Journal of Pragmaticsdistinct communicative purposes (e.g., more code glosses in academic writing; more boosters and engagement markers in colloquial writing). Finally, the use of

Journal of Pragmatics 139 (2019) 22e39

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Journal of Pragmatics

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Metadiscourse: Variation across communicative contexts

Wenjuan Qin a, b, *, Paola Uccelli b

a Fudan University, College of Foreign Languages and Literature, 220 Handan Road, Literal Arts Building 431, Shanghai, 200433, Chinab Harvard University, Graduate School of Education, 14 Appian Way, Larsen Hall 302, Cambridge, MA 02138, USA

a r t i c l e i n f o

Article history:Received 15 February 2018Received in revised form 8 October 2018Accepted 10 October 2018

Keywords:MetadiscourseAcademic writingColloquial writingEnglish as a Foreign Language (EFL)Communicative contexts

* Corresponding author. Fudan University, CollegeChina.

E-mail address: [email protected] (W.

https://doi.org/10.1016/j.pragma.2018.10.0040378-2166/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t

Writers interact with readers using explicit signals of textual organization (e.g., firstly;however) and stance (e.g., possibly, surprisingly), known as metadiscourse markers(MDMs). Most metadiscourse studies, however, focus on academic writing exclusively,with minimal efforts comparing MDMs usage across communicative contexts. The presentstudy examines English-as-Foreign-Language (EFL) learners’ use of MDMs in academic andcolloquial writing. Each participant produced two texts on the same topic: a personal emailto a close friend (colloquial) and an academic report to school principals (academic). Thecorpus of 704 texts were coded using Hyland’s metadiscourse model (2005). Trained EFLteachers scored texts for overall writing quality. The study first presented a distributionalmap of MDMs used in the EFL academic and colloquial writing corpora. Then, multi-levelmodels revealed both similarities and differences in the use of MDM subtypes to servedistinct communicative purposes (e.g., more code glosses in academic writing; moreboosters and engagement markers in colloquial writing). Finally, the use of MDMs wasanalyzed in relation to overall writing quality within and across communicative contexts.The results highlighted the strengths and weaknesses in EFL learners’ use of MDMs andinformed the design of pedagogical approach attuned to the pragmatic functions of MDMs.

© 2018 Elsevier B.V. All rights reserved.

1. Introduction

A recent research report from the British Council estimates that 750 million people are learning English as a foreignlanguage (EFL) worldwide (British Council, 2014). These learners are expected to be taught and prepared to become not onlyskilled test takers, but also competent language users capable of communicating with different audiences and for differentpurposes. However, while enormous efforts have been dedicated to improving EFL learners' standardized English proficiencyscores (e.g., TOEFL, IELTS), less attention has been paid to understand their strengths and weaknesses in navigating variousacademic, professional, and social contexts in the real world (Cheng, 2008; Choi, 2008). The present study attempts to un-derstand this less studied area through the lens of metadiscourse in writing.

Writing is a process of social engagement in which the writers interact with an imagined or real audience through thepurposeful use of language. For instance, writers may use explicit signals of textual organization (e.g., first of all, in other words,in conclusion) and stance (e.g., it is possibly true that…; surprisingly; in my opinion) based not only on their own viewpoints, butalso on their projection of the perceptions, interests, and needs of a potential reader. These signals, also called metadiscoursemarkers (MDMs), refer to the linguistic resources employed by writers to “help readers to organize, interpret and evaluatewhat is being said” (Hyland, 2017, p. 17). Attending to MDMs is useful in analyzing writing across communicative contexts

of Foreign Languages and Literature, 220 Handan Road, Literal Arts Building 431, Shanghai, 200433,

Qin).

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W. Qin, P. Uccelli / Journal of Pragmatics 139 (2019) 22e39 23

because they reflect how writers project themselves as well as their readers into the discourse that they construct. Thus,studying MDMs allows for an analysis of writing as social engagement, which goes beyond conceiving writing just as anexchange of information. UsingMDMs appropriately can transformwhat may otherwise be a lifeless text into a discourse thatresponds to the needs of the communicative context.

In recent years, metadiscourse has attracted increasing attention from researchers who focused on writing in both nativeand later acquired languages (€Adel, 2006; Uccelli et al., 2013; Hong and Cao, 2014; Hyland, 2017). A brief review of theliterature, however, has revealed a few important gaps in the research conducted so far. First, the majority of metadiscoursestudies have focused on an academic register, such as research articles (Gillaerts and Velde, 2010; Rubio, 2011), textbooks(Hyland, 2004), and academic essays (€Adel, 2006). While this line of research was highly informative, it has not addressed thevariation of MDMs in different contexts, such that MDMs used in a personal email could be dramatically different from thosein an academic essay. Based on the sociocultural theories of language acquisition, language learning is the result of in-dividuals' socialization and enculturation into certain discourse communities (Ninio and Snow, 1996; Ochs, 1993; Uccelli andPhillips Galloway, 2017). That is to say, being a skilled language user in some social contexts does not guarantee languageproficiency in other contexts. Thus, it is not unusual to see learners who can conduct fluent colloquial conversations/writingstruggle with academic writing, or vice versa. Second, previous metadiscourse studies have mostly used corpora of writtentexts composed only by adult language users (e.g., university students or academic scholars) (Crosthwaite and Jiang, 2017; Leeand Casal, 2014; Lee and Deakin, 2016; Wu, 2006, 2007). Less is known to date about how language learners at various agesand proficiency levels deploy the forms and functions of MDMs in writing. Compared to adult EFL learners, adolescent EFLlearners are an especially understudied population. Adolescence, however, represents a critical period for language devel-opment, as language learners develop new skills to navigate an increasing number of social contexts (Berman and Ravid,2009). That is why, in the present study, we have included high school EFL learners in the sample. Finally, though a num-ber of studies explored the association between using MDMs and writing quality (Dobbs, 2014; Lee and Deakin, 2016; Wu,2007), how this relation may differ across different register elicitation conditions remains understudied. For instance,certain subtypes of MDMs might matter more in academic writing than colloquial writing, or the other way around.

To begin to fill these research gaps, the present mixed-methods study compared the use of MDMs in 352 academic essayswritten to school principals (academic register condition) and 352 personal emails written to a close friend (colloquialregister condition) composed by a sample of EFL learners with diverse sociocultural backgrounds, different ages/levels ofeducation and various English proficiency levels. The study was driven by three goals: 1) to present an empirically baseddistributional map of MDMs used in an EFL learner corpus of academic and colloquial writing; 2) to identify individualvariability in MDMs across register conditions; 3) and to explore the predictive relations between MDMs and overall writingquality within and across register conditions.

2. Literature review

2.1. Defining metadiscourse

The termmetadiscoursewas first introduced by Harris (1959) to refer to the way in which language is used by the writer orspeaker to guide a receiver's perception of a text. The conceptwas later refined and operationalized by scholars including VandeKopple (1985), Crismore (1989), Williams (1997), and more recently Hyland (2005), as well as €Adel and Mauranen (2010).Metadiscourse has been frequently related to or understood as synonymous with other terms, including but not limited tometalanguage (Jaworski et al., 2004), metatalk (Schiffrin, 1980), discourse reflexivity (€Adel, 2006; Mauranen, 2010) and meta-pragmatics (Caffi, 2006). Researchers utilizing these terms tend to focus on different aspects of metadiscursive analysis, andtherefore, have not reached consensus on a single precise definition. The core conceptualization of metadiscourse, and whatresearchers commonly agree on, centers on discourse about discourse. The present study, combining insights from previousconceptualizations (Crismore et al., 1993; Hyland, 2005, 2017), defines metadiscourse as the non-propositional linguistic re-sources employed by writers to help their readers understand the organization of a text and the writer's stance towards the message.

