mystery (activity 6.13), an activity which reminds me of some of the material in Jill Hadfield’s much-loved Communication Games books.

In common with many recipe books, Interaction Online is not an easy book to use, in the sense that it is hard to navigate. The authors have divided up the tasks into five kinds of interaction (personal, factual, creative, critical and fanciful), but it is not always clear precisely why one activity has been assigned to one category rather than another. In any case, the kind of interaction is likely to be less important to many teachers than the kind and amount of language that will be generated (among other considerations), and the table of contents is less than helpful. The index at the back of the book helps to some extent, but a clearer tabulation of activities by interaction type, level, time required, topic and language focus (if any) would be very welcome. Teachers will need to devise their own system of referencing so that they can easily find activities they want to try out.

Again, like many recipe books, Interaction Online is a mix of generic task-types and activities that will only work with the supporting materials that are provided. Teachers will enjoy the latter, but will want to experiment with the former and it is these generic task-types that they are most likely to add to their repertoire. In activity 2.7 (‘Foodies’ – personal interaction), for example, students post pictures of items of food and drink, to which other students must respond with questions. The procedure is clear and effective, but, as the authors note, the pictures could be of practically anything. ‘From pictures to questions’ might be a better title for the activity than ‘Foodies’. Similarly, activity 3.4 (‘Find a festival’ –factual interaction) uses a topic (‘festivals’), rather than a picture, to generate questions and responses. The procedure is slightly different from activity 2.7, but the interactional procedures of the two activities could be swapped around as easily as the topics could be changed.

Perhaps the greatest strength of this book is the variety of interactional procedures that is suggested. The majority of activities contain (1) suggestions for a stimulus, (2) suggestions for managing initial responses to this stimulus, and (3) suggestions for further interaction. As readers work their way through the book, they will be struck by similarities between the activities. The final chapter (chapter 8: ‘Task design’) provides an excellent summary of the possibilities of communicative online interaction, and more experienced teachers may want to read this chapter first.

Chapter 7 provides a useful, but necessarily fairly brief, overview of considerations regarding feedback and assessment

Overall, Interaction Online is a very rich resource, and one that will be best mined in multiple visits. For most readers, I would suggest an initial flick through and a cherry-picking of a small number of activities to try out. For materials writers and course designers, a better starting point may be the final two chapters, followed by a sampling of activities. For everyone, though, Online Interaction is a powerful reminder that technology-assisted language learning could and should be far more than what is usually is.

(This review first appeared in the International House Journal of Education and Development.)

 

