## Revision of Business Content on Corporate Social Responsibility: Measuring the Impact of Training on the Cognitive Effort of SecondLanguage University Students

## Abstract

With more and more people interested in how sustainable and socially responsible companies are, the comprehensibility of  content on corporate social responsibility (CSR) has become paramount. Producing easy-to-read business content either by writing it from scratch or revising it - is a cognitively demanding undertaking, especially for second-language non-professional  writers.  Both  formal  training  and  sustained  practice  can  help  writers  build  expertise  and,  in  turn,  be considerate of their intended audience. In particular, research on the impact of training has usually yielded positive results when examining the texts produced following specific instruction. However, the extent to which training has a positive effect on the process of writing and revision is still under-researched, especially in second language. To address this gap, we  report  on  an  experimental  study  that  examines  the  impact  of  reader-oriented  training  on  the  cognitive  effort experienced  by  47  second-language  university  students  when  revising  CSR  content.  We  adopted  a  pre-test  post-test design, and we used keystroke logging and retrospective interviews to collect data on students' pausing behaviour, use of online sources, and strategies to approach the revision task. Our training seemed to reduce the cognitive effort linked with lexical choices. Furthermore, it provided some students with procedural knowledge on how to approach the revision task in a more efficient way. We also observed a general tendency to rewrite from scratch (rather than revise) CSR content despite  the  higher  cognitive  effort  required  by  rewriting.  We  discuss  implications  for  training,  limitations,  and  future research avenues.

## Keywords:

corporate social responsibility; text revision; second-language writing; plain language training; cognitive effort; keystroke logging; retrospective interviews

## 1. Introduction

Writing and revising texts in order to suit the needs and expectations of the intended reader is a complex  task,  which  tends  to  be  particularly  effortful  for  non-native  speakers  of  a  language (Barkaoui  2007).  Training  and  sustained  practice  can  help  manage  the  cognitive  effort  required (Kellogg 2008). Therefore, in this article, we report on an empirical investigation of the impact of reader-oriented  training  on  the  revision  process  intended  in  a  broad  sense,  whether  it  involved starting a text from scratch ( rewriting ), or modifying an existing text by trying to preserve as much of its original features as possible ( narrow revision ). Our focus was specifically on the cognitive effort  experienced  by  second-language  university  students  before  and  after  taking  part  in  our (online)  training.  To  this  end,  we  collected  data  using  keystroke  logging  and  retrospective interviews.  The  reader-oriented  training  that  we  developed  (i.e.  the  intervention  of  this  study) revolved around the accessible communication of business content on corporate social responsibility (CSR). The selection of this domain was motivated, on the one hand, by the growing interest  of  customers  and  lay  people  in  the  extent  to  which  companies  engage  with  socially responsible activities (Golob/Podnar 2019) and, on the other hand, by the low readability of CSR content, particularly in corporate reports (Smeuninx et al. 2020).

*  Alessandra Rossetti Department of Management Universiteit Antwerpen alessandra.rossetti@uantwerpen.be

**  Luuk Van Waes Department of Management Universiteit Antwerpen luuk.vanwaes@uantwerpen.be We  start  this  article  with  a  review  of  previous  work  and  related  research  gap  (Section  2), followed by a description of the method that we adopted (participants, tools, tasks, and texts) in Section 3. Section 4 delves into data preparation and analysis, while Section 5 reports the results of our quantitative and qualitative analyses. Section 6 summarises our findings and Section 7 presents limitations and avenues for future research.

## 2. Literature review

## 2.1. Training on reader-oriented communication

Being  able  to  adopt  the  perspective  of  the  reader  when  writing  or  revising  requires  decades  of deliberate  and  sustained  practice  (Kellogg  2008).  It  is  therefore  not  surprising  that  researchers, professional  trainers,  and  school  teachers  have  invested  time  and  effort  in  identifying  the techniques and approaches that can foster the adoption of a reader-oriented perspective (Barkaoui 2007).  Matching  the  needs  and  expectations  of  the  reader  is a  requirement  for  effective communication not only in monolingual texts (produced and revised in one language), but also in texts  that  are  translated  from  one  language  into  another  (Schrijver  et  al.  2016). As  such,  readeroriented  communication  is  also  an  important  component  of  translation  curricula  and  modules (Federici/Cadwell 2018; Lee 2016).

Previous works varied in terms of their focus of analysis, genres, and domains. For instance, focusing specifically on paragraphing, Eden/Mitchell (1986) pointed out that training should take into consideration the reader's expectations and patterns of comprehension, rather than being purely text oriented. Paragraphing was also an important aspect in a study conducted by Kuteeva (2011), who analysed the use of a course wiki for the production of academic and professional texts among students  of  English  as  a  second  language.  Specifically,  the  wiki  revolved  around  paragraph structure, coherence, and argumentation. Results showed that the use of the wiki made the majority of students consider their audience and, in turn, pay attention to the organisation of the texts and to grammatical correctness. In her seminal work,  Schriver (1992) tested the impact of using thinkaloud  protocol  transcripts  produced  by  readers  when  training  writers  to  anticipate  readers' comprehension needs. Her results showed that protocols helped writers to more accurately predict and diagnose readers' problems, as well as to attend to global textual features. Positive results were also  reported  by  Sengupta  (2000),  who  trained  students  of  English  as  a  second  language  in  the revision  of  texts  aimed  at  making  them  more  reader-friendly,  with  'reader-friendliness' being defined as appropriateness, sufficiency, and organization of information.

Training on reader-oriented communication is carried out not only in educational settings, but also in the workplace. An example is the training provided to government agencies in the United States  by  the  Plain  Language  Action  and  Information  Network  (Schriver  2017).  Research  on reader-oriented writing  in the workplace  is, however,  quite  limited and  results about  the effectiveness of such training tends to be low or moderate (Janssen 2001).

In  the  medical  domain,  Warde  et  al.  (2018)  advocate  for  the  integration  of  plain  language training into medical curricula as a way of preparing healthcare professionals for the challenges of a  multilingual  and  globalised  society.  Similarly,  Sagi  et  al.  (2021)  describe  the  ETGAR  course whose goal is to train medical students to rewrite formal discharge letters in plain language with lay patients as the  intended  readers.  Students  are instructed  to  go  beyond the  mere simplification  of vocabulary and to approach the text globally, for instance by restructuring the information, using headings, and adding/removing content, so that texts are concise, well-organised, and clear (Kools et  al.  2008).  Moving from medicine to subtitling and audio description, the recent EASIT (Easy Access  for  Social  Inclusion  Training)  project  seeks  to  develop  a  curriculum  for  the  training  of experts in easy reading (Arias Badia/Matamala Ripoll 2020). In the business domain, Greer (2012) presents resources and exercises that instructors can introduce in their courses. An example of such exercises  is  asking  students  to  revise  a  business  advertisement  or  a  brochure  following  plain language guidelines.

As this section has shown, training on reader-oriented communication is relevant to a variety of settings  and  domains.  In  the  business  domain,  very  little  attention  has  however  been  devoted  to making CSR content easier to comprehend for a non-specialist audience (Rossetti/Van Waes 2022). This gap is surprising when considering the increasing interest in companies' sustainability efforts from a variety of stakeholders (Zamir/Saeed 2020), as well as previous research highlighting the difficulty of CSR content, particularly in corporate reports (Nilipour et al. 2020). Accordingly, in the  current  study,  we  selected  CSR  content  as  the  focus  of  our  training  on  reader-oriented communication (Section 3.2).

## 2.2. Expertise and language proficiency in revision

The goal of training is  to  help  students/trainees  bridge  the  expertise  and  knowledge  gap  (Carter 1990). When it comes to the revision of texts, higher writing expertise levels result in the ability to consider the text as a whole and to make global, structural changes that consider genre, medium, and  intended  reader  (Leijten/Van  Waes  2020;  Lindgren  et  al.  2011).  Furthermore,  while  the revision process of experts tends to be recursive and to involve different rounds (Becker 2006), less experienced writers tend to apply local, small changes to texts that preserve original meaning and structure (Faigley/Witte 1981). Interestingly, Whyatt (2018) found that the translation expertise of professionals is also transferable to monolingual paraphrasing tasks as this group produced higherquality texts (in terms of readability, communicative quality, and errors) compared with language students. Wallace/Hayes (1991) ascribe these differences in expertise levels to the way in which revision  tasks  are  represented  in  the  mind  of  the  writer/reviser.  Specifically,  after  delivering  8minute  instruction  to  freshmen  on  how  experienced  revisers  operate,  the  authors  observed  an improvement in revision quality (Wallace/Hayes 1991).

Similar to writing and translation expertise, levels of language proficiency can also determine the amount and types of revisions made. In his review of the research on the revision practices of experienced and less experienced writers in a first language and in a second language,  Barkaoui (2007)  discusses  the  dimensions  along  which  these  groups  differ,  namely:  revision  beliefs; audience awareness; revision time and frequency; revision processes; revision focus; and revision outcomes. In particular, with regard to audience awareness, Barkaoui (2007: 84) reports that:

Skilled  writers  [...]  tend  to  spend  more  time  thinking  about  the  effect  they  have  on  their reader, how they wish to present themselves to the reader, what background knowledge the reader needs to have to comprehend their text, and what may interest the reader.

The author points out that these revision practices should be regarded as a continuum, since novice writers  can  gradually  develop  their  expertise  in  (second  language)  writing  through  practice (Barkaoui 2007). Overall, despite some evidence that certain revision behaviours are transferable from first language to second language, revisions in a second language appear to be more frequent and more time consuming (Hall 1990; Lindgren/Sullivan 2006), with cognitive demands increasing also  as  a  result  of  task  complexity  (Xu  et  al.  2021).  Furthermore,  native  speakers  and  highproficiency speakers of a second language tend to make more content and global edits, as opposed to the local edits (e.g., spelling or grammar) usually made by low-proficiency speakers (Barkaoui 2016; Zhang et al. 2017).

## 2.3. Managing cognitive effort

The  observable  differences  among  various  expertise  and  language  proficiency  levels  are  linked with the way in which the cognitive demand imposed by revision is managed. Revising involves planning solutions to problems detected in a text, transcribing those solutions into new text and, if necessary, revising new and existing text (Hayes 2012). In conducting these activities, the reviser has  to  maintain  a  mental  representation  of  the  content  planned  by  the  author  (when  revising somebody else's text), of the text as it is at any given moment, and of the needs and expectations of the prospective reader within a context (Hayes 2012; Kellogg 2008; Schriver 2012). These actions put a strain on the reviser's working memory, which is responsible for the temporary storage and processing of information during writing and revision (Olive 2012).

The limited working memory resources are easily depleted when used for tasks that have not become  automatised  enough  (as  is  the  case  with  children  struggling  with  orthography,  writers working  with  texts  of  an  unfamiliar  genre/topic,  or  low-proficiency  second-language  writers) (McCutchen  2000,  2011).  Skilled  and  experienced  writers/revisers,  on  the  other  hand,  have internalised  knowledge  and  strategies,  thus  being  able  to  retrieve  them  from  their  long-term memory as needed by the task at hand and, in turn, circumventing working memory constraints (McCutchen 2011). However, the types of issues that need to be addressed in a text also play a role, with the notion of revision moving away from mere error correction to include different forms of text improvement (Conijn et al. 2021). In particular, while surface errors of spelling and grammar might require little cognitive effort for experienced writers and/or native speakers, the ill-defined considerations on readership and global text features (e.g., cohesion) will still require more mental resources and more text readings (Piolat et al. 2004). For writers of English as a second language, for  example,  the  use  of  discourse  features  -  such  as  cohesive  devices  -  might  require  specific instruction (Kobayashi/Rinnert 2001).

When  confronted  with  a  difficult  task,  writers  and  revisers  can  adopt  different  strategies  to manage cognitive  effort.  One  of  these  strategies  is  related  to  the  interaction  with  the  text  to  be revised. In their model of revision, Hayes et al. (1987) argue that, when a reviser realises that a text contains too many issues - and/or that they do not know how to solve them (e.g., in the case of novices) - they might find it sometimes simpler to extract the gist of the text and rewrite it from scratch, rather than addressing individual problems  (Hayes 2004). Such rewriting might be local (e.g., paraphrasing individual sentences) or global (redrafting entire sections) (Hayes et al. 1987). Therefore,  the  authors  distinguish  between,  on  the  one  hand,  revision  in  a  narrow  sense  henceforth 'narrow revision' (see Section 5.2), whereby the reviser tries to address issues in a text while preserving as much of the original structure and content as possible - and, on the other hand, rewriting (Hayes et al. 1987). We regarded the model by Hayes et al. (1987) particularly relevant for this study since it focuses on the revision of already existing texts, which was the task carried out  by  our  participants  (Hayes  2004)  (Section  3.1).  A  similar  distinction  is  also  reported  in Allal/Chanquoy (2004) between 'editing' - as corrections and modifications that do not change the meaning of the text - and 'rewriting', which results in transformations of content, organisation, and meaning.