While some analysts have narrowed the focus of metadiscourse to features of either textual organization (Mauranen,1993;Valero-Garces, 1996) or textual stance (Hong and Cao, 2014; Yoon, 2017b; Zhao, 2013), the present study explores both di-mensions of metadiscourse use, adapted from Hyland's (2005) models of metadiscourse: 1) organizational markers (inter-active markers in Hyland's model), those markers that guide the reader through the discourse structure of the texts byexplicitly signaling relationships between ideas, clauses, and paragraphs; and 2) stance markers (interactional markers inHyland's model), those that add evaluative viewpoints on what is being said.

2.2. A pragmatic view of metadiscourse

The role of metadiscourse as a resource that connects the writer, the reader, and the message makes it a central concept inpragmatics. The appropriateness of metadiscourse use is crucially dependent on the rhetorical expectations of a specificcommunicative context (Hyland,1998). For instance, inacademicdiscourse,writers are typicallyexpected touse “stepwise logicalargumentation explicitly signaled by organizational markers” and “impersonal or authoritative stance that […] requires a non-dialogical and distant construction of opinion” (SnowandUccelli, 2009, p.118). On the other hand, an informalmessage betweenfriendsmight involve loose flow of information and personal stance that conveymessages in an affective and dialogicalmanner.

W. Qin, P. Uccelli / Journal of Pragmatics 139 (2019) 22e3924

Misunderstanding of context-specific rhetorical expectations may lead to the lack of or overuse of certain subtypes of MDMs,resulting in ineffective communication. It is critical to acknowledge that academic and colloquial language should not be viewedas a binary set of two completely distinct categories (Uccelli et al., 2015; Uccelli et al., in press). Similarly, MDMs should not becategorized as being either “colloquial” or “academic”. Understanding how MDMs are used across academic and colloquialregister elicitation conditions is, therefore, a critical step in understanding the continuum of pragmatic functions of differentMDMs e i.e., markers used more frequently in colloquial contexts versus those used more frequently in academic contexts.

So far metadiscourse studies have been conducted on a very narrow range of registers (see detailed review in Hyland,2017), with the vast majority of studies focusing on an academic register. A dominant number of researchers analyzedpublished research articles (Abdollahzadeh, 2011; Dahl, 2004; Gillaerts and Velde, 2010; P�erez-Llantada, 2010; Rubio, 2011).Other studies have focused on postgraduate theses (Kawase, 2015; Soler-Monreal et al., 2011), textbooks (Hyland, 2004) andacademic essays written by second or foreign language learners (€Adel, 2006; Crosthwaite and Jiang, 2017; Hong and Cao,2014; Intaraprawat and Steffensen, 1995; Li and Wharton, 2012; Rustipa, 2014; Simin and Tavangar, 2009). These studieshave repeatedly shown metadiscourse to be a prevalent linguistic resource that facilitates writers' communication with theirreaders in the academic discourse community. Interestingly, even within the academic register, researchers have foundvariation in writers' use of MDMs across genres, disciplines and modalities. For instance, Hyland (1999) found that authorsuse different subtypes of MDMs in textbooks and research articles to represent themselves, organize arguments, and signalattitude. Hyland (2010) also compared postgraduate students' use of MDMs across six disciplines (e.g., Electronic Engineering,Biology, Applied Linguistics, etc.) and identified different means of persuasion across disciplines. In comparingmetadiscourseuses in 30 spoken university lectures and 130 essays by highly proficient graduate students, €Adel (2010) revealed bothsimilarities and differences in the distribution of metadiscourse functions across modalities.

While there are a few studies comparing discourse markers in written and spoken languages (e.g., Katzenberger, 2004;Ravid and Berman, 2006), only two studies reviewed so far have particularly concentrated on comparing formal andinformal written registers (Qin, 2018; Qin and Uccelli, under review; Zhang, 2016). Zhang (2016) compared themetadiscourseused in corpora of academic prose, fiction, and journalistic prose, and concluded that MDMs were more pervasive in moreinformational registers (e.g., academic prose, general prose, and editorials), whereas they were relatively rare in narrativeregisters (e.g., fiction and press reports). On the other hand, another study comparing adolescent and adult EFL learners' use ofMDMs in academic and colloquial writing found no cross-register differences in the total frequencies of organizationalmarkers and higher frequencies of stance markers in the colloquial register (Qin, 2018; Qin and Uccelli, under review). Thisfinding intrigued authors to further explore how cross-register differences might or might not exist in subtypes of MDMs.

2.3. Metadiscourse and writing quality

One of the primary purposes of using MDMs is to signal the textual organization and stance in a way that facilitates thecomprehension and evaluation of the text ideas by its readers (Hyland, 2005). From a language learning perspective, if EFLwriters learn to use MDMs appropriately, then MDMs should function to enhance the clarity, coherence, and ultimately, theoverall writing quality of texts. Empirical research investigating the relations between the use of MDMs and writing quality,however, have yielded mixed findings. A number of studies have identified positive relations between a variety of meta-discourse measures and overall writing quality. For instance, Intaraprawat and Steffensen (1995) compared the use of MDMsin good and poor undergraduate ESL essays, reporting that good essays showed a greater diversity of MDMs than the pooressays. Lee and Deakin (2016) found high-rated essays contain significantly greater instances of hedges than low-rated essays,in both L1 and ESL essays written by university students. Similarly, Uccelli et al. (2013) examinedMDMs used in native Englishspeaking high schoolers' persuasive essays, and found that frequency of organizational markers as well as epistemic hedgessignificantly and positively predicted writing quality. Other studies, however, have reported mixed findings. For instance, in astudy of metadiscourse use in undergraduate Chinese EFL learners, no significant associationwas found between frequency ofMDMs and writing quality for lower-proficiency L2 writers, but a slightly positive association was found for higher-proficiency L2 writers (Xu and Gong, 2006). In a large sample of 6th to 8th graders in the U.S., Dobbs (2014) found thatthe use of two subtypes of organizational markers (i.e., evidencemarkers and code glosses) were negatively related towritingquality. Moreover, the variety of stance markers was not predictive of writing quality for longer essays. In a longitudinal studyof metadiscourse development, Crosthwaite and Jiang (2017) found a rise in EFL learners' use of hedges but an overallreduction in the use of boosters and self-mentioning as a result of instruction in English for Academic Purposes (EAP).Similarly, Galloway et al. (in press) found negative relations between the use of self-mentioning and writing quality.