In the last post, I suggested a range of activities that could be used in class to ‘activate’ a set of vocabulary before doing more communicative revision / recycling practice. In this, I’ll be suggesting a variety of more communicative tasks. As before, the activities require zero or minimal preparation on the part of the teacher.
1 Simple word associations
Write on the board a large selection of words that you want to recycle. Choose one word (at random) and ask the class if they can find another word on the board that they can associate with it. Ask one volunteer to (1) say what the other word is and (2) explain the association they have found between the two words. Then, draw a line through the first word and ask students if they can now choose a third word that they can associate with the second. Again, the nominated volunteer must explain the connection between the two words. Then, draw a line through the second word and ask for a connection between the third and fourth words. After three examples like this, it should be clear to the class what they need to do. Put the students into pairs or small groups and tell them to continue until there are no more words left, or it becomes too difficult to find connections / associations between the words that are left. This activity can be done simply in pairs or it can be turned into a class / group game.
As a follow-up, you might like to rearrange the pairs or groups and get students to see how many of their connections they can remember. As they are listening to the ideas of other students, ask them to decide which of the associations they found the most memorable / entertaining / interesting.
2 Association circles (variation of activity #1)
Ask students to look through their word list or flip through their flashcard set and make a list of the items that they are finding hardest to remember. They should do this with a partner and, together, should come up with a list of twelve or more words. Tell them to write these words in a circle on a sheet of paper.
Tell the students to choose, at random, one word in their circle. Next, they must find another word in the circle which they can associate in some way with the first word that they chose. They must explain this association to their partner. They must then find another word which they can associate with their second word. Again they must explain the association. They should continue in this way until they have connected all the words in their circle. Once students have completed the task with their partner, they should change partners and exchange ideas. All of this can be done orally.
3 Multiple associations
Using the same kind of circle of words, students again work with a partner. Starting with any word, they must find and explain an association with another word. Next, beginning with the word they first chose, they must find and explain an association with another word from the circle. They continue in this way until they have found connections between their first word and all the other words in the circle. Once students have completed the task with their partner, they should change partners and exchange ideas. All of this can be done orally.
4 Association dice
Prepare two lists (six in each list) of words that you want to recycle. Write these two lists on the board (list A and list B) with each word numbered 1 – 6. Each group in the class will need a dice.
First, demonstrate the activity with the whole class. Draw everyone’s attention to the two lists of the words on the board. Then roll a dice twice. Tell the students which numbers you have landed on. Explain that the first number corresponds to a word from List A and the second number to a word from List B. Think of and explain a connection / association between the two words. Organise the class into groups and ask them to continue playing the game.
Conduct feedback with the whole class. Ask them if they had any combinations of words for which they found it hard to think of a connection / association. Elicit suggestions from the whole class.
5 Picture associations #1
You will need a set of approximately eight pictures for this activity. These should be visually interesting and can be randomly chosen. If you do not have a set of pictures, you could ask the students to flick through their coursebooks and find a set of images that they find interesting or attractive. Tell them to note the page numbers. Alternatively, you could use pictures from the classroom: these might include posters on the walls, views out of the window, a mental picture of the teacher’s desk, a mental picture generated by imagining the whiteboard as a mirror, etc.
In the procedure described below, the students select the items they wish to practise. However, you may wish to select the items yourself. Make sure that students have access to dictionaries (print or online) during the lesson.
Ask the students to flip through their flashcard set or word list and make a list of the words that they are finding hardest to remember. They should do this with a partner and, together, should come up with a list of twelve or more words. The students should then find an association between each of the words on their list and one of the pictures that they select. They discuss their ideas with their partner, before comparing their ideas with a new partner.
6 Picture associations #2
Using the pictures and word lists (as in the activity above), students should select one picture, without telling their partner which picture they have selected. They should then look at the word list and choose four words from this list which they can associate with that picture. They then tell their four words to their partner, whose task is to guess which picture the other student was thinking of.
7 Rhyme associations
Prepare a list of approximately eight words that you want to recycle and write these on the board.
Ask the students to look at the words on the board. Tell them to work in pairs and find a word (in either English or their own language) which rhymes with each of the words on the list. If they cannot find a rhyming word, allow them to choose a word which sounds similar even if it is not a perfect rhyme.
The pairs should now find some sort of connection between each of the words on the list and their rhyming partners. When everyone has had enough time to find connections / associations, combine the pairs into groups of four, and ask them to exchange their ideas. Ask them to decide, for each word, which rhyming word and connection will be the most helpful in remembering this vocabulary.
Conduct feedback with the whole class.
8 Associations: truth and lies
In the procedure described below, no preparation is required. However, instead of asking the students to select the items they wish to practise, you may wish to select the items yourself. Make sure that students have access to dictionaries (print or online) during the lesson.
