Posts Tagged ‘vocabulary’

A few years ago, I wrote a couple of posts about the sorts of things that teachers can do in classrooms to encourage the use of vocabulary apps and to deepen the learning of the learning items. You can find these here and here. In this and a future post, I want to take this a little further. These activities will be useful and appropriate for any teachers wanting to recycle target vocabulary in the classroom.

The initial deliberate learning of vocabulary usually focuses on the study of word meanings (e.g. target items along with translations), but for these items to be absorbed into the learner’s active vocabulary store, learners will need opportunities to use them in meaningful ways. Classrooms can provide rich opportunities for this. However, before setting up activities that offer learners the chance to do this, teachers will need in some way to draw attention to the items that will be practised. The simplest way of doing this is simply to ask students to review, for a few minutes, the relevant word set in their vocabulary apps or the relevant section of the word list. Here are some more interesting alternatives.

The post after this will suggest a range of activities that promote communicative, meaningful use of the target items (after they have been ‘activated’ using one or more of the activities below).

1             Memory check

Ask the students to spend a few minutes reviewing the relevant word set in their vocabulary apps or the relevant section of the word list (up to about 20 items). Alternatively, project / write the target items on the board. After a minute or two, tell the students to stop looking at the target items. Clean the board, if necessary.

Tell students to work individually and write down all the items they can remember. Allow a minute or two. Then, put the students into pairs: tell them to (1) combine their lists, (2) check their spelling, (3) check that they can translate (or define) the items they have, and (4) add to the lists. After a few minutes, tell the pairs to compare their lists with the work of another pair. Finally, allow students to see the list of target items so they can see which words they forgot.

2             Simple dictation

Tell the class that they are going to do a simple dictation, and ask them to write the numbers 1 to X (depending on how many words you wish to recycle: about 15 is recommended) on a piece of paper or in their notebooks. Dictate the words. Tell the students to work with a partner and check (1) their spelling, and (2) that they can remember the meanings of these words. Allow the students to check their answers in the vocabulary app / a dictionary / their word list / their coursebook.

3             Missing vowels dictation

As above (‘Simple dictation’), but tell the students that they must only write the consonants of the dictated words. When comparing their answers with a partner, they must reinsert the missing vowels.

4             Collocation dictation

As above (‘Simple dictation’), but instead of single words, dictate simple collocations (e.g. verb – complement combinations, adjective – noun pairings, adverb – adjective pairings). Students write down the collocations. When comparing their answers with a partner, they have an additional task: dictate the collocations again and identify one word that the students must underline. In pairs, students must think of one or two different words that can collocate with the underlined word.

5             Simple translation dictation

As above (‘Simple dictation’), but tell the students that must only write down the translation into their own language of the word (or phrase) that you have given them. Afterwards, when they are working with a partner, they must write down the English word. (This activity works well with multilingual groups – students do not need to speak the same language as their partner.)

6             Word count dictation

As above (‘Simple translation dictation’): when the students are doing the dictation, tell them that they must first silently count the number of letters in the English word and write down this number. They must also write down the translation into their own language. Afterwards, when they are working with a partner, they must write down the English word. As an addition / alternative, you can ask them to write down the first letter of the English word. (This activity works well with multilingual groups – students do not need to speak the same language as their partner.)

I first came across this activity in Morgan, J. & M. Rinvolucri (2004) Vocabulary 2nd edition. (Oxford: Oxford University Press).

7             Dictations with tables

Before dictating the target items, draw a simple table on the board of three or more columns. At the top of each column, write the different stress patterns of the words you will dictate. Explain to the students that they must write the words you dictate into the appropriate column.

Stress patterns

As an alternative to stress patterns, you could use different categories for the columns. Examples include: numbers of syllables, vowel sounds that feature in the target items, parts of speech, semantic fields, items that students are confident about / less confident about, etc.

8             Bilingual sentence dictation

Prepare a set of short sentences (eight maximum), each of which contains one of the words that you want to recycle. These sentences could be from a vocabulary exercise that the students have previously studied in their coursebooks or example sentences from vocab apps.

Tell the class that they are going to do a dictation. Tell them that you will read some sentences in English, but they must only write down translations into their own language of these sentences. Dictate the sentences, allowing ample time for students to write their translations. Put the students into pairs or small groups. Ask them to translate these sentences back into English. (This activity works well with multilingual groups – students do not need to speak the same language as their partner.) Conduct feedback with the whole class, or allow the students to check their answers with their apps / the coursebook.

From definitions (or translations) to words

An alternative to providing learners with the learning items and asking them to check the meanings is to get them to work towards the items from the meanings. There are a very wide variety of ways of doing this and a selection of these follows below.

9             Eliciting race

Prepare a list of words that you want to recycle. These lists will need to be printed on a handout. You will need at least two copies of this handout, but for some variations of the game you will need more copies.

Divide the class into two teams. Get one student from each team to come to the front of the class and hand them the list of words. Explain that their task is to elicit from their team each of the words on the list. They must not say the word that they are trying to elicit. The first team to produce the target word wins a point, and everyone moves on to the next word.

The race can also be played with students working in pairs. One student has the list and elicits from their partner.

10          Eliciting race against the clock

As above (‘Eliciting race’), but the race is played ‘against the clock’. The teams have different lists of words (or the same lists but in a different order). Set a time limit. How many words can be elicited in, say, three minutes?

11          Mime eliciting race

As above (‘Eliciting race’), but you can ask the students who are doing the eliciting to do this silently, using mime and gesture only. A further alternative is to get students to do the eliciting by drawing pictures (as in the game of Pictionary).

12          The fly-swatting game

Write the items to be reviewed all over the board. Divide the class into two teams. Taking turns, one member of each group comes to the board. Each of the students at the board is given a fly-swatter (if this is not possible, they can use the palms of their hands). Choose one of the items and define it in some way. The students must find the word and swat it. The first person to do so wins a point for their team. You will probably want to introduce a rule where students are only allowed one swat: this means that if they swat the wrong word, the other player can take as much time as they like (and consult with their tem members) before swatting a word.

13          Word grab

Prepare the target items on one or more sets of pieces of paper / card (one item per piece of paper). With a smallish class of about 8 students, one set is enough. With larger classes, prepare one set per group (of between 4 – 8 students). Students sit in a circle with the pieces of paper spread out on a table or on the floor in the middle. The teacher calls out the definitions and the students try to be the first person to grab the appropriate piece of paper.

As an alternative to this teacher-controlled version of the game, students can work in groups of three or four (more sets of pieces of paper will be needed). One student explains a word and the others compete to grab the right word. The student with the most words at the end is the ‘winner’. In order to cover a large number of items for recycling, each table can have a different set of words. Once a group of students has exhausted the words on their table, they can exchange tables with another group.

