Investigating L2 performance in text chat

Applied Linguistics: 31/4: 554–577 � Oxford University Press 2010

doi:10.1093/applin/amq007 Advance Access published on 3 March 2010

Investigating L2 Performance in Text Chat

1SHANNON SAURO and 2BRYAN SMITH1University of Texas at San Antonio and 2Arizona State University

E-mail: [email protected]; [email protected]

This study examines the linguistic complexity and lexical diversity of both overt

and covert L2 output produced during synchronous written computer-mediated

communication, also referred to as chat. Video enhanced chatscripts produced

by university learners of German (N = 23) engaged in dyadic task-based chat

interaction were coded and analyzed for syntactic complexity (ratio of clauses

to c-units), productive use of grammatical gender, and lexical diversity (Index of

Guiraud). Results show that chat output that exhibits evidence of online plan-

ning in the form of post-production monitoring displays significantly greater

linguistic complexity and lexical diversity than chat output that does not exhibit

similar evidence of online planning. These findings suggest that L2 learners do

appear to use the increased online (i.e. moment-by-moment) planning time

afforded by chat to engage in careful production and monitoring.

INTRODUCTION

Computer-mediated environments represent a growing context for L2 learners

to study and use the target language (Chapelle 2008). This can be seen, for

example, in the use of computer-mediated communication (CMC) used among

students in the same classroom (e.g. Abrams 2003; Shekary and Tahririan

2006) or as part of larger collaborative class projects that link learners with

target language speakers in other cities and countries (e.g. Belz 2002; Lee

2004). Such technology-enhanced L2 environments call for research on

second language learning processes and outcomes that arise during or are

influenced by CMC (Skehan 2003). A small but growing body of research

has begun to examine claims and constructs from SLA during written synchro-

nous computer-mediated communication (SCMC), text-chat, in particular.

These include, for example, uptake following negotiated interaction (Smith

2005), the influence of task type on negotiation episodes during chat (Blake

2000; Pellettieri 2000), the amount and type of negotiation strategies that

occur during chat (Kotter 2003), the effectiveness of computer-mediated cor-

rective feedback on the development of L2 grammar (Loewen and Erlam 2006;

Sachs and Suh 2007; Sauro 2009), the provision of and responses to linguistic

affordances during NS/NNS telecollaboration (Darhower 2008), the use of

dynamic assessment to observe learner development during chat (Oskoz

2005), and the nature of CMC self-initiated self-repair (Smith 2008).

While findings have largely supported certain trends found in face-to-face

interaction, more dynamic data capture technologies have recently been used

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to identify features of learner strategies and L2 performance that may be a

function of the chat environment. This can be seen in Smith’s (2008) use of

screen capture software to record all mouse movement, typing, and deletion to

examine self-initiated self-repair (SISR) strategies during chat. By examining

videos of the participants’ computer screens, Smith was able to document all

instances of self-repair, including CMCovert self-repair, defined as self-repair

attempts that were deleted during the editing and rewording of chat turns prior

to transmission. Smith’s findings indicated that accounting for both overt SISR

(that which is ‘sent’ to the interlocutor and appears on traditional chat logs)

and CMCovert SISR (that which is not sent and which does not appear on

traditional chat logs) revealed far more self-repair during chat than had been

previously assumed. Furthermore, examination of the video revealed that a

substantial portion of L2 output was deleted during the composition of each

message and did not appear in the final transmitted turn.

Smith’s findings raised questions concerning the deleted portion of the L2

output; in particular, what was being deleted (e.g. simple spelling errors, devel-

opmentally more advanced but less automatic IL forms), and how the deleted

text compared linguistically to the text that was eventually transmitted. In

other words, did the deleted text show evidence of hypothesis testing and

risk-taking through the use of more varied lexical items and more complex

morphosyntax relative to that found in the text that was eventually transmit-

ted? As has been demonstrated by research on planning time and L2 perfor-

mance, increased online planning time has been found to benefit the

complexity and accuracy of L2 performance (Yuan and Ellis 2003; Ellis and

Yuan 2004). Synchronous text chat has been argued to afford learners more

processing time (Pellettieri 1999; Shehadeh 2001; Smith 2004) and, by exten-

sion, increased online planning time during these ‘conversations in slow

motion’ (Beauvois 1992). Certainly, learners do engage in significant monitor-

ing of target language output during text chat (Smith 2008). Accordingly, this

study uses screen capture video records of learner interaction in order to more

closely explore the relationship between planning time and L2 performance in

SLA—specifically, the linguistic complexity and lexical diversity of L2 output

in a chat environment.

WRITTEN SYNCHRONOUS CMC

Written synchronous CMC, often referred to as chat or text-chat, is typically

characterized by multiple overlapping turns, an enduring as opposed to

ephemeral trace, and greater lag time between turns than afforded by

spoken interaction. Early work on small group interaction in L2 classrooms

identified overlapping turns as a characteristic of chat that permitted greater

participation for members in small group interaction than permitted during

spoken small group interaction, in which only one person at a time can hold

the floor (Kern 1995). That is to say, in chat, multiple interlocutors may be

composing messages simultaneously and hitting the return key within

S. SAURO and B. SMITH 555



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moments of each other without having to wait for a break in conversation to

reply to prior comments or introduce new content.