We hypothesize that the mixed findings could be attributed to three factors that have not been fully addressed in previousresearch. First, writers' proficiency level in the target language may play a critical role in the relations betweenmetadiscourseuse and writing quality, such that more proficient language learners could more skillfully use these linguistic markers to adegree that enhances the overall writing quality, while less proficient learners might demonstrate less skillful or redundantuses (Dobbs, 2014; Xu and Gong, 2006). Second, most studies treated metadiscourse as a single index by summing up theconstellation of markers. However, investigating subtypes of MDMs (e.g., code glosses, hedges) might contribute to revealmore specific associations between specificMDMs use andwriting quality (Crosthwaite and Jiang, 2017; Dobbs, 2014). Finally,all studies reviewed above analyzed the relations between MDMs and writing quality only in academic argumentativewriting. This study advances prior research by examining whether the relations vary across academic and colloquial writing.

The current study will be guided by the following three research questions:


1) What MDMs are used in EFL learners' writing and how are they distributed across the academic and colloquial learnercorpora?

2) Does individual EFL learners' use of MDMs differ by register? If so, does the cross-register difference vary by learners'characteristics (i.e., English proficiency or educational level)?

3) Is the use of MDMs associated with overall writing quality? Does the association vary by register and/or learners' Englishproficiency?

3. Methods

3.1. Sample

Data of the present study were collected as part of a larger research project collaborated with a language educationinstituted that ran language immersion programs worldwide. Participants enrolled in the research project on a voluntarybasis; and all participants and parents (if under 16 years) signed a consent form before completing the assessments. Thesample consists of 352 adolescents and adults enrolled in two English immersion programs in the U.S. or U.K. The programsused standard curricula appropriate for various proficiency levels. All participants completed the assessments during theirfirst week in the language immersion program to minimize the instructional effects. They were considered EFL learnersbecause their English was acquired in countries where English was not a primary language (e.g., China, Mexico, France), andthey self-reported having had limited prior exposure to native English environments. Participants included 142 highschoolers (40%), 155 undergraduates (44%) and 55 graduate students (16%). The sample had a slightly larger proportion offemales (64%) than males (36%). Three native language groups were represented in the sample: 74 Chinese speakers (21%), 95French speakers (27%) and 183 Spanish speakers (52%).

We measured learners' English proficiency using a 50-min standardized English proficiency test (EFSET, 2014). EFSET hasan overall reliability coefficient of 0.94, which is comparable toTOEFL iBT (a ¼ 0:85). The test measures EFL learners' listeningand reading comprehension in English. It uses a computer multi-stage adaptive test design, whereby the difficulty level of thetest content is adjusted in real-time according to the test takers' performances. The EFSET score scale ranges from 1 to 100 andassigns students to corresponding levels of the Common European Framework of Reference for Language (CEFR). Participants'English proficiency ranged from basic (A1/A2: 21%) or intermediate (B1/B2: 56%) to advanced levels (C1/C2: 23%).

3.2. Corpus description

The total corpus contained 704 texts (135,972 words) written by the 352 EFL learners. Each participant produced twotexts: one in response to an academic register condition and one in response to a colloquial register condition. Data werecollected on the same day in a computer lab using a previously piloted instrument e the Communicative Writing Instrument(CW-I) e that was designed by the author to examine EFL learners' writing performance across communicative contexts(Table 1). Students were given 50 min to complete the assessment, and they were free to distribute the time between the twoscenario-based writing tasks. The tasks required students to write two persuasive texts on the same topic, but certain factors(i.e., participants, social status and channel of communication) (Biber and Conrad, 2009) in the scenarios weremanipulated toreflect the distinct language requirements in colloquial and academic contexts.

Table 1Designing framework of the communicative writing instrument (CW-I) (adapted from the register analysis framework in Biber andConrad, 2009).

Colloquial Academic

Participants FriendeFriend Student - PrincipalsSocial status Close and equal Distanced and hierarchicalChannel Personal email Argumentative essay in academic reportPurpose PersuasiveTopic Whether students should take a gap year from regular school work to participate in a

study-abroad program?

Half of the sample was randomly assigned to write the colloquial text before the academic texts, whereas the other halffollowed the reversed order. Participants with only one response were dropped from the sample. Therefore, the final corpuscontained a balanced sample of 352 academic texts (65,293 words) and 352 colloquial texts (70,679 words).

3.3. Research measures

Texts were originally typed on a digital platform and exported into plain text files. To ensure accurate linguistic featuretagging and reduce the possibility of bias in human coding/scoring, we removed all mechanical mistakes, including theunconventional use of spellings, capitalizations, and punctuations, and saved the cleaned essays in separate files. We inte-grated automatic computer linguistic analysis, using programs such as CLAN, SiNLP and AntConc, with human coding/scoringto generate a series of linguistic and quality measures.


3.3.1. Text length and lexico-syntactic measuresUsing CLAN (MacWhinney, 2000), five linguistic indices were generated automatically to measure the length of text and

basic lexico-syntactic features of texts.

� Text length was measured by the total number of words.� Lexical diversity was measured through the widely used VocD measure. (McKee et al., 2000; Yoon, 2017a; Qin et al., inpress).

� Lexical densitywas measured by the proportion of content words (i.e., nouns, verbs, adjectives, adverbials) per 100 words(Ure, 1971).

� Mean Length of words was measured by the mean proportion of polysyllabic words, i.e., three or more syllables, out oftotal words per text (Berman and Nir-sagive, 2007; Read, 2000).

� Syntactic complexity was measured by words per clause. This syntactic measure was selected because it showed thestrongest association with writing quality in the current corpus as well as in previous research compared to other mea-sures (e.g., words per sentence/T-unit, clauses per T-unit) (Berman and Nir-Sagiv, 2009; Biber et al., 2011; Lu, 2011; Wolfe-Quintero et al., 1998).

3.3.2. Writing quality measureEach textwas scored forwritingqualityusing the6þ1Trait®Writing rubric. Four experiencedEFL practitionerswere trained

to score texts' overall writing quality. The quality scores ranged from 1 to 6. Scorers weremade aware of the different demandsexpected in each of the two writing tasks and the rubric includes the assessment of “whether the text elicited appropriate in-formation and language style to address the specific audiences”, and “whether it is effectively persuasive in this particular commu-nicative context”. Scorerswere also providedwith a packet of prototypical examples, selected by an experienced native-English-speaking scorer and a senior researcher, which represented different levels of writing quality in both academic and colloquialregisters. The writing quality measure was comparable across registers in that, for instance, a 6-point academic essay and a 6-point personal email both represented thebest possiblewritingperformance in thecorresponding context in thecurrent corpus.Moreover, scorers were blind to the research objectives and coding scheme of linguistic features. All texts were doubly scored.Following standard SAT scoring practices, scores with exact or adjacent agreements were added up to form the final score,resulting in a final scoring scale from 2 to 12. When the difference between two scorers' evaluation was more than 2 points, athird scorer intervened to resolve the disagreement. Formative reliability was calculated throughout the scoring process (afterscoring 20%, 50% and 100% of the samples) to ensure at least 90% of adjacent or exact agreement between scorers.

3.3.3. Metadiscourse markers (MDMs)We analyzed two dimensions of metadiscourse function adapted from Hyland's (2005) metadiscourse model:

1) Organizational markers: language resources used to organize propositional information in ways that support a targetaudience's understanding of a text as logical and coherent.

2) Stance markers: language resources used to express authors' viewpoint by explicitly commenting on the message usingevaluative language.Both organizational and stanceMDMswere further classified into subtypes following the coding scheme in Hyland (2005).The full list of MDMs codes applied is described and illustrated in Table 2.

Table 2Coding scheme of subtypes of MDMs (adapted from Hyland, 2005).