Rewriting  -  namely,  starting  a  text  from  scratch  rather  than  trying  to  preserve  as  much  as possible of the original text - might originally appear as the most efficient option since it allows the writer to avoid the constraints represented by the characteristics of the original text. However, rewriting might also prove unexpectedly challenging for less fluent and less experienced writers (e.g.,  low-proficiency  second-language  speakers),  who  might  experience  concern  for  issues  of language or genre conventions with which they are not familiar (Barkaoui 2007). To the best of our knowledge,  no  previous  work  in  the  area  of  writing  research  has  empirically  compared  the cognitive  effort  of  rewriting  vs  revising  somebody's  else  text  in  a  second  language.  However, comparable research related to (machine) translation shows that translating a text from scratch  similar to the activity of rewriting - tends to require more effort than revising the output produced by  a  machine  translation  system  when  the  quality  of  such  output  is  above  a  certain  threshold (Koponen 2016). Therefore, it can be hypothesised that - when a text is of acceptable quality revising it would require less effort than rewriting it from scratch.

## 2.4. Measuring cognitive effort

Researchers have used and combined various methods to measure the effort involved in (re)writing or  revising  (in  a  second  language),  such  as  questionnaires  with  self-reporting,  think-aloud protocols, dual task, eye tracking, keystroke logging, and retrospective interviews (with stimulated recall) (see e.g., Kellogg et al. 2007; Lu/Révész 2021; Révész et al. 2019; Xu et al. 2021). In this section, we are going to focus on the two methods that we selected for our study, namely keystroke logging and retrospective interviews.

At  the  high  level,  keystroke  logging  has  been  used  to  understand  the  cognitive  processes  including  mental  effort  -  involved  in  writing  and  revision.  The  analysis  of  pauses  is  especially central in most keystroke logging studies (see Hall et al. (2022) for an overview). Pauses (namely interruptions  in  the  flow  of  typing  that  can  be  recorded  by  keystroke  logging  tools)  have  been linked with effortful processes. The analysis of pauses in writing has shown that they occur, for instance, when the author has to plan new content, retrieve existing content from their memory, or revise the  text  produced  so  far,  among  others (Olive  et  al.  2009).  Most  keylogging studies  have shown that pauses are longer and occur more often at more global text boundaries, involving more high level and complex cognitive processes. Of course, pausing behaviour (pause length and pause distribution)  is  an  indirect  indicator  of  cognitive  effort,  and  we  should  be  aware  of  the  fact  that writers  might  lose  focus  at  a  certain  moment,  feel  the  need  for  some  'downtime',  or  engage  in 'sociopsychological' activities  (e.g.,  daydreaming)  (Révész et  al.  2019).  However,  there is  ample evidence that pause indicators are reliable indicators of cognitive effort.

The  need  to  understand  cognitive  processes  in  writing  has  translated  into  analyses  of  pause locations  (e.g.,  within  or  between  words,  between  sentences,  or  between  paragraphs)  and  pause length, as well as fluency, text elements revised, reading activity, and use of sources, among others (Chukharev-Hudilainen  et  al.  2019;  Leijten  et  al.  2019;  Révész  et  al.  2019;  Van  Waes/Leijten 2015).  Moreover,  findings  from  previous  research  have  shown  that  text  production  in  second language tends to be slower than in first language, involving more pauses and decreased fluency (Chukharev-Hudilainen  et  al.  2019).  Additionally,  second-language  text  production  tends  to involve a more varied use of sources, which has an impact on the fragmentation of writing/revision processes and can increase cognitive effort (Leijten et al. 2019).

Retrospective  interviews  have  also  been  used  to  collect  data  on  multiple  aspects  related  to writing  and  revision,  such  as  reasons  behind  revisions  (in  a  second  language);  use  of  feedback while revising; and explanations of pausing behaviour (Shen/Chen 2021; Suzuki 2008; Zhao 2010). For  instance,  the  less-skilled  writers  in  the  study  by  Shen/Chen  (2021)  reported  struggling  with linguistic encoding (spelling, grammar, or vocabulary).

## 2.5. Addressing a research gap

As  this  review  of  previous  literature  has  shown,  adapting  texts  to  the  intended  audience  is  an effortful task, particularly for less experienced and second-language writers/revisers. Training and practice can help bridge the expertise gap. However, the effect of training on the revision process carried out in a second language is still an under-researched area. For this reason, this experimental study sought to answer the following research question: What is the impact of reader-oriented training  on  the  revision  process  of  second-language  university  students? We  focused  in particular  on  the  cognitive  effort  of  the  revision  process,  as  indicated  by  students'  pausing behaviour,  use  of  sources,  and  strategies  to  approach  the  revision  task  (i.e.,  narrow  revision  vs. rewriting). Moreover, regarding the strategies, it should be noted that, throughout this paper, we will use the umbrella term 'revision' to include both narrow revision and rewriting.

In addition to filling this research gap, this study contains other elements of novelty. First of all, differently  from  the  majority  of  previous  works,  our  participants  carried  out  their  revisions  on already existing texts produced by others, rather than on their own texts. Secondly, this is the first empirical  investigation  of  the  differences  in  pausing  behaviour  associated  with  rewriting  versus revising in a second language. Thirdly, to the best of our knowledge, this study is the first to focus on  the  revision  of  business  texts  dealing  with  CSR.  In  Section  3  below  we  report  on  the methodology adopted to answer our research question and to fill these research gaps.

## 3. Method

## 3.1. Set-up: participants, data collection, and tasks

We collected data from a total of 47 students from the Faculty of Business and Economics at the University of Antwerp between October and November 2021. Data collection took place in person in  computer laboratories  on  campus.  Upon signing up for the study, participants  were randomly divided into an experimental group and a control group. Participants in both groups took part in a pre-test session and a post-test session.

During the pre-test session, participants read an information sheet and signed a consent form. Subsequently,  they  were  asked  to  read  an  extract  of  a  corporate  report  produced  by  a  fictitious tobacco company (SmokIT) and to convert it into an easy-to-read and engaging post that could be published  on  the  company's  website.  We  informed  the  students  that  they  could  be  creative  and inventive with their revisions, that they could do online searches, and that they could take as much time as needed. The revision task took place in Microsoft Word while the keystroke logging tool Inputlog was running in the background. Therefore, we could record the students' typing, pauses, and  use  of  online  sources  (Leijten/Van  Waes  2013).  Furthermore,  Inputlog  saved  intermediate versions of the participants' drafts every 2 minutes. Following the revision task, participants were asked to access an online module. The experimental group was assigned a  module on accessible communication applied  to  CSR  content,  while  the  control  group  was  assigned  a  module  on  the topic  of  CSR  (Section  3.2).  Students  were  instructed  to  spend  at  least  45  minutes  familiarising themselves  with  the  theory  and  the  exercises  in  the  modules.  Finally,  at  the  end  of  the  pre-test session, all participants completed a short demographic questionnaire (Section 3.4).

The post-test sessions took place 2-3 days after the pre-test sessions. As part of the post-test, the students conducted the revision task in the case study of their respective modules. Similar to the pre-test,  this  revision  task  involved  converting  another  extract  of  a  corporate  report  into  an accessible and engaging post that could be published on the website of the same tobacco company (see Section 3.3 for details about the texts to be revised). For this task as well, students could be creative and inventive, do online searches, and take as much time as needed. However, differently from the pre-test, in the post-test the participants could also consult their respective modules and use  them  to  guide  the  revision  task.  This  task  also  took  place  in  Microsoft  Word  while  the keystroke  logging  tool  Inputlog  was  collecting  data  in  the  background.  Finally,  all  participants completed  a  fidelity  questionnaire  with  multiple-choice  questions  about  the  content  of  their respective modules. Even though correct answers to multiple-choice questions might be guessed, we treated this fidelity questionnaire as a 'quick and dirty' way of checking that the participants had actually interacted with content from the modules. Since the experimental group and the control group had been assigned different modules, the multiple-choice questions were of course different. Results of the fidelity questionnaire are reported in Section 3.4.

In  the  post-test  session,  we  also  randomly  selected  a  sub-group  of  participants  (3  from  the experimental group and 4 from the control group) and we collected screen recorded and interview data from them so as to allow for a more qualitative analysis. The screen-recorded data will not be analysed in this paper. With regard to the interviews, they involved short follow-up questions at the end  of  the  revision  task.  These  retrospective  interviews  focused  on  the  process/product  of  the revision  task  and  the  potential  benefits  of  taking  part  in  our  training.  We  used  semi-structured interviews, which allowed us to discuss the topics of interest but also to adjust the questions to the natural flow of the conversation and to the emergence of additional topics of interest (Matthews/Ross 2010).

## 3.2. Modules

In order to empirically test the impact of training on accessible/reader-oriented communication, the experimental  group  received  a  link  to  an  online  module  where  the  principles  of  accessible communication were applied to CSR content. In contrast, the control group received a link to an online  module  dealing  exclusively  with  CSR 1 .  Both  modules  were  hosted  by  the  online  writing center Calliope, where content was divided into introduction, theory, exercises, and case study, and students  could  move freely between the different components depending on their learning styles (Van Waes et al. 2014). Furthermore, in both modules, theoretical content was provided both in textual  and  in  audio-visual  format.  The  revision  task  carried  out  by  the  students  in  the  post-test session was located on the case study page of the respective modules.

## 3.2.1 Module for experimental group

The  theoretical  content  in  the  experimental  module  was  divided  into  three  main  sections:  (i) accessible  communication;  (ii)  CSR;  and  (iii)  the  revision  process.  The  section  on  accessible communication  focused  on  vocabulary,  sentence  length/structure,  cohesion,  visual  aspects,  and relevance.  For  instance,  the  students  were  shown  how  to  simplify  sentences,  how  to  use connectives to build cohesion, or how to replace complex vocabulary with simpler synonyms. The section  on  CSR  focused  on  its  definition  and  on  the  differences  between  two  channels  of communication (i.e., corporate reports and corporate websites), in line with the goal of the revision task (Section 3.1). The section on the revision process relied on Inputlog process graphs and videos in order to show students how expert and novice revisers differ in terms of their revision processes (Leijten/Van  Waes  2013).  We  included  this  section  in  order  to  foster  observational  learning, whereby  students  learn  by  observing  the  actions  and  hearing  the explanations  of  experts (Rijlaarsdam et al. 2008). For a more detailed description of the module given to the experimental group, see Rossetti/Van Waes (2022).

## 3.2.2 Module for control group

The  theoretical  content  in  the  control  module  was  divided  into  three  main  sections,  namely definition,  evolution,  and  communication  of  CSR.  The  section  on  the  communication  of  CSR contained links to the websites of companies well known for their CSR activities and for their clear communications.  Students  in  the  control  group  could  of  course  consult  these  pages  but  they received no formal instruction on how to write accessible texts.

In the interest of clarity, Figure 1 presents a visual overview of the procedure described in the previous sections.

1  The experimental module is available at the following link: https://hosting.uantwerpen.be/calliope/plantra/. The control module is available here: https://hosting.uantwerpen.be/calliope/plantra-control-training/.

Data

collection

method:

keystroke

logging

Data collection methods:

keystroke logging

screen recording (sub-group

of participants)

retrospective interviews (sub-

group of participants)

Control group

Corporate social

Figure 1. Overview of data collection procedure

<!-- image -->

## 3.3. Materials (texts)

In order to avoid a learning effect (whereby students would learn from the pre-test task rather than from our module/intervention), the texts that we selected for revision in the pre-test and in the posttest  differed  in  terms  of  topic.  However,  they  both  dealt  with  the  socially  responsible  activities carried  out  by  a  fictitious  tobacco  company  called  SmokIT.  The  pre-test  text  dealt  with  the company's efforts  to reduce  their  carbon  emissions,  while  the  post-test  text  revolved  around  the company's  investment  in  smoke-free  alternatives.  Both  texts  were  extracts  of  a  real  corporate report, but we manipulated some of their features to make sure that the two revision tasks were similar  and  that  the  texts  contained  some  of  the  problems  mentioned  in  the  Calliope  module assigned  to  the  experimental  group  (Section  3.2).  The  decision  to  assign  students  extracts  of corporate reports to be revised into website posts (Section 3.1) was motivated by previous research showing that, while CSR content in corporate reports tends to be too complex and too technical for lay  customers,  websites  offer  an  opportunity  for  companies  to  present  CSR  information  in accessible language and engaging format (Smeuninx et al. 2020; Wei 2020).