14          Word hold-up

The procedures above can be very loud and chaotic! For a calmer class, ensure that everyone (or every group) has a supply of blank pieces of paper. Do the eliciting yourself. The first student or team to hold up the correct answer on a piece of paper wins the point.

15          Original contexts

Find the words in the contexts in which they were originally presented (e.g. in the coursebook); write the sentences with gaps on the board (or prepare this for projection). First, students work with a partner to complete the gaps. Before checking that their answers are correct, insert the first letter of each missing word so students can check their own answers. If you wish, you may also add a second letter. Once the missing words have been checked, ask the students to try to find as many different alternatives (i.e. other words that will fit syntactically and semantically) as they can for the missing words they have just inserted.

Quick follow-up activities

16          Word grouping

Once the learning items for revision have been ‘activated’ using one of the activities above, you may wish to do a quick follow-up activity before moving on to more communicative practice. A simple task type is to ask students (in pairs, so that there is some discussion and sharing of ideas) to group the learning items in one or more ways. Here are a few suggestions for ways that students can be asked to group the words: (1) words they remembered easily / words they had forgotten; (2) words they like / dislike; (3) words they think will be useful to them / will not be useful to them; (4) words that remind them of a particular time or experience (or person) in their life; (5) words they would pack in their holiday bags / words they would put in the deep-freeze and forget about for the time being (thanks to Jeremy Harmer for this last idea).

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

Plonsky & Ziegler

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

What we know about glosses

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

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

Bliu_Bliu_example_2

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

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

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

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

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

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

 

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

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

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

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

Learning from meta-analyses

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

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

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

References

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

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

Boulton, A. 2016. ‘Quantifying CALL: significance, effect size and variation’ in S. Papadima-Sophocleus, L. Bradley & S. Thouësny (eds.) CALL Communities and Culture – short papers from Eurocall 2016 pp.55 – 60 http://files.eric.ed.gov/fulltext/ED572012.pdf

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

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

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

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

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

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

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

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

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

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

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

Every now and then, someone recommends me to take a look at a flashcard app. It’s often interesting to see what developers have done with design, gamification and UX features, but the content is almost invariably awful. Most recently, I was encouraged to look at Word Pash. The screenshots below are from their promotional video.

word-pash-1 word-pash-2 word-pash-3 word-pash-4

The content problems are immediately apparent: an apparently random selection of target items, an apparently random mix of high and low frequency items, unidiomatic language examples, along with definitions and distractors that are less frequent than the target item. I don’t know if these are representative of the rest of the content. The examples seem to come from ‘Stage 1 Level 3’, whatever that means. (My confidence in the product was also damaged by the fact that the Word Pash website includes one testimonial from a certain ‘Janet Reed – Proud Mom’, whose son ‘was able to increase his score and qualify for academic scholarships at major universities’ after using the app. The picture accompanying ‘Janet Reed’ is a free stock image from Pexels and ‘Janet Reed’ is presumably fictional.)

According to the website, ‘WordPash is a free-to-play mobile app game for everyone in the global audience whether you are a 3rd grader or PhD, wordbuff or a student studying for their SATs, foreign student or international business person, you will become addicted to this fast paced word game’. On the basis of the promotional video, the app couldn’t be less appropriate for English language learners. It seems unlikely that it would help anyone improve their ACT or SAT test scores. The suggestion that the vocabulary development needs of 9-year-olds and doctoral students are comparable is pure chutzpah.

The deliberate study of more or less random words may be entertaining, but it’s unlikely to lead to very much in practical terms. For general purposes, the deliberate learning of the highest frequency words, up to about a frequency ranking of #7500, makes sense, because there’s a reasonably high probability that you’ll come across these items again before you’ve forgotten them. Beyond that frequency level, the value of the acquisition of an additional 1000 words tails off very quickly. Adding 1000 words from frequency ranking #8000 to #9000 is likely to result in an increase in lexical understanding of general purpose texts of about 0.2%. When we get to frequency ranks #19,000 to #20,000, the gain in understanding decreases to 0.01%[1]. In other words, deliberate vocabulary learning needs to be targeted. The data is relatively recent, but the principle goes back to at least the middle of the last century when Michael West argued that a principled approach to vocabulary development should be driven by a comparison of the usefulness of a word and its ‘learning cost’[2]. Three hundred years before that, Comenius had articulated something very similar: ‘in compiling vocabularies, my […] concern was to select the words in most frequent use[3].

I’ll return to ‘general purposes’ later in this post, but, for now, we should remember that very few language learners actually study a language for general purposes. Globally, the vast majority of English language learners study English in an academic (school) context and their immediate needs are usually exam-specific. For them, general purpose frequency lists are unlikely to be adequate. If they are studying with a coursebook and are going to be tested on the lexical content of that book, they will need to use the wordlist that matches the book. Increasingly, publishers make such lists available and content producers for vocabulary apps like Quizlet and Memrise often use them. Many examinations, both national and international, also have accompanying wordlists. Examples of such lists produced by examination boards include the Cambridge English young learners’ exams (Starters, Movers and Flyers) and Cambridge English Preliminary. Other exams do not have official word lists, but reasonably reliable lists have been produced by third parties. Examples include Cambridge First, IELTS and SAT. There are, in addition, well-researched wordlists for academic English, including the Academic Word List (AWL)  and the Academic Vocabulary List  (AVL). All of these make sensible starting points for deliberate vocabulary learning.

When we turn to other, out-of-school learners the number of reasons for studying English is huge. Different learners have different lexical needs, and working with a general purpose frequency list may be, at least in part, a waste of time. EFL and ESL learners are likely to have very different needs, as will EFL and ESP learners, as will older and younger learners, learners in different parts of the world, learners who will find themselves in English-speaking countries and those who won’t, etc., etc. For some of these demographics, specialised corpora (from which frequency-based wordlists can be drawn) exist. For most learners, though, the ideal list simply does not exist. Either it will have to be created (requiring a significant amount of time and expertise[4]) or an available best-fit will have to suffice. Paul Nation, in his recent ‘Making and Using Word Lists for Language Learning and Testing’ (John Benjamins, 2016) includes a useful chapter on critiquing wordlists. For anyone interested in better understanding the issues surrounding the development and use of wordlists, three good articles are freely available online. These are:making-and-using-word-lists-for-language-learning-and-testing

Lessard-Clouston, M. 2012 / 2013. ‘Word Lists for Vocabulary Learning and Teaching’ The CATESOL Journal 24.1: 287- 304

Lessard-Clouston, M. 2016. ‘Word lists and vocabulary teaching: options and suggestions’ Cornerstone ESL Conference 2016

Sorell, C. J. 2013. A study of issues and techniques for creating core vocabulary lists for English as an International Language. Doctoral thesis.