Category Function Examples

Organizational markersFrame markers to sequence, label, predict and shift arguments on the other hand; in conclusion; finallyCode glosses to supply additional information by rephrasing, explaining or elaborating what has

been saidfor example; in other words; defined as

Transitions to signal additive, causative and contrastive relations between main clauses in addition; because; thoughStance markersHedges to acknowledge alternative voices by implying that a statement is based on the

writer's plausible reasoning rather than certain knowledgepossible; might; as far as I am concerned

Boosters to confront alternative voices by expressing their certainty in a single, confidentvoice

obviously, definitely, it has been shown…

Attitude markers to convey affective, rather than epistemic, attitude towards propositions, such assurprise, agreement, importance, obligation, frustration, etc.

surprisingly, unfortunately, important

Engagement to explicitly address readers, either to focus their attention or include them asdiscourse participants

you, your, inclusive we, should,have to, must

Self-mention to explicitly mention author presence in the text via first-person pronouns andpossessive adjectives

I, me, my, our, us, exclusive we

*Note: Hyland's (2005) original metadiscourse framework proposed more subtypes of MDMs than those selected in the present study. Endophoric markersand evidential markers were not selected because of their scarce appearance in the current corpus.


Some researchers have expressed concerns regarding the commonly adopted metadiscourse coding approaches, whichheavily rely on “counting surface linguistic forms rather than analyzing discourse functions of linguistic markers” (€Adel andMauranen, 2010; Hyland, 2017). Thus, we conducted a fine-grained coding approach to make sure that forms were notidentified asMDMs unless they served aMD function. First, all possible forms of MDMwere retrieved by SiNLP (Crossley et al.,2014) using a pre-defined list of lexical terms (e.g., however; in other words, possible) identified as MDMs in large corpusstudies and adapted fromHyland (2015). Second, using concordance lines in AntConc (Anthony, 2016), all retrieved individualwords and phrases were carefully examined by two trained human coders in their sentential contexts to ensure they wereperforming metadiscourse functions. For certain multifunctional MDMs (e.g. must could serve as a hedge or an obligationmodal that express engagement), the coders made the judgment based on the meaning in the context. Coders were blind tothe research objectives and the writing quality scoring rubric. The inter-rater reliability between the two human coders wask ¼ 0:91.

3.4. Data analytic approach

We started by examining the total frequencies of all MDMs used in the learners' corpus and ranked them on a distribu-tional map from being used “more frequently in the colloquial context” to “more frequently in the academic context”. Afterexamining the distribution of variables, we conducted multi-level Poisson modeling to address the second research questionbecause theMDMmeasures are count variables with strongly skewed distribution.We used subtypes ofMDMs aswell as totalfrequencies/diversity as the outcome variables, register as the within-subject covariate and learners' characteristics (i.e.,English proficiency, education level) as the between-subject covariates. To answer the third research question, we firstchecked the bivariate relations between each subtype MDM and writing quality. Markers that showed non-linear relationswith writing quality were transformed to meet the regression assumptions. The variety of lexico-syntactic measures werecombined into a single composite via a Principal Component Analysis. Next, we built a series of multi-level linear modelsusing writing quality score as the outcome variable, English proficiency level, text length and lexico-syntactic composite asthe control variables, and then entered the question predictors (e.g., MDM subtypes and total frequency/diversity) one at atime. Finally, we examined the interaction between each predictor and register/English proficiency level to see if the pre-dictive relations vary.

4. Results

4.1. A distributional map of MDMs across learners' registers

Across the entire corpus, higher frequencies of organizational markers and stance markers occurred in EFL learners'colloquial writing compared to academic writing (Table 3). Such discrepancies were manifested in all subtypes of markers,except for code glosses. On the contrary, the academic writing corpus displayed a slightly higher diversity of organizationalmarkers. A distributional map of all forms of MDMs identified in both corpora is presented in the Appendix and illustrated inFig. 1. EFL learners' use of MDMs seemed to rely heavily on a small subset of metadiscourse forms with minimal use of thewider constellation of options. For instance, only three types of transitionmarkers, i.e., because, but and also, werewidely usedin learners' writing and accounted for over 83% of all transition markers in the academic corpus and 87% in the colloquialwriting. Similarly, the use of hedges was limited to four marker types (could, may, maybe, might), accounting for 73% of hedgesin the academic corpus and 76% in the colloquial corpus. Though the overall frequency of organizational and stance markerswas comparable across the academic and colloquial corpus in most cases, some subtypes of markers were used more often inone register. As shown in Fig. 1, markers listed on the left side of the continuum (in blue) were used more frequently inparticipants' colloquial writing (e.g., second, secondly, also, but), whereas some others were used more frequently in partic-ipants' academic writing (e.g., whereas, moreover, however). It is interesting to note that some markers that prior research has

Table 3Overall frequency and diversity of organizational and stance markers across the academic and colloquial corpora.

Freq. per 1000 words Diversity

Academic Colloquial Academic Colloquial

Organizational markers 25.45 26.41 69 54Frame markers 3.80 4.57 25 18Code glosses 2.66 1.81 12 9Transition markers 18.99 20.03 32 27

Stance markers 79.91 157.14 70 70Hedges 7.78 8.56 22 22Boosters 4.66 6.10 15 13Attitude 3.40 3.24 11 13Engagement 40.68 101.02 16 16Self-mentioning 23.39 38.22 6 6

Fig. 1. The Distributional Map of MDMs used in EFL learners' writing: similarities and differences between the academic and colloquial corpora. The position onx-axis indicates the relative frequencies across registers e markers used more in colloquial texts are to the left, and likewise markers to the right were used morein academic texts. The numbers on the bottom of the graphs indicate the absolute differences across registers (per 1000 words).



considered more academic in experts' writing were used more frequently in EFL learners' colloquial texts (e.g., first/firstly,second/secondly, in contrast, on the contrary).

4.2. Individual variability in using MDMs across learners' registers

Table 4 summarizes descriptive statistics and statistical tests of cross-register variation for all variables investigated inindividual writings. The average number of organizational markers was 4.86 per text in academic writing, and, somewhatsurprisingly, slightlymore in colloquial writing (5.22 per text). Similarly, no significant differencewas found in the diversity oforganizational markers by register. Yet, looking at subtypes of MDMs, the estimated ratio of coded glosses (e.g., for example)was 60% more in academic writing than in colloquial writing (irr ¼ 1:60; p<0:001). Colloquial writing contains a slightlyhigher number of frame markers and transitions, but neither of these differences was statistically significant. On the otherhand, both frequency and diversity of stance markers were significantly higher in colloquial writing than academic writing,with an estimated difference of 49% in total frequency (p<0:001) and 25% in diversity (p<0:001). The cross-register dif-ference was mainly manifested in the use of boosters, engagement markers and self-mentioning.

Table 4Cross-register variation in text length, subtypes and total frequencies of organizational markers and stance markers.

