Language Learning & Technology
ISSN 1094-3501

October 2020, Volume 24, Issue 3
pp. 176–193

ARTICLE

Data-driven learning of academic lexical bundles
below the C1 level
Keith J. Lay, Cyprus International University*
Mehmet A. Yavuz, Cyprus International University*

Abstract
This study investigates the possibility and efficacy of paper-based, in-class, data-driven learning (DDL) of
academic lexical bundles below the C1 level of proficiency described by the Common European Frameworkof Reference (CEFR; advanced high ACTFL). A two-stage experimental design involving three groups (n= 41) and 24 two-to-four word academic items was implemented. First, the question of whether this typeof learning works with these items below the C1 level is addressed through a nonequivalent-groups quasiexperimental design covering a five-week period. The results indicate that this technique is effective at theB2 level, but not at the A2-B1 level. Next, an equivalent-groups experimental design compares this style oflearning to conventional techniques at the B2 level. The results of this stage suggest that paper-based, inclass DDL is more effective than conventional learning with academic lexical bundles at the B2 level.
Keywords: Academic lexical bundles, Corpus, Data-driven learning (DDL), English for academic purposes(EAP)
Language(s) Learned in This Study: English
APA Citation: Lay, K. J., & Yavuz, M. A. (2020). Data-driven learning of academic lexical bundles below
the C1 level. Language Learning & Technology, 24(3), 176–193. http://hdl.handle.net/10125/44741

Introduction
As increasing numbers of lower proficiency learners are applying for and entering tertiary level education
in English Medium Instruction (EMI) contexts (Coxhead, 2011), the need to innovate in order to help thoselower proficiency learners cope with English for Academic Purposes (EAP) is greater than ever before. Ashigh academic performance is more likely from those who write with a broader vocabulary (Cooper, 2017),
the challenge to compete with native speakers is significant. This is further illustrated by Csomay & Prades(2018), who found essay scores to be higher in writers with a broader academic vocabulary.
One form of academic vocabulary that has been shown to be both particularly useful and particularly
challenging is that of the academic collocation. Works such as the Academic Collocations List (Ackermann& Chen, 2013) and the Academic PhraseBank (Morley, 2017) are testimony to the place of academic
collocations in EAP development, an area in which second language learners at the university level may
need greater assistance. However, academic multi-word items are difficult for even native speakers to learn
and start using in a short amount of time, as shown by Cortes (2007). Therefore, it seems that introducing
academic collocations as early as is feasible would be desirable, yet therein lies a problem: How can lower
proficiency learners cope with academic collocations?
One innovation that may help these learners comes in the form of corpus study for the purpose of language
proficiency development, commonly referred to as data-driven learning (DDL). In the last decade, studies
such as those by Boulton (2008a, 2009, 2010), Chujo et al. (2012), Vyatkina (2016), and Brown (2017),
among others, have served to map the possibilities of DDL with lower-level learners. In addition to proving
successful with such learners, as a tool for vocabulary learning, DDL answers many of the central issues
regarding vocabulary learning successfully. As a result, there is potential to explore EAP areas in such
*Cyprus International University, Nicosia, Northern Cyprus, Mersin 10, Turkey

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contexts. This paper reports on a two-stage study aimed at investigating the possibility of incorporating
DDL into the multi-word EAP vocabulary development of learners below the C1 (Advanced-high ACTFL)
threshold.

Literature Review
Academic Lexical Bundles
Academic collocations represent an important and sizeable area of EAP vocabulary and those collocations
come in a variety of forms. Resources such as the Academic Collocations List (ACL) compiled byAckermann and Chen (2013) demonstrate that a great number of academic collocations are in the form ofnoun collocations; often adjective-noun combinations, such as ‘contextual factors’ and ‘significant impact’.
A further resource, the Academic PhraseBank (Morley, 2017), consists of phrases that are of a usually
greater length (often several words), such as ‘An example of X is…’, representing a collocation type morein line with terms such as lexical phrases (Li & Schmitt, 2009, DeCarrico & Nattinger, 1988), lexicalbundles (Biber et al., 2004), formulas, and formulaic sequences (Ellis et al., 2008), among others. These
collocation types differ from the typical noun combinations noted above as they perform a lexicogrammatical function above the level of merely labelling. In order to distinguish the item types focusedupon here and denote their more common usage within academic discourse, the term academic lexicalbundles is selected, fitting with the recurrent formulaic sequences that Pan et al. (2016) describe lexicalbundles as representing.
Collocations that perform functions, such as those described as lexical bundles here, are significant because
they have the potential to affect the coherence and cohesion of writing and as such can greatly influence
performance. Academic lexical bundles are one of the defining and key factors of academic vocabulary,acting as ‘a component of fluent linguistic production’ (Hyland & Jiang, 2018). Pan et al. (2016) found thateven at the highest levels of academic discourse native and non-native speakers use lexical bundles
differently, with the former more likely to use phrasal type bundles and the latter incorporating a higher
proportion of clausal bundles into their writing. The consequences of such differences can be significant;Csomay and Prades (2018) found that essay scores were higher in those learners who display greater
academic vocabulary, while Cooper (2017) notes the contrast in the vocabulary ranges of low and highachievers.
EAP collocations pose a particular challenge to even advanced level non-native speakers of English (Elliset al., 2008, p. 378). Byrd and Coxhead (2010) describe a number of challenges for both the teacher andlearner in EAP relating to lexical bundles, including a lack of pedagogical advice, issues relating to item
type, and justifying the time spent on them when the individual components may already be known. Indeed,
academic collocations also pose a challenge to native speakers; a study focusing on the learning of 35 fourword bundles (selected from an 800,000 word history corpus and classified by function) during five microlessons in a ten week period, found that the participants’ use of the target bundles was rare both before andafter the experiment (Cortes 2007, p. 395).
While it seems that a strong set of academic lexical bundles would likely improve academic scores andlearner confidence, the challenge these items pose demands a learning strategy. One possible answer may
come from the world of corpora.
Data-Driven Learning for Vocabulary Learning
Data-driven learning (DDL) is the term, coined by Johns (1991), which refers to language learning throughthe study of corpora (a collection of texts, usually computerized and vast in size). For example, the Corpus
of Contemporary American English consists of over 560 million words and is searchable in a variety ofways. This allows language analysis and, as such, language learner study of the text of a corpus, or DDL.
The manner of study can vary from the student having direct access to the corpus, so-called hands-on
concordancing, to groups/classes using teacher-selected concordance lines, often referred to as hands-off