This overlapping of turn-taking in chat is supported by the enduring nature

of the interaction, which, unlike spoken conversation, is captured and pre-

served in the chat window on the computer screen. Thus, in contrast to the

fleeting nature of spoken interaction, the chat window provides interlocutors

with a more enduring and reviewable visual record of the exchange. According

to Smith (2005), this accessible record affords L2 learners the opportunity to

reread or scroll back to prior turns in a manner that may mirror the benefits of

repetition and redundancy.

Although unfolding in real time, chat interaction supports longer pauses

between turns than commonly found in spoken interaction. Figure 1 illustrates

the length of pauses between turns that occurred during the NNS/NNS chat

examined in this study. In this particular exchange, wait time between turns

ranged from 8 to 47 s. Protracted wait time between turns in chat results in part

from typing, which is intrinsically slower than speaking, as well as from a delay

in message transmission inherent in most pedagogical (e.g. Blackboard Chat)

and commercial (e.g. Skype, Yahoo! Instant Messenger) chat clients. Whereas

in spoken conversation, in which interlocutors can listen to their partners’

utterances as they unfold, interlocutors in most chat programs are not privy

to unfolding utterances but must wait until the return key is pressed and the

completed and edited message is transmitted in full.

emiTgaLspmatSemiThtiwsnruT#nruT

3

4

5

6

7

8

9

CW: Der Mann mit dem weiss hembt gibt der andere Mann ein Portmonet 11:58:31 CW: The Man with the white shirt gives the other man a wallet

CW: Eine Frau sagt etwas 11:59:01 CW: A woman says something

CW: Der anderer Mann sagt etwas und gibt der Portmonet zum Mann mit weiss Hembt 11:59:48 CW: The other man says something and gives the wallet to the man with white shirt

MT: In der ersten bild ein mann offnen sein Portemonnai 12:00:30 MT: In the first picture a man opens his wallet

CW: Der Mann mit weiss Hemd fragt die Frau 12:00:59 CW: The man with white shirt asks the woman

CW: dass sind meine 4 Bilder 12:01:10 CW: those are my 4 pictures

MT: ok 12:01:18 MT: ok

0:30

0:47

0:42

0:29

0:11

0:08

Figure 1: Lag time between turns in NNS/NNS chat

556 INVESTIGATING L2 PERFORMANCE IN TEXT CHAT



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Taken together, the speed of typing relative to speaking as well as the

software-induced lag time between turns mean that interlocutors in chat

have more time to both process incoming messages and produce and monitor

their output. Indeed, Smith’s (2008) screen capture of self-repair provided

evidence that L2 learners often do, in fact, monitor and edit their output

during this period of lag time.

Examples of this output monitoring can be seen in Figure 2, which depicts

the coded video enhanced chatscript of the exchange shown initially in

Figure 1. In this coding system (see online supplementary material for

Appendix 1), a strikethrough indicates text that was typed and then deleted

before the message was sent (e.g. turn 5) while underlined text (e.g. turn 7)

signifies text with embedded deletions, that is, a larger string of text which is

deleted and which contains subcomponents that were edited prior to the full

deletion. While certain turns show no evidence of message editing and mon-

itoring (e.g. turn 4), others show evidence of deletion of words or word parts

(e.g. turns 5 and 8), and still others contain deletion of full phrases or sen-

tences (e.g. turn 7). As Figure 2 illustrates, whether at the word or sentence

level, these deleted segments comprise a portion of the total learner output

during chat. Thus L2 chat interaction includes the production of both overt

and covert output with the former comprising the output that is transmitted

to the interlocutor and the latter encompassing the deleted elements.

The significance of the production of overt and covert output during chat

for L2 development can be understood in terms of planning time and L2

performance.

PLANNING AND L2 PERFORMANCE

Research on planning time and L2 performance has focused in particular on

two overarching types of planning which are differentiated based on when the

Turn # Transmitted and Deleted Output with Time Stamps Lag Time

3

4

5

6

7

8

9

CW: Der Mann mit dem weiss hembt gibt der andere Mann ein Poerrtmonet 11:58:31

CW: Eine Frau sagt etwas 11:59:01

CW: Der andere maner Mann sagt etwas und gibt der Portmonet zum Mann mit He h weiss Hembt 11:59:48

MT: In der ersten bild ein mann offnen sein Portemonnai 12:00:30

CW: Der mMann mit weiss Hembd [6] sagt etwas Der Mann mit weidss Hemd fragt die Frau 12:00:59

CW: dallss sind meine 4 Bilder 12:01:10

MT: ok 12:01:18

0:30

0:47

0:42

0:29

0:11

0:08

Figure 2: Overt and covert output during chat




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planning occurs: pre-task planning, and online planning. As the name sug-

gests, pre-task planning is either rehearsal (i.e. a practice run-through of the

task) or strategic planning (i.e. deliberation of content and code) which occurs

during a preparation period prior to the performance of a language production

task (Ortega 1999; Ellis 2005). In contrast, online planning refers to ‘the

moment-by-moment planning during the task performance’ (Yuan and Ellis

2003: 4). Both types of planning have been hypothesized to contribute to L2

performance by freeing up attentional resources, thereby enabling L2 learners

to attend to linguistic form. Pre-task planning, it has been suggested, affords

learners the opportunity to consider message content in advance (Ellis 2005)

while online planning enables learners to attend carefully to message formu-

lation and to engage in pre- and post-production monitoring (Yuan and Ellis

2003).