Academic Colloquial Irra

Freq. per Text Range Freq. per Text Range

Word token 197.43 40e459 188.71 52e552 e

Organizational markers frequency 4.86 0e17 5.22 0e19 0.97Organizational markers diversity 3.36 0e13 3.46 0e10 1.01Frame 1.31 0e9 1.54 0e13 0.89Code glosses 0.56 0e4 0.37 0e4 1.60*

Transition 2.99 0e12 3.32 0e14 0.95Stance markers frequency 16.61 0e66 33.98 2e95 0.51*

Stance markers diversity 5.76 0e15 8.02 1e18 0.75*

Hedges 1.47 0e14 1.69 0e9 0.92Boosters 2.41 0e14 4.16 0e17 0.61*

Attitude 0.64 0e4 0.64 0e5 1.06Engagement 8.84 0e42 20.72 0e53 0.44*Self-mentioning 3.25 0e34 6.77 0e34 0.51*

*p < 0.004.ba Incidence-rate ratio (irr) was estimated using multi-level Poisson modeling with each subtype of MDM as the outcome variable and register as the

within-subject covariate. The total number of words was used as the exposure factor in the Poisson models. Thus, the irr coefficient indicates the ratio of aparticular subtype of MDM in academic writing in comparison to colloquial writing. For instance, the coefficient for code glosses (1.60) indicates that theestimated incident-rate ratio for code glosses was 60% more in academic writing than colloquial writing.

b Given that we are investigating twelve measures and therefore performing twelve tests on the same dataset simultaneously, we employed the Bon-ferroni correction to avoid spurious positives. This sets the alpha value for each comparison to 0.05/12, or 0.004.

To further test whether the cross-register patterns found above held for all types of EFL participants, we conducted afollow-up analysis to test interactions between register and learners' characteristics (i.e., native language, English proficiencyand educational level). In this analysis, we found a significant interaction between register and educational level for the fre-quency of hedges. As shown in Table 5 and Fig. 2, while high schoolers and undergraduate students used more hedges incolloquial writing, graduate students used more hedges in academic writing. The interactionwas significant even controllingfor learners' English proficiency. No other interactions were detected.

Table 5Multi-level Poisson models describing the cross-register differencesin using MDMs varied by learners' educational background.

HedgesFixed effects

Register (Academic) 1.10English proficiency 1.13***

Educational levelHigh school 0.96College 0.97

InteractionAcademic � High school 0.78*

Academic � College 0.77*

Intercept 0.01***

Random effectss2u 0.25***

Goodness of fitLog likelihood �923.03

*p < 0.05; **p < 0.01; ***p < 0.001.

Fig. 2. Cross-register variation in hedges differed by educational level.


4.3. Relations between MDMs and writing quality

4.3.1. Bivariate analysis, PCA and variable transformationWe addressed the third research question by first examining the pairwise correlations between writing quality, lexico-

syntactic features and MDMs frequency and diversity, by register. The four types of lexico-syntactic features demonstratedsignificant positive associations with each other, and a Principal Component Analysis indicated that they load onto a singleprimary composite (Table 6). This composite was used as an important control variable in subsequent analysis.

Table 6Principal component analysis of lexico-syntactic complexity.

Comp 1 Screeplot of eigenvalues after PCA

Eigenvalue 1.80% of variance 0.45Loading of linguistic indicesLexical diversity 0.51Lexical density 0.57Mean length of words 0.42Words per clause 0.49

The total frequencies/diversity of organizational and stance markers, as well as subtype MDMs, were all positively andmoderately correlated with the writing quality scores, but it is necessary to test whether the association exists after ac-counting for length. We also graphed the bivariate relations between each MDM and writing quality. We found that therelations between certain subtypes (e.g., frame markers, hedges) and writing quality appeared to be non-linear, so wetransformed these markers using square root transformations to meet the regression assumptions.


4.3.2. Regression analysisPrior to entering the question predictors, learners' English proficiency levels,1 text length, lexico-syntactic composite and

register (academic vs. colloquial) were entered to construct a baseline model. Not surprisingly, higher writing quality scoreswere associated with higher levels of English proficiency, longer texts, andmore complex lexico-syntactic features. Moreover,academic writing, on average, displayed a lower level of quality than colloquial writing.

Table 7 displayed the significant results when testing the association between MDM subtypes and writing quality con-trolling for holding other conditions constant. Frequency of hedges had a statistically significant interactionwith register (b ¼� 0:36; p ¼ 0:014). That is, the effect of hedges on writing quality varied between academic and colloquial writing. Fig. 3aillustrates this interaction, with a slightly positive slope in the colloquial register but a slightly negative slope in the academicregister condition. Though neither slope was particularly steep, the contrast between them foreshadowed an intriguingpatternworthy of further study. Engagement markers also demonstrated a promising relationship with writing quality. It hada positive and significant associationwith writing quality overall (b ¼ 0:02; p ¼ 0:028) and also a significant interactionwithregister (b ¼ � 0:03; p ¼ 0:05). Fig. 3b illustrates this interaction, showing a positive association in the colloquial context butnot in the academic context. Other subtypes of MDMs were also tested, but none was a significant predictor in either register.No significant interactions were found between the use of MDMs and English proficiency, indicating that the main effectsfound in the analyses held across all proficiency levels in the sample.

Table 7Taxonomy of fitted multilevel models describing the relationship between overall writing quality and subtypes of MDMs, controlling for text length, lexico-syntactic complexity.

M0 M1 M2 M3 M4

Fixed effectEnglish proficiency 0.46*** 0.46*** 0.45*** 0.46*** 0.46***

Text length 1.00*** 0.98*** 0.98*** 0.88*** 0.88***

Academic register �0.88*** �0.88*** �0.53*** �0.69*** �0.31Lexico-syntactic composite 0.29*** 0.29*** 0.30*** 0.34*** 0.34***

Hedges 0.06 0.23*

Hedges � Academic ¡0.36**

Engagement 0.02* 0.03**

Engagement � Academic ¡0.03*

Random effects2u 1.06 1.06 1.06 1.07 1.06s2ε

1.19 1.19 1.18 1.18 1.18icc 0.45 0.45 0.45 0.45 0.45Goodness of fitLog likelihood �1031.53 �1031.33 �1028.31 �1029.14 �1027.27

*p < 0.05; **p < 0.01; ***p < 0.001.Note. The table only displayed significant results. All other MDM subtypes were tested but showed non-significant relationship with writing quality.

Fig. 3. Estimated association between subtypes of MDMs and writing quality.

1 Other learner characteristics (i.e., educational level and native language background) were also entered into the model in a first step, but neithershowed significant associations with writing quality. Thus, they were dropped to achieve more parsimonious models.


Finally, the total frequencies and diversity of organizational markers and stance markers were used as question predictors.As shown in Table 8, neither frequency nor diversity of organizational markers showed significant relationship with writingquality at the 0.05 significance-level. On the contrary, frequency and diversity of stance markers displayed a more promisingrelationship with writing quality with significant interactions with the register. Specifically, frequency/diversity of stancemarkers demonstrated a positive relationship with colloquial writing quality, but slightly negative relationship was found inacademic writing (Fig. 4).

Table 8Taxonomy of fitted multilevel models describing the relationship between overall writing quality and total frequencies/diversity of MDMs, controlling fortext length and lexico-syntactic complexity.