Ask students to flip through their flashcard set or word list and make a list of the words that they are finding hardest to remember. Individually, they should then write a series of sentences which contain these words: the sentences can contain one, two, or more of their target words. Half of the sentences should contain true personal information; the other half should contain false personal information.
Students then work with a partner, read their sentences aloud, and the partner must decide which sentences are true and which are false.
9 Associations: questions and answers
Prepare a list of between 12 and 20 items that you want the students to practise. Write these on the board (in any order) or distribute them as a handout.
Demonstrate the activity with the whole class before putting students into pairs. Make a question beginning with Why / How do you … / Why / How did you … / Why / How were you … which includes one of the target items from the list. The questions can be rather strange or divorced from reality. For example, if one of the words on the list were ankle, you could ask How did you break your ankle yesterday? Pretend that you are wracking your brain to think of an answer while looking at the other words on the board. Then, provide an answer, using one of the other words from the list. For example, if one of the other words were upset, you might answer I was feeling very upset about something and I wasn’t thinking about what I was doing. I fell down some steps. If necessary, do another example with the whole class to ensure that everyone understand the activity.
Tell the students to work in pairs, taking it in turns to ask and answer questions in the same way.
Conduct feedback with the whole class. Ask if there were any particularly strange questions or answers.
(I first came across a variation of this idea in a blog post by Alex Case ‘Playing with our Word Bag’
10 Associations: question and answer fortune telling
Prepare for yourself a list of items that you want to recycle. Number this list. (You will not need to show the list to anyone.)
Organise the class into pairs. Ask each pair to prepare four or five questions about the future. These questions could be personal or about the wider world around them. Give a few examples to make sure everyone understands: How many children will I have? What kind of job will I have five years from now? Who will win the next World Cup?
Tell the class that you have the answers to their questions. Hold up the list of words that you have prepared (without showing what is written on it). Elicit a question from one pair. Tell them that they must choose a number from 1 to X (depending on how many words you have on your list). Say the word aloud or write it on the board.
Tell the class that this is the answer to the question, but the answer must be ‘interpreted’. Ask the students to discuss in pairs the interpretation of the answer. You may need to demonstrate this the first time. If the question was How many children will I have? and the answer selected was precious, you might suggest that Your child will be very precious to you, but you will only have one. This activity requires a free imagination, and some classes will need some time to get used to the idea.
Continue with more questions and more answers selected blindly from the list, with students working in pairs to interpret these answers. Each time, conduct feedback with the whole class to find out who has the best interpretation.
11 Associations: narratives
In the procedure described below, no preparation is required. However, instead of asking the students to select the items they wish to practise, you may wish to select the items yourself. Make sure that students have access to dictionaries (print or online) during the lesson.
This activity often works best if it is used as a follow-up to ‘Picture Associations’. The story that the students prepare and tell should be connected to the picture that they focused on.
Ask students to flip through their flashcard set and make a list of the words that they are finding hardest to remember. They should do this with a partner and, together, should come up with a list of twelve or more words.
Still in pairs, they should prepare a short story which contains at least seven of the items in their list. After preparing their story, they should rehearse it before exchanging stories with another student / pair of students.
To extend this activity, the various stories can be ‘passed around’ the class in the manner of the game ‘Chinese Whispers’ (‘Broken Telephone’).
12 Associations: the sentence game
Prepare a list of approximately 25 items that you want the class to practise. Write these, in any order, on one side of the whiteboard.
Explain to the class that they are going to play a game. The object of the game is to score points by making grammatically correct sentences using the words on the board. If the students use just one of these words in a sentence, they will get one point. If they use two of the words, they’ll get two points. With three words, they’ll get three points. The more ambitious they are, the more points they can score. But if their sentence is incorrect, they will get no points and they will miss their turn. Tell the class that the sentences (1) must be grammatically correct, (2) must make logical sense, (3) must be single sentences. If there is a problem with a sentence, you, the teacher, will say that it is wrong, but you will not make a correction.
Put the class into groups of four students each. Give the groups some time to begin preparing sentences which contain one or more of the words from the list.
Ask a member from one group to come to the board and write one of the sentences they have prepared. If it is an appropriate sentence, award points. Cross out the word(s) that has been used from the list on the board: this word can no longer be used. If the sentence was incorrect, explain that there is a problem and turn to a member of the next group. This person can either (1) write a new sentence that their group has prepared, or (2) try, with the help of other members of their group to correct a sentence that is on the board. If their correction is correct, they score all the points for that sentence. If their correction is incorrect, they score no points and it is the end of their turn.
The game continues in this way with each group taking it in turns to make or correct sentences on the board.

(There are a number of comedy sketches about word associations. My favourite is this one. I’ve used it from time to time in presentations on this topic, but it has absolutely no pedagogical value (… unlike the next autoplay suggestion that was made for me, which has no comedy value).

word associations