But, back to ‘general purposes’ …. Frequency lists are the obvious starting point for preparing a wordlist for deliberate learning, but they are very problematic. Frequency rankings depend on the corpus on which they are based and, since these are different, rankings vary from one list to another. Even drawing on just one corpus, rankings can be a little strange. In the British National Corpus, for example, ‘May’ (the month) is about twice as frequent as ‘August’[5], but we would be foolish to infer from this that the learning of ‘May’ should be prioritised over the learning of ‘August’. An even more striking example from the same corpus is the fact that ‘he’ is about twice as frequent as ‘she’[6]: should, therefore, ‘he’ be learnt before ‘she’?

List compilers have to make a number of judgement calls in their work. There is not space here to consider these in detail, but two particularly tricky questions concerning the way that words are chosen may be mentioned: Is a verb like ‘list’, with two different and unrelated meanings, one word or two? Should inflected forms be considered as separate words? The judgements are not usually informed by considerations of learners’ needs. Learners will probably best approach vocabulary development by building their store of word senses: attempting to learn all the meanings and related forms of any given word is unlikely to be either useful or successful.

Frequency lists, in other words, are not statements of scientific ‘fact’: they are interpretative documents. They have been compiled for descriptive purposes, not as ways of structuring vocabulary learning, and it cannot be assumed they will necessarily be appropriate for a purpose for which they were not designed.

A further major problem concerns the corpus on which the frequency list is based. Large databases, such as the British National Corpus or the Corpus of Contemporary American English, are collections of language used by native speakers in certain parts of the world, usually of a restricted social class. As such, they are of relatively little value to learners who will be using English in contexts that are not covered by the corpus. A context where English is a lingua franca is one such example.

A different kind of corpus is the Cambridge Learner Corpus (CLC), a collection of exam scripts produced by candidates in Cambridge exams. This has led to the development of the English Vocabulary Profile (EVP) , where word senses are tagged as corresponding to particular levels in the Common European Framework scale. At first glance, this looks like a good alternative to frequency lists based on native-speaker corpora. But closer consideration reveals many problems. The design of examination tasks inevitably results in the production of language of a very different kind from that produced in other contexts. Many high frequency words simply do not appear in the CLC because it is unlikely that a candidate would use them in an exam. Other items are very frequent in this corpus just because they are likely to be produced in examination tasks. Unsurprisingly, frequency rankings in EVP do not correlate very well with frequency rankings from other corpora. The EVP, then, like other frequency lists, can only serve, at best, as a rough guide for the drawing up of target item vocabulary lists in general purpose apps or coursebooks[7].

There is no easy solution to the problems involved in devising suitable lexical content for the ‘global audience’. Tagging words to levels (i.e. grouping them into frequency bands) will always be problematic, unless very specific user groups are identified. Writers, like myself, of general purpose English language teaching materials are justifiably irritated by some publishers’ insistence on allocating words to levels with numerical values. The policy, taken to extremes (as is increasingly the case), has little to recommend it in linguistic terms. But it’s still a whole lot better than the aleatory content of apps like Word Pash.

[1] See Nation, I.S.P. 2013. Learning Vocabulary in Another Language 2nd edition. (Cambridge: Cambridge University Press) p. 21 for statistical tables. See also Nation, P. & R. Waring 1997. ‘Vocabulary size, text coverage and word lists’ in Schmitt & McCarthy (eds.) 1997. Vocabulary: Description, Acquisition and Pedagogy. (Cambridge: Cambridge University Press) pp. 6 -19

[2] See Kelly, L.G. 1969. 25 Centuries of Language Teaching. (Rowley, Mass.: Rowley House) p.206 for a discussion of West’s ideas.

[3] Kelly, L.G. 1969. 25 Centuries of Language Teaching. (Rowley, Mass.: Rowley House) p. 184

[4] See Timmis, I. 2015. Corpus Linguistics for ELT (Abingdon: Routledge) for practical advice on doing this.

[5] Nation, I.S.P. 2016. Making and Using Word Lists for Language Learning and Testing. (Amsterdam: John Benjamins) p.58

[6] Taylor, J.R. 2012. The Mental Corpus. (Oxford: Oxford University Press) p.151

[7] For a detailed critique of the limitations of using the CLC as a guide to syllabus design and textbook development, see Swan, M. 2014. ‘A Review of English Profile Studies’ ELTJ 68/1: 89-96

In December last year, I posted a wish list for vocabulary (flashcard) apps. At the time, I hadn’t read a couple of key research texts on the subject. It’s time for an update.

First off, there’s an article called ‘Intentional Vocabulary Learning Using Digital Flashcards’ by Hsiu-Ting Hung. It’s available online here. Given the lack of empirical research into the use of digital flashcards, it’s an important article and well worth a read. Its basic conclusion is that digital flashcards are more effective as a learning tool than printed word lists. No great surprises there, but of more interest, perhaps, are the recommendations that (1) ‘students should be educated about the effective use of flashcards (e.g. the amount and timing of practice), and this can be implemented through explicit strategy instruction in regular language courses or additional study skills workshops ‘ (Hung, 2015: 111), and (2) that digital flashcards can be usefully ‘repurposed for collaborative learning tasks’ (Hung, ibid.).

nakataHowever, what really grabbed my attention was an article by Tatsuya Nakata. Nakata’s research is of particular interest to anyone interested in vocabulary learning, but especially so to those with an interest in digital possibilities. A number of his research articles can be freely accessed via his page at ResearchGate, but the one I am interested in is called ‘Computer-assisted second language vocabulary learning in a paired-associate paradigm: a critical investigation of flashcard software’. Don’t let the title put you off. It’s a review of a pile of web-based flashcard programs: since the article is already five years old, many of the programs have either changed or disappeared, but the critical approach he takes is more or less as valid now as it was then (whether we’re talking about web-based stuff or apps).

Nakata divides his evaluation for criteria into two broad groups.

Flashcard creation and editing

(1) Flashcard creation: Can learners create their own flashcards?

(2) Multilingual support: Can the target words and their translations be created in any language?

(3) Multi-word units: Can flashcards be created for multi-word units as well as single words?

(4) Types of information: Can various kinds of information be added to flashcards besides the word meanings (e.g. parts of speech, contexts, or audios)?

(5) Support for data entry: Does the software support data entry by automatically supplying information about lexical items such as meaning, parts of speech, contexts, or frequency information from an internal database or external resources?