Research on planning time has primarily investigated the impact of pre-task

and online planning time on three aspects of L2 performance, hypothesized to

map on to phases of the learning process (Skehan 2003). The first of these,

complexity, which describes the use of more advanced or diverse target language

features, corresponds to the growth and subsequent restructuring of the lear-

ner’s interlanguage system (Skehan and Foster 1999). Accuracy, or the avoid-

ance of error during production, has been hypothesized to reflect the increase

of control over newly acquired language features while fluency, defined as

real-time rapid language production, reflects more advanced or native-like

control of target language structures (Skehan and Foster 1999).

In studies of planning time, a variety of measures have been used to evaluate

these three aspects of L2 performance. Typical indicators of complexity include

measures of syntactic complexity (e.g. ratio of clauses to T-units) (Ellis and

Yuan 2004, 2005; Kawauchi 2005), syntactic variety (e.g. range of verb forms

used) (Ellis and Yuan 2005), lexical diversity (e.g. modified type-token ratios)

(Daller et al. 2003), and length of c-units (Elder and Iwashita 2005). Indicators

of accuracy include percentage of error-free clauses (Skehan and Foster 2005),

error rates (e.g. per 100 words, per T-unit) (Sangarun 2005), and target-like

use analysis of select morphology (e.g. article usage, verbal morphology) (Pica

1983). Indices of fluency include speech rate (e.g. syllables per minute) (Ellis

and Yuan 2005), mean length of run (Towell, et al. 1996; Tavakoli and Skehan

2005), and measurements of pausing (e.g. number of pauses, total pausing

time) (Kormos and Denes 2004).

Studies have demonstrated a benefit for pre-task planning on these three

aspects of L2 performance. In particular pre-task planning has been found to

consistently benefit fluency (Foster and Skehan 1996; Mehnert 1998;

Kawauchi 2005) and syntactic complexity (Crookes 1989; Mehnert 1998;

Ortega 1999; Yuan and Ellis 2003; Kawauchi 2005) during oral production.

Though limited, research on planning time and written production (Ellis and

Yuan 2004) has also found an advantage for pre-task planning on fluency in

writing and complexity as measured by syntactic variety (the number of verb

forms used). However, findings regarding the effect of pre-task planning on




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accuracy are less consistent and suggest that, other factors such as the type and

complexity of the language production task (Skehan and Foster 1997, 1999),

the target language features used to measure accuracy (Ellis 2005), the com-

peting processing demands of fluency and complexity (Foster and Skehan

1996), as well as the proficiency of the learner (Kawauchi 2005) may mitigate

the advantage of pre-task planning on accuracy.

In contrast, studies of online planning in both oral and written production,

though still quite limited, have found a benefit for accuracy and complexity

but not for fluency. Relative to non-planning conditions, these findings also

point to the following trends: (i) similar advantages for both pre-task and

online planning on complexity over non-planning conditions, (ii) an advan-

tage for online planning over pre-task planning for accuracy, and (iii) an

advantage for pre-task planning over online planning for fluency.

ONLINE PLANNING AND CHAT

As Skehan and Foster (2005) have argued, ‘[p]lanning is an unobservable

activity’ (p. 197) and claims regarding the influence of planning time on L2

performance rely on the assumption that learners are in fact making use of

allotted time to actually plan or monitor language production. Studies of

pre-task planning have looked to notes taken during the pre-task planning

phase or to post-task stimulated recall (Ortega 1999) to support this assump-

tion. However, in the case of online planning, operationalized as additional or

unlimited time for language task performance, no similar product or evidence

is generated to support the assumption that learners are in fact making use of

additional online time to plan and monitor their utterances (Skehan and Foster

2005).

It is here that the case of covert output generated by learners during chat

may provide evidence of online planning in the form of post-production mon-

itoring (Yuan and Ellis 2003). By post-production monitoring, we simply mean

learners’ self-correction of their own output that occurs after a message is

produced. In the current study such post-production monitoring is apparent

in the form of (covert) self-repair. If, as suggested by prior research on online

planning during speech and writing, online planning time also benefits L2

production during chat, then comparison of covert and overt output may

reveal differences in L2 performance. In particular, if learners do in fact

engage in more careful production1 and monitoring during synchronous

chat, we may expect a qualitative difference in the use of developmentally

more advanced or varied TL features in the target language produced prior to,

during and following covert output.

RESEARCH QUESTIONS

The present study, therefore, uses a portion of the data from Smith’s (2008)

study and delves more deeply into describing the L2 performance in chat by




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examining whether there is a difference in the linguistic complexity and vari-

ety of covert and overt output produced by learners. The following research

questions were posed:

1. Is there a difference in the linguistic complexity of chat outputthat shows evidence of on-line planning in the form of post-productionmonitoring versus chat output that does not?

2. Is there a difference in the lexical diversity of chat output thatshows evidence of on-line planning in the form of post-production mon-itoring versus chat output that does not?

METHODOLOGY

Participants and tasks

For this analysis, one task was chosen for further analysis from the larger study

reported in Smith (2008). Data from this task consisted of 23 usable chat

and Camtasia records of beginner-high level learners of German-as-a-

foreign-language. There were 12 dyads that completed the task, however,

one participant experienced technical problems of some sort and did not ini-

tiate the screen capture software correctly, thus resulting in the odd number.

These students participated in this study as part of their regularly scheduled

German language course at a major southwestern university in the United

States. In the larger study students were required to meet once every other

week in the foreign language micro-computing lab over the course of the

semester. All students were undergraduates and all were native speakers of

English. None were German majors. Their proficiency level and placement in

the German sequence was determined by an in-house online placement test.