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concordancing, and all the combinations therein.
A corpus can offer thousands of seemingly contextualized examples of an item at the click of a button. At
first glance then, corpora would seem to be a magic answer in searching for meaningful, real-world
examples. However, a more in-depth consideration of the central issues of vocabulary learning highlights
not only positive factors, but also some potentially negative factors DDL may bring to the vocabulary
learning field.
Laufer (2017) concisely defines the current debate between two schools of thought in vocabulary learning:
The first group believes that the best way to acquire many words is by reading large quantities ofmaterial. The second group claims that to meet new words 12 times during extensive reading—12 times
being the average number of encounters required for acquisition to occur, according to the available
body of research—L2 learners will have to read about a million words per year, which is unrealistic
since they have neither the time nor the ability to do this. Hence, the argument goes, word-focused
instruction is indispensable (Laufer, 2017, p. 6).
While one recent study found that 15 encounters of a collocation was a suitable number for effective
retention (Webb et al., 2013), a counter argument from Laufer and Rozovski-Roitblat (2015) suggests thatwhat is done with new lexis appears to be more significant than the number of encounters itself.
Consequently, factors such as context, the style and interaction pattern that form the situation in which theitem is encountered, and the spacing between encounters must all contribute to retention rates.
First, the degree to which the study of corpora provides context is disputed. While some note the benefit ofconcordance analysis in providing a ‘feel’ for items and enabling visualization due to the context (Boulton
2008b, p. 40), others put forward the notion that the concordance results of corpus analysis are removed
from the wider context of the text and are therefore decontextualized (Flowerdew, 2005, p. 324). Many
concordancers allow the selection of concordances in full sentence form and some allow the user to see theoriginal text in full to provide the context. However, keyword in context (KWIC) concordances, a common
form used in DDL, are comprised of the item and a certain number of items in each direction from that key
word, limiting context potential. On the other hand, recent works have begun offering a more interactive
and context-heavy interface; the TED Corpus Search Engine compiled by Hasebe (2015) being one suchconcordancer. Therefore, it seems likely that context will soon be less disputed as an advantage of using
corpora in language learning.
In terms of style and interaction, the study of lexical items in corpus concordances involves intentional
vocabulary learning. This type of learning can be described as ‘‘aiming at committing lexical information
to memory’’ (Hulstijn, 2001, p. 272), much in line with the word focused tasks mentioned by Laufer (2017).
Choo et al. (2012, p. 855), referring to Rosszell (2007), state that a combination of incidental and intentional
vocabulary learning may be the best solution, with intentional study providing a boost to vocabulary
learning. On that basis, paper-based, in-class DDL for specific items can be considered a potential meansof providing that boost.
The retention level of new lexical items is also affected by the spacing (in terms of time) between
encounters, of which Nation (2001, p. 56) makes the distinction between spaced repetition and mass
repetition. The former involves spacing between encounters and the latter involves a high number ofencounters at the initial stage. Ergo, specific-item DDL involves mass repetition. Early research studies,such as those by Pimsleur (1967, in Nation, 2001), show strong empirical evidence in favour of spaced
repetition as opposed to mass repetition. Subsequent second language research strengthens this position of
spaced repetition (for a more recent example see Hirschel & Fritz, 2013). Thus, a lack of spacing is a clear
limitation. This could perhaps be somewhat countered in DDL practice by encouraging recycling, byactively recapping, or by explicitly teaching learners the benefits of spaced repetition.
The vocabulary learning benefits of DDL presented here are: The provision of a sufficient number ofencounters, the potential for contextualization, the opportunity for intentional learning, and the relative ease