(6) Flashcard set: Does the software allow learners to create their own sets of flashcards?

Learning

(1) Presentation mode: Does the software have a presentation mode, where new items are introduced and learners familiarise themselves with them?

(2) Retrieval mode: Does the software have a retrieval mode, which asks learners to recall or choose the L2 word form or its meaning?

(3) Receptive recall: Does the software ask learners to produce the meanings of target words?

(4) Receptive recognition: Does the software ask learners to choose the meanings of target words?

(5) Productive recall: Does the software ask learners to produce the target word forms corresponding to the meanings provided?

(6) Productive recognition: Does the software ask learners to choose the target word forms corresponding to the meanings provided?

(7) Increasing retrieval effort: For a given item, does the software arrange exercises in the order of increasing difficulty?

(8) Generative use: Does the software encourage generative use of words, where learners encounter or use previously met words in novel contexts?

(9) Block size: Can the number of words studied in one learning session be controlled and altered?

(10) Adaptive sequencing: Does the software change the sequencing of items based on learners’ previous performance on individual items?

(11) Expanded rehearsal: Does the software help implement expanded rehearsal, where the intervals between study trials are gradually increased as learning proceeds? (Nakata, T. (2011): ‘Computer-assisted second language vocabulary learning in a paired-associate paradigm: a critical investigation of flashcard software’ Computer Assisted Language Learning, 24:1, 17-38)

It’s a rather different list from my own (there’s nothing I would disagree with here), because mine is more general and his is exclusively oriented towards learning principles. Nakata makes the point towards the end of the article that it would ‘be useful to investigate learners’ reactions to computer-based flashcards to examine whether they accept flashcard programs developed according to learning principles’ (p. 34). It’s far from clear, he points out, that conformity to learning principles are at the top of learners’ agendas. More than just users’ feelings about computer-based flashcards in general, a key concern will be the fact that there are ‘large individual differences in learners’ perceptions of [any flashcard] program’ (Nakata, N. 2008. ‘English vocabulary learning with word lists, word cards and computers: implications from cognitive psychology research for optimal spaced learning’ ReCALL 20(1), p. 18).

I was trying to make a similar point in another post about motivation and vocabulary apps. In the end, as with any language learning material, research-driven language learning principles can only take us so far. User experience is a far more difficult creature to pin down or to make generalisations about. A user’s reaction to graphics, gamification, uploading time and so on are so powerful and so subjective that learning principles will inevitably play second fiddle. That’s not to say, of course, that Nakata’s questions are not important: it’s merely to wonder whether the bigger question is truly answerable.

Nakata’s research identifies plenty of room for improvement in digital flashcards, and although the article is now quite old, not a lot had changed. Key areas to work on are (1) the provision of generative use of target words, (2) the need to increase retrieval effort, (3) the automatic provision of information about meaning, parts of speech, or contexts (in order to facilitate flashcard creation), and (4) the automatic generation of multiple-choice distractors.

In the conclusion of his study, he identifies one flashcard program which is better than all the others. Unsurprisingly, five years down the line, the software he identifies is no longer free, others have changed more rapidly in the intervening period, and who knows will be out in front next week?

 

About two and a half years ago when I started writing this blog, there was a lot of hype around adaptive learning and the big data which might drive it. Two and a half years are a long time in technology. A look at Google Trends suggests that interest in adaptive learning has been pretty static for the last couple of years. It’s interesting to note that 3 of the 7 lettered points on this graph are Knewton-related media events (including the most recent, A, which is Knewton’s latest deal with Hachette) and 2 of them concern McGraw-Hill. It would be interesting to know whether these companies follow both parts of Simon Cowell’s dictum of ‘Create the hype, but don’t ever believe it’.

Google_trends

A look at the Hype Cycle (see here for Wikipedia’s entry on the topic and for criticism of the hype of Hype Cycles) of the IT research and advisory firm, Gartner, indicates that both big data and adaptive learning have now slid into the ‘trough of disillusionment’, which means that the market has started to mature, becoming more realistic about how useful the technologies can be for organizations.

A few years ago, the Gates Foundation, one of the leading cheerleaders and financial promoters of adaptive learning, launched its Adaptive Learning Market Acceleration Program (ALMAP) to ‘advance evidence-based understanding of how adaptive learning technologies could improve opportunities for low-income adults to learn and to complete postsecondary credentials’. It’s striking that the program’s aims referred to how such technologies could lead to learning gains, not whether they would. Now, though, with the publication of a report commissioned by the Gates Foundation to analyze the data coming out of the ALMAP Program, things are looking less rosy. The report is inconclusive. There is no firm evidence that adaptive learning systems are leading to better course grades or course completion. ‘The ultimate goal – better student outcomes at lower cost – remains elusive’, the report concludes. Rahim Rajan, a senior program office for Gates, is clear: ‘There is no magical silver bullet here.’

The same conclusion is being reached elsewhere. A report for the National Education Policy Center (in Boulder, Colorado) concludes: Personalized Instruction, in all its many forms, does not seem to be the transformational technology that is needed, however. After more than 30 years, Personalized Instruction is still producing incremental change. The outcomes of large-scale studies and meta-analyses, to the extent they tell us anything useful at all, show mixed results ranging from modest impacts to no impact. Additionally, one must remember that the modest impacts we see in these meta-analyses are coming from blended instruction, which raises the cost of education rather than reducing it (Enyedy, 2014: 15 -see reference at the foot of this post). In the same vein, a recent academic study by Meg Coffin Murray and Jorge Pérez (2015, ‘Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning’) found that ‘adaptive learning systems have negligible impact on learning outcomes’.

future-ready-learning-reimagining-the-role-of-technology-in-education-1-638In the latest educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Reimagining the Role of Technology in Education’, 2016) the only mentions of the word ‘adaptive’ are in the context of testing. And the latest OECD report on ‘Students, Computers and Learning: Making the Connection’ (2015), finds, more generally, that information and communication technologies, when they are used in the classroom, have, at best, a mixed impact on student performance.

There is, however, too much money at stake for the earlier hype to disappear completely. Sponsored cheerleading for adaptive systems continues to find its way into blogs and national magazines and newspapers. EdSurge, for example, recently published a report called ‘Decoding Adaptive’ (2016), sponsored by Pearson, that continues to wave the flag. Enthusiastic anecdotes take the place of evidence, but, for all that, it’s a useful read.

In the world of ELT, there are plenty of sales people who want new products which they can call ‘adaptive’ (and gamified, too, please). But it’s striking that three years after I started following the hype, such products are rather thin on the ground. Pearson was the first of the big names in ELT to do a deal with Knewton, and invested heavily in the company. Their relationship remains close. But, to the best of my knowledge, the only truly adaptive ELT product that Pearson offers is the PTE test.