All participants were characterized by the instructor as roughly at the ACTFL

Novice-High proficiency level and indicated verbally to the researcher that

they were familiar with the chat function in Blackboard. However, participants

did complete one 50-min training session prior to data collection to ensure

they were familiar with the chat interface as well as the task type and proce-

dures since they were not necessarily accustomed to performing similar

task-based CMC activities in their German class.

Materials

The current task was a sequential ordering task, a type of jigsaw. As with all of

the tasks in the larger study this task type was chosen because of its structural

requirement of two-way information exchange by participants who are striv-

ing to reach a convergent goal (Pica et al. 1993).

The current task provided learners each with a task sheet that contained a

series of color stills from a two-minute dramatic video clip that corresponded to

the week’s assigned course content. Care was taken to select a series of stills,




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which, when viewed without having seen the video clip, had no obvious

sequential order. These same stills were easily sequenced upon viewing the

full video clip, however. Learners were instructed to describe their series of

pictures to their partner and collaboratively arrive at a proposed correct and

logical ordering for the series of eight pictures. After the pairs had a solution,

they were instructed to view the short video clip and then reconvene to eval-

uate their solution/sequence and make any changes before proposing a final

sequence/solution.

Given the length of the class participants realistically had about 40 min to

complete the task. All students worked collaboratively online with a partner.

Each participant was given task sheet A or B. All of those holding task sheet A

were grouped in one area of the computer lab while those holding task sheet B

were grouped in another. This was done in order to reduce the chance that

any participant would gain visual access to their interlocutor’s (partner’s)

task sheet.

Participants interacted with one another via the chat function in Blackboard

and were assigned to one of various paired ‘groups’ under Blackboard’s

Communication Tool, Virtual Classroom.

DATA COLLECTION AND ANALYSIS

Capturing the interaction

The dynamic screen capture software Camtasia 3 recorded exactly what

appeared on each participant’s computer screen in real time. The Camtasia

files were recorded to a networked drive and copied by one of the researchers

for later analysis. The chat logs of these interactions were saved automatically

in Blackboard.

Coding the interaction

Hard copies of the chat transcripts were converted to individual MS Word

documents. One copy of each chat transcript was renamed in preparation for

coding the interaction with the screen capture. These versions are referred to

as the video-enhanced chatscripts. The video-enhanced chatscripts were coded

by one of the researchers using the procedure outlined in Smith (2008), which

used Appendix 1 (see online supplementary material) to signify text that was

typed and subsequently deleted before being sent, text that was inserted later

in the composition phase of any given message, as well as to show the timing/

location of each of these moves. In order to code the chat interaction in this

way, each participant’s video file (a screen capture of the chat interaction) was

played back in its entirety, pausing the playback where necessary. The second

researcher coded each of the chatscripts for c-units,2 clauses, and instances

of productive use of grammatical gender. To establish inter-rater reliability,




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the first researcher also coded two full chat transcripts for c-units, clauses, and

instances of productive use of grammatical gender. The initial overall agree-

ment statistic was .93 and was considered sufficiently high. In the few cases

where there was initial disagreement the researchers discussed the item

until 100% agreement was reached. Each segment of text from these

coded chatscripts was then placed into one of five columns, representing the

different sub-categories of output produced with and without evidence of

post-production monitoring.

Operationalizations

Output generated without evidence of post-production monitoring included

the following two categories: (i) pristine text, and (ii) pre-deleted text. Output

that showed evidence of post-production monitoring included the following

three categories: (iii) post-deleted text, (iv) deleted text and (v) post-deleted

deleted text. Descriptions of each follow as does an example from the coded

data in Figure 3.

Pristine text was a complete stretch of text that contained no deletions

of any kind. Such text was written from beginning to end without any

self-corrections or alterations and sent to the interlocutor. Pristine text is not

necessarily error-free, however. Pristine text can be considered overt in nature.

SCMC Chatscript Column A

Hard copy of transcript (with English translation)

Column B 1a. K: ok das ist so wie photo D von mir 1:08:57

2a. D: Die menner mit dem Rose ros Hemd ist die TZetiung [1a] [zu] halten [+]. 1:09:23

3a. D: die manner sind look looki to look at? 1:10:12

4a. K: shen 1:10:37

5a. K: sehen** 1:10:40

6a. K: oder sieht etwas an 1:10:50

7a. K: ok im welches photo nehmt den mann mit der rosa hemd die zeitung 1:12:14

8a. D: die manne [6a] e ist an a sie[g]t ht [-] e dieder Man in der Rose hemd rose Hemd istt shieght anddieser man mit dem blau Hemd.[7a] 1:12:15

1b. K: ok das ist so wie photo D von mir 1:08:57 K: ok that is like picture D of mine 2b. D: Die menner mit dem ros Hemd ist die Zeitung zu halten. 1:09:23

D: The men with the pink shirt is holding the newspaper3b. D: wie sagt man to look at? 1:10:12 D: How do you say to look at?4b. K: shen 1:10:37 K: Misspells the verb “to look”5b. K: sehen** 1:10:40

K: to look 6b. K: oder sieht etwas an 1:10:50

K: or ”to look at something”7b. K: ok im welches photo nehmt den mann mit der rosa hemd die zeitung 1:12:14 K: ok in which picture does the man with the pink shirt take the newspaper8b. D: der Man in der rose Hemd sieht an dieser man mit dem blau Hemd. 1:12:15

D: the man with the pink shirt looks at the man with the blue shirt.