M5 M6 M7 M8 M9 M10

Fixed effectEnglish Proficiency 0.46*** 0.46*** 0.46*** 0.46*** 0.46*** 0.45***

Text length 0.95*** 0.93*** 0.92*** 0.93*** 0.95*** 0.94***

Academic �0.88*** �0.86*** �0.78*** �0.28 �0.83*** �0.08Lexico-syntactic composite 0.29*** 0.27*** 0.32*** 0.31*** 0.30*** 0.30***

Org (Freq) 0.02Org (Dive) 0.07Stance (Freq) �0.01 0.01Stance(Freq) £ Academic ¡0.02*

Stance (Dive) 0.03 0.08*

Stance (Dive) £ Academic ¡0.11*

Random effects2u 1.06 1.05 1.06 1.05 1.04 1.05s2ε

1.19 1.19 1.19 1.18 1.19 1.18icc 0.44 0.44 0.45 0.44 0.44 0.44Goodness of fitLog likelihood �1031.17 �1029.85 �1030.98 �1028.38 �1031.07 �1028.61

*p < 0.05; **p < 0.01; ***p < 0.001.

Fig. 4. Predicted association between Frequency/diversity of stance markers and writing quality.

To summarize, participants' texts demonstrated some patterns of contrast in using subtypes of MDMs to address theacademic and colloquial communicative contexts. Specifically, more boosters, engagement markers and self-mentioningwere found in colloquial writing, whereas more code glosses were found in academic writing. Cross-register variation inthe use of hedges differed by educational level, with graduate students using more hedges in academic writing, while highschoolers and undergraduates showed the opposite pattern. In addition, hedges and engagementmarkers appeared to be twopromising MDM subtypes in predicting writing quality, but the relationship varied by register. Finally, the total frequency anddiversity of stance markers, rather than organizational markers, were significantly associated with writing quality and therelationship also differed between academic and colloquial writing.

5. Discussion

The present study compared the use of MDMs in academic essays written to school principals and personal emails writtento a close friend by 352 EFL learners. The study contributes to the literature by first presenting an empirically based


distributional map of the MDMs identified in an EFL learner corpus of academic and colloquial writing. We demonstrated acontinuum of metadiscourse forms and functions, from those more prevalent in learners' colloquial texts to those moreprevalent in academic texts. Second, the study revealed individual variability in the use of subtype MDMs across registers.While some cross-register patterns were consistent with expectations, others were rather surprising and might be uniquecharacteristics of this specific learner corpus and worthy of further exploration. Salient among these was the lack of cross-register difference in using frame markers and transitions. We will illustrate these quantitative results using specificwriting samples in the following section. Finally, by revealing the contribution of MDMs use to overall writing quality, thesefindingsmade visible to EFL learners and practitioners a repertoire of metadiscourse resources that could be incorporated intoEFL writing instruction across communicative contexts.

5.1. Cross-register variation in using MDMs

5.1.1. Organizational markersAmong the three subtypes of organizational markers investigated, only code glosses were found to vary significantly by

register on average. It is not surprising to see the more prevalent use of code glosses in academic writing, as writers are morelikely to use “rephrasing, explaining or elaborating” (Hyland, 2005, p. 22) to ensure the more “distanced” reader is able torecover the writer's intended meaning. They may, however, feel less motivated to do so when writing to a “close” audience,assuming they have more shared knowledge and background. On the other hand, it is somewhat surprising to find the lack ofdifference in using frame markers and transitions across registers, meaning that EFL learners in the sample used a similar setof linguistic devices to label text stages (first, second), to announce discourse goals (my purpose is…), or to indicate topic shift(resume). Below is an excerpt from the colloquial corpus showing how framemarkers were frequently presented in a learner'scolloquial writing:

Colloquial Writing [Participant #089]

“Hi, my dear friend, I heard that you have a chance to study abroad. I have some advices for you below and I would like youto think about it carefully. There are 3 advantages and a disadvantage for you. First of all it is about your language learning[…] For example, everyone you meet there will be the teacher who teach you the correct way to use this language […] Thesecond thing that I want to talk with you is the culture differences…The last important advantage is about your inde-pendent ability…Overall, I think you should seize this opportunity to improve yourself.”

Looking at the specific markers used, some could be considered on the colloquial side of the continuum (e.g., I would likeyou to…), while others were more academic (e.g., first of all, for example) (Hyland, 2005). Actually, this is not an atypical casein the sample. Across the entire corpus, there were 1.56 incidences of first or firstly in the academic corpus per 1000 words,but 2.37 in the colloquial corpus. Similarly, the normalized frequency of second or secondly was 0.38 in the academic corpusand almost doubled in the colloquial corpus (see Appendix). Other frame markers and transitions, including “on the otherhand, furthermore, accordingly, additionally, on the contrary, in contrast”, which were documented as more frequently used inacademic writing of expert language users (Hyland, 2005) have all shown the opposite pattern e i.e., higher frequencies incolloquial writing. This phenomenon might be explained by Slobin's famous language acquisition principle, such that newforms are first expressed old function and new functions are first expressed by old forms (Slobin, 1973). This sort of naturalinteractive dance between forms and functions, though, may be less smooth in the EFL learning context given the limitedlearning opportunities. For instance, learners might have first acquired the forms of MDMs in EFL classrooms or textbooks,yet not have the opportunities to practice their functions in authentic diverse communicative contexts. While acquiring thelinguistic forms could be as easy as memorizing a formula, it takes multiple exposures to the forms in distinct contexts aswell as explicit instruction to understand when to use them (e.g., the linguistic markers) and how to use themappropriately.

5.1.2. Stance markersEFL learners across the sample used a higher frequency of boosters, engagementmarkers and self-mentioning in colloquial

writing. The high school and undergraduate learners also used more hedges in colloquial writing. The sample of graduatelearners used more hedges in academic writing e the only group aligned with our expected pattern. More prevalent use ofboosters in colloquial writing, to some extent, demonstrated that writers were more likely to express their certainty in whatthey say to a close audience. It was also possible that the essays were written in a short time frame where writers were notgiven a chance to search for evidence from external sources to support the arguments. Therefore, the lack of evidentialsupport might also result in relatively less “confidence or commitment” to the expressed opinions in a more formal academicwriting.

Among all stance markers coded, hedges were believed to be the “most suitable to capture the epistemically cautiousstance” (Uccelli et al., 2013, p. 52), an advanced argumentative skill typically valued in academic register. A variety ofdevelopmental linguistic and cognitive studies have identified a shift from deontic to epistemic stance in adolescents'discourse, which typically refers to the development from a more egocentric or categorical judgment to more relativistic


view that acknowledge multiple perspectives (Berman and Katzenberger, 2004; Crosthwaite and Jiang, 2017; Reilly et al.,2002; Selman, 2003). Hedges were commonly found in academic articles to imply the writer's decision to recognizealternative voices and viewpoints, and therefore open that opinion for discussion (Hyland, 2005). In the current corpus, it isparticularly interesting to view that cross-register variation in the use of hedges differed by learners' educational back-ground, even after accounting for language proficiency. Graduate learners, as the only group who used more hedges inacademic than colloquial writing, might be more socialized into the academic discourse (through the reading of academicarticles, participating in academic discussions, for example) than the younger groups. However, whether this is related tosocio-cognitive maturity or just to the understanding of rhetorical expectations goes beyond the scope of the present study.Future research could further explore the interaction between socio-cognitive and language development during adoles-cence and early adulthood.