Macmillan signed a contract with Knewton in May 2013 ‘to provide personalized grammar and vocabulary lessons, exam reviews, and supplementary materials for each student’. In December of that year, they talked up their new ‘big tree online learning platform’: ‘Look out for the Big Tree logo over the coming year for more information as to how we are using our partnership with Knewton to move forward in the Language Learning division and create content that is tailored to students’ needs and reactive to their progress.’ I’ve been looking out, but it’s all gone rather quiet on the adaptive / platform front.

In September 2013, it was the turn of Cambridge to sign a deal with Knewton ‘to create personalized learning experiences in its industry-leading ELT digital products for students worldwide’. This year saw the launch of a major new CUP series, ‘Empower’. It has an online workbook with personalized extra practice, but there’s nothing (yet) that anyone would call adaptive. More recently, Cambridge has launched the online version of the 2nd edition of Touchstone. Nothing adaptive there, either.

Earlier this year, Cambridge published The Cambridge Guide to Blended Learning for Language Teaching, edited by Mike McCarthy. It contains a chapter by M.O.Z. San Pedro and R. Baker on ‘Adaptive Learning’. It’s an enthusiastic account of the potential of adaptive learning, but it doesn’t contain a single reference to language learning or ELT!

So, what’s going on? Skepticism is becoming the order of the day. The early hype of people like Knewton’s Jose Ferreira is now understood for what it was. Companies like Macmillan got their fingers badly burnt when they barked up the wrong tree with their ‘Big Tree’ platform.

Noel Enyedy captures a more contemporary understanding when he writes: Personalized Instruction is based on the metaphor of personal desktop computers—the technology of the 80s and 90s. Today’s technology is not just personal but mobile, social, and networked. The flexibility and social nature of how technology infuses other aspects of our lives is not captured by the model of Personalized Instruction, which focuses on the isolated individual’s personal path to a fixed end-point. To truly harness the power of modern technology, we need a new vision for educational technology (Enyedy, 2014: 16).

Adaptive solutions aren’t going away, but there is now a much better understanding of what sorts of problems might have adaptive solutions. Testing is certainly one. As the educational technology plan from the U.S. Department of Education (‘Future Ready Learning: Re-imagining the Role of Technology in Education’, 2016) puts it: Computer adaptive testing, which uses algorithms to adjust the difficulty of questions throughout an assessment on the basis of a student’s responses, has facilitated the ability of assessments to estimate accurately what students know and can do across the curriculum in a shorter testing session than would otherwise be necessary. In ELT, Pearson and EF have adaptive tests that have been well researched and designed.

Vocabulary apps which deploy adaptive technology continue to become more sophisticated, although empirical research is lacking. Automated writing tutors with adaptive corrective feedback are also developing fast, and I’ll be writing a post about these soon. Similarly, as speech recognition software improves, we can expect to see better and better automated adaptive pronunciation tutors. But going beyond such applications, there are bigger questions to ask, and answers to these will impact on whatever direction adaptive technologies take. Large platforms (LMSs), with or without adaptive software, are already beginning to look rather dated. Will they be replaced by integrated apps, or are apps themselves going to be replaced by bots (currently riding high in the Hype Cycle)? In language learning and teaching, the future of bots is likely to be shaped by developments in natural language processing (another topic about which I’ll be blogging soon). Nobody really has a clue where the next two and a half years will take us (if anywhere), but it’s becoming increasingly likely that adaptive learning will be only one very small part of it.

 

Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning. Boulder, CO: National Education Policy Center. Retrieved 17.07.16 from http://nepc.colorado.edu/publication/personalized-instruction

I have been putting in a lot of time studying German vocabulary with Memrise lately, but this is not a review of the Memrise app. For that, I recommend you read Marek Kiczkowiak’s second post on this app. Like me, he’s largely positive, although I am less enthusiastic about Memrise’s USP, the use of mnemonics. It’s not that mnemonics don’t work – there’s a lot of evidence that they do: it’s just that there is little or no evidence that they’re worth the investment of time.

Time … as I say, I have been putting in the hours. Every day, for over a month, averaging a couple of hours a day, it’s enough to get me very near the top of the leader board (which I keep a very close eye on) and it means that I am doing more work than 99% of other users. And, yes, my German is improving.

Putting in the time is the sine qua non of any language learning and a well-designed app must motivate users to do this. Relevant content will be crucial, as will satisfactory design, both visual and interactive. But here I’d like to focus on the two other key elements: task design / variety and gamification.

Memrise offers a limited range of task types: presentation cards (with word, phrase or sentence with translation and audio recording), multiple choice (target item with four choices), unscrambling letters or words, and dictation (see below).

Screenshot_2016-05-24-08-10-42Screenshot_2016-05-24-08-10-57Screenshot_2016-05-24-08-11-24Screenshot_2016-05-24-08-11-45Screenshot_2016-05-24-08-12-51Screenshot_2016-05-24-08-13-44

As Marek writes, it does get a bit repetitive after a while (although less so than thumbing through a pack of cardboard flashcards). The real problem, though, is that there are only so many things an app designer can do with standard flashcards, if they are to contribute to learning. True, there could be a few more game-like tasks (as with Quizlet), races against the clock as you pop word balloons or something of the sort, but, while these might, just might, help with motivation, these games rarely, if ever, contribute much to learning.

What’s more, you’ll get fed up with the games sooner or later if you’re putting in serious study hours. Even if Memrise were to double the number of activity types, I’d have got bored with them by now, in the same way I got bored with the Quizlet games. Bear in mind, too, that I’ve only done a month: I have at least another two months to go before I finish the level I’m working on. There’s another issue with ‘fun’ activities / games which I’ll come on to later.

The options for task variety in vocabulary / memory apps are therefore limited. Let’s look at gamification. Memrise has leader boards (weekly, monthly, ‘all time’), streak badges, daily goals, email reminders and (in the laptop and premium versions) a variety of graphs that allow you to analyse your study patterns. Your degree of mastery of learning items is represented by a growing flower that grows leaves, flowers and withers. None of this is especially original or different from similar apps.

Screenshot_2016-05-24-19-17-14The trouble with all of this is that it can only work for a certain time and, for some people, never. There’s always going to be someone like me who can put in a couple of hours a day more than you can. Or someone, in my case, like ‘Nguyenduyha’, who must be doing about four hours a day, and who, I know, is out of my league. I can’t compete and the realisation slowly dawns that my life would be immeasurably sadder if I tried to.