Figure 3: Examples of text categories




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When considering this category of text we may say that learners have

had the regular benefit of increased processing time afforded by the SCMC

interface.

Deleted text was that text which appears only on the video-enhanced chat-

scripts. That is to say, this category of text would not appear on traditional chat

transcripts. We consider such deleted text to be covert in nature. This text was

captured by examining the Camtasia video files of the chat interaction in a line

by line fashion.

Post-deleted text was that text learners typed immediately following any dele-

tions/corrections participants made and as such can be considered overt in

nature. We counted as post-deleted text that text which started immediately

after a deletion/correction and ended with sentence-ending punctuation, or

the sending of the message.

Post-deleted deleted text was that post-deleted text that was subsequently

deleted and as such can be considered covert in nature. A discussion of this

category follows in the Data analysis section below.

Pre-deleted text was developed simply to account for all of the text written

by an individual. Since Pristine text required that there be absolutely no

deletions or corrections in a given stretch of discourse, the pre-deleted text

category was needed in order to code and account for that text which came

immediately before a deletion or correction in the same turn. The pre-deleted

text category was not used explicitly in the data analysis but is considered overt

in nature.

There were, then, actually two kinds of deleted text; deleted text and

post-deleted deleted text. Deleted text of both sorts (deleted text and

post-deleted deleted text) are argued here to constitute evidence that

learners are indeed making use of the additional time afforded by SCMC

for online planning in the form of post-production monitoring of their

‘utterances’.

We may expect that output which comes immediately after such

post-production monitoring will show the benefits of this planning time in

the way of more sophisticated language production since there is arguably a

heightened degree of attention to form immediately following the execution of

deleted text. When one deletes text, one’s attention is drawn to the fact that

this text was unacceptable in some way. Thus, it seems reasonable to assume

that learners will focus on form (and arguably content) more in their writing

immediately following self-repair.3

In summary, then, we argue that the methodology presented here,

which captures text that is subsequently deleted gets us closer to accounting

for the possible positive effects of online planning. Likewise, this deleted

text allows us to distinguish between evidence of online planning and the

possible effects of online planning as measured by post-deleted text production.

As such the current (CMC) study accesses information about the nature

of learner–learner interaction that is not readily available in comparable

face-to-face studies.




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Data analysis

Because there were two categories of deleted text in the chat interaction data

examined here, two separate comparisons of these data were required. That is,

as mentioned above, occasionally text was at the same time ‘post-deleted text’

and ‘deleted text’. Such a coding dilemma occurs when text which is

‘post-deleted’ in nature is subsequently deleted (post-deleted deleted text)

before being sent. The problem was what to do with this post-deleted deleted

text. Of course, such an ‘overlap’ or ‘double counting’ of the data would

be problematic for any statistical analysis, so simultaneously counting

post-deleted deleted text as both deleted text and post-deleted text in the

same analysis was not an option. In order to address this problem, we decided

to conduct two separate levels of analysis of the data. Pristine text was not

affected by this problem since by definition there was never any overlap

between pristine text and deleted or post-deleted text. We shall call the first

level of analysis the deleted text focus (Analysis 1) and the second level of ana-

lysis the post-deleted text focus (Analysis 2).

Analysis 1

In the deleted text focus we coded and quantified all deleted text and assigned

it to the deleted text category irrespective of whether this text could/should also

be coded as post-deleted deleted text as well. Thus, in this analysis only that

post-deleted text which was not then subsequently deleted remained in the

post-deleted text category. In summary then, for this first level of analysis,

all text that was typed and then subsequently deleted was assigned to the

deleted text category.

Analysis 2

In the post-deleted text focus all text that was initially assigned to the post-deleted

text category remained there irrespective of whether it was subsequently

deleted (post-deleted deleted text). In this way we were able to avoid any

overlaps in the statistical comparisons of the data since we conducted two

independent and parallel statistical analyses. Figures 4 and 5 illustrate the

analysis conducted.

Calculating measures of linguistic complexity and lexical diversity

We calculated the linguistic complexity and lexical diversity for each category

of text (pristine text, deleted text, and post-deleted text) for each participant

(n = 23) and for each focus (deleted text focus and post-deleted text focus).

Thus, participants served as their own controls. We operationalized linguistic

complexity in two ways. First, we calculated the syntactic complexity for each

text category for each learner, and second, we calculated the complexity of form

by productive use of grammatical gender.4 We arrive at the syntactic complexity of




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a text by dividing the number of clauses by the number of c-units. The mea-

sure of grammatical gender simply reflected the number of occurrences in the

chatscript of grammatical gender in German. According to DeKeyser (2005),

grammatical gender is among those elements of a second language that are

notoriously hard to acquire for native speakers of L1s that do not have them or

that use a very different system.5 These elements of grammar also seem to be

strongly resistant to instructional treatments. In addition, due to the nature of

the communication tasks in which no specific complex German form was

deemed task essential (Loschky and Bley-Vroman 1993), it was determined

that grammatical gender would be the target language feature most likely to

occur in sufficient quantity for statistical analysis. For our measure of lexical

diversity we employed the Index of Guiraud (or Guiraud value/coefficient),

which is the number of lexical types, operationalized as unique content and

function words, divided by the square root of tokens. This measure reduces the

influence of the token length or the length of the text under consideration. The

higher the score of the Guiraud value (G), the greater the variety of vocabulary

included in a text. (See Daller et al. 2003 for a detailed discussion.)