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of access and use. The potential downsides include the nature of mass repetition, the questions regarding
contextualization, and the lack of incidental or peripheral learning opportunities. As a result, with the stakes
high in terms of the effect of a strong foundation of academic lexical bundles on the academic performanceand confidence of learners, it is important to establish whether or not DDL is a solution for the learning of
those item types, and at which level this can be done.
Data-Driven Learning with Lower Proficiency Learners
A meta-analysis of several hundred papers in a search for empirical evidence of DDL alludes to the levelof participants being mostly made up of high level learners: ‘‘the majority of the studies are concerned withmore or less advanced learners.’’ (Boulton 2007, p. 13). In the 13 years since the publication of that paper,many studies have concentrated on investigating DDL with lower proficiency learners. Although reported
on according to different language proficiency descriptions, studies have focused on the whole range oflower proficiency. Here, those studies are aligned to the levels of the CEFR, a comprehensive current toolfor describing language proficiency. They include: ‘basic user’ learners at the A1 (beginner) and A2(elementary/pre-intermediate) levels (such as Vyatkina, 2016, Chujo et al., 2012, and Boulton, 2008astudied), as well as the ‘independent user’ range at the B1 (intermediate) and B2 (high intermediate) levels(studied by Boulton, 2010, 2008b, and Brown, 2017). The mapping of DDL below the high level thresholdis significant because for a technique such as DDL to filter through to language learning materials,
classrooms, and teacher education, there is a need for strong evidence of its value. Therefore, it is useful toconsider the findings of these studies in relation to both the learner level and the language area in focus.
Some recent studies have focused on the very lowest levels. Vyatkina (2016) focused on beginner level
learners of German as a foreign language in a study comparing the learning of eleven verb-preposition
collocations through either DDL or through a textbook. The participants, 88 American English native
speakers, were mostly at A2 level or below in German, and the results indicated that DDL was more
effective than traditional learning. Chujo et al. (2012) also found evidence of the superiority of DDL overconventional methods with beginner level learners, this time focusing on noun phrases.
Moving up in level, a study of 132 learners ranging from elementary to intermediate according to a TOEIC
test, investigated the possibilities of DDL with linking adverbials. The adverbials ranged in length, withseven one-word items, and one each of two-word, three-word, and four-word adverbials. The findings
showed DDL to again be effective, although the overall scores were ‘very low’ at just 13.92% (Boulton,
2009, p. 9), prompting the question, were the items too difficult for the level, regardless of the method oflearning?
Further positive results with lower levels come from a study focusing on phrasal verbs, measuring theperformance of 113 lower intermediate participants with paper-based DDL (Boulton 2008a). As with thestudy above, the scores were considered low; only two items were focused upon (‘look up’ and ‘pick up’),yet the overall score was just 57% (where 50% should be returned by chance), although again thecomparison between DDL and conventional methods favoured DDL (Boulton, 2008a, p. 9–10). Thesestudies seem to suggest a pattern that DDL, while more effective, is not a miracle cure for the problem of
early exposure to difficult language items. However, some respite may come when the focus turns toproblematic areas that are learner-generated.
Boulton (2010) undertook a further study with lower proficiency learners and similarly to that describedabove, this was done in a hands-off, paper-based style of DDL. A selection of 15 problematic areas in thewriting of the participants (produced in a pre-test session) were identified and selected for inclusion in thetreatment, meaning the items were not being learned in the study, but developed. As the author notes, these
items were typical problematic areas for French learners of English (Boulton, 2010, p. 7). This is asignificant move away from the focus on intentional learning in the studies discussed above. Although theresearcher again noted low scores, this study also found DDL to be more successful than conventional
learning (Boulton: 2010, p. 16).
When collating the evidence, it seems that while DDL is more effective than conventional methods with

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lower proficiency learners across levels and item types, what can and cannot be done at lower levels is still
limited, which could be a reason for the slow progress of DDL with regards to becoming incorporated into
mainstream teaching.
As the academic performance of non-native speakers of English relies on their ability to perform well interms of vocabulary (Cooper, 2017, Csomay & Prades, 2018), one area of EAP vocabulary that may aid thecohesion and coherence that learners perceive to be problematic (Al Badi, 2015) is that of academic lexicalbundles. The earlier they can start acquiring these valuable items, the greater their chance of success lateron will be, and as items such as these pose a considerable challenge (Cortes, 2007), identifying the mostefficient way of learning them is desirable. However, while even at low levels DDL has proven to be more
effective than conventional methods (Vyatkina, 2016, Boulton, 2008a, 2009, 2010, Chujo et al., 2012),
those studies have often reported low scores. Therefore, it seems prudent to establish at which level these
items become a realistic option for DDL instruction in the EAP classroom.
This study was carried out in two stages. The first stage was designed to answer the first two researchquestions, establishing the lower limit of this type of learning with these item types.
Research Questions:Stage One
RQ1. Can B2 level learners learn academic lexical bundles to a significant degree using paper-based inclass DDL?
RQ2. Can A2-B1 level learners learn academic lexical bundles to a significant degree using paper-based
in-class DDL?
The results of the first stage justified a second stage in which this technique was compared withconventional learning at the B2 level.
Stage Two
RQ3. Can B2 level learners learn academic lexical bundles to a significant degree using conventional
methods?
RQ4. Is paper-based, in-class DDL more effective than conventional learning with academic lexical bundles
at the B2 level?

Methodology
DesignParticipants
Three groups of participants (n = 41), all studying at a large EMI university in Cyprus, took part in thisstudy; groups A and B participated in the first stage and group C in the second. Groups A and C consistedof students in their first year of undergraduate study and group B consisted of students learning (foundation)
English prior to faculty entry. In order to involve participants of the same level and stage of learning as thefirst treatment, the second stage was conducted in the following academic year.
The participants, 29 females and 12 males with a mean age of 20.17 and predominantly of Turkish
nationality, had the following first languages: Turkish (35), Arabic (3), Kurdish (2), and French (1).
Group A (n = 12) and group C participants (n = 17) had already passed a general proficiency test pegged
to the CEFR and had an average CEFR level of B2 according to the mutual opinion of three of the
instructors (experienced language teaching professionals) of those classes. Group B participants (n = 12)
were studying at the pre-intermediate level of a foundation programme pegged to the CEFR and are
therefore best described as A2-B1. The faculty participants were studying eight courses, all delivered inKeith J. Lay & Mehmet A. Yavuz