Having said that, I have tried to compete and the way to do so is by putting in the time on the ‘speed review’. This is the closest that Memrise comes to a game. One hundred items are flashed up with four multiple choices and these are against the clock. The quicker you are, the more points you get, and if you’re too slow, or you make a mistake, you lose a life. That’s how you gain lots of points with Memrise. The problem is that, at best, this task only promotes receptive knowledge of the items, which is not what I need by this stage. At worst, it serves no useful learning function at all because I have learnt ways of doing this well which do not really involve me processing meaning at all. As Marek says in his post (in reference to Quizlet), ‘I had the feeling that sometimes I was paying more attention to ‘winning’ the game and scoring points, rather than to the words on the screen.’ In my case, it is not just a feeling: it’s an absolute certainty.

desktop_dashboard

Sadly, the gamification is working against me. The more time I spend on the U-Bahn doing Memrise, the less time I spend reading the free German-language newspapers, the less time I spend eavesdropping on conversations. Two hours a day is all I have time for for my German study, and Memrise is eating it all up. I know that there are other, and better, ways of learning. In order to do what I know I should be doing, I need to ignore the gamification. For those, more reasonable, students, who can regularly do their fifteen minutes a day, day in – day out, the points and leader boards serve no real function at all.

Cheating at gamification, or gaming the system, is common in app-land. A few years ago, Memrise had to take down their leader board when they realised that cheating was taking place. There’s an inexorable logic to this: gamification is an attempt to motivate by rewarding through points, rather than the reward coming from the learning experience. The logic of the game overtakes itself. Is ‘Nguyenduyha’ cheating, or do they simply have nothing else to do all day? Am I cheating by finding time to do pointless ‘speed reviews’ that earn me lots of points?

For users like myself, then, gamification design needs to be a delicate balancing act. For others, it may be largely an irrelevance. I’ve been working recently on a general model of vocabulary app design that looks at two very different kinds of user. On the one hand, there are the self-motivated learners like myself or the millions of other who have chosen to use self-study apps. On the other, there are the millions of students in schools and colleges, studying English among other subjects, some of whom are now being told to use the vocabulary apps that are beginning to appear packaged with their coursebooks (or other learning material). We’ve never found entirely satisfactory ways of making these students do their homework, and the fact that this homework is now digital will change nothing (except, perhaps, in the very, very short term). The incorporation of games and gamification is unlikely to change much either: there will always be something more interesting and motivating (and unconnected with language learning) elsewhere.

Teachers and college principals may like the idea of gamification (without having really experienced it themselves) for their students. But more important for most of them is likely to be the teacher dashboard: the means by which they can check that their students are putting the time in. Likewise, they will see the utility of automated email reminders that a student is not working hard enough to meet their learning objectives, more and more regular tests that contribute to overall course evaluation, comparisons with college, regional or national benchmarks. Technology won’t solve the motivation issue, but it does offer efficient means of control.

If you’re going to teach vocabulary, you need to organise it in some way. Almost invariably, this organisation is topical, with words grouped into what are called semantic sets. In coursebooks, the example below (from Rogers, M., Taylore-Knowles, J. & S. Taylor-Knowles. 2010. Open Mind Level 1. London: Macmillan, p.68) is fairly typical.

open mind

Coursebooks are almost always organised in a topical way. The example above comes in a unit (of 10 pages), entitled ‘You have talent!’, which contains two main vocabulary sections. It’s unsurprising to find a section called ‘personality adjectives’ in such a unit. What’s more, such an approach lends itself to the requisite, but largely, spurious ‘can-do’ statement in the self-evaluation section: I can talk about people’s positive qualities. We must have clearly identifiable learning outcomes, after all.

There is, undeniably, a certain intuitive logic in this approach. An alternative might entail a radical overhaul of coursebook architecture – this might not be such a bad thing, but might not go down too well in the markets. How else, after all, could the vocabulary strand of the syllabus be organised?

Well, there are a number of ways in which a vocabulary syllabus could be organised. Including the standard approach described above, here are four possibilities:

1 semantic sets (e.g. bee, butterfly, fly, mosquito, etc.)

2 thematic sets (e.g. ‘pets’: cat, hate, flea, feed, scratch, etc.)

3 unrelated sets

4 sets determined by a group of words’ occurrence in a particular text

Before reading further, you might like to guess what research has to say about the relative effectiveness of these four approaches.

The answer depends, to some extent, on the level of the learner. For advanced learners, it appears to make no, or little, difference (Al-Jabri, 2005, cited by Ellis & Shintani, 2014: 106). But, for the vast majority of English language learners (i.e. those at or below B2 level), the research is clear: the most effective way of organising vocabulary items to be learnt is by grouping them into thematic sets (2) or by mixing words together in a semantically unrelated way (3) – not by teaching sets like ‘personality adjectives’. It is surprising how surprising this finding is to so many teachers and materials writers. It goes back at least to 1988 and West’s article on ‘Catenizing’ in ELTJ, which argued that semantic grouping made little sense from a psycho-linguistic perspective. Since then, a large amount of research has taken place. This is succinctly summarised by Paul Nation (2013: 128) in the following terms: Avoid interference from related words. Words which are similar in form (Laufer, 1989) or meaning (Higa, 1963; Nation, 2000; Tinkham, 1993; Tinkham, 1997; Waring, 1997) are more difficult to learn together than they are to learn separately. For anyone who is interested, the most up-to-date review of this research that I can find is in chapter 11 of Barcroft (2105).

The message is clear. So clear that you have to wonder how it is not getting through to materials designers. Perhaps, coursebooks are different. They regularly eschew research findings for commercial reasons. But vocabulary apps? There is rarely, if ever, any pressure on the content-creation side of vocabulary apps (except those that are tied to coursebooks) to follow the popular misconceptions that characterise so many coursebooks. It wouldn’t be too hard to organise vocabulary into thematic sets (like, for example, the approach in the A2 level of Memrise German that I’m currently using). Is it simply because the developers of so many vocabulary apps just don’t know much about language learning?

References

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

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

Ellis, R. & N. Shintani, N. 2014. Exploring Language Pedagogy through Second Language Acquisition Research. Abingdon, Oxon: Routledge

West, M. 1988. ‘Catenizing’ English Language Teaching Journal 6: 147 – 151

It’s practically impossible to keep up to date with all the new language learning tools that appear, even with the help of curated lists like Nik Peachey’s Scoop.it! (which is one of the most useful I know of). The trouble with such lists is that they are invariably positive, but when you actually find the time to look at the product, you often wish you hadn’t. I decided to save time for people like me by occasionally writing short posts about things that you can safely forget about. This is the first.