RESULTS

Analysis 1: Deleted text focus

For comparisons of the syntactic complexity, grammatical gender, and lexical

diversity of pristine text, deleted text, and post-deleted text, a series of

Deleted text (DT)

Post-deleted text (PDT)

Post-deleted deleted text (PDDT)

Analysis 1: Deleted text focus Analysis 2: Post-deleted text focus

Figure 4: Illustration of the relationship between deleted text, post-deleted text,and post-deleted deleted text

Analysis Pristine text Deleted Text Post-deleted text1. Deleted text focus DT + PDDT PDT – PDDT

2. Post-deleted text focus All PT DT - PDDT PDT + PDDT

Note: PT = Pristine text; DT = Deleted text; PDT = Post-deleted text; and PDDT = Post-deleted deleted text.

Figure 5: Two separate analyses of DT focus and PDT focus data




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pair-wise Wilcoxon Matched Pairs tests were conducted. In all cases alpha

was set at .017 (Bonferroni adjustment) to account for the fact that multiple

comparisons of the same data were made. Table 1 shows the descriptive data

for analysis 1. Figures 6, 7, and 8 show graphical representations of these

comparisons.

Results for this pair-wise comparison for syntactic complexity showed that

syntactic complexity was significantly higher for post-deleted text than pristine

text (z = 3.458, p = .001, r = .72)6 as well as deleted text (z = 4.010, p< .001,

r = .84). Syntactic complexity for pristine text was also significantly higher

than deleted text (z = 2.581, p = .010, r = .54). Thus, the syntactic complexity

for post-deleted text was the highest followed by the pristine text. Deleted text

had the lowest degree of syntactic complexity.

A similar analysis was run for the measure of grammatical gender. Results of

the multiple Wilcoxon Matched Pairs tests showed that grammatical gender

was significantly higher for post-deleted text than pristine text (z = 2.471,

p = .013, r = .52) as well as deleted text (z = 3.656, p< .001, r = .76). There

was no significant difference found between pristine text and deleted text

(z = 0.179, p = .858). Post-deleted text, then, showed significantly more use of

grammatical gender than did pristine text and deleted text.

Finally, a similar analysis was performed on the measure of lexical diversity

(G). Results of the Wilcoxon Matched Pairs tests showed that post-deleted text

had significantly higher scores than pristine text (z = 2.464, p = .014, r = .51) as

well as deleted text (z = 4.197, p< .001, r = .88). Pristine text scores were also

significantly higher than deleted text scores as well (z = 2.433, p = .015, r = .51).

We can say, then, that the post-deleted text showed the highest degree of

lexical diversity followed by the pristine text. Deleted text showed the

lowest relative lexical diversity score.

Table 1: Descriptive data—deleted text focus (n = 23)

Variable Text type Mean SD Min. Max.

Syntactic complexity PDT 0.811 0.815 0 4.29

PT 0.396 0.269 0 1.0

DT 0.205 0.208 0 0.80

Grammaticalgender

PDT 9.65 6.18 1.0 25.0

PT 4.60 5.48 0 21.0

DT 4.39 3.11 0 14.0

Lexical diversity PDT 4.85 0.890 2.71 6.36

PT 4.07 1.35 1.0 6.55

DT 3.20 0.779 1.0 4.57

PDT = post-deleted text; PT = pristine text; DT = deleted text.




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Analysis 2: Post-deleted text focus

The second level of analysis focused on the nature of the post-deleted text in

the chat interaction. As mentioned above, all text that was composed after

some deleted text was assigned to the post-deleted text category for this ana-

lysis regardless of whether this text was eventually deleted prior to being sent

(see coding explanation in the Operationalizations section). The rationale here

stemmed from the working hypothesis that immediately following a deletion,

learners would have a heightened state of attention to form. For this compar-

ison, whether post-deleted text eventually becomes post-deleted deleted text is

irrelevant. For comparisons of the syntactic complexity, grammatical gender,

and lexical diversity of pristine text, deleted text, and post-deleted text in this

second analysis, a series of pair-wise Wilcoxon Matched Pairs tests were

conducted. In all cases alpha was set at .017 (Bonferroni adjustment) to

account for the fact that multiple comparisons of the same data were made.

Table 2 shows the descriptive data for the post-deleted text focus (analysis 2),

with Figures 9, 10, and 11 depicting the graphical representations of these

same comparisons.

Figure 6: Analysis 1—comparisons of syntactic complexity across the variabletext type




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Results showed that syntactic complexity was significantly higher for

post-deleted text than pristine text (z = 2.902, p = .004, r = .61) as well as

deleted text (z = 3.702, p< .001, r = .77). There was no significant difference

found between pristine text and deleted text (z = 2.062, p = .039).

Results for grammatical gender showed a similar outcome. Grammatical

gender scores were significantly higher for post-deleted text than pristine

text (z = 3.079, p = .002, r = .64), as well as deleted text (z = 4.109, p< .001,

r = .86). No significant difference was found in the comparison of deleted

text and pristine text (z = 1.730, p = .084).