The  length  and  the  original  level  of  readability  of  the  selected  texts  might  have  acted  as confounding variables. Therefore, we ensured that the two texts were as comparable as possible. Using the Coh-Metrix Common Core Text Ease and Readability Assessor (T.E.R.A.), we observed that both texts had a low level of narrativity (which means that they contained a low proportion of common words and verbs, and a high proportion of complex noun phrases), with the pre-test text having  a  particularly  low  percentage  (Jackson  et  al.  2016).  Both  texts  were  also  similar  in  their levels of syntactic simplicity (low, especially in the post-test) and referential cohesion (average, but lower  in  the  pre-test  text).  Referential  cohesion  refers  to  the  repetition  of  words,  phrases,  and concepts  across  sentences  (Jackson  et  al.  2016).  Deep  cohesion  -  determined  by  the  amount  of connections among the events and ideas in the text (Jackson et al. 2016) - was higher in the pre-test text, but the narrativity and referential cohesion were lower in this text so we assumed that these readability  features  would  compensate  each  other.  Regarding  text  length  and  structure,  the  two texts had a very similar number of words (274 vs. 278 words) and they were both divided into three paragraphs.  Furthermore,  they  both  lacked  (sub-)headings  and  visual  devices  to  support  the processing of content.

·

Experimental group

Accessible Pre-test text revision module background questionnaire

## 3.4. Demographic profiles and interaction with modules

As  explained  in  Section  3.1,  students  filled  out  a  demographic  questionnaire  after  the  pre-test session,  and  a  fidelity  questionnaire  after  the  post-test  session.  The  goal  of  the  demographic questionnaire  was  to  ensure  that  participants  in  the  experimental  and  in  the  control  group  had similar  background  characteristics  and  prior  knowledge.  Furthermore,  participants  self-reported how much they had learnt from the respective modules. The goal of the fidelity questionnaire, on the other hand, was to more objectively assess participants' levels of interaction with the modules by asking them 4-5 multiple-choice questions about the theoretical contents. We assigned a score of 1  to  correct  answers  and  a  score  of  0  to  wrong  answers.  'I  don't  know'  answers  were  treated  as missing values. We calculated the mean score for each question, and then the grand mean of all questions. In the interests of clarity, we report these data as percentage values. Table 1 contains an overview of demographic and fidelity data. The control group was not asked the questions about pre-module and post-module knowledge about accessible communication. Therefore, we marked those cells as 'N/A' in Table 1.

It  can  be  observed  that  participants  in  both  groups  were  similar  in  terms  of  age,  gender distribution, linguistic profile (most of them reported Dutch as native language), average number of years spent studying English (the language of the texts to be revised), and current programme of study  (specifically,  most  of  the  participants  were  starting  a  Master  in  Multilingual  Professional Communication).  Furthermore,  the  majority  of  them  had  not  previously  attended  training  on accessible  communication.  However,  in  the  experimental  group,  most  participants  were  already somewhat  familiar  with  the  principles  of  accessible  communication  even  before  looking  at  the assigned  Calliope  module.  On  average,  they  gave  quite  a  high  score  about  their  ability  to  write accessible/easy-to-read texts following participation in our module. With regard to CSR, in both the experimental and the control group, there was some increase in their self-reported knowledge about the topic after looking at the modules, although with quite some within-group variability. Finally, the  grand  mean  scores  from  the  fidelity  questionnaire  seem  to  indicate  that  participants  in  both groups engaged thoroughly with the theoretical contents provided in the modules.

Table 1. Participants' profiles and interaction with Calliope modules

|                                                                                                                                                                                             | Experimental (N=24)             | Control (N=23)                                       |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|------------------------------------------------------|
| Age                                                                                                                                                                                         | Mean=23 Min=19 Max=38           | Mean=23 Min=22 Max=34                                |
| Gender                                                                                                                                                                                      | Female=18 Male=6                | Female=20 Male=3                                     |
| Dutch as native language                                                                                                                                                                    | N=24                            | N=21                                                 |
| Additional native language(s)                                                                                                                                                               | Tamazight, Armenian, Polish     | Frisian, Armenian, French, Finnish, Spanish, Italian |
| Years studying English                                                                                                                                                                      | Mean=9 Min=2 Max=26             | Mean=8 Min=4 Max=15                                  |
| Study programme                                                                                                                                                                             | Master=23 Bachelor=1            | Master=23                                            |
| Previously attended training on accessible communication                                                                                                                                    | Yes=6 No=18                     | Yes=8 No=15                                          |
| After looking at the module, how much do you think you know about writing accessible/easy-to-read texts, on a scale from 1 (I don't know anything at all) to 10 (I have become an expert on | Mean=7 Min=5 Max=8              | N/A                                                  |
| the topic)? Before looking at the module, how much did you know about corporate social responsibility on a scale from 1 (I knew nothing at all) to 10 (I was an expert on the topic)?       | Mean=4 Min=1 Max=8              | Mean=4 Min=1                                         |
| After looking at the module, how much do you think you know about corporate social responsibility on a scale from 1 (I still don't know anything at all) to 10 (I have become               |                                 | Max=8                                                |
| an expert on the topic)?                                                                                                                                                                    | Mean=6 Min=3 Max=9              | Mean=7 Min=4 Max=9                                   |
| Correct answers about module content                                                                                                                                                        | Grand mean=93% Min=83% Max=100% | Grand mean=87% Min=57% Max=100%                      |

The  results  of  the  fidelity  questionnaires  were  also  supported  by  the  keystroke  logging  data provided  by  Inputlog.  Specifically,  the  tool  recorded  the  time  spent  by  the  participants  on  their respective modules during the pre-test session, after the revision task (Section 3.1). We observed that the experimental group spent on average 46 minutes on the module, while the control group spent on average 34 minutes, possibly because of the more limited theoretical content (the control group did not have access to theory on accessible communication).

## 4. Analysis

## 4.1. Keystroke logging data

The  analysis  of  keystroke  logging  data  was  first  carried  out  in  Inputlog  and  then  transferred  to SPSS.  It revolved around pausing behaviour and source use. Furthermore, we  classified participants'  overall  revision  strategies  as  either  narrow  revision  or  rewriting  -  following  the classification  reported  in  Hayes  et  al.  (1987)  -  and  analysed  the  extent  to  which  these  different strategies were represented by keystroke logging data.

In order to examine the effect of our reader-oriented training, for each variable of interest, we compared the  pre-test  with  the  post-test  data  of  all  participants  (within-subjects),  as  well  as  the experimental group with the control group at the pre-test and post-test stage (between-subjects). A series  of  Shapiro-Wilk tests  showed that the  data  were  not  normally  distributed  for  all the  (sub)variables of interest. Therefore, in the interests of consistency, we ran non-parametric tests (MannWhitney U test for the between-group analyses and Wilcoxon signed rank test for the within-group analyses).

## 4.1.1. Data preparation

Before  starting  data  analysis,  we  checked  and  time-filtered  the  raw  IDFX  files  produced  by Inputlog, as described in Leijten/Van Waes (2020). This filtering ensured that we removed noise and irrelevant data (such as the time spent between our start of Inputlog and the start of the revision task  by  the  participants).  Firstly,  we  used  Inputlog  to  produce  a  general  analysis  of  all  the  raw IDFX  files.  In  the  general  analysis,  every  row  corresponds  to  a  log  event  (keyboard  or  mouse action),  for  which  cursor  position,  document  length,  and  pauses  are  reported,  along  with  the environment (focus) in which that event takes place (e.g., main Microsoft Word document, URLs of online pages, Calliope modules, and so on) (Leijten/Van Waes 2013). Based on the information reported  in  the  general  analysis  files,  we  filtered  the  IDFX  files  from  the  focus  in  the  main Microsoft  Word  document  immediately  preceding  the  first  action  (e.g.,  pressing  RETURN  or typing  a  letter)  to  the  last  action  in  the  same  document,  where  the  revision  task  took  place. Therefore, the filtered IDFX files contained keystroke logging data exclusively on the revision task.

Subsequently,  we  produced  general  analyses  and  process  graphs  from  the  filtered  IDFXs. Process  graphs  are  visual  representations  of  the  writing  processes  that  include  information  on cursor position, text length at any given point, number of characters typed/edited/deleted, pauses (location  and  length),  and  source  use  (Vandermeulen  et  al.  2020).  In  examining  all  the  process graphs and the general analysis files, we noticed that - for two participants in the pre-test and for eight  participants  in  the  post-test 2 -  there  were  time  ranges  when  Inputlog  incorrectly  recorded actions  as  being  carried  out  outside  of  the  Microsoft  Word  document.  In  other  words,  events (typing, revisions, etc.) that were part of the revision task were not recorded as such. However, as the  data  loss  represented  only  a  small  percentage  of  the  overall  task  duration,  we  also  included these IDFX files in the analysis.

Finally,  we  excluded  4  participants  from  the  pre-test  and  2  participants  from  the  post-test because their IDFX files were irreparably damaged, and no data could be retrieved. This exclusion influenced the number of participants that could be included in the within-subjects analyses.

## 4.1.2. Pause analysis

As parameters in the pause analysis, we used a pause threshold of 200 milliseconds and a P-Burst threshold  of  2000  milliseconds.  Pauses  that  are  longer  than  200  milliseconds  are  of  interest because, for this cohort of participants, they are mainly linked with high-level, effortful cognitive processes (e.g., content planning) that exceed the low-level transitions between keys (Van Waes et al.  2009).  P-Bursts  are  fluent  writing  episodes  that  are  not  interrupted  by  a  pause  (Leijten  et  al. 2019). In addition to general measures (e.g., overall task duration, total number of pauses, or total pause time), we examined and compared pausing behaviour at different levels (i.e., within words, between words, between sentences, and before revisions). Furthermore, to account for individual differences in task duration, we included proportional values out of overall process time.

2  For two of these participants, we were able to manually correct the IDFX files since the missing time ranges involved linear production of content. Specifically, the correction involved counting the actions within the Microsoft Word document and adjusting document length, character production, and cursor position accordingly.

## 4.1.3. Analysis of revision strategies

Informed by the model discussed in Hayes et al. (1987), we categorised each participant's strategy of approach to the task either as narrow revision or rewriting (despite the fact that both strategies were part of a larger revision task) (Section 3.1). We used the start of the task as the determining criterion in our classification. Concretely, if the first edit (addition, deletion, or substitution) that a participant made was located within the assigned text, we treated the overall strategy as 'narrow revision'. In contrast, if the first edit made was located outside the assigned text (e.g., under it), we treated the overall strategy as 'rewriting'.

When making this categorisation,  we  excluded  outlines,  instructions,  and  titles/headings.  For instance, if a participant wrote a title on top of the assigned text but then continued to make the revisions  within  the  text  (rather  than  starting  a  new  one),  that  strategy  was  treated  as  narrow revision. This categorisation was possible because we set Inputlog to save versions of the revised documents every 2 minutes. Therefore, in addition to the final texts produced by the participants, we had also access to their intermediate drafts. This way of categorising the revision strategies is certainly not perfect since participants might have switched from a rewriting to a narrow revision strategy (or vice versa) throughout the task. However, this categorisation allowed us to focus on the way in which these second-language students first interacted with the text to be revised using either of these strategies. Furthermore, as we will show in Section 5.2, this manual classification is partly supported by patterns in the keystroke logging data.

First,  we  examined  if  and  how  keystroke  logging  data  related  to  pauses  reflected  the  two different strategies of rewriting and narrow revision both in the pre-test and in the post-test. To this end,  we  merged  the  data  from  the  control  and  from  the  experimental  group.  Subsequently,  we zoomed in on the participants who changed their revision strategies moving from pre-test to posttest session.

## 4.1.4. Analysis of source use

We also examined how our participants interacted with the main Word document and with external sources  (e.g.,  module,  online  pages,  etc.)  during  the  revision  task.  The  keystroke  logging  tool Inputlog  allowed  us  to  collect  data  on  time  spent  and  on  number  of  keystrokes  made  in  each environment (Leijten/Van Waes 2013). In order to account for the differences in task duration, we report the time and keystrokes per source as a proportion of the overall process times. Prior to data analysis,  we  recoded  the  IDFX  files  that  we  had  filtered  (Section  4.1.1)  by  grouping  and categorising the different sources used by the participants. Specifically, we created 8 categories: (i) main  document;  (ii)  language  searches;  (iii)  topic  searches;  (iv)  search  start;  (v)  modules;  (vi) session  instructions;  (vii)  calculations;  and  (viii)  irrelevant.  A  description  of  the  categories  is available in Table 2.