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English, while the foundation programme participants were studying English in 25 separate 50-minutelessons each week. The usual form of English instruction at the university is a focus on skills development
and communicative competence. With very few classes being monolingual, English is the language of
instruction in language lessons too. DDL is not usually carried out in the institution at which the research
was conducted and academic lexical bundles are not a feature of courses taken by the participants, except
for the occasional item occurring during normal learning conditions. The sessions of this research wereincorporated into their programmes of study for the purposes of vocabulary building and English
language development.
Mechanics
Six 50-minute training sessions were conducted with each group, giving a total in-class treatment timeof 300 minutes per group. For the first stage, a nonequivalent-groups quasi-experimental design waschosen, incorporating a pre-test, training sessions, and post-test (the same for both groups to allow
statistical comparison). The first stage took place over a period of five weeks. The second stage utilized
an equivalent-groups quasi-experimental design. In order to use the results of group A as theexperimental data, a control group (group C) was required. Participants in the control group were given
the same pre and post-test, and received the same amount of instruction time with the same lexicalbundles as groups A and B. However, the control group was taught in a conventional manner, which
is described in more detail later in this section.
Planning
A total of 24 academic lexical bundles were included in the pre and post-test stages, chosen through theimplementation of a selection and piloting process. The lexical bundle selection process involved five items(chosen through a randomizer) from each of the seventeen function categories listed on the Academic
Phrasebank (Morley 2017) being subjected to a cross-reference search by hit per million words in the British
Academic Written English (BAWE) corpus (see Appendix A). The number of hits for each lexical bundle
in each category was then calculated to determine the most common functions according to the selected
lexical bundles. The resultant functions were causality, signalling transition, compare and contrast, andgiving examples. A second search was then conducted on each lexical bundle in the Phrasebank (Morley2017) for those selected functions (see Appendix B), resulting in the selection of 24 lexical bundles
according to the most common six per function.
A gap-fill with a phrase bank was designed for the pre/post-test (sample in Appendix C). This style oftest was chosen as it is similar to the materials used in the DDL sessions of the study as well as to tasks
included in existing language learning materials. Also, the degree of control provided by a gap-fill task
reduced the chances of validity and reliability issues that may have arisen from alternative tasks types,
such as a written task. To this end, the test items were initially divided into two 12-item gap-fill tasks forthe pilot test.
A piloting process was then conducted to ascertain the most desirable experimental design. A panel ofexperts consisting of three academic professionals in the field of English teaching (PhD holders) wereconsulted prior to and following the administration of a pilot test and the re-selecting of lexical bundles.
The pilot test participants (n = 7) were of approximately the C1 level of the CEFR and were in their final
year of undergraduate study. Despite the participants of the pilot test being above the level of the intended
study participants, this sample was accepted as a convenience sample owing to the benefit of the material
for the participants and the assumption that should they be able to complete the task, a test on learners
below their level would be worthwhile.
This process confirmed that changes were desirable regarding the test design as well as both the functions
and lexical bundles. The pilot test participants said they thought it would be better if the items were split
into smaller sets, which was then done to form four sets of six items. As for the functions and lexicalbundles, with the basis of discourse at all levels involving the basic functions performed by the words “and,”“but,” “so,” and “because,” something voiced during discussion with the panel of experts, it was agreed

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Language Learning & Technologythat the four functions would be adding information, compare and contrast, cause, and effect. These
functions and the selected items are shown in Table 1.
Table 1: Lexical bundles of the experiment by function
Function

Lexical bundles

Adding information and furthermore, in addition , as well as,
and what is more, not to mention, further toCompare/contrast
Causality
cause/effectCausality effect/cause

is similar to, in contrast to, in the same way, significantly different from, iscomparable to, on the other handwhich leads to, may cause, which causes, as a consequence, have contributed
to, may affectstems from, is linked to, caused by, due to, owing to, as a result ofMaterials
The style of DDL used here was hands off concordancing. That is to say, the participants did not interact
with a corpus themselves, but were exposed to corpus extracts selected by the researchers. The corpus fromwhich the extracts were taken is the BAWE corpus, a 6.9 million word collection of written academic texts
in British English. The number of citations given per item was 12 and a sample can be seen in Appendix
D. It is important to note that the citations were given in full sentences, not KWIC format concordances.
Although there are advantages to KWIC format concordances, such as the increased noticing of surroundingwords and the potential for ‘vertical or paradigmatic reading’ (Ballance, 2016, p. 1208), full sentences werechosen to allow more familiarity in early stage DDL. The citations in the concordance sheets were selected
randomly and then checked for any inappropriate content in line with ethical considerations.
Implementation
Stage One: Experimental Groups
The pre and post-tests (see Appendix C for a sample) were carried out in test conditions. Participants were
informed that they would complete four tasks as part of a 20-minute test. It was stated that they should
answer every question, even if they were unsure of the answer. The participants were also told not to worry
about the difficulty of the task and that they should not try to understand every unknown word. Although
the post-test was conducted in identical circumstances, the participants were of course fully aware of what
was expected of them having completed a pre-test and training sessions.
Each session followed an “observe, hypothesize, experiment” (OHE) procedure (Appendix E) based on thesuggestions put forward by Lewis (1993). This procedure is in line with the “identify, classify, generalize”
procedure of Johns (1991) and the “look, familiarize, practice, create” procedure described by Thurston andCandlin (1998). As can be seen by the prompts in the plan used (Appendix E), the aim was to encouragelearner-generated noticing and hypothesizing from the outset. Oral instructions were used to achieve this(Appendix E).
Four items were included in the first training session, with instructions given prior to the first sheet ofconcordances being distributed. Participants were asked to circle the lexical bundle in each sentence (which
was already highlighted in bold). Participants were then given time to observe the concordances. This wasfollowed by a period in which the group voiced their observations and were encouraged to hypothesize.
These observations and hypotheses were discussed and debated by the group. The second sheet was thenhanded out and participants repeated the process. Following the second item, participants were asked to try
and form a simple sentence (orally) to show how the item works. Participants were then given theopportunity to discuss their sentences in terms of accuracy of meaning and use, with a particular emphasisKeith J. Lay & Mehmet A. Yavuz