The more prevalent appearance of engagement markers and self-mentions in the colloquial context is within the au-thors' expectation. This finding is aligned with the widely known contrast between colloquial and academic stance e withthe former featuring more “involved stance” where the writer could use more personal pronouns (e.g., I went to studyabroad last year and I learned a lot.), imperatives (e.g., Go for it!) and obligation modals (e.g., You should take this oppor-tunity!) to engage the reader and express personal opinions. The academic texts, in contrast, are expected to demonstratemore “distanced stance” with generic or generic/abstract subjects and more epistemic attitude (e.g., Studying abroad couldbe a beneficial experience if students work for it during the time in another country) (Berman et al., 2002). It is also particularlyworthy noting how the same marker performed distinct discourse functions in different registers, such that the inclusivepronouns (we, our, us) were used more frequently in colloquial writing, whereas the exclusive we, our, us were moreprevalent in academic writing.

5.2. Predictive relations between MDMs and writing quality

5.2.1. Organizational markers did not predict writing qualityIn contrast to previous research (Uccelli et al., 2013; Qin and Uccelli, 2016), the present study revealed the lack of sig-

nificance for organizational markers to predict writing quality in either context. In-depth discourse analyses supported thefinding that the overuse or repetitive use of MDMs did not necessarily contribute to higher writing quality overall. Forinstance, the corpus contains an overwhelming number of transition markers (2,656). Many of these were used as simpleclause-level connectives (e.g., because). In some cases, the overuse of transition markers led to essays filled with run-onsentences, for example:

Academic Writing [Participant #128]

“[…] they can discover a new world because of the different culture and this is very good for the students because a lot ofpeople can't discover a new place […] If you know another language it can improve your CV because people think that youknow another culture so that is really good for the students.”

It is worth clarifying that the authors were not trying to claim organizational markers are not important in writing, butrather, EFL learners need guidance to learn how to strategically use them to enhance writing quality. Though we did notidentify statistically significant relationship in the current sample, the diversity of organizational markers did demonstrate anemerging positive association with writing quality, which was worthy of further exploration (b ¼ 0:07; p ¼ 0:087). Thefollowing example illustrates how skillful use of organizational markers might enhance overall writing quality:


“Nowadays, there has been a considerable growth in the popularity of studying abroad […], but does this decision really asbeneficial as most people think it is? Certainly, studying in a different country carries a number of advantages. First of all, itcan help students to improve their language […]. Secondly, since one country's education system cannot possibly cover allthe knowledge, being able to be exposed to two sets of education systems greatly enlarges a person's knowledge in his/herspecialized area, therefore brings him/her more chance in the future. Also, studying in another country allows people toknow the culture of this country better. Not only does this enrich the experience and inner fulfillment of the person himself/herself, but this also helps push the world globalization trend to expand faster. However, I believe that there are still severalpotential problems for […]. For example, two different education systems, languages, and cultures could easily make aperson feel confused […].Moreover, long-term exposure to a completely different culture maymake people think less of theirown cultures. All in all, although studying abroad can be quite problematic, in my personal opinion, the advantages it bringscould still outweigh the disadvantages. That is to say, studying abroad is definitely more of an enrichment than aninterruption.”

Despite the obvious room for improvement, this essay obtained a quality score of 12 points, one of the highest-qualitywritings in the corpus. It contains a diverse repertoire of organizational markers that were purposefully deployed to guidethe readers through the textual organization (e.g., first of all; that is to say; all in all) in a coherent way.


5.2.2. Stance markers predict writing quality: contrast between academic and colloquial writingThe study found two subtypes of stancemarkers were predictive of writing qualitye i.e., hedges and engagementmarkers,

but the relationship was only positive and significant in the colloquial context. Similarly, the total frequency and diversity ofstancemarkers was positively associated with colloquial writing quality, but was negatively associatedwith academic writingquality. This finding suggested the enormous challenges of establishing appropriate authorial voice in academic writing,which many more experienced writers continue to struggle with (Lee and Deakin, 2016; Yoon, 2017b; Zhao, 2010). Thefollowing excerpt illustrates the unskillful use of hedges in an academic essay:


“The possible advantage of studying abroad could be that student could learn variety of skills and abilities related to theeducation field […] but also they could improve aspect such as social ability and perhaps how to interact with others. […]The possible problems that we could find could be: student would have to Skype classes here in our school, andmaybe theycannot afford it.”

The student used a total of 13 hedges in her writing, with each argument or statement hedged at least once. Zhao (2010)observed similar pattern in her study and explained, “her raters tended to associate the overuse of hedges to a lack ofconfidence in the L2 writer, or a lack of a clear stance on a particular topic under discussion” (p.141). This “lack of confidence”feeling described by the raters might be due to the fact that most of the hedges used in the sample text above were marking“probability of a hypothetical situation” (e.g., “they could improve aspect such as social ability”; “maybe they cannot afford it”)rather than “propositional certainty/uncertainty” that are indicative of an epistemic stance (e.g., “in my personal opinion, theadvantages it brings could still outweigh the disadvantages”). In light of this distinction, future research might need todistinguish the different functions of hedges in the coding scheme and analyze their relations to writing quality separately. Itis also worth noting that the writer used an overwhelming number of “could” in his/her writing, which made the authorsquestion whether the marker was a real indicator of stance or just habitual use of language. Interestingly, the colloquialwriting written by the same writer contained only two hedges throughout the rest of the text (possible and might). Thiscontrast might indicate that the hedges were purposefully chosen by the writer to entail an authorial stance that sheconsidered appropriate for this particular context.

These findings highlight the needs to conduct metadiscourse studies in more diverse samples of language learners,especially those at younger ages and emergent language proficiency levels. In addition, the association between MDM fre-quencies and overall writing quality could differ by subtype (e.g., hedges, engagement markers) and register (academic vs.colloquial). These finding indicate that the teaching and learning of MDMs is not a single-ruler formula but deserves explicitreflection on the metadiscourse functions of MDM subtypes as well as their situated communicative contexts.

5.3. Limitations and implications

The current findings should be viewed with consideration of a few limitations. First, the list of possible MDMs wasretrieved from a pre-defined lexical list of markers from Hyland (2005). While lengthy, this list was not comprehensive,omitting MDM forms such as metadiscursive nouns (e.g., fact, analysis) (Jiang and Hyland, 2016) and metadiscursive sen-tences (e.g., Just to give you a map of where we are going) (Mauranen, 2010). Next, the writing tasks were designed to assesslanguage learners' performance in writing across registers, but the single-time prompt-based writing activity has limitationsin capturing the full range of learners' writing knowledge and skills. Thus, it is important to acknowledge that this analysisreflects EFL learners' performance, not their writing proficiency. Future research could further explore the topic using naturallanguage data, such as comparing real personal email messages and academic essays written by the same writers. Finally, thesample of participants of the present study came from diverse educational and English proficiency levels. Though they wereenrolled in the same language education institute at the time when our study was conducted, we were not able to collectinformation about their educational background (e.g., degree of exposure to different English learning contexts, EFL curric-ulum in the local schools, etc.). Future research could more thoroughly explore these factors in relation to writing proficiencyacross communicative contexts.