<entry\_5>alleviate - Vertaling Engels-Nederlands - Google Chrome</entry\_5>

<entry\_7>an alternative to - Vertaling naar Nederlands - voorbeelden Engels | Reverso Context - Google Chrome</entry\_7>

<entry\_6>an alternative - Vertaling naar Nederlands - voorbeelden Engels | Reverso Context - Google Chrome</entry\_6>

<entry\_8>BELANG HECHTEN AAN - Engelse vertaling - bab.la Nederlands-Engels woordenboek - Google Chrome</entry\_8>

<entry\_10>betaalbaar - Vertaling naar Engels - voorbeelden Nederlands | Reverso Context - Google Chrome</entry\_10>

<entry\_9>belangrijk daarbij is - Vertaling naar Engels - voorbeelden Nederlands | Reverso Context - Google Chrome</entry\_9>

<entry\_11>bijvoorbeeld - Vertaling naar Engels - voorbeelden Nederlands | Reverso Context - Google Chrome</entry\_11>

<entry\_12>brand onder de voeten - Vertaling naar Engels - voorbeelden Nederlands | Reverso Context - Google Chrome</entry\_12>

Table 2. Categorisation of sources

| Sources   | Descriptions   |
|-----------|----------------|

| Main document        | Word document in which students carried out the revision task                                                                                                                                                                                           |
|----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Language searches    | Internet searches aimed at finding the meaning, synonyms, and/or translation of words                                                                                                                                                                   |
| Topic searches       | Online searches whose goal was to find information on the CSR topics addressed in the texts (e.g., climate change or smoke-free products)                                                                                                               |
| Search starts        | Interruption of the writing flow: typing a search in an Internet browser and scrolling the results without opening a specific page                                                                                                                      |
| Modules              | Note: this category includes different types of searches (language- and topic- related), but we did not assign these searches a specific goal because that was not always clear from the words typed Access to the modules assigned to the participants |
| Session instructions | Checking of task instructions provided in digital format                                                                                                                                                                                                |
| Calculations         | Internet searches aimed at calculating values and percentages related to the text                                                                                                                                                                       |
| Irrelevant           | Use of online pages not related to the task at hand                                                                                                                                                                                                     |

Such categorisation was needed considering the high variability in students' use of sources. To give an example, Figure 2 below contains some of the language-related online searches made by the participants:

Figure 2: Examples of language-related searches

## 4.2. Retrospective interview data

In the post-test session, for a randomly selected sub-group of participants (3 from the experimental group  and  4  from  the  control  group),  we  collected  qualitative  data  in  the  form  of  short  semistructured  retrospective  interviews.  The  goal  of  these  interviews  was  to  complement  the  more quantitative  keystroke  logging  data.  Specifically,  the  questions  revolved  around  the  general approach to the task, perceived difficulty, planning and revision strategies, level of satisfaction with the final text, and helpfulness of the Calliope module.

After transcribing the audio recordings, the first author read and coded participants' answers in NVivo using an inductive and iterative approach. The goal of this coding was to identify important units of meaning in the data. After organising the codes into main themes and sub-themes, the first author and the second author discussed the (sub-)themes and reached agreement on their naming and categorisation.

We  identified  six  themes,  namely:  (i)  revision  strategies;  (ii)  interaction  with  module;  (iii) overall approach awareness; (iv) previous knowledge and experience; (v) reader orientedness; and (vi)  source  use.  The  theme  'revision  strategies' was in  turn  divided  into  seven  sub-themes,  each referring  to  a  different  type  of  issue  that  the  participants  addressed  in  the  text  when  revising  it. Most  of  these  sub-themes  are  in  line  with  the  contents  of  the  Calliope  module  assigned  to  the experimental  group  (Section  3.2).  Specifically,  they  refer  to  vocabulary,  syntax,  text  structure, visual aspects, relevance, tone, and contextual information. The theme 'overall approach awareness' was also divided into two sub-themes, respectively referring to planning how to address the text and to the revision of content.

## 5. Results

In this section, we report the results of our quantitative (Sections 5.1-5.3) and qualitative (Section 5.4) analyses. We use the code 'PE' to refer to participants from the experimental group, and the code 'PC' to refer to participants from the control group.

## 5.1. Pauses

Table 3 reports the pause-related results of the between-group analyses comparing the experimental and the control group during the pre-test and during the post-test. The table contains only a subset of all the pause-related sub-variables that we analysed. Specifically, we report the data related to the entire process (e.g., total active writing time) and to the word-, sentence-, and revision-level. The full list of pause-related sub-variables is available in Appendix A.

Overall,  the  pausing  behaviour  of the experimental group and of the control group was quite similar both in the pre-test and in the post-test, for instance in terms of average duration of pauses, proportion of pauses out of overall process time, number of P-Bursts per minute, and their average duration. In other words, taking part in training did not lead to differences in pausing behaviour between the groups. In the pre-test, the control group showed a significantly higher proportion of between-sentence pauses (compared with the experimental group), which might be indicative of effortful planned sentence production and idea generation (Baaijen et al. 2012; Leijten et al. 2019). This difference became non-significant in the post-test, possibly due to acquired familiarity with the revision task and/or with the topic of smoke-free products (Section 3.3).

When  focusing  on  the  within-group  comparisons,  the  control  group  showed  a  significantly higher proportion of between-word pauses in the post-test (compared with the pre-test) ( Z =-2.455, p =.01). This result might be due to the higher cognitive effort needed by this group when making lexical decisions (Leijten et al. 2019). As the experimental group did not show a higher proportion of  between-word  pauses  in  the  post-test,  we  can  hypothesize  that  our  training  in  plain  language assisted them with lexical choices, for example in finding lay synonyms of specialised terms. This hypothesis is supported by the keystroke logging data linked with rewriting vs revision strategies (Section 5.2) and by the interview data (Section 5.4).

Table 3. Between-group analysis of pausing behaviour in the pre- and post-test

|                                                              | Pre-test            | Pre-test            | Pre-test       | Pre-test       |          | Post-test           | Post-test           | Post-test      | Post-test      |          |
|--------------------------------------------------------------|---------------------|---------------------|----------------|----------------|----------|---------------------|---------------------|----------------|----------------|----------|
|                                                              | Experimental (N=24) | Experimental (N=24) | Control (N=19) | Control (N=19) | p- value | Experimental (N=23) | Experimental (N=23) | Control (N=22) | Control (N=22) | p- value |
|                                                              | M                   | SD                  | M              | SD             |          | M                   | SD                  | M              | SD             |          |
| Overall process                                              |                     |                     |                |                |          |                     |                     |                |                |          |
| Total process time (min:secs)                                | 45:42               | 20:42               | 45:31          | 15:52          | .769     | 49:55               | 15:00               | 48:01          | 15:42          | .803     |
| Total active writing time (min:secs)                         | 14:01               | 06:28               | 16:47          | 09:00          | .293     | 16:18               | 06:39               | 17:04          | 06:57          | .666     |
| Pausing                                                      |                     |                     |                |                |          |                     |                     |                |                |          |
| behaviour Proportion of pause time (%)                       | 68.68%              | 8.02%               | 64.57%         | 10.4%          | .221     | 67.4%               | 8.43%               | 64.49%         | 6.56%          | .107     |
| Number of P-                                                 | 3.49                | .59                 | 3.46           | .68            | .912     | 3.52                | .46                 | 3.36           | .59            | .184     |
| Bursts per minute Mean process time of P-Bursts (secs)       | 10.9                | 3.2                 | 12.7           | 5.3            | .359     | 11.2                | 2.9                 | 12.4           | 3.5            | .256     |
| Pause locations                                              |                     |                     |                |                |          |                     |                     |                |                |          |
| Number of within- word pauses per second                     | .17                 | .06                 | .14            | .04            | .056     | .17                 | .06                 | .14            | .04            | .146     |
| Geometric mean of within-word pauses (secs)                  | .32                 | .02                 | .31            | .01            | .625     | .32                 | .02                 | .31            | .02            | .919     |
| Number of between-word pauses per second                     | .10                 | .04                 | .09            | .04            | .42      | .10                 | .03                 | .10            | .03            | .602     |
| Geometric mean of between-word pauses (secs)                 | .57                 | .13                 | .54            | .12            | .525     | .55                 | .14                 | .51            | .10            | .601     |
| Number of between-sentence                                   | .002*               | .002                | .003*          | .001           | < .05    | .003                | .002                | .004           | .002           | .102     |
| pauses per second Geometric mean of between- sentence pauses | 1.06                | .97                 | .82            | .32            | .778     | 1.04                | 1.19                | .69            | .26            | .503     |
| Number of revision pauses per second                         | .06                 | .02                 | .06            | .02            | .903     | .06                 | .02                 | .06            | .03            | .856     |
| Geometric mean of revision pauses (secs)                     | .57                 | .09                 | .54            | .06            | .399     | .55                 | .05                 | .53            | .07            | .51      |

## 5.2. Revision strategies: narrow revision vs. rewriting

The most common strategy involved rewriting (more specifically, redrafting) (Hayes et al. 1987), whereby the participants abandoned the assigned text and started a new text from scratch, either above, below, or in the middle of the assigned text (e.g., when they rewrote one paragraph at a time). Overall, the rewriting strategy was used by 38 participants in the pre-test (90% of the total) and  by  31  in  the  post-test  (73%  of  the  total).  However,  in  some  (less  frequent)  cases,  the participants decided to revise the given text. This happened in total 4 times during the pre-test and 11 times during the post-test. It is also interesting to note that 6 participants in the control group and 7 participants in the experimental group switched between strategies depending on the session (pre-test vs post-test). Table 4 provides an overview of the strategies and their evolution.

Table 4. Participants' revision strategies

|                     | Pre-test   | Pre-test   | Post-test   | Post-test   | Strategy change       | Strategy change       |
|---------------------|------------|------------|-------------|-------------|-----------------------|-----------------------|
|                     | Rewriting  | Revision   | Rewriting   | Revision    | Rewriting to revision | Revision to rewriting |
| Group               |            |            |             |             |                       |                       |
| Control (N=19)      | 16/19      | 3/19       | 16/19       | 3/19        | 3/19                  | 3/19                  |
| Experimental (N=23) | 22/23      | 1/23       | 15/23       | 8/23        | 7/23                  | 0/23                  |

Table 5 shows that, in the pre-test, rewriting involved a significantly higher proportion of pause time and significantly higher proportions of pauses at multiple text levels (within words, between words,  and  between  sentences).  These  results  seem  to  indicate  that  rewriting  required  more cognitive effort than revision as the participants (who were not native speakers of English) had to pay attention to spelling and grammar, come up with their own lexical choices, and generate ideas for  the  new  text  (Leijten  et  al.  2019).  In  the  post-test,  the  differences  between  rewriting  and revision became less pronounced, possibly because the students had familiarised themselves with the task of rewriting a text on CSR in their second language, and/or could draw on a more concrete set of criteria following the training. However, the participants who chose rewriting still needed, on average,  significantly  more  time  than  the  participants  who  chose  the  narrow  revision  strategy, especially  because  of  higher  pausing  times.  It  is  also  interesting  to  note  that,  despite  the  low number of participants  - especially in the narrow revision group  - these keystroke logging data show that using different strategies leads to changes in pausing behaviour.