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on the item meaning. The first item was then subjected to the same treatment and participants had now
understood the process of OHE that was desired. The process was then followed for items three and four.
The session ended with participants forming example sentences (orally) of all four items. Four items werealso provided in session two, with the now-established process being followed for all items.
In the subsequent four sessions, participants of both groups experimented by making simple sentences forproblematic items (such as ‘I like chocolate in the same way I like pizza’) and checking with otherparticipants. The instructor also confirmed some of these examples as being correct in meaning or use,when participants expressly requested instructor feedback.
Stage Two: Control GroupThe second stage was carried out one year after the first stage in order to include a group with a comparable
academic and English background to group A. The pre-test and post-test were conducted in identicalconditions to those of the first stage and the same items were involved in each session.
The method of learning, in order to give a measure of conventional learning, was in line with how modern
course books present items. The first three sessions followed a deductive “present, practice, produce”
procedure, moving from controlled to semi-controlled tasks. The students were allowed and encouraged touse reference materials when they felt it necessary. However, it should be noted that learners do notinstinctively turn to dictionaries in the setting of this study (this applies to all participants) due to learnertraining away from a reliance on total comprehension at all times and towards facilitating generalcomprehension. This is in contrast to the type of learning described as typical in France by Boulton (2010).
In the second three sessions, participants were given context (teacher-provided, spoken example sentencesof the items and an academic reading task) and encouraged to inductively work towards understanding (inboth cases these activities were supported by matching tasks). Meaning was clarified and again referencematerials were consulted where participants so desired. Participants then produced examples and practiced
substituting one-word connectors with the academic lexical bundles in focus here. In all six conventionalsessions, there was a focus on meaning, form (including pronunciation and spelling), and use, and learners
were allowed and encouraged to ask questions of the teacher at any time.
In order to carry out statistical analysis of the data, the tests were marked. Each question was marked ascorrect if the original bundle was used. It should be noted that there was the possibility of a few of the items
being somewhat interchangeable within the selected sentences, but it was decided that “correct” would
mean completing the original sentence here. The data sets were then put through a series of tests, the resultsof which are reported below.

Results
Paper-Based, In-Class DDL with Academic Lexical Bundles below the C1 Level
Paired-samples t-tests were conducted for both experimental groups as a measurement of performance
between the pre-test and post-test. Further to these, where significant results were found, effect sizes were
calculated using the relevant Cohen’s d formula. These results are shown in Table 2 (group A) and Table 3(group B) respectively.
RQ1. Can B2 level learners learn academic lexical bundles to a significant degree using paper-based inclass DDL?
Table 2: Group A pre-test & post-test results
N

M

M (%)

σ

t

p

x̅ 2 - x̅ 1

Cohen’s dPre-test

12

6.92

28.82

11.16

4.05240.0019

10.07

1.05

Post-test

12

9.33

38.89

7.61

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N: Number of participants, M: Raw mean, M(%) Mean as %, σ: Standard deviation, t: t-test value, p: level ofsignificance, x̅ 2 - x̅ 1 =the difference in mean values

The mean of the pre-test scores of group A is 28.82% and the post-test mean is 38.89%. This difference, a34.94% increase in performance, is statistically significant at the alpha p < .01 level. The effect size
calculation gives a Cohen’s d figure of 1.05.
The results represent an increase that is significant to a strong level (.01), a confidence level of 99.8%,
coupled with an effect size that is above the ‘large’ effect size level of 0.8 put forward by Cohen (1988),
and that of 1.00 suggested by Plonsky and Oswald (2014, p. 12) for second language research studies,indicating that the answer to the first research question is affirmative; B2 level learners can learn this item
type to a significant degree through paper-based, in-class DDL.
RQ2: Can A2-B1 level learners learn academic lexical bundles to a significant degree using paper-based
in-class DDL?
Table 3: Group B pre-test & post-test
M

M(%)

σ

t

p

x̅ 2 - x̅ 1

12 6.025.0

6.15-0.1205

.9062

-0.35

Post-test 12 5.92

24.65

7.84

N
Pre-test

N: Number of participants, M: Raw mean, M(%): Mean as %, σ: Standard deviation, t: t-test value, p: level ofsignificance at p<.05, x̅ 2 - x̅ 1 =the difference in mean values

The group B pre-test mean score is 25% and the post-test mean is 24.65%. The difference, a 1.4% decreasein performance, is not significant at the alpha p > .05.
This result suggests that A2-B1 level learners cannot learn academic lexical bundles with paper-based inclass DDL. Consequently, it can be seen that the combination of findings related to RQ1 and RQ2 imply
that B2 is a cut-off point for this item type when combined with this form of learning.
Conventional Learning of Academic Lexical Bundles at the B2 Level
A paired-sample t-test for the pre-test and post-test data of group C is detailed in Table 4 with a Cohen’s dcalculation of the effect size in order to address the third research question.
RQ3: Can B2 level learners learn academic lexical bundles to a significant degree using conventional
methods?
Table 4: Group C pre-test & post-test
N

M

M(%)

σ

17

7.29

30.39

9.17 1.1863

Post-test 17

8.06

33.58

12.45

Pre-testtpx̅ 2 - x̅ 1

Cohen’s d

0.25283.17

0.29

N: Number of participants, M: Raw mean, M(%): Mean as %, σ: Standard deviation, t: t-test value, p: level ofsignificance at p<.05, x̅ 2 - x̅ 1 =the difference in mean values

The group C pre-test mean score is 30.39% and the post-test mean is 33.58%. The difference is not
significant at the alpha p > .05. The effect size calculation returns a Cohen’s d figure of 0.29, slightly above
Cohen’s (1988) small effect threshold of 0.20, but below the small effect size of 0.40 recommended forsecond language research by Plonsky and Oswald (2014, p. 12).
The results suggest that B2 level learners cannot learn academic lexical bundles to a significant degree
through conventional methods.