Nik’s take on Vocabulist was this:

Nik_Peachey

It sounds useful,  but for anyone involved in language teaching or learning, there is, unfortunately, nothing remotely useful about this tool.

Here’s how it works:

Vocabulist is super easy to use!

Here’s how:

1.Upload a Word, PDF, or Text document. You could also copy and paste text.

2.Wait a minute. Feel free to check Facebook while Vocabulist does some thinking.

3.Select the words that you want, confirm spelling, and confirm the correct definition.

4.All Done! Now print it, export it, and study it.

To try it out, I copied and pasted the text above. This is what you get for the first two lines:

vocabulist

The definitions are taken from Merriam-Webster. You scroll down until you find the definition for the best fit, and you can then save the list as a pdf or export it to Quizlet.

export

For language learners, there are far too many definitions to choose from. For ‘super’, for example, there are 24 definitions and, because they are from Merriam-Webster, they are all harder than the word being defined.

The idea behind Vocabulist could be adapted for language learners if there was a selection of dictionary resources that users could choose from (a selection of good bilingual or semi-bilingual dictionaries and a good monolingual learner’s dictionary). But, as it stands, here’s an app you can forget.

Screenshot_2016-04-29-09-48-05I call Lern Deutsch a vocabulary app, although it’s more of a game than anything else. Developed by the Goethe Institute, the free app was probably designed primarily as a marketing tool rather than a serious attempt to develop an educational language app. It’s available for speakers of Arabic, English, Spanish, Italian, French, Italian, Portuguese and Russian. It’s aimed at A1 learners.

Users of the app create an avatar and roam around a virtual city, learning new vocabulary and practising situational language. They can interact in language challenges with other players. As they explore, they earn Goethe coins, collect accessories for their avatars and progress up a leader board.Screenshot_2016-04-29-09-50-12

As they explore the virtual city, populated by other avatars, they find objects that can be clicked on to add to their vocabulary list. They hear a recording of an example sentence containing the target word, with the word gapped and three multiple choice possibilities. They are then required to type the missing word (see the image below). After collecting a certain number of words, they complete exercises which include the following task types:

  • Jumbled sentences
  • Audio recording of individual words and multiple choice selection
  • Gapped sentences with multiple choice answers
  • Dictation
  • Example sentences containing target item and multiple choice pictures
  • Typing sentences which are buried in a string of random letters

Screenshot_2016-05-02-14-23-07Screenshot_2016-05-02-14-26-13

Screenshot_2016-05-02-14-27-21Screenshot_2016-05-02-14-31-49

 

 

 

 

 

 

 

 

 

The developers have focused their attention on providing variety: engagement and ‘fun’ override other considerations. But how does the app stand up as a language learning tool? Surprisingly, for something developed by the Goethe Institute, it’s less than impressive.

The words that you collect as you navigate the virtual city are all nouns (Hotel, Auto, Mann, Banane, etc), but some (e.g. Sehenswurdigkeit) seem out of level. Any app that uses illustrations as the basic means of conveying meaning runs into problems when it moves away from concrete nouns, but a diet of nouns only (as here) is of necessarily limited value. Other parts of speech are introduced via the example sentences, but no help with meaning is provided so when you come across the word for ‘egg’, for example, your example sentence is ‘Ich möchte das Frühstück mit Ei.’ It’s all very well embedding the target vocabulary in example sentences that have a functional value, but example sentences are only of value if they are understandable: the app badly needs a look-up function for the surrounding language.

The practice exercises are varied, too, but they also vary in their level of difficulty. It makes sense to do receptive / recognition tasks before productive ones, but there is no evidence that I could see of pedagogical considerations of this kind. Neither does there seem to be any spaced repetition at work: the app is driven by the needs of the game design rather than any learning principles.

It’s unclear to me who the app is for. The functional language that is presented is adult: the situations are adult situations (buying a bed, booking a hotel room, ordering a beer). However, the graphic design and the gamification features are juvenile (adding a pirate patch to your avatar, for example).

The lack of attention to the business of learning is especially striking in the English of the English language version that I used. The number of examples of dodgy English that I came across do not inspire confidence.

  • Quite alright! You win your first Goethe coin.
  • What sightseeings do you spot in the city center and the train station?
  • Have a picknick in the park. You now have a picnic in the park with the musician.
  • You still search for your teacher. Whom do you meet in the park? What do they work?

 

All in all, it’s an interesting example of a gamified approach to language, and other app developers may find ideas here that they could do something with. It’s of less interest, though, to anyone who wants to learn a bit of German.

Having spent a lot of time recently looking at vocabulary apps, I decided to put together a Christmas wish list of the features of my ideal vocabulary app. The list is not exhaustive and I’ve given more attention to some features than others. What (apart from testing) have I missed out?

1             Spaced repetition

Since the point of a vocabulary app is to help learners memorise vocabulary items, it is hard to imagine a decent system that does not incorporate spaced repetition. Spaced repetition algorithms offer one well-researched way of improving the brain’s ‘forgetting curve’. These algorithms come in different shapes and sizes, and I am not technically competent to judge which is the most efficient. However, as Peter Ellis Jones, the developer of a flashcard system called CardFlash, points out, efficiency is only one half of the rote memorisation problem. If you are not motivated to learn, the cleverness of the algorithm is moot. Fundamentally, learning software needs to be fun, rewarding, and give a solid sense of progression.

2             Quantity, balance and timing of new and ‘old’ items

A spaced repetition algorithm determines the optimum interval between repetitions, but further algorithms will be needed to determine when and with what frequency new items will be added to the deck. Once a system knows how many items a learner needs to learn and the time in which they have to do it, it is possible to determine the timing and frequency of the presentation of new items. But the system cannot know in advance how well an individual learner will learn the items (for any individual, some items will be more readily learnable than others) nor the extent to which learners will live up to their own positive expectations of time spent on-app. As most users of flashcard systems know, it is easy to fall behind, feel swamped and, ultimately, give up. An intelligent system needs to be able to respond to individual variables in order to ensure that the learning load is realistic.

3             Task variety

A standard flashcard system which simply asks learners to indicate whether they ‘know’ a target item before they flip over the card rapidly becomes extremely boring. A system which tests this knowledge soon becomes equally dull. There needs to be a variety of ways in which learners interact with an app, both for reasons of motivation and learning efficiency. It may be the case that, for an individual user, certain task types lead to more rapid gains in learning. An intelligent, adaptive system should be able to capture this information and modify the selection of task types.