Table 5. Differences in pausing behaviour between revision strategies

|                                               | Pre-test         | Pre-test         | Pre-test              | Pre-test              | Pre-test   | Post-test        | Post-test        | Post-test              | Post-test              |          |
|-----------------------------------------------|------------------|------------------|-----------------------|-----------------------|------------|------------------|------------------|------------------------|------------------------|----------|
|                                               | Rewriting (N=38) | Rewriting (N=38) | Narrow revision (N=4) | Narrow revision (N=4) | p- value   | Rewriting (N=31) | Rewriting (N=31) | Narrow revision (N=11) | Narrow revision (N=11) | p- value |
|                                               | M                | SD               | M                     | SD                    |            | M                | SD               | M                      | SD                     |          |
| Overall process                               |                  |                  |                       |                       |            |                  |                  |                        |                        |          |
| Total process time (min:secs)                 | 45:59            | 19:18            | 41:09                 | 12:51                 | .668       | 51:39            | 14:27            | 42:12                  | 16:12                  | < .05    |
| Pausing behaviour                             |                  |                  |                       |                       |            |                  |                  |                        |                        |          |
| Total pause time (min:secs)                   | 31:10            | 13:15            | 20:57                 | 05:32                 | .134       | 34:15*           | 09:56            | 26:46*                 | 09:50                  | < .05    |
| Proportion of pause time (%)                  | 68.15%*          | 7.8%             | 52.46%*               | 10.73%                | < .05      | 66.45%           | 7.5%             | 63.82%                 | 8.3%                   | .466     |
| Total number of pauses per second             | .57*             | .09              | .45*                  | .01                   | < .05      | .56              | .09              | .58                    | .08                    | .637     |
| Pause locations                               |                  |                  |                       |                       |            |                  |                  |                        |                        |          |
| Number of within- word pauses per second      | .17*             | .06              | .09*                  | .01                   | < .05      | .16              | .04              | .15                    | .07                    | .597     |
| Number of between- word pauses per second     | .10*             | .04              | .04*                  | .01                   | < .05      | .10              | .03              | .10                    | .03                    | .539     |
| Number of between- sentence pauses per second | .003*            | .002             | .001*                 | .001                  | < .05      | .004             | .002             | .003                   | .001                   | .391     |

With regard to the participants who showed a change in their revision strategy, some interesting patterns can be observed (Table 4). In the experimental group, the seven participants who changed revision  strategies  all  switched  from  rewriting  to  narrow  revision.  In  contrast,  the  control  group showed more variability (3 people switched from narrow revision to rewriting, and 3 people from rewriting to narrow revision). In the experimental group, the change from rewriting (in the pre-test) to revision (in the post-test) resulted in significantly shorter between-word pauses (indicating lower cognitive  effort  for  lexical  choices),  as  shown  by  a  Wilcoxon  signed-rank  test  ( Z =  -2.366, p = .018). In the control group, such difference was not observed.

## 5.3. Source use

In Table 6, it can be observed that the source-related behaviour of experimental and control group was quite similar across pre- and post-sessions. Overall, participants devoted most of their time and their  typing  to  working  on  the  documents  where  the  revision  tasks  took  place,  as  would  be expected,  also  based  on  previous  research  (Leijten  et  al.  2019).  For  instance,  between  90%  and 80% of all keystrokes - and about 80% of the overall task duration - were devoted to the main document during the pre-test session. These percentages decreased slightly in the post-test, but they continued to represent the majority.

Across  sessions,  the  control  group  dedicated  consistently  more  time  and  more  typing  to language searches, and especially during the post-test session. When considering that the control group showed a significantly higher average number and proportion of between-word pauses in the post-test (Section 5.1), this result related to language searches seems again to point to the difficulty experienced by this group in finding appropriate lexical choices. Previous research on source-based writing in English as a second language has also highlighted students' use of online resources such as  dictionaries  and  grammars  (Leijten  et  al.  2019).  Regarding  topic  searches,  the  relative  time devoted  to  them  increased  for  all  participants  in  the  post-test.  Based  on  the  results  of  the retrospective interviews (Section 5.4), these topic searches were mainly aimed at the addition of contextual information on the topics of tobacco and smoke-free products.

Table 6. Between-group source analysis

|                     | Pre-test            | Pre-test            | Pre-test       | Pre-test       | Pre-test   | Post-test           | Post-test           | Post-test      | Post-test      | Post-test   |
|---------------------|---------------------|---------------------|----------------|----------------|------------|---------------------|---------------------|----------------|----------------|-------------|
|                     | Experimental (N=24) | Experimental (N=24) | Control (N=19) | Control (N=19) | p-value    | Experimental (N=23) | Experimental (N=23) | Control (N=22) | Control (N=22) | p- value    |
|                     | M                   | SD                  | M              | SD             |            | M                   | SD                  | M              | SD             |             |
| Relative time       |                     |                     |                |                |            |                     |                     |                |                |             |
| Main document       | .84                 | .14                 | .80            | .15            | .271       | .78                 | .10                 | .69            | .15            | .093        |
| Search start        | .05                 | .05                 | .06            | .05            | .769       | .03                 | .03                 | .03            | .03            | .937        |
| Topic searches      | .02                 | .03                 | .01            | .04            | .801       | .05                 | .06                 | .07            | .05            | .078        |
| Language searches   | .05                 | .09                 | .09            | .10            | .076       | .03                 | .04                 | .11            | .15            | .078        |
| Relative keystrokes |                     |                     |                |                |            |                     |                     |                |                |             |
| Main document       | .91                 | .10                 | .84            | .15            | .112       | .85                 | .13                 | .77            | .21            | .238        |
| Search start        | .04                 | .06                 | .05            | .05            | .378       | .02                 | .04                 | .02            | .03            | .624        |
| Topic searches      | .004                | .01                 | .001           | .004           | .414       | .002                | .005                | .003           | .009           | .818        |
| Language searches   | .03                 | .07                 | .09            | .13            | .051       | .03                 | .06                 | .14            | .21            | .08         |

We  also  examined  differences  within  each  group.  A  Wilcoxon  signed  rank  test  showed  that, compared with the pre-test, in the post-test the experimental group used less time ( Z = -2.068, p = .039)  and  fewer  keystrokes  ( Z = -2.305, p =  .021)  for  starting  online  searches.  Similarly,  in  the post-test, the control group spent less time starting online searches ( Z = -2.052, p = .040), but more time for topic searches ( Z = -3.385, p = <.001), compared with the pre-test.

With regard to the time spent interacting with the Calliope modules while revising during the post-test session, participants in both groups spent on average between 3 and 4 minutes consulting their respective modules during the revision task. This might seem like a short time, but it should be remembered that participants had already familiarised themselves with the modules during the pre-test session, after completing the revision task (see Section 3.1). Specifically, the experimental group  had  spent  on  average  46  minutes  on  the  module,  while  the  control  group  had  spent  on average 34 minutes.

## 5.4. Retrospective interviews (post-test)

With regard to revision strategies, participants in both the experimental and the control group reportedly  focused  to  a  large  extent  on  the  same  issues  when  revising  the  text.  Mentions  of vocabulary,  syntax,  visual  aspects,  tone,  structure,  relevance,  and  contextual  information  were present in interviews with both groups. For instance, with regard to vocabulary:

PC19: [D]ifficult words... I tried to look them up and explain them.

PE10: I would just remove all the jargon and the specific terms used in a report.

As far as visual aspects are concerned:

PC22: I used some bullet points as well.

PE10: And what I also did while revisioning my text is use more words that are highlighted so that it's more scannable.

In  terms  of  tone,  participants  tried  to  make  the  text  more  personal,  for  example  by  using exclamation  marks  or  replacing  the  tobacco  company's  name  with  the  pronoun  'We'.  Syntaxoriented edits  involved  shortening  the  sentences.  Relevance-oriented  comments  were  quite frequent, but some participants pointed out the difficulty of deciding which information to leave in and which information to leave out when revising:

PC13: Like, sometimes I was thinking, like 'Do I have to say this? Is this important? Is this too...?' And that was a little bit tricky for me.

Linked with relevance comments, one participant in the control group and one participant in the experimental group also pointed out the need to add some contextual information, specifically on the available portfolio of smoke-free products. The addition of such contextual information often involved doing online searches on the topic of tobacco and smoke-free products:

PE10:  When  I  looked  at  the  case,  I  saw  a  link  to  more  information  about  smoke-free cigarettes, and when I was reading that article, I thought 'Oh, maybe it would be interesting to focus on what the product is, why you will switch to it and then what we do already for those smoke free cigarettes'.

Structure is the textual aspect that received the most attention from participants, who used titles and (sub-)headings  to  give  structure  to  the  text  and  to  merge  similar  content  together.  It  should  be remembered that our texts lacked these elements (Section 3.3):

PE23: So this text, I think, some parts were kind of similar. Well, the content was the same. So I put them together and I left out what was extra. [...] When I saw what the paragraph was about, the main idea, I tried to put that into a title and then work on it further because the titles were missing.

The motivation behind the various changes made to the text seems to be reader-orientedness for both the control and the experimental group:

PC07:  The  difficulty  may  be  some  of  the  more  technical  aspects,  how  to  properly communicate them to an audience that may not be fully familiar with some of the concepts.

PC19: [I]t's easier for the clients to understand them with an explanation, or an example, or something like that.

PC22: I tried to bring as much structure to the text as I could because I think it helps the readers on a website a lot when they're reading a text.

PE10: I just focused on what the products are about and the important information that a customer needs to know to switch, for example, to smoke-free products.

PE23: And then I was searching something about exclamation marks. I was thinking, if that was... If we could add that. But I did because I thought it was a text to the customers and it was on the website. So I thought, well, that it was OK to do that, that it wasn't too, well, business-like.

The fact that participants in  the  control  group showed  reader  orientedness might  be  due to their prior  knowledge  of  the  topic  and  experience  with  plain  language,  also  mentioned  during  the interviews:

PC07: [N]ot to blow my own horn too much, but I do believe that I have a firm grasp of how to write a text that will convince people to at least look at the product that is being sold.

PC19: And second [text] it was easier because I think  I know some more about this topic than the other topic.

When prompted by the questions, most participants were able to reflect upon their overall approach to the task, e.g., whether they followed a specific order, why they rewrote a new text from scratch rather than revising an existing one (Section 5.2), whether they already had a structure in mind, and so  on.  Overall,  participants'  processes  showed  features  of  expertise,  for  instance,  in  the  use  of multiple revision rounds or in the whole-text planning approach:

PC19: Sometimes I just write the structure that I want to have in my text. So, for instance, if I want to have a structure that is problem-solution based, I just write these small questions beforehand and then I answer them along the way.

PE18: [B]efore I did anything, I looked at the whole text. And then I started revising and I went...  I  did  it  a  second  time.  Like,  I  wrote  a  paragraph,  revised  it,  wrote  a  second,  then revised both, wrote three and then revised the whole bit to see if it looked like a whole.

Interestingly, the choice to rewrite a new text from scratch rather than revising the given one seems a way to manage the cognitive difficulty of the task, as highlighted in our review of earlier work (Section 2.3):

PC13: [W]hat's very handy for me, was to not adapt the text in the text while I was writing, but just start a new text, and then read. So I was watching and then writing. Sometimes I wrote the same and then I deleted it and then... So I was just typing a new text under the actual text. [...] I didn't do that the first time. So I was just like writing inside the text and it was like a soup. I didn't remember what I wrote, what was already in there. And it was, like, very... No, so, I said, now I'm going to, like, divide it up. And for every line I'm going to write, start  writing,  from zero. [...]  So then  I  had  like  a  better  overview of  what  I  already said. What is essential. What is not essential. And then I just deleted the whole part.

One striking difference that emerged between the control and the experimental group refers to the sub-theme about planning how to approach the task. Specifically, participants in the control group talked  about  their  difficulty  with  planning.  In  contrast,  participants  in  the  experimental  group seemed to benefit from the module as it provided them with stepwise guidance on how to approach the text:

PC19: So I'm actually a bit relieved now, to be honest, because I thought I went through the entire Calliope and I thought to myself 'Where is the part where we learn how to write?' [...] [W]hen  I  was  writing,  it  was  easy  going  and  I  found  some  solutions  here  and  there,  but starting the writing process was rather difficult.

PE18: This time I had more of a plan in my head because the first time when I revised it, I just  started.  This  time  I  had  a  clear  image  what  I  was  going  to  look  at.  Like  vocabulary, sentence structures... So I kind of had a building plan in my head of what I wanted it to look like before I started writing.

Experimental participants also expanded on how interacting with the module helped them manage the cognitive demands of the revision task:

PE10: But the module was very clear and gave me a good idea of what I really need to focus on. It was very clear, it was bulleted, so I knew that I need to focus on that and that I need to focus on that.

PE18:  Because  it  [the  module]  provided  a  step-by-step,  like...  Because  it  gave  a  clear definition of what is a good text, what makes a text simple, what makes it easier, easy to read and accessible. And because before I started revising... So, on Monday, [day of the pre-test session] I didn't really have a clear image in my mind, in my head, what a good text was, and now I had like a plan with steps and I follow them. And that made it easier for me.

As  the  participants  in  the  control  group  did  not  receive  formal  instruction  on  how  to  write accessible  texts  (Section  3.2),  it  is  not  surprising  that,  when  interacting  with  their  module,  they found the concrete examples of accessible web communications particularly helpful:

PC19: [T]here was a lot of theory and it was interesting, but a lot of it I already knew, but I never saw a concrete example, so that was really interesting.

PC22:  It helped me that there were some examples of websites that already had a page on CSR. I did use them. I did go back and forth and look at them. And so that helped me a lot.

However, exposure to these examples did not seem to translate into the same stepwise guidance that the experimental module provided.

## 6. Discussion and conclusions

We carried out a study to test the impact of reader-oriented training on the revision process. Our reader-oriented  training -the  intervention of this  study -  revolved  around  the  accessible communication of CSR content. Using keystroke logging and retrospective interviews in a pre-test post-test design, we collected data on the cognitive effort (pauses, strategies, and source use) of 47 Master students (with English as a second language) revising extracts of corporate reports in order to  make  them  accessible  enough for  a  corporate  website.  Students  were  randomly  assigned  to  a control and to an experimental group, with the latter receiving our reader-oriented training.