The study is unique in its comparative lens on metadiscourse analysis across academic and colloquial writing. It extendsprevious research by focusing on EFL learners with diverse English proficiency and educational levels from high school tograduate students. Understanding the strengths and weaknesses of EFL learners' use of MDMs across registers is relevant forthe design of evidence-based EFL writing instruction that prepares learners for the range of communicative contexts of thereal world beyond the classroom. For instance, rather than asking students to memorize a list of MDM forms that theysubsequently apply in drill exercises, teachers could scaffold learners' reflections about and use of MDM forms and func-tions by producing their own texts and comparing others' texts across communicative contexts. Through multiple exposuresto MDMs use in authentic contexts, teachers could highlight which markers are used by skilled writers/texts in specificcontexts to accomplish which functions. Far from a rigid division between colloquial and academic forms, learners need tolearn a wide repertoire of forms and understand how to convey which function in what context. EFL learners ought to beencouraged to express their voices and to flexibly use the language resources but with a solid knowledge of the registerpatterns prevalent in proficient writers.


Acknowledgements

This research was supported by the EF Education First Grant at the Harvard Graduate School of Education. The opinionsexpressed are those of the authors and do not represent the views of the EF Education First. We express our gratitude to Dr.Catherine Snow and Dr. Luke Miratrix for their valuable insights and support to this work. Special thanks are given to thestudents and teachers who participated in the study.

Appendix. Normalized frequencies of MDMs and distributions across registers

Academic corpus: 65,293 words; Colloquial Corpus: 70,679 words.*Frequencies were normalized to be occurrences per 1000 words.

Distribution of Organizational markers in Academic and Colloquial Writing

Frame Markers Aca Col

Sequencing first 1.36 1.97 first of all 0.43 0.40 firstly 0.20 0.40 finally 0.25 0.34 last 0.05 0.16 lastly 0.02 0.01 next 0.02 0.00 second 0.15 0.35 secondly 0.23 0.33 subsequently 0.05 0.00 then 0.14 0.08 third 0.06 0.06 thirdly 0.06 0.04 to begin with 0.02 0.00 to start with 0.03 0.00

Code glosses Aca Col

( ) 0.02 0.01called 0.03 0.01e.g. 0.03 0.00for example 1.41 0.95for instance 0.17 0.11I mean 0.12 0.18in fact 0.21 0.16in other words 0.03 0.03known as 0.05 0.00say 0.02 0.00specifically 0.00 0.01such as 0.57 0.34that is to say 0.02 0.00Normed Freq 2.66 1.81

Transi�ons Aca Col

also 3.12 3.23accordingly 0.00 0.01addi�onally 0.00 0.01again 0.02 0.01as a consequence 0.03 0.00as a result 0.06 0.03at the same �me 0.05 0.07even though 0.06 0.04further 0.02 0.00hence 0.02 0.00in addi�on 0.15 0.10in contrast 0.00 0.01in the same way 0.02 0.00lead to 0.06 0.03likewise 0.02 0.00on the contrary 0.02 0.03

Label stages all in all 0.03 0.03 in conclusion 0.25 0.07 in short 0.02 0.00 overall 0.03 0.01 to conclude 0.18 0.04 to sum up 0.09 0.04 Announce goals aim 0.05 0.00 purpose 0.02 0.00 want to 0.00 0.11 I would like to 0.08 0.13 Shi� topic resume 0.02 0.00 Normed Freq 3.80 4.57

on the other hand 0.29 0.34since 0.14 0.10so as to 0.02 0.01the result is 0.03 0.00although 0.26 0.20because 6.92 6.85besides 0.06 0.08but 5.76 7.46consequently 0.06 0.03furthermore 0.14 0.16however 0.70 0.57moreover 0.40 0.28nevertheless 0.08 0.06nonetheless 0.03 0.00therefore 0.14 0.14though 0.11 0.08thus 0.09 0.03whereas 0.11 0.00yet 0.03 0.07Normed Freq 18.99 20.03

Distribution of Stance Markers in Academic and Colloquial Writing

Hedges Aca Col

almost 0.14 0.11 claimed 0.02 0.00

Boosters Aca Col

actually 0.12 0.11certainly 0.09 0.13

A�tude Aca Col

agree 0.15 0.06amazing 0.47 0.72


feel 0.00 0.01 perspec�ve 0.00 0.01 generally 0.05 0.00 guess 0.00 0.04 in general 0.05 0.04 in my opinion 0.21 0.33 in my view 0.02 0.00 likely 0.08 0.03 mostly 0.06 0.03 o�en 0.21 0.04 perhaps 0.06 0.04 possible 0.06 0.11 probable 0.00 0.01 quite 0.05 0.13 seem 0.03 0.04 supposed 0.08 0.00 tend to 0.05 0.01 usually 0.15 0.01 could 2.76 2.49 may 1.15 1.10 maybe 1.16 2.14 might 0.58 0.76 probably 0.29 0.44 some�mes 0.54 0.61

clear 0.08 0.00definitely 0.15 0.17indeed 0.29 0.04no doubt 0.02 0.07must 0.03 0.07obviously 0.21 0.03of course 0.26 0.25shown 0.06 0.00surely 0.06 0.42truly 0.11 0.04always 0.87 0.95never 0.72 0.92really 1.58 2.89Normed Freq 4.66 6.10

appropriate 0.05 0.00astonished 0.02 0.00disagree 0.02 0.01essen�al 0.08 0.00fortunate 0.02 0.03hopefully 0.02 0.03importantly 0.00 0.01interes�ng 0.18 0.18prefer 0.05 0.06surprised 0.00 0.01unbelievable 0.00 0.01understandable 0.02 0.00unexpected 0.00 0.01unfortunately 0.00 0.04important 2.34 2.05Normed Freq 3.40 3.24

Normed Freq 7.78 8.56

Engagement Aca Col You 21.58 63.34 your 9.16 18.80 we (inclusive) 0.93 1.58 our (inclusive) 0.49 1.33 us (inclusive) 0.60 1.16 ? 1.00 2.58 do not 0.26 1.73 go 0.29 1.30 have to 1.16 1.27 imagine 0.20 0.45 let’s 0.23 0.71 must 1.36 1.88 need to 1.38 1.22 remember 0.20 0.76 should 1.53 1.98 think about 0.31 0.92 Normed Freq 40.68 101.02

Self-mentioning Aca Col I 11.09 24.60 me 1.41 3.32 my 4.70 6.37 our (exclusive) 1.42 0.98 us (exclusive) 0.64 0.55 we (exclusive) 4.12 2.41 Normed Freq 23.39 38.23

Appendix B. Supplementary data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.pragma.2018.10.004.

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Dr.WenjuanQin is a graduate from the Harvard Graduate School of Education and an assistant professor at Fudan University. Her research focuses onwritingdevelopment of learners using English as a Foreign Language (EFL). Departing from the pragmatic-based view of language development, she is particularlyinterested in how EFL writers learn to flexibly and effectively deploy a variety of lexical, syntactic and discourse features that are attuned to differentcommunicative contexts, genres and registers. She is engaged in transferring knowledge obtained from empirical research into pedagogical practices thatenhance EFL learners' communicative competence.

Dr. Paola Uccelli is a professor at the Harvard Graduate School of Education. With a background in linguistics, she studies socio-cultural and individualdifferences in language and literacy development throughout the school years. Her research focuses on how different language skills (at the lexical,grammatical, and discourse levels) interact with each other to either promote or hinder advances in language expression and comprehension in monolingualand bilingual students.



































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Journal of Pragmaticsdistinct communicative purposes (e.g., more code glosses in academic writing; more boosters and engagement markers in colloquial writing). Finally, the use of