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Comparing DDL & Conventional Learning
Finally, an independent-samples t-test to compare the differences in means of the pre-test and post-testscores of groups A and C together with a Cohen’s d calculation of effect size are detailed in Table 5 in orderto answer the final research question.
RQ4: Is paper-based, in-class DDL more effective than conventional learning with academic lexicalbundles at the B2 level?
Table 5: Comparison of Group A & Group CGroup A 12 2.41x̅σ10.07 8.61

Group C 17 0.77

3.17

N

X

t

p

x̅ 2 - x̅ 1

Sig

Cohen’s d

1.7993

.0832

6.90

> .05

0.70

11.08

N: Number of participants, X: difference in raw mean pre/post test, x̅: Difference in mean % pre/post-test, σ: Standard
deviation, t: t-test value, p: level of significance at p>.05, x̅ 2 - x̅ 1 =the difference between group A and group C

Table 5 shows the results of an independent samples t-test of the differences in performance of participantsin groups A (experimental group) and C (control group) from pre-test to post-test. The difference of group
A is 10.07, a 34.94% improvement, while group C has a 3.17 difference, a 10.5% increase. Although bothgroups show an increase, with that of group A greater than that of group C, the result is not significant at
the .05 level.
In terms of effect size, the calculation of Cohen's d returns a figure of 0.70. This figure is between the 0.50
medium effect and 0.80 large effect that Cohen (1988) tentatively identified, with 0.70 being closer to thelarge effect. However, as Plonsky and Oswald (2014 p. 12) point out, effect size interpretation varies
according to discipline, and according to their recommendation, 0.40 indicates a small effect size, 0.70 a
medium effect size, and 1.00 a large effect size for L2 research. According to this interpretation, there is a
firm medium effect size. In sum, it can be said that the improvement in performance of group A was better
than that of group C, although a larger statistical difference would have allowed more confident
generalization.

Discussion
This study set out to investigate the learning of academic lexical bundles below the C1 level through paperbased, in-class DDL. The results suggest that this is possible at the B2 level, but not so below that point. A
comparison then found that this type of learning is more efficient than conventional learning for academic
lexical bundles in keeping with the many studies that have found DDL to be more effective thanconventional learning, as reported by Boulton and Cobb (2017: 34). However, the scores achieved in allconditions were low, and the effect sizes varied. These two areas are therefore in need of further scrutiny.
Boulton and Cobb’s (2017) meta-analysis of DDL, which covered two and a half decades up to 2014,provides a great deal of insight. That study consists of a 64-study, 88-sample meta-analysis of DDL
research, with effect size calculations made for many combinations of variables. A sub-group of 13 studies
with a sample size less than 20 and a pre/post-test design returned a mean effect size of d = 1.17, which issomewhat stronger than the effect size found for group A in this study (d = 1.05). Also, 13 studies with apre/post-test design were paper-based and had a mean effect size of d = 1.06, very close to that of group A(d = 1.05). Therefore, it can be seen that the effect size for group A was within expectations and isparticularly consistent with previous findings from studies with similar designs. Furthermore, the effectsize calculation of 0.70 for the comparison of group A and group C is larger than that of the mean effectsize of another subset of fifteen studies which were paper-based and involved control and experimentalgroups (d = 0.52) reported in that meta-analysis.
However, when compared to the overall effect sizes for pre/post-test designs of d = 1.50, the effect size of186