On average, students had little prior knowledge about CSR (i.e., the topic of the texts). Most of them had not previously attended training on accessible communication. However, based on their academic studies in the domain of professional communication, our participants were between the knowledge-transforming and the knowledge-crafting stage of writing development, characterised by the ability to consider and balance the author's intentions, the features of the text at any given moment, and the reader's interpretation (Kellogg 2008). In other words, our students were already showing some traits of writing and revision expertise. For instance, as highlighted by the interview data,  they  made  different  rounds  of  revisions  and  they  concerned  themselves  with  a  number  of issues during their revisions, such as vocabulary, syntax, visual aspects, tone, structure, relevance, and contextual information (Becker 2006). Participants went so far as to remove and add content by relying on online searches about the topics of the texts. Such ability to consider global text features is also indicative of their high level of English proficiency. In other words, the students seemed to have automatised procedures to deal with local issues (e.g., spelling and grammar), which left them with  enough  working memory resources to consider the  text  as a  whole  and its  intended reader (Barkaoui 2016; McCutchen 2011).

Our training did not have an impact on the general pausing behaviour - and, by extension, on the  cognitive  effort  -  linked  with  the  revision  task.  However,  it  seemed  to  reduce  the  cognitive effort linked with lexical choices for the experimental participants in the post-test, as indicated by the  lower  proportion  of  between-word  pauses  in  the  post-test,  and  by  the  consistently  lower proportion of time and keystrokes devoted to language searches. For second-language writers and revisers,  lexical  decisions  tend  to  be  determined  by  level  of  language  proficiency,  with  more proficient writers/revisers showing a higher degree of lexical sophistication (Kim/Crossley 2018). However, in our study, participants in the experimental and control group were similar in terms of average number of years of English study (and all quite proficient in English, as mentioned above). The fact that our intervention training - although limited in time and scope - assisted students in the  experimental  group  with  vocabulary  selection  is  in  line  with  previous  research  showing  that instruction  fostering  awareness  about  the  external  context  of  a  text  (including  the  intended audience) can have a positive effect on second-language writing fluency and lexical sophistication (Yasuda 2011).

In examining the revision strategies adopted by the students, we observed a general preference for rewriting a text from scratch rather than carrying out internal revisions within the assigned text, despite the fact that rewriting was not presented as a strategy in either of the modules. Based on the qualitative comments, the decision to rewrite seems to be determined by the need to manage the complexity of the task and the resulting cognitive effort, as also pointed out in Hayes et al. (1987). In  other  words,  our  participants  might  have  perceived  the  texts  as  too  problematic  and/or might have been unsure of how to address the issues identified, thus deciding that rewriting would be the more  'economical'  approach.  However,  keystroke  logging  data  related  to  pausing  showed  that  compared with narrow revision - rewriting actually required more cognitive and temporal effort. Accordingly, our students might have underestimated the effort required to write a semi-specialised text  from  scratch  in  a  second  language  (Schoonen  et  al.  2009).  Based  on  this  observation,  we contend  that  there  is  a  need  for  providing  less  experienced  and/or  less  proficient  writers  with training  on  how  to  accurately  gauge the  effort required  by revision  following  a  diagnosis  of  the issues  in  a  text.  Such  decisions  on  how  to  approach  a  text  might  be  particularly  relevant  in  the workplace (e.g., in a corporate environment), where content tends to be produced and revised under time- and resource-constraints (Schriver 2012).

Some participants also switched strategy (from rewriting to revision, or vice versa) between pretest and post-test. While the control group varied in their strategy switches, the experimental group showed  clear  patterns  of  preferring  narrow  revision  to  rewriting  in  the  post-test  session.  This decision allowed experimental participants to save cognitive effort linked with lexical decisions, as indicated by their significantly shorter between-word pauses. The fact that some of the students in the experimental group switched to a less cognitively demanding revision strategy in the post-test might be the result of the module on accessible communication in which they took part. In other words, if we assume that the choice between rewriting and revising is determined by the need to manage the (expected) cognitive effort of the task at hand (Hayes et al. 1987), then the content in the  module  -  and  the  exemplary  process  videos  included  -  might  have  shown  students  how  to address issues in a text (e.g. in terms of vocabulary, syntax, cohesion, and so on) without having to start a new document from scratch and, in turn, saving mental effort. The interview data seem to confirm these assumptions since the experimental participants reported that the training provided them with procedural knowledge on how to approach the text, and specifically: (i) knowledge on the textual aspects to be considered; and (ii) criteria against which to assess the quality/accessibility of a text. Such positive impact of training has not always been observed. For instance, Terryn et al. (2017)  found  that  a  course  on  translation  revision  did  not  significantly  improve  their  students' procedural knowledge as they did not always adopt the most efficient procedure.

## 7. Limitations and future research

This study has some limitations which point to avenues for future research. The relatively small and  homogenous  group  of  participants  makes  our  results  only  partially  generalisable  to  other contexts. Some analyses in particular - such as the keystroke logging data related to rewriting vs revision  strategies  -  would  benefit  from  a  larger  sample  of  participants.  Future  research  should focus on the extent to which the adoption of these strategies influences multiple aspects related to the  revision  process,  from  pausing,  to  source  use,  to  number  and  types  of  revisions.  Using retrospective  interviews  or  think  aloud  protocols  with  a  larger  group  of  participants  might  also provide  more  insights  into  the  reasons  why  second-language  students  prefer  one  strategy  over another (and why they switch between strategies). Our participants were already proficient users of English as a second language and had quite some writing experience. In follow-up work, it might be interesting to test the impact of this (or similar) modules on less experienced/proficient students, and maybe also on expert writers.

We observed that, following participation  in the training,  the  control  group  carried  out  more online topic searches, which is likely to be linked with the exclusive focus of their module on the topic of CSR. Therefore, another interesting research avenue would be to investigate if and how training  affects  source  use  during  writing  or  revision.  Furthermore,  asking  second-language students to revise texts of a different domain or genre (e.g., financial reports), might complement our data and shed light on other aspects of revision in a second language.

Finally,  our  focus  in  this  paper  was  exclusively  on  the  revision  process.  We  are  currently analysing  the  products (or  texts)  produced  by  the  students  from  several  perspectives  to  see  if differences and similarities in the final texts are reflected in the observed processes.

## Authors' note

This study was approved by the Ethics Committee for the Social Sciences and Humanities at the University of Antwerp.

## Funding

This  project  has  received  funding  from  the  European  Union's  Horizon  2020  research  and innovation programme under the Marie Sklodowska-Curie grant agreement No 888918.

## References

- Allal,  Linda/Chanquoy, Lucile 2004: Introduction: Revision Revisited. In Allal, Linda/Chanquoy, Lucile/Largy, Pierre (eds.), Revision Cognitive and Instructional Processes . New York: Springer Science+Business Media, 1-7. https://doi.org/10.1007/978-94-007-1048-1\_1
- Arias Badia, Blanca/Matamala Ripoll, Anna 2020: Audio Description Meets Easy-to-Read and Plain Language: Results from a Questionnaire and a Focus Group in Catalonia. In Zeitschrift für Katalanistik 33, 251-270.

http://www.romanistik.uni-freiburg.de/pusch/zfk/33/13\_Arias-Badia\_Matamala.pdf

- Baaijen, Veerle/Galbraith, David/De Glopper, Kees 2012: Keystroke Analysis: Reflections on Procedures and Measures. In Written Communication 29(3), 246-277. https://doi.org/10.1177/0741088312451108
- Barkaoui, Khaled 2016: What and When Second-Language Learners Revise when Responding to Timed Writing Tasks on the Computer: The Roles of Task Type, Second Language Proficiency, and Keyboarding Skills. In The Modern Language Journal 100(1), 320-340. https://doi.org/10.1111/modl.12316

- Barkaoui, Khaled 2007: Revision in Second Language Writing: What Teachers Need to Know. In TESL Canada Journal 25(1), 81-92. https://doi.org/10.18806/tesl.v25i1.109
- Becker,  Anne  2006:  A  Review  of  Writing  Model  Research  Based  on  Cognitive  Processes.  In  Horning,  Alice/Becker, Anne (eds.), Revision: History, Theory, and Practice. West Lafayette, Indiana: Parlor Press, 25-49. https://wac.colostate.edu/docs/books/horning\_revision/chapter3.pdf
- Carter, Michael 1990: The Idea of Expertise: An Exploration of Cognitive and Social Dimensions of Writing. In College Composition and Communication 41(3), 265-286. https://doi.org/10.2307/357655
- Chukharev-Hudilainen, Evgeny/Saricaoglu, Aysel/Torrance, Mark/Feng, Hui-Hsien 2019: Combined Deployable Keystroke  Logging  and  Eyetracking  for  Investigating  L2  Writing  Fluency.  In Studies  in  Second  Language Acquisition 41(3), 583-604. https://doi.org/10.1017/S027226311900007X
- Conijn, Rianne/Dux Speltz, Emily/Chukharev-Hudilainen, Evgeny 2021: Automated Extraction of Revision Events from Keystroke Data. In Reading and Writing , 1-26. https://doi.org/10.1007/s11145-021-10222-w
- Eden, Rick/Mitchell, Ruth 1986: Paragraphing for the Reader. In College Composition and Communication 37(4), 416441. https://doi.org/10.2307/357912
- Faigley, Lester/Witte, Stephen 1981: Analyzing Revision. In College Composition and Communication 32(4), 400-414. https://doi.org/10.2307/356602
- Federici, Federico/Cadwell, Patrick 2018: Training Citizen Translators: Design and Delivery of Bespoke Training on the Fundamentals of Translation for New Zealand Red Cross. In Translation Spaces 7(1), 20-43. https://doi.org/10.1075/ts.00002.fed
- Golob, Urša/Podnar, Klement 2019: Researching CSR and Brands in the Here and Now: An Integrative Perspective. In Journal of Brand Management 26, 1-8. https://doi.org/10.1057/s41262-018-0112-6
- Greer,  Rachelle  2012:  Introducing  Plain  Language  Principles  to  Business  Communication  Students.  In Business  and Professional Communication Quarterly 75(2), 136-152. https://doi.org/10.1177/1080569912441967
- Hall,  Chris  1990:  Managing  the  Complexity  of  Revising  Across  Languages.  In Tesol  Quarterly 24(1),  43-60. https://doi.org/10.2307/3586851
- Hall, Sophie/Baaijen, Veerle/Galbraith, David 2022: Constructing Theoretically Informed Measures of Pause Duration in Experimentally Manipulated Writing. In Reading & Writing . https://doi.org/10.1007/s11145-022-10284-4
- Hayes, John 2012: Modeling and Remodeling Writing. In Written Communication 29(3), 369-388. https://doi.org/10.1177/0741088312451260
- Hayes, John 2004: What Triggers Revision? In Allal, Linda/Chanquoy, Lucile/Largy, Pierre (eds.), Revision Cognitive and  Instructional  Processes .  New  York:  Springer  Science+Business  Media,  9-20.  https://doi.org/10.1007/978-94007-1048-1\_2
- Hayes,  John/Flower,  Linda/Schriver,  Karen/Stratman,  James/Carey,  Linda  1987:  Cognitive  Processes  in  Revision.  In Rosenberg, Sheldon (ed.), Advances in Applied Psycholinguistics: Reading, Writing, and Language Learning (Vol. 2). Cambridge: Cambridge University Press, 176-240.
- Jackson, Tanner/Allen, Laura/McNamara, Danielle 2016: Common Core TERA: Text Ease and Readability Assessor. In Crossley, Scott/McNamara, Danielle (eds.), Adaptive Educational Technologies for Literacy Instruction . New York: Routledge, 49-68.
- Janssen, Daniël 2001: Improving the Quality of Public Documents or: Why Training Public Writers Often Doesn't Work. In  Janssen,  Daniël/Neutelings,  Rob  (eds.), Reading  and  Writing  Public  Documents .  Amsterdam:  John  Benjamins Publishing Company, 105-130. https://doi.org/10.1075/ddcs.1.06jan
- Kellogg, Ronald 2008: Training Writing Skills: A Cognitive Developmental Perspective. In Journal of Writing Research 1(1), 1-26. https://doi.org/10.17239/jowr-2008.01.01.1
- Kellogg, Ronald/Olive, Thierry/Piolat, Annie 2007: Verbal, Visual, and Spatial Working Memory in Written Language Production. In Acta Psychologica 124(3), 382-397. https://doi.org/10.1016/j.actpsy.2006.02.005
- Kim, Minkyung/Crossley, Scott 2018: Modeling Second Language Writing Quality: A Structural Equation Investigation of Lexical, Syntactic, and Cohesive Features in Source-Based and Independent Writing. In Assessing Writing 37, 3956. https://doi.org/10.1016/j.asw.2018.03.002
- Kobayashi,  Hiroe/Rinnert, Carol 2001:  Factors Relating to  EFL  Writers' Discourse Level  Revision Skills. In International Journal of English Studies 1(2), 71-101.
- Kools,  Marieke/Ruiter,  Robert/Van  De  Wiel,  Margaretha/Kok,  Gerjo  2008:  The  Effects  of  Headings  in  Information Mapping on Search Speed and Evaluation of a Brief Health Education Text. In Journal of Information Science 34(6), 833-844. https://doi.org/10.1177/0165551508089719
- Koponen, Maarit 2016: Is Machine Translation Post-Editing Worth the Effort? A Survey of Research into Post-Editing and Effort. In The Journal of Specialised Translation 25, 131-148. https://www.jostrans.org/issue25/art\_koponen.pdf
- Kuteeva, Maria 2011: Wikis and Academic Writing: Changing the Writer-Reader Relationship. In English for Specific