Results for lexical diversity (G), showed that post-deleted text scores were

significantly higher than pristine text (z = 2.403, p = .016, r = .50), as well as

deleted text (z = 4.167, p< .001, r = .87). Pristine text scores were also signifi-

cantly higher than deleted text scores (z = 2.768, p = .006, r = .58).

DISCUSSION

In answer to research question 1, ‘Is there a difference in the linguistic com-

plexity of chat output, which shows evidence of online planning in the form of

post-production monitoring, versus chat output that does not?’, the present

Figure 7: Analysis 1—comparisons of grammatical gender across the variabletext type




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Figure 8: Analysis 1—comparisons of lexical diversity across the variabletext type

Table 2: Descriptive data—post-deleted text focus (n = 23)

Variable Text type Mean SD Min. Max.

Syntactic complexity PDT 0.642 0.285 0 1.5

PT 0.396 0.269 0 1.0

DT 0.215 0.260 0 1.0

Grammatical gender PDT 12.82 9.65 1.0 41.0

PT 4.60 5.48 0 21.0

DT 2.26 2.19 0 8.0

Lexical diversity PDT 4.80 0.879 2.59 6.39

PT 4.07 1.35 1.0 6.55

DT 2.89 0.777 1.0 4.166

PDT = post-deleted text; PT = pristine text; DT = deleted text.




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Figure 9: Analysis 2—comparisons of syntactic complexity across the variabletext type

Figure 10: Analysis 2—comparisons of grammatical gender across thevariable text type




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data suggest a partial advantage for one subset of chat output that shows evi-

dence of online planning. For both measures of linguistic complexity (syntactic

complexity and grammatical gender) post-deleted text was significantly higher

than both pristine text and deleted text. The results regarding the relationship

between pristine text and deleted text for syntactic complexity are less clear.

In answer to research question 2, ‘Is there a difference in the lexical diversity

of chat output, which shows evidence of online planning in the form of

post-production monitoring, versus chat output that does not?’, the present

data suggest a partial advantage for one subset of output that shows evidence

of online planning. The results show that post-deleted text was significantly

more lexically diverse than both pristine text and deleted text. Likewise, pris-

tine text was significantly more lexically diverse than deleted text. Table 3

illustrates the nature of the relationships between the variables in both the

deleted text focus (Analysis 1) and post-deleted text focus (Analysis 2).

Most interesting is the fact that post-deleted text was significantly higher

than the pristine text in every comparison made. We might expect the

post-deleted text and the pristine text to be higher than the deleted text

since the learner decides to delete the deleted text because it is arguably

faulty in some way. Furthermore, we notice that for lexical diversity pristine

Figure 11: Analysis 2—comparisons of lexical diversity across the variabletext type




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text is also significantly higher than deleted text in both the deleted text focus

and post-deleted text focus. Overall, then, the results suggest that learners

create more complex or sophisticated language in the post-deleted text envi-

ronment. That is to say, subsequent to the deletion of covert chat output, an

indicator of post-production monitoring, this group of learners used more

complex target language features.

In most cases, dyads did not arrive at the ‘correct’ sequential order for the

video stills. Indeed, the task was intentionally constructed to be challenging for

this group in order to force them to sufficiently stretch their interlanguage

resources. The task itself is best viewed as a means to an end, namely, to

lead learners to engage in meaningful and arguably beneficial target language

interaction with another learner. Success was not measured in terms of cor-

rectly ordering the picture sequence, but rather in terms of whether each dyad

was able to arrive at a mutually acceptable joint resolution to the task. Keeping

this in mind, we can say that although only two of the dyads arrived at the

prescribed ‘correct’ sequence (17%), all dyads were successful in completing

the task (100%).

Limitations of the current study

The current study has several limitations. The first of these is the small sample

size (n = 23) and the resulting possibility of a Type II7 error. In addition to

sample size limitations the use of a specific intact population makes it difficult

to make generalizations about the nature of L2 performance during chat. That

said, these ‘limitations’ can also be viewed as design strengths. That is, if we are

to posit any strong pedagogical relevance to our findings, it is important to

make the study as naturalistic as possible (rather than artificially simulated). It

is our view that such a design has a high degree of ‘ecological validity’ in that

learners were engaged in a communicative task with another learner in a

familiar setting (the language computer lab). The data collection was sched-

uled during their regularly scheduled class time and the underlying ‘tracking’

software employed in no way interfered with their typical experience with the

learning management system (Blackboard). Investigation of chat produced by

learners of varying proficiencies, particularly by more advanced L2 learners,

may have produced more robust effects. A third limitation stems from char-

acteristics of the communication task used and the subsequent nature of the

Table 3: Overview of relationship between each variable

Syntacticcomplexity

Grammaticalgender

Lexicaldiversity

Deleted text focus PDT > PT > DT PDT > PT & DT PDT > PT > DT

Post-deleted text focus PDT > PT & DT PDT > PT & DT PDT > PT > DT




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language this task elicited. Although promoting a two-way flow of interaction,

this particular jigsaw task (Pica et al. 1993) did not require the use of specific

task-essential or task-useful TL forms (Loschky and Bley-Vroman 1993). In

some instances, learners were able to avoid complex syntax by relying on

letters to agree on the order of their pictures (e.g. ‘Vielleicht, F, E, B?’) instead

of describing the actions depicted in the pictures. A task that necessitated the

use of more sophisticated syntax or specifically targeted a complex TL form

may have generated different effects.

