professional writing from multiple sources
DESCRIPTION
Introduction Previous writing research has lead to various well-known writing process models. However, these models are primarily based on observations made in educational environments and relate to single texts. Professional writers in the workplace on the other hand often use multiple (digital) sources to succesfully write their business texts. Writing a business text, e.g. a report for a merger, is a very complex activity during which a wide variety of sources are consulted. This project, therefore, addresses the following research question: What characterizes the writing processes of professional writers 'designing' business texts from multiple (digital) sources? Method In this research project (2010-2013) we describe the activities of writing professionals when writing in their organisational setting (via keystroke logging and participative observation). In this stage, we have gathered a variety of writing process data, ranging from proposals to tweets. The writing process data are collected with Inputlog. Inputlog is a keystroke logging program that registers an identification of every activated window environment (e.g. program, document, or web page) which is very important for the source analysis. Results During the presentation we describe the main concepts of this research project via case studies: the use of multiple sources, the implications of sources on the fragmentation and fluency of the writing process, and we will end with an example of a linguistic analysis of the data. To show the complexity of professional writing we have, for instance, transferred the Inputlog data to a network analysis program (Pajek). A network analysis shows the relative time spent reading/writing the different sources and the direction/quantity of the transitions between the sources when producing a text. In one of our cases (a project proposal that took 10 hours to producs), we observed that this writer on average switches 5 times per minute between documents and programs and that he spends about 75% of the time consulting other (re)sources while writing. The conclusions will be related to the excisting writing models. Especially the decision process a writer needs to make to either retrieve information from the long term memory or consult an external (re)source seems to be an important aspect that influences the organisation of the writing process.TRANSCRIPT
The 13th International Conference of the EARLI Special Interest Group on Writing
Wednesday July 11, Porto, Portugal
Presentation
Professional Writing from Multiple Sources
www.ua.ac.be/marielle.leijten
Leijten, M., & Van Waes, L. (2012). Professional writing from multiple sources. Paper presented at the The 13th
International Conference of the EARLI Special Interest Group on Writing, Porto.
Mariëlle Leijten Flanders Research Foundation University of Antwerp [email protected] Luuk Van Waes University of Antwerp [email protected]
Professional writing from multiple sources
Mariëlle Leijten & Luuk Van Waes
Program
introduction
method
case
reflections
Introduction
I think I will start
writing a ... I will change the formulation of this sentence
into...
Flower & Hayes, 1981
1980’s
1996
Introduction
Hayes, 1996
?
?
External digital sources: Task schemas Topic knowledge Audience knowledge Linguistic knowledge Genre knowledge
Long term memory: Task schemas Topic knowledge Audience knowledge Linguistic knowledge Genre knowledge
Method
Observations (participative & Inputlog)
Interviews
Versions of documents
(Logbooks)
Inputlog 5.0.* Beta
Record Logging of sources: focus events
Procedures for professional writing
create new
open existing
continue previous
Case Study
Midsized Design Consulting Agency in BrusselsExperience in engineering, cognitive ergonomics, visual design & social sciences
Professional: Aiden 45 years old
Background in Economics and Management
No background in technical or professional writing
Case study
Proposal for Flemish Government
In cooperation with contractor
Duration 8:37:54
17 pages
55.000 lines of logging data
Session Date Duration % Sessionssession 1 4/04/2011 5:32:41 64,24%
session 2 4/04/2011 0:19:20 3,73%
session 3 5/04/2011 1:29:59 17,37%
session 4 7/04/2011 0:34:45 6,71%
session 5 7/04/2011 0:41:09 7,95%
Data preparation
Merging
Filtering
Coding sources
Graph of writing process
Percentage of time spend inProposal: 26%Sources: 74%
Type of text productionCopied: 75%New: 25%
template
searching
meeting
constructing
inserting/rewriting
searching
distraction
contextualizing
commenting
re‐reading
deleting
re‐reading
searching
constructing
constructing
connecting
Aiden says:
"I usually start working from a template‐based document. ... In this case I have used Google Docs also quite a lot. I made a distinction between Google Docs and our company Wiki. The Wiki holds more general company information and Google Docs contains more specific information that relates to a project. Because we have worked with this contractor before, we have a document that is constantly updated and shared. It contains all the agreements with contractor A and the information that needs to be included in the project proposal."
Aiden says:
"Ideally, we take a comparable proprosal based on our proposal template, or a comparable project that contains a lot of standard wordings. ... Sometimes the data is a bit what outdated. ... Standard tekst kan be retrieved from the Wiki or from previous texts. However, nowadays the previous texts are more recent than the Wiki. Then I choose a recent project proposal. It is a pragmatic decision."
Aiden says:
"I use a different document in which I save separate cases. It is just a large Word‐document in which I include all the case studies that I have ever written (e.g. case on usability of Sony webpages). Just, so I know they won't get lost. This could also be done in the Wiki, but to save images in a Wiki is terrible.”
Text construction
Network analyses (step 1)
Number of switchesTotal: 2759
Network analyses (step 2)
Number of switchesWithout transitions: 1118
Network analyses (step 3)
Relative time spent in:
Proposal: 29 %
Other documents: 24 %Mail: 18 %Other: 13 %Projectmanagement: 9 %Internet: 4%
Remainder: 3 %
Reflections on writing modelsExpertise in professional writing
Schriver, 2012Hayes, 1996
digital sources
search for re‐sources
Reflections: new features of Inputlog
Filters Time Filter
Event type Filter
Window Filter
Focus analyses
New feature of Inputlog 5.1
Focus analyses
227
148
60%
39%
New feature of Inputlog 5.1
Implementation of focus analyses
Thank you
Eric Van Horenbeeck (technical coordinator Inputlog)
Tom Pauwaert (programmer Inputlog)
Aiden S.
More information
Mariëlle Leijten, Flanders Research Foundation, Belgium
[email protected] ~ www.ua.ac.be/marielle.leijten
Luuk Van Waes, University of Antwerp, Belgium
[email protected] ~ www.ua.ac.be/luuk.vanwaes
www.writingpro.eu
www.inputlog.net
www.jowr.org
Inputlog 5.1 available