- Purposes 30, 44-57. https://doi.org/10.1016/j.esp.2010.04.007
- Lee, Vivian (2016). Considerations of Relevance to the Target Reader in Korean into English Translation. In Asia Pacific Translation and Intercultural Studies 3(1), 62-75. https://doi.org/10.1080/23306343.2015.1131880
- Leijten,  Mariëlle/Van  Waes,  Luuk  2013:  Keystroke  Logging  in  Writing  Research:  Using  Inputlog  to  Analyze  and Visualize Writing Processes. In Written Communication 30(3), 358-392. https://doi.org/10.1177/0741088313491692
- Leijten, Mariëlle/Van Waes, Luuk 2020: Designing Keystroke Logging Research in Writing Studies. In Chinese Journal of Second Language Writing 1(1), 18-39.
- Leijten, Mariëlle/Van  Horenbeeck, Eric/Van Waes, Luuk 2019: Analysing Keystroke Logging Data from a Linguistic Perspective. In Lindgren, Eva/Sullivan, Kirk (eds.), Observing Writing . Leiden: Brill, 71-95. https://doi.org/10.1163/9789004392526\_005
- Leijten, Mariëlle/Van Waes, Luuk/Schrijver, Iris/Bernolet, Sarah/Vangehuchten, Lieve 2019: Mapping Master's Students' Use of External Sources in Source-Based Writing in L1 and L2. In Studies in Second Language Acquisition 41(3), 555-582. https://doi.org/10.1017/S0272263119000251
- Lindgren, Eva/Leijten, Mariëlle/Van Waes, Luuk 2011: Adapting to the Reader during Writing. In Written Language & Literacy 14(2), 188-223. https://doi.org/10.1075/wll.14.2.02lin
- Lindgren, Eva/Sullivan, Kirk 2006: Analysing Online Revision. In Lindgren, Eva/Sullivan, Kirk (eds.), Computer KeyStroke Logging and Writing . Leiden: Brill, 157-188. https://doi.org/10.1163/9780080460932\_010
- Lu,  Xiaojun/Révész,  Andrea  2021:  Revising  in  a  Non-Alphabetic  Language:  The  Multi-Dimensional  and  Dynamic Nature Of Online Revisions in Chinese as a Second Language. In System 100, 102544. https://doi.org/10.1016/j.system.2021.102544
- Matthews,  Bob/Ross,  Liz  2010: Research  Methods:  A  Practical  Guide  for  the  Social  Sciences .  London:  Pearson Education Ltd.
- McCutchen, Deborah 2011: From Novice to Expert: Implications of Language Skills and Writing-Relevant Knowledge for Memory during the Development of Writing Skill. In Journal of Writing Research 3(1), 51-68. https://doi.org/10.17239/jowr-2011.03.01.3
- McCutchen, Deborah 2000: Knowledge, Processing, and Working Memory: Implications for a Theory of Writing.  In Educational Psychologist 35(1), 13-23. https://doi.org/10.1207/S15326985EP3501\_3
- Nilipour, Azadeh/De Silva, Tracy-Anne/Li, Xuedong 2020: The Readability of Sustainability Reporting in New Zealand over Time. In Australasian Accounting, Business and Finance Journal 14(3), 86-107. https://doi.org/10.14453/aabfj.v14i3.7
- Olive,  Thierry  2012:  Working  Memory  in  Writing.  In  Berninger,  Virginia  Wise  (ed.), Past,  Present,  and  Future Contributions  of  Cognitive  Writing  Research  to  Cognitive  Psychology .  New  York:  Psychology  Press,  Taylor  & Francis Group, 485-503.
- Olive, Thierry/Alves, Rui Alexandre/ Castro, São Luís 2009: Cognitive Processes in Writing during Pause and Execution Periods. In European Journal of Cognitive Psychology 21(5), 758-785. https://doi.org/10.1080/09541440802079850
- Piolat,  Annie/Roussey,  Jean-Yves/Olive,  Thierry/Amada,  Murielle  2004:  Processing  Time  and  Cognitive  Effort  in Revision: Effects of Error Type and of Working Memory Capacity. In Allal, Linda/Chanquoy, Lucile/Largy, Pierre (eds.), Revision  Cognitive  and  Instructional  Processes .  New  York:  Springer  Science+Business  Media,  21-38. https://doi.org/10.1007/978-94-007-1048-1\_3
- Révész, Andrea/Michel, Marije/Lee, Minjin 2019: Exploring Second Language Writers' Pausing and Revision Behaviors: A Mixed-Methods Study. In Studies in Second Language Acquisition 41(3), 605-631. https://doi.org/10.1017/S027226311900024X
- Rijlaarsdam,  Gert/Braaksma,  Martine/Couzijn,  Michel/Janssen,  Tanja/Raedts,  Mariet/Van  Steendam,  Elke/Toorenaar, Anne/Van den Bergh, Huub 2008: Observation of Peers in Learning to Write. Practice and Research. In Journal of Writing Research 1(1), 53-83. https://doi.org/10.17239/jowr-2008.01.01.3
- Rossetti, Alessandra/Van Waes, Luuk 2022: Accessible Communication of Corporate Social Responsibility: Development  and  Preliminary  Evaluation  of  an  Online  Module.  In Business  Professional  and  Communication Quarterly 85(1), 52-79. https://doi.org/10.1177/23294906221074324
- Sagi, Doron/Spitzer-Shohat, Sivan/Schuster, Michal/Daudi, Ligat/Rudolf, Mary  Catharine  2021: Teaching Plain Language to Medical Students: Improving Communication with Disadvantaged Patients. In BMC Medical Education 21(407), 1-8. https://doi.org/10.1186/s12909-021-02842-1
- Schoonen,  Rob/Snellings,  Patrick/Stevenson,  Marie/Van  Gelderen,  Amos  2009:  Towards  a  Blueprint  of  the  Foreign Language Writer: The Linguistic and Cognitive Demands of Foreign Language Writing. In Manchón, Rosa (ed.), Writing in  Foreign  Language Contexts: Learning, Teaching, and Research .  Bristol:  Multilingual  Matters,  77-101. https://doi.org/10.21832/9781847691859-007
- Schrijver,  Iris/Van  Vaerenbergh,  Leona/Leijten,  Mariëlle/Van  Waes,  Luuk  2016:  The  Impact  of  Writing  Training  on Transediting in Translation, Analyzed from a Product and Process Perspective. In Perspectives 24(2), 218-234.

- Schriver, Karen 2017: Plain Language in the US Gains Momentum: 1940-2015. In IEEE Transactions on Professional Communication 60(4), 343-383. https://doi.org/10.1109/TPC.2017.2765118
- Schriver,  Karen  2012:  What  We  Know  about  Expertise  in  Professional  Communication.  In  Berninger,  Virginia  Wise (ed.), Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology .  New York: Psychology Press, Taylor & Francis Group, 275-312.
- Schriver,  Karen  1992:  Teaching  Writers  to  Anticipate  Readers'  Needs:  A  Classroom-Evaluated  Pedagogy.  In Written Communication 9(2), 179-208. https://doi.org/10.1177/0741088392009002001
- Sengupta,  Sima  2000:  An  Investigation  into  the  Effects  of  Revision  Strategy  Instruction  on  L2  Secondary  School Learners. In System 28(1), 97-113. https://doi.org/10.1016/S0346-251X(99)00063-9
- Shen, Cuiping/Chen, Ningyang 2021: Profiling the Pausing Behaviour of EFL Learners in Real-Time Computer-Aided Writing: A Multi-Method Case Study. In Asian-Pacific Journal of Second and Foreign Language Education 6(1), 126. https://doi.org/10.1186/s40862-021-00118-1
- Smeuninx, Nils/De Clerck, Bernard/Aerts, Walter 2020: Measuring the Readability of Sustainability Reports: A CorpusBased Analysis through Standard Formulae and NLP. In International Journal of Business Communication 57(1), 5285. https://doi.org/10.1177/2329488416675456
- Suzuki, Manami 2008: Japanese Learners' Self Revisions and Peer Revisions of Their Written Compositions in English. In Tesol Quarterly 42(2), 209-233. https://doi.org/10.1002/j.1545-7249.2008.tb00116.x
- Terryn,  Ayla/Robert,  Isabelle/Ureel,  Jim/Remael,  Aline/Hanoulle,  Sabien  2017:  Conceptualizing  Translation  Revision Competence: A Pilot Study on The Acquisition of The Knowledge about Revision and Strategic Subcompetences. In Across Languages and Cultures 18(1), 1-27. https://doi.org/10.1556/084.2017.18.1.1
- Van  Waes,  Luuk/Leijten,  Mariëlle  2015:  Fluency  in  Writing:  A  Multidimensional  Perspective  on  Writing  Fluency Applied to L1 and L2. In Computers and Composition 38, 79-95. https://doi.org/10.1016/j.compcom.2015.09.012
- Van  Waes,  Luuk/Leijten,  Mariëlle/Van  Weijen,  Daphne  2009:  Keystroke  Logging  in  Writing  Research:  Observing Writing Processes with Inputlog. In German as a Foreign Language 2, 41-64.
- Van Waes, Luuk/Van Weijen, Daphne/Leijten, Mariëlle 2014: Learning to Write in an Online Writing Center: The Effect of Learning Styles on the Writing Process. In Computers & Education 73, 60-71. https://doi.org/10.1016/j.compedu.2013.12.009
- Vandermeulen,  Nina/Leijten,  Mariëlle/Van  Waes,  Luuk  2020:  Reporting  Writing  Process  Feedback  in  the  Classroom Using  Keystroke  Logging  Data  to  Reflect  on  Writing  Processes.  In Journal  of  Writing  Research 12(1),  109-139. https://doi.org/10.17239/jowr-2020.12.01.05
- Wallace, David/Hayes, John 1991: Redefining Revision for Freshmen. In Research in the Teaching of English 25, 54-66.
- Warde,  Fiona/Papadakos,  Janet/Papadakos,  Tina/Rodin,  Danielle/Salhia,  Mohammad/Giuliani,  Meredith  2018:  Plain Language Communication as a Priority Competency for Medical Professionals in a Globalized World. In Canadian Medical Education Journal 9(2), e52-e59.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044302/
- Wei, Lewen 2020: Examining Corporate Communications of Environmental Responsibility on Corporate Websites: Main Themes, Linguistic Features, and Text Reuse. In Journal of Promotion Management 26(7), 1013-1037. https://doi.org/10.1080/10496491.2020.1746467
- Whyatt, Bogusława 2018: Testing Indicators of Translation Expertise in an Intralingual Task. In HERMES-Journal of Language and Communication in Business 57, 63-78. https://doi.org/10.7146/hjlcb.v0i57.106194
- Xu, Ting Sophia/Zhang, Lawrence Jun/Gaffney, Janet 2021: Examining the Relative Effects of Task Complexity and Cognitive Demands on Students' Writing in a Second Language. In Studies in Second Language Acquisition ,  1-24. https://doi.org/10.1017/S0272263121000310
- Yasuda,  Sachiko  2011:  Genre-Based  Tasks  in  Foreign  Language  Writing:  Developing  Writers'  Genre  Awareness, Linguistic Knowledge, and Writing Competence. In Journal of Second Language Writing 20(2), 111-133. https://doi.org/10.1016/j.jslw.2011.03.001
- Zamir,  Farah/Saeed,  Abubakr  2020:  Location  Matters:  Impact  of  Geographical  Proximity  to  Financial  Centers  on Corporate Social Responsibility (CSR) Disclosure in Emerging Economies. In Asia Pacific Journal of Management 37(1), 263-295. https://doi.org/10.1007/s10490-018-9619-3
- Zhang,  Fan/Hashemi,  Homa/Hwa,  Rebecca/Litman,  Diane  2017:  A  Corpus  of  Annotated  Revisions  for  Studying Argumentative Writing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Stroudsburg: The Association for Computational Linguistics, 1568-1578. https://doi.org/10.18653/v1/P17-1144
- Zhao,  Huahui  2010:  Investigating  Learners'  Use  and  Understanding  of  Peer  and  Teacher  Feedback  on  Writing:  A Comparative Study in a Chinese English Writing Classroom. In Assessing Writing 15(1), 3-17. https://doi.org/10.1016/j.asw.2010.01.002