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group A (d = 1.05) is considerably lower. The 0.70 effect size for the difference between group A and group
C is also lower than the overall experimental/controled design effect size of 0.95 calculated by Boulton and
Cobb (2017). With the combination of results and effect sizes of this study being somewhat limited, a closer
look at the scores may shed more light on the situation.
The scores were low in both the pre-tests and the post-tests of all three groups. The pre-test mean scores of
28.82% (group A), 25% (group B), and 30.39% (group C) represent an overall correct response rate of
28.35%. The post-test mean scores of 38.89%, 24.65%, and 33.58% return a mean score rate of 32.52%.
This is in keeping with past studies. However, as mentioned earlier, the group B participants’ scores may
have been further subdued by the difficulty of the test used. An alternative test design may have yieldedhigher scores all round, although that may have also impacted upon the validity of the test were it to be tooeasy or not include sentences similar to those used in the training sessions.
In terms of the content of the concordances used in both the test and the DDL sessions, it could be said thatthe selected sentences were heavy going. One solution could have been to incorporate the use of a scaled
down level of English, such as a graded reader corpus. On the other hand, the advantage of using a graded
reader corpus for DDL is not proven (see results reported in Hadley & Charles, 2017) and the likelihood offinding enough academic lexical bundles in such materials is low. Another possibility would naturally be
the careful selection of sentences by reading difficulty tests such as Gunning’s Fog Index (discussed in
Gunning, 1969), but this would inevitably cause difficulties in practice due to time constraints. Nonetheless,the content could have affected performance, although low scores are not uncommon.
Several studies have reported low scores in lower proficiency DDL. Boulton (2008a, 2009, 2010) has
reported several studies of DDL with lower proficiency learners, each demonstrating the place of DDL
as superior to conventional learning. However, those studies also carry a further commonality in having
low scores. The earliest study involved two items and so 50% would be expected by chance only, yet
the overall score returned was 57% (Boulton, 2008a, pp. 9–10). The following study involved linking
adverbials and produced what the author described as very low scores with a mean of 13.92% (Boulton,
2009, p. 9). The next study returned overall mean scores of 48.53% in the pre-test and 57.97% in the
post-test (Boulton, 2010, pp, 11). In contrast, Chujo et al. (2012) reported scores which wereconsiderably higher with pre-test mean scores ranging from 43.6% to 65.2% and post-test means
ranging from 64.6% to 88.5%. Of course, the scores that participants achieve depends on how they aretested and by no means negates the beneficial findings of these studies. However, teachers and learners
alike prefer the appeal of higher scores to lower ones and this could be detrimental to encouraging theuptake of DDL.
Another likely factor to the low scores is the language item type. Previous works have noted the difficultyof academic collocations (such as Cortes, 2007 and Ellis et al., 2008) as they require a depth of knowledge(Cooper, 2017) that puts a strain on the learner and academic vocabulary is already a more challenging
prospect than general vocabulary.
On a more positive note, several observations can be made about the treatment in terms of benefits to theparticipants. Participants were subjected to intentional vocabulary learning—the kind of word-focusedlearning noted by Laufer (2017)—which is one side of the current debate in vocabulary learning. Intentional
vocabulary learning means that language educators are able to find and provide concrete examples that
Cooper (2017) suggests are needed to target learner idiosyncrasies. The participants were also able tobenefit from encountering the bundles more than the 12 times recommended by Laufer (2017) and the 15
times needed for collocation learning stated by (Webb et al., 2013).
When one considers the nature of the DDL that took place, participants showed signs of the type of detective work
detailed by Johns (1991). Participants of both groups quickly noticed that the items performed functions and were
therefore able to experiment relatively quickly, connecting items together and experimenting with replacing someitems with others, often those which performed the same function. An example of this was a Group B participant,
who gave an example sentence comparing the weather in his country (Libya) with that of Cyprus. He initially used

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187

the bundle “is comparable to,” but wanted to express that Cyprus was in fact hotter than Libya. The participant thenasked if he could say “Libya weather it is good, on the other hand Cyprus is hot.” Another strategy participants
adopted involved focusing on key semantic clues; the lexical bundles that contained key content words signifying
the core meaning seemed easier for learners to recall. Items such as “in the same way” and “is comparable to”
seemed easier than items such as “due to” or “owing to,” despite the number of words. However, this was not borneout in the results of the post-test, and may have been a false strategy.

Conclusion
Several limitations of this study require mention. First and foremost, the sample of the study can be
criticized in a few ways. The size of the groups (n = 12, n = 12, and n = 17) could clearly have been
larger and more diverse culturally and linguistically to allow for a more generalizable result. The
pre/post-test instrument is a limitation in terms of test difficulty and this difficulty could, of course, haveaffected the lower level learners to a greater extent than other participants. It also lacks productive use oflanguage, although this would have heightened the effect of test difficulty on the lower level learners.
Finally, the acceptance of only the original bundles as being correct may have allowed well-formed
sentences to be deemed wrong. Further research could address these limitations with a larger, morediverse sample being tested in both receptive and productive ways through a test that is of a more suitable
degree of difficulty.
There are some other possible research implications that arise from this study. It may be useful to conducta hands-on version of this study. Based on Boulton and Cobb’s (2017) meta-analysis findings, an increasein effect size would be expected. This might be particularly useful with a context-heavy corpus such as the
TED Corpus of Spoken English compiled by Hasebe (2015).
This study has demonstrated that learners at the B2 level, but no lower, can learn academic lexical bundles
through paper-based, in-class DDL more effectively than through conventional methods. As the demandfor EAP at lower proficiency levels continues to rise, can word-focused vocabulary learning of academic
lexical bundles therefore be incorporated into the B2 level curriculum through means of paper-based inclass data-driven learning? If stakeholders start listening to effect sizes, perhaps. On the other hand, should
they focus on scores, as is the nature of teachers and learners alike, perhaps not.

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Acknowledgements
The data in this study come from the British Academic Written English (BAWE) corpus, which wasdeveloped at the Universities of Warwick, Reading and Oxford Brookes under the directorship ofHilary Nesi and Sheena Gardner (formerly of the Centre for Applied Linguistics [previously called
CELTE], Warwick), Paul Thompson (Department of Applied Linguistics, Reading) and Paul Wickens(Westminster Institute of Education, Oxford Brookes), with funding from the ESRC (RES -000-230800).