Most younger learners and some adult learners will respond well to the inclusion of games within the range of task types. Examples of such games include the puzzles developed by Oliver Rose in his Phrase Maze app to accompany Quizlet practice.Phrase Maze 1Phrase Maze 2

4             Generative use

Memory researchers have long known about the ‘Generation Effect’ (see for example this piece of research from the Journal of Verbal Learning and Learning Behavior, 1978). Items are better learnt when the learner has to generate, in some (even small) way, the target item, rather than simply reading it. In vocabulary learning, this could be, for example, typing in the target word or, more simply, inserting some missing letters. Systems which incorporate task types that require generative use are likely to result in greater learning gains than simple, static flashcards with target items on one side and definitions or translations on the other.

5             Receptive and productive practice

The most basic digital flashcard systems require learners to understand a target item, or to generate it from a definition or translation prompt. Valuable as this may be, it won’t help learners much to use these items productively, since these systems focus exclusively on meaning. In order to do this, information must be provided about collocation, colligation, register, etc and these aspects of word knowledge will need to be focused on within the range of task types. At the same time, most vocabulary apps that I have seen focus primarily on the written word. Although any good system will offer an audio recording of the target item, and many will offer the learner the option of recording themselves, learners are invariably asked to type in their answers, rather than say them. For the latter, speech recognition technology will be needed. Ideally, too, an intelligent system will compare learner recordings with the audio models and provide feedback in such a way that the learner is guided towards a closer reproduction of the model.

6             Scaffolding and feedback

feebuMost flashcard systems are basically low-stakes, practice self-testing. Research (see, for example, Dunlosky et al’s metastudy ‘Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology’) suggests that, as a learning strategy, practice testing has high utility – indeed, of higher utility than other strategies like keyword mnemonics or highlighting. However, an element of tutoring is likely to enhance practice testing, and, for this, scaffolding and feedback will be needed. If, for example, a learner is unable to produce a correct answer, they will probably benefit from being guided towards it through hints, in the same way as a teacher would elicit in a classroom. Likewise, feedback on why an answer is wrong (as opposed to simply being told that you are wrong), followed by encouragement to try again, is likely to enhance learning. Such feedback might, for example, point out that there is perhaps a spelling problem in the learner’s attempted answer, that the attempted answer is in the wrong part of speech, or that it is semantically close to the correct answer but does not collocate with other words in the text. The incorporation of intelligent feedback of this kind will require a number of NLP tools, since it will never be possible for a human item-writer to anticipate all the possible incorrect answers. A current example of intelligent feedback of this kind can be found in the Oxford English Vocabulary Trainer app.

7             Content

At the very least, a decent vocabulary app will need good definitions and translations (how many different languages?), and these will need to be tagged to the senses of the target items. These will need to be supplemented with all the other information that you find in a good learner’s dictionary: syntactic patterns, collocations, cognates, an indication of frequency, etc. The only way of getting this kind of high-quality content is by paying to license it from a company with expertise in lexicography. It doesn’t come cheap.

There will also need to be example sentences, both to illustrate meaning / use and for deployment in tasks. Dictionary databases can provide some of these, but they cannot be relied on as a source. This is because the example sentences in dictionaries have been selected and edited to accompany the other information provided in the dictionary, and not as items in practice exercises, which have rather different requirements. Once more, the solution doesn’t come cheap: experienced item writers will be needed.

Dictionaries describe and illustrate how words are typically used. But examples of typical usage tend to be as dull as they are forgettable. Learning is likely to be enhanced if examples are cognitively salient: weird examples with odd collocations, for example. Another thing for the item writers to think about.

A further challenge for an app which is not level-specific is that both the definitions and example sentences need to be level-specific. An A1 / A2 learner will need the kind of content that is found in, say, the Oxford Essential dictionary; B2 learners and above will need content from, say, the OALD.

8             Artwork and design

My wordbook2It’s easy enough to find artwork or photos of concrete nouns, but try to find or commission a pair of pictures that differentiate, for example, the adjectives ‘wild’ and ‘dangerous’ … What kind of pictures might illustrate simple verbs like ‘learn’ or ‘remember’? Will such illustrations be clear enough when squeezed into a part of a phone screen? Animations or very short video clips might provide a solution in some cases, but these are more expensive to produce and video files are much heavier.

With a few notable exceptions, such as the British Councils’s MyWordBook 2, design in vocabulary apps has been largely forgotten.

9             Importable and personalisable lists

Many learners will want to use a vocabulary app in association with other course material (e.g. coursebooks). Teachers, however, will inevitably want to edit these lists, deleting some items, adding others. Learners will want to do the same. This is a huge headache for app designers. If new items are going to be added to word lists, how will the definitions, example sentences and illustrations be generated? Will the database contain audio recordings of these words? How will these items be added to the practice tasks (if these include task types that go beyond simple double-sided flashcards)? NLP tools are not yet good enough to trawl a large corpus in order to select (and possibly edit) sentences that illustrate the right meaning and which are appropriate for interactive practice exercises. We can personalise the speed of learning and even the types of learning tasks, so long as the target language is predetermined. But as soon as we allow for personalisation of content, we run into difficulties.

10          Gamification

Maintaining motivation to use a vocabulary app is not easy. Gamification may help. Measuring progress against objectives will be a start. Stars and badges and leaderboards may help some users. Rewards may help others. But gamification features need to be built into the heart of the system, into the design and selection of tasks, rather than simply tacked on as an afterthought. They need to be trialled and tweaked, so analytics will be needed.

11          Teacher support

Although the use of vocabulary flashcards is beginning to catch on with English language teachers, teachers need help with ways to incorporate them in the work they do with their students. What can teachers do in class to encourage use of the app? In what ways does app use require teachers to change their approach to vocabulary work in the classroom? Reporting functions can help teachers know about the progress their students are making and provide very detailed information about words that are causing problems. But, as anyone involved in platform-based course materials knows, teachers need a lot of help.

12          And, of course, …

Apps need to be usable with different operating systems. Ideally, they should be (partially) usable offline. Loading times need to be short. They need to be easy and intuitive to use.

It’s unlikely that I’ll be seeing a vocabulary app with all of these features any time soon. Or, possibly, ever. The cost of developing something that could do all this would be extremely high, and there is no indication that there is a market that would be ready to pay the sort of prices that would be needed to cover the costs of development and turn a profit. We need to bear in mind, too, the fact that vocabulary apps can only ever assist in the initial acquisition of vocabulary: apps alone can’t solve the vocabulary learning problem (despite the silly claims of some app developers). The need for meaningful communicative use, extensive reading and listening, will not go away because a learner has been using an app. So, how far can we go in developing better and better vocabulary apps before users decide that a cheap / free app, with all its shortcomings, is actually good enough?

I posted a follow up to this post in October 2016.