Appendix A: Full list of pause-related sub-variables examined

Between-group analysis of pausing behaviour in the pre- and post-test Within-group analysis of pausing behaviour for the experimental and control group Note: There are slight differences in scores between the within-subjects and the between-subjects analysis. These slight differences are due to the fact that, for the within-subjects analysis involving paired observations, some participants had to be excluded (see Section 4.1.1 on data preparation).

|                                                        | Pre-test            | Pre-test            | Pre-test       | Pre-test       |          | Post-test           | Post-test           | Post-test      | Post-test      |          |
|--------------------------------------------------------|---------------------|---------------------|----------------|----------------|----------|---------------------|---------------------|----------------|----------------|----------|
|                                                        | Experimental (N=24) | Experimental (N=24) | Control (N=19) | Control (N=19) | p- value | Experimental (N=23) | Experimental (N=23) | Control (N=22) | Control (N=22) | p- value |
|                                                        | M                   | SD                  | M              | SD             |          | M                   | SD                  | M              | SD             |          |
| Overall process                                        |                     |                     |                |                |          |                     |                     |                |                |          |
| Total process time (secs)                              | 2741.98             | 1241.95             | 2730.71        | 951.6          | .769     | 2995.09             | 899.7               | 2880.65        | 941.84         | .803     |
| Total process time (min:secs)                          | 45:42               | 20:42               | 45:31          | 15:52          | .769     | 49:55               | 15:00               | 48:01          | 15:42          | .803     |
| Total active writing time (secs)                       | 841.95              | 388.14              | 1007.09        | 540.31         | .293     | 978.26              | 399.03              | 1023.6         | 416.62         | .666     |
| Total active writing time (min:secs)                   | 14:01               | 06:28               | 16:47          | 09:00          | .293     | 16:18               | 06:39               | 17:04          | 06:57          | .666     |
| Pausing                                                |                     |                     |                |                |          |                     |                     |                |                |          |
| behaviour Total pause time (secs)                      | 1900.03             | 917.9               | 1723.61        | 556.31         | .714     | 2016.82             | 631.71              | 1857.04        | 622.37         | .388     |
| Total pause time (min:secs)                            | 31:40               | 15:18               | 28:44          | 09:16          | .714     | 33:37               | 10:32               | 30:57          | 10:22          | .388     |
| Proportion of pause time                               | 68.68%              | 8.02%               | 64.57%         | 10.4%          | .221     | 67.4%               | 8.43%               | 64.49%         | 6.56%          | .107     |
| Total number of pauses                                 | 1533.12             | 710.24              | 1535.15        | 603.59         | .961     | 1735.95             | 612.2               | 1607.31        | 670.18         | .467     |
| Total number of                                        | .56                 | .11                 | .56            | .08            | .845     | .57                 | .09                 | .55            | .10            | .570     |
| pauses per second Geometric mean of pauses (secs)      | .55                 | .07                 | .56            | .05            | .456     | .55                 | .07                 | .56            | .08            | .683     |
| Number of P- Bursts                                    | 159.5               | 73.7                | 155            | 55.158         | .922     | 176.91              | 59.71               | 161.59         | 57.05          | .340     |
| Number of P-                                           | 3.49                | .59                 | 3.46           | .68            | .912     | 3.52                | .46                 | 3.36           | .59            | .184     |
| Bursts per minute Mean process time of P-Bursts (secs) | 10.92               | 3.18                | 12.69          | 5.27           | .359     | 11.19               | 2.85                | 12.41          | 3.48           | .256     |
| Pause locations                                        |                     |                     |                |                |          |                     |                     |                |                |          |
| Number of within- word pauses                          | 499.5               | 275.33              | 388.47         | 185.31         | .115     | 528.78              | 253.68              | 429.81         | 224.9          | .220     |
| Number of within- word pauses per                      | .17                 | .06                 | .14            | .04            | .056     | .17                 | .06                 | .14            | .04            | .146     |
| second Geometric mean of within-word                   | .32                 | .02                 | .31            | .01            | .625     | .32                 | .02                 | .31            | .02            | .919     |
| pauses (secs) Number of between-word                   | 293.75              | 151.53              | 248.89         | 122.77         | .31      | 315.39              | 136.93              | 305.55         | 138.64         | .674     |
| pauses Number of between-word                          | .10                 | .04                 | .09            | .04            | .42      | .10                 | .03                 | .10            | .03            | .602     |
| pauses per second Geometric mean of between-word       | .57                 | .13                 | .54            | .12            | .525     | .55                 | .14                 | .51            | .10            | .601     |
| pauses (secs) Number of between-sentence               | 7.67*               | 7.16                | 10.47*         | 5.45           | < .05    | 9.21                | 7.15                | 12.13          | 6.38           | .133     |

| pauses Number of                                      |   .002* |   .002 |   .003* |   .001 |   < .05 |   .003 |   .002 |   .004 |   .002 |   .102 |
|-------------------------------------------------------|---------|--------|---------|--------|---------|--------|--------|--------|--------|--------|
| between-sentence pauses per second Geometric mean     |    1.06 |    .97 |     .82 |    .32 |    .778 |   1.04 |   1.19 |    .69 |    .26 |   .503 |
| Number of before- paragraph pauses                    |   14.58 |   9.42 |   23.26 |  24.68 |    .265 |  18.82 |  10.83 |  18.27 |  10.86 |   .657 |
| Number of before- paragraph pauses                    |    .005 |   .003 |    .007 |   .004 |    .129 |   .006 |   .003 |   .006 |   .004 |   .964 |
| per second Geometric mean of before- paragraph pauses |     .57 |    .23 |     .57 |    .14 |    .434 |    .64 |    .32 |    .58 |    .11 |   .716 |
| Number of revision pauses                             |  173.08 | 112.67 |     169 |  93.15 |     .99 | 195.08 | 109.06 | 187.09 | 105.23 |   .768 |
| Number of revision pauses per second                  |     .06 |    .02 |     .06 |    .02 |    .903 |    .06 |    .02 |    .06 |    .03 |   .856 |
| Geometric mean of revision pauses (secs)              |     .57 |    .09 |     .54 |    .06 |    .399 |    .55 |    .05 |    .53 |    .07 |   .510 |

|                                      | Experimental (N=23)   | Experimental (N=23)   | Experimental (N=23)   | Experimental (N=23)   |          | Control (N=19)   | Control (N=19)   | Control (N=19)   | Control (N=19)   |          |
|--------------------------------------|-----------------------|-----------------------|-----------------------|-----------------------|----------|------------------|------------------|------------------|------------------|----------|
|                                      | Pre-test              | Pre-test              | Post-test             | Post-test             | p- value | Pre-test         | Pre-test         | Post-test        | Post-test        | p- value |
|                                      | M                     | SD                    | M                     | SD                    |          | M                | SD               | M                | SD               |          |
| Overall process                      |                       |                       |                       |                       |          |                  |                  |                  |                  |          |
| Total process time (secs)            | 2731.16               | 1268.71               | 2995.09               | 899.71                | .121     | 2730.72          | 951.60           | 2896.19          | 963.95           | .227     |
| Total process time (min:secs)        | 45:31                 | 21:09                 | 49:55                 | 15:00                 | .121     | 45:31            | 15:52            | 48:16            | 16:04            | .227     |
| Total active writing time (secs)     | 847.01                | 396.06                | 978.26                | 399.03                | .101     | 1007.10          | 540.32           | 1054.68          | 439.71           | .469     |
| Total active writing time (min:secs) | 14:07                 | 06:36                 | 16:18                 | 06:39                 | .101     | 16:47            | 09:00            | 17:35            | 07:20            | .469     |
| Pausing                              |                       |                       |                       |                       |          |                  |                  |                  |                  |          |
| behaviour Total pause time (secs)    | 1884.15               | 935.16                | 2016.83               | 631.71                | .144     | 1723.62          | 556.31           | 1841.52          | 608.40           | .277     |
| Total pause time (min:secs)          | 31:24                 | 15:35                 | 33:37                 | 10:32                 | .144     | 28:44            | 09:16            | 30:42            | 10:08            | .277     |
| Proportion of pause time             | 68.38%                | 8.05%                 | 67.4%                 | 8.43%                 | .605     | 64.57%           | 10.4%            | 63.78%           | 6.54%            | .629     |
| Total number of pauses               | 1543.00*              | 724.52                | 1735.96*              | 612.21                | < .05    | 1535.16          | 603.60           | 1649.68          | 704.34           | .295     |
| Total number of pauses per second    | .57                   | .11                   | .58                   | .10                   | .627     | .56              | .09              | .56              | .09              | .841     |
| Geometric mean of pauses (secs)      | .55                   | .07                   | .55                   | .07                   | .574     | .56              | .05              | .56              | .07              | .546     |
| Number of P- Bursts                  | 158.30                | 75.13                 | 176.91                | 59.71                 | .181     | 155.00           | 55.16            | 160.58           | 56.20            | .546     |
| Number of P-                         | 3.48                  | .60                   | 3.53                  | .47                   | .412     | 3.47             | .69              | 3.34             | .63              | .260     |

| Bursts per minute Mean process                        |   11.04 |   3.21 |   11.19 |   2.85 |   .879 | 12.69   |   5.28 | 12.68   |   3.57 | .355   |
|-------------------------------------------------------|---------|--------|---------|--------|--------|---------|--------|---------|--------|--------|
| Pause locations Number of within-word                 |  505.00 | 280.17 |  528.78 | 253.68 |   .447 | 388.47  | 185.32 | 444.32  | 229.57 | .227   |
| pauses Number of within-word pauses per               |     .18 |    .07 |     .18 |    .06 |   .976 | .14     |    .05 | .15     |    .04 | .601   |
| Geometric mean of within-word pauses (secs)           |     .33 |    .02 |     .32 |    .03 |    .08 | .32     |    .02 | .32     |    .02 | .629   |
| Number of between-word pauses                         |  296.78 | 154.19 |  315.39 | 136.93 |   .148 | 248.89* | 122.77 | 317.05* | 139.56 | < .05  |
| Number of between-word pauses per                     |     .11 |    .05 |     .10 |    .04 |   .523 | .10*    |    .04 | .11*    |    .03 | < .05  |
| second Geometric mean of between-word                 |     .57 |    .13 |     .55 |    .14 |   .094 | .55     |    .13 | .51     |    .12 | .073   |
| pauses (secs) Number of between-                      |    7.83 |   7.28 |    9.22 |   7.15 |   .231 | 10.47   |   5.45 | 12.95   |   5.98 | .117   |
| sentence pauses Number of between- sentence pauses    |    .002 |   .002 |    .003 |   .002 |   .259 | .003    |   .001 | .004    |   .002 | .469   |
| per second Geometric mean of between- sentence pauses |    1.08 |   1.00 |    1.05 |   1.19 |   .848 | .82     |    .33 | .73     |    .22 | .147   |
| (secs) Number of before- paragraph                    |   14.65 |   9.63 |   18.83 |  10.84 |   .094 | 23.26   |  24.68 | 19.26   |  11.33 | .747   |
| pauses Number of before- paragraph                    |    .005 |   .003 |    .006 |   .003 |   .162 | .007    |   .004 | .007    |   .004 | .809   |
| second Geometric mean of before- paragraph            |     .57 |    .24 |     .65 |    .33 |   .627 | .57     |    .14 | .58     |    .11 | .841   |
| pauses (secs) Number of revision pauses               |  175.13 | 114.75 |  195.09 | 109.07 |   .132 | 169     |  93.15 | 192.16  | 111.36 | .338   |
| Number of revision pauses per second                  |     .06 |    .03 |     .06 |    .03 |   .831 | .06     |    .03 | .07     |    .03 | .546   |
| Geometric mean of revision pauses (secs)              |     .57 |    .09 |     .55 |    .06 |   .533 | .55     |    .07 | .54     |    .08 | .546   |