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Appendices
Appendix A:
Selection process in stage one: Result samples from two of the 17 categories on Morley’s Academic
Phrasebank (2017).
Describing Quantities - Five most common lexical bundles by hit on the BAWE corpus
Item
5 Items Selected (Below)
the highest proportion
dropped from * tohalf of thoselower than averagein the range ofTotal Hits
38
3
4
2
1
28

Explaining Causality - Five most common items by hit on the BAWE corpus
Item
5 Items Selected (Below)
as a result
as a consequence ofBecause of thismay have contributed toowing toTotal Hits
1570
1287
83
103
14
83

Appendix B:
Selection process in stage two sample.
Giving Examples
Item
A/An * example of * is
For example
This is exemplified
Another example ofIs further exemplified
An example of this isHas been exemplified in
Seen in the case of
Is a good illustration of
Illustrates this point
Can be illustrated
By way of illustration
Illustrate that
Such asSupports the view that

Total Hits Frequency
Per Million
20
2.4
3348
401.6
12
1.44
66
7.92
1
0.12
55
6.6
0
0
6
0.72
2
0.24
3
0.36
26
3.12
5
0.6
134
16.08
6926
830.8
23
2.76

Keith J. Lay & Mehmet A. Yavuz

This * demonstrates

191

23

2.76

Appendix C:
Sample from the pre/post-testa) stems fromb) is similar toc) and furthermore

1. The next point to consider relates to the aims of the spells ___________________ the olderd) is linked toe) may causef) in contrast to
Egyptian spells.
2. This concern with the implications mimesis has for the search for truth ______________________
Plato's hierarchical conception of society.

3. Never apply ice directly onto the skin, as this ______________________ tissue damage.
4. What should be noted, however, is that experiences were diverse, with differing experiences between, as
well as within states, ______________________ between different social groups.
5. The accessibility awareness at the Hippodrome ______________________ that at the Playhouse.
6. It is though very unlikely that it ______________________ human selection pressures, as human-tohuman transmission does not occur.
Appendix D:
Concordance for ‘which leads to’.
Possible hypotheses for this concordance include: The item is used to connect ideas, the first idea causes
the second idea (desired hypothesis), the ideas are/are not opposed, the second idea adds detail, the second
idea changes the first and so on.

An advantage of using qualitative methods is that we can study behaviour in the context of which
it occurs which leads to a greater understanding to the meaning behind the behaviour (Billig,1997).
There is a reduction in an individual's self-restraint and normative regulation of behavior whichleads to a newfound obedience to any form of authority.
The trophoblasts invade the tubal wall, weakening it which leads to bleeding.
However, we can never observe all instances of a phenomenon, which leads to infinity.
He employs such verbs as 'punir...réfuser...détourner...abuser'to describe her behaviour and his
sentiments include 'tendre...l'amour...l'amitié'. This is an effective technique and Madame de
Tourvel genuinely wrestles with her conscience and eventually capitulates which leads to her
perfidy.
Thus it is not surprising that activation of the phototransduction pathway functions in a similar
way to inhibition of the olfactory transduction pathway, which leads to rapid desensitisation of
olfactory neurons in case of high intensity or prolonged presence of the odourant.

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However, this gradient only holds true for the continental crust, a vital error which leads to an
overestimation of the temperature at the centre by a magnificent factor of forty.
If the economy is actually facing deflation and is in a liquidity trap, output below the natural level
leads to more deflation over time, which leads to a further increase in the real interest rate andleads to a further shift of the IS curve to the left.
This shift leads to a further decrease in output, which leads to more deflation, as shown below.
This has been proven by blocking adenosine receptors which leads to the increase in the rate ofethanol release to the water (Nilsson, 2001).
There are a number of methodological flaws when examining "Islam" which leads to limitationsin its findings and conclusions.
They find that 60% of file sharers use downloading to sample music, which leads to them buying
at least one CD.
Appendix E:
OHE Based Plan
Warm-up: Greet learners, introduce the task of the lesson – show sheets that will be studied. Use instructions
as a guide (below).
Observe: Give learners first sheet, encourage observations.
Hypothesize: Allow students to hypothesize on their own first then share their ideas. Encourage debate andrationalization.
Experiment: Encourage learners to produce utterances that test their hypotheses. Allow learner to learnercorrection and commenting. Only clarify when they absolutely need.
Repeat process for each item.
Instructions Guide(given orally, teacher monitoring and repeating instructions to individuals to check comprehension andcompliance)
Before they look:
I am going to give you some lists.
The lists have examples of some of the phrases from the original test.
The language in the examples is very high level academic English.
Please don’t worry about words you don’t know or examples you don’t understand.
Please don’t use dictionaries or the internet to look up the phrases or words you don’t understand.
After giving:
Look at the examples.
Think of as many different things that you notice.

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Are the examples similar?
Do similar words appear?
Do similar parts of speech appear (nouns, verbs, prepositions, etc.)?
How are the parts that go before the phrase similar?
How are the parts that go after the phrase different?
If you wanted to change the phrase for a different phrase or word, what would you change it to?
Do you think the phrase shares the same/a similar meaning to something you already know?
How could you describe the phrase to help a friend understand it?

About the Authors
Keith John Lay is a teacher educator at Cyprus International University in Northern Cyprus. He
gained his BA in Criminology from the Department of Sociology, School of Social Sciences,
Nottingham Trent University, in 2000. He earned his CELTA at Gloucester College, UK, in 2010,
marking the start of his teaching English career. Earning his MA in English Language Teachingin 2016 and being awarded his PhD in 2020, both at Cyprus International University, his areas ofinterest include data-driven learning, corpus analysis, language assessment, higher education, andnonverbal communication.
Email: klay@ciu.edu.tr
Dr. Mehmet Ali Yavuz is a professor of linguistics and language teaching at Cyprus International
University in Northern Cyprus. He earned his degrees (BA, MA, and PhD) in English Language
Teaching. He taught at Dokuz Eylül University in Turkey. His areas of interest include error
analysis, contrastive analysis, syntactic analysis, and semantic and pragmatic issues in humancommunication.
Email: mayavuz@ciu.edu.tr