CONCLUSIONS AND FUTURE DIRECTIONS

The results of this study suggest that learners do appear to use the increased

online planning time afforded by chat to engage in careful production that

results in more complex language. If post-deleted text output is considered a

product of online planning, these findings support the results of other

(face-to-face) studies which also found an effect for increased online planning

time and complexity (Yuan and Ellis 2003; Ellis and Yuan 2004).

These findings hold pedagogical implications for the use of synchronous

text-chat in language classrooms. Instructors in face-to-face contexts wishing

to provide learners with output opportunities that allow learners to produce

complex language may consider incorporating task-based chat into their les-

sons. Similarly, instructors of online distance language courses may also con-

sider incorporating a synchronous task-based chat component to their course

to allow remote language learners this opportunity for careful target language

production.

Additionally, and perhaps most importantly, pedagogical implications deriv-

ing from these findings call for the design and selection of communication tasks

that take advantage of the additional online planning time afforded by chat.

Optimally, such tasks should be designed to elicit complex target language fea-

tures or precise and descriptive vocabulary from learners. Thus a picture

sequencing task, such as the one used in this study, might be replaced with a

video sequencing task, in which learners view and narrate different segments

from a larger video clip in order to determine the sequence of segments.

Successful completion of such a video reconstruction task (see, for example,

Sullivan and Caplan 2003) may call upon more complex syntax as learners

must narrate the events of each clip instead of relying on abbreviated labels

for pictures, as was observed in this study. In addition, chat may also be an

optimal tool for the use of more specifically form-focused activities that require

careful production or monitoring of complex or difficult language features.

Understanding the nature of L2 performance during chat and the role of

increased online planning time requires research which examines L2 chat

interaction among learners of varying proficiencies. This includes studies

which examine the complexity of covert and overt output generated by lear-

ners of differing proficiencies while completing tasks that necessitate or are at

least more likely to elicit more complex language during chat interaction. It is




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also worth investigating whether differences in online planning time afford an

advantage for L2 performance in chat relative to face-to-face interaction and to

what degree any such advantage is mitigated by typing ability, digital literacy,

and target language proficiency. In addition, the current study made no attempt

to investigate whether there might be a relationship between the amount and

complexity of post-deleted text with the complexity of language production

(whether written or spoken) subsequent to the chat sessions. Such research

might uncover potential benefits from chat for continued L2 development.

While the current study investigated syntactic complexity and lexical diver-

sity, future research should also examine the relative accuracy of both covert

and overt output. Finally, further inquiry into the nature of chat interaction

should investigate factors influencing deletion during chat, including identify-

ing under what conditions learners choose to either delete or transmit their

output, what elements of the chat tool or interlocutor responses facilitate

noticing or prompt more complex and accurate output. In particular, the use

of stimulated recall could help us better understand other underlying fac-

tors that prompt learners to delete output during chat.

Chat environments remain a promising site for research on second language

development; however, to date, few studies have examined the full range of

target language output learners produce during chat. The results of this study

demonstrate how capturing the full range of learner output through screen

capture technology can be used to investigate how the online planning time

afforded by the medium is in fact used by L2 learners to produce more complex

language during dyadic interaction.

SUPPLEMENTARY DATA

Supplementary material is available at Applied Linguistics online.

NOTES

1 Yuan and Ellis (2003) contrast

careful production with rapid produc-

tion, which lacks opportunity for self-

repair.

2 In keeping with prior L2 research on

CMC (e.g. Gonzales-Lloret 2003), c-

units (communication units) were

used instead of T-units to account for

isolated words and phrases that did

not necessarily contain verbs yet

conveyed information. c-Units,

unlike T-units, include the single

words and phrases that characterize a

great deal of chat discourse.

3 See Smith and Sauro (2009) for an

exploration of additional factors in

the chat context that might also influ-

ence a learner’s decision to delete text

during chat.

4 These numbers reflect learner attempts

at target-like use and do not imply that

these instances were accurate, gram-

matical, or target-like. Rate of target-

like use, which corresponds to accu-

racy, the second dimension of L2 per-

formance according to Skehan (2003),

was beyond the scope of this current

study.




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5 As one anonymous reviewer pointed

out, grammatical gender is a feature

introduced early in formal German

language instruction. Despite its inclu-

sion in introductory texts, grammati-

cal gender, remains a cognitively

complex form for second language

learners who are faced with recogniz-

ing and learning a complex underlying

system of semantic, morphological and

phonological rules that guide gender

assignment, so that this feature of

German continues to pose a challenge

for even advanced learners (Menzel

2005).

6 There does not seem to be an ‘industry

standard’ for calculating effect sizes for

many nonparametric measures. More

commonly used (parametric) effect

size measures such as Cohen’s d are

not appropriate when employing non-

parametric analyses since such effect

size estimates are adversely affected

by departures from normality and het-

erogeneity of variances (as is largely

the case with the present data). In

order to provided some indication of

the meaningfulness of observed signif-

icant differences in these data effect

sizes (r) were calculated by dividing

the relevant z score by the square

root of N. An effect size of r = 0.10

was defined as small, r = 0.30 as

medium, and r=0.50 or larger as

large. We also chose to provide confi-

dence intervals along with graphical

representations of each statistic com-

parison in Figures 6–11.

7 Type II error results when a researcher

fails to reject a null hypothesis that is

false. This contrasts against Type I

error, which results when a researcher

rejects a null hypothesis that is true.

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Investigating L2 performance in text chat

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