Exploring the Linguistic Complexity of On-Task and Off-Task Interaction

During ChatShannon Sauro

University of Texas at San Antonioshannon.sauro@utsa.edu

Just What Kind of Language Are Students Producing during Task-Based

CMC?

http://www.ishkur.com/posters

Style of Chat

“No normal person, and no normal community, is limited to a single style of speech …”

(Hymes, 1974: 30)

Why assume that there is only a single style of chat?

Language Within the Same Chat

Mina Malmö:

The pollution in Sweden is not that bad, but even thoug the industry not letting out that much industrial waste we still have waste coming from countrys surrounding us

Steve Penn:

good, but one of your nouns could use the zero article.

During the Task

Mina Malmö:

Steve Penn:

1,8 kids

on average

Mina Malmö:

Steve Penn:

yes.

how is it in USA?

After the Task

Task-Based Research in Chat

Tasks As the Object of Research– Negotiation of Meaning Studies

• (e.g., Blake, 2000; Pelletieri, 2000; Smith, 2003)

Tasks As Data Elicitation Tools– Quality and quantity of self-repair

• (Smith, 2008)– Comparison of corrective feedback

effectiveness• (Loewen & Erlam, 2006; Sachs & Suh,

2007; Sauro, 2009)– Comparison of ACMC and SCMC

• (Sotillo, 2000)

The Study

Research Questions

1. Is the lexical diversity of on-task interaction greater than that of off-task interaction during chat?

2. Is the syntactic complexity of on-task interaction greater than that of off-task interaction during chat?

The Participants

Collaborative Writing Task: Environmental Issues

Word Bank

nature global warming

space nuclear power

mankind industrial waste

carbon dioxide pollution

wind energy industry

On-Task Language

“Learner discourse related either directly or indirectly to completion of the assigned task” (Keller-Lally, 2007, p. 105)

• Opinion exchange using the target words• Task meta-talk• Negotiation of meaning• Self-repair moves• Responses to feedback moves by interlocutors

Off-Task Language

• Exchanges that preceded the beginning of the task • opening sequences, introductions

• Responses to interlocutor questions not that did not relate to completing the task

• tangential topics, personal or general questions

• Exchanges following statements of the task being finished,

• closing sequences, personal or general questions

Transition from On to Off Task Chat

Diana Malmö:

Diana Malmö:Anna Penn:Anna Penn:Diana Malmö:Diana Malmö:Anna Penn:Diana Malmö:Diana Malmö:

Industry is to blame for much of the polluting.=)I couldn’t agree more!Great job!thanks!so how’s the us today? where are you?What is your major?english.and you?

Tangential Topics

Carlos Penn: What if a missile hits a nuclear power plant? What happens?

Carsten Malmö: Because of how the core of the powerplant is built

Carsten Malmö: Nothing would happen actually

Carlos Penn: Is it stabalized or something?

Carlos Penn: stabilized, I mean

Carsten Malmö: I did study science, but I’m not sure how to explain it in english to be honest :)

Carlos Penn: Is that all the words?

Carsten Malmö: two to go

Repairing the Task

Susan Penn: what’s it called in Swedish?

MC Malmö: It’s okay. I don’t really know myself

MC Malmö: Wind maler perhaps haha… no I really don’t know

Susan Penn: nevermind – that’s okay!

Susan Penn: which other words can we discuss?

RQ1: Calculating Lexical Diversity

• Excluded tokens:– Use of the L1, participants names, laughter (e.g. “haha”),

emoticons, numbers

• Included tokens:– Abbreviations (e.g., ex, etc.), texting shorthand (np),

ontomatopoetic formulations of surprise (oh, ah)

• Determining types:– Different inflections of the same word (industry, industries)

and use of contracted forms (ya’ll, he’s) were treated as different types

• Index of Guiraud:– The ratio of types to the square root of tokens

Lexical Diversity: Descriptive Data

Tokens M SD Min Max

On-Task

Off-Task

Total

3851

3318

7169

160.46

138.25

54.55

87.98

53

36

307

344

N= 24

MLT On-Task: 10.05

MLT Off-Task: 7.65

Results: Lexical Diversity

Tokens Types Mean Index of

G

SD

On-Task

Off-Task

Total

3851

3318

7169

2244

2125

4369

7.38

7.47

0.83

1.56

N= 24

RQ2: Determining Syntactic Complexity

• Analysis of Speech Unit (AS-unit):– “a single speaker’s utterance consisting of an independent clause

or sub-clausal unit with any subordinate clause(s) associated with either” (Foster, Tonkyn & Wigglesworth, 2000, p. 365)

• Clause:– “a finite or non-finite verb element plus at least one other clause

element (Subject, Object, Complement or Adverbial)” (p.366)

• Measure of Complexity:– Ratio of clauses (independent and subordinate) to AS-units

RQ2: Syntactic Complexity Coding

Examples

Natalie Malmö : |When we use others alternative fuels like gasoline we are putting the global warming at risk.| (2 clauses, 1 AS-unit)

Hanna Malmö: *|on or over?| (0 clauses, 1 AS-unit)

Lena Malmö: |You’ve been very helpful… |Must be hard chatting with people that aren’t very good at English.| (4 clauses, 2 AS-units)

Results: Syntactic Complexity

Clauses AS Units

Mean C/AS Ratio

SD

On-Task

Off-Task

Total

449

542

991

420

600

1020

1.06

.93

0.32

.29

N= 24

Limitations and Future Directions

• Use of screen capture video to record the full range of learner chat production (e.g. Smith, 2008; Smith & Sauro, 2009)

Limitations and Future Directions

• Identifying measures of complexity and accuracy that best reflect the nature of CMC language

• Analysis of Chat Unit?

• Evaluating lexical diversity through comparison to word frequency lists (Daller, Van Hout & Treffers-Daller, 2003)

Limitations and Future Directions

Comparison of on-task and off-task chat for less proficient learners and interaction during different types of tasks

• Clarissa : 3. richtig? Samuel : 4. ? Clarissa : ich weiss nicht Samuel : Falsch Clarissa : ja Clarissa : 5. richtig? Samuel : ja Samuel : 6. Falsch Clarissa : ja

Internet Oriented Tasks

• Tasks that more closely resemble the technology-mediated tasks and tools that language learners actually engage with outside the classroom

References

Baron, N. (2008). Always on: Language in an online and mobile world. New York: Oxford University Press.Blake, R. (2000). Computer mediated communication: A window on L2 Spanish interlanguage. Language

Learning and Technology, 4(1), 120-136. Available from http://llt.msu.edu/vol4num1/blake/default.html Crystal , D. (2001). Language and the Internet. Cambridge: Cambridge University Press.Daller, H., Van Hout, Roeland, & Treffers-Daller, Jeanine. (2003). Lexical richness in the spontaneous speech of

bilinguals. Applied Linguistics, 24(2), 197-222.Foster, P., Tonkyn, A., & Wigglesworth, G. (2000). Measuring spoken language: A unit for all reasons. Applied

Linguistics, 21(3), 354-375.Hymes, D. (1974). Foundations in sociolinguistics: An ethnographic approach. Philadelphia: University of

Pennsylvania Press. Keller-Lally, A.M. (2007). Effects of task-type and group size on foreign language learner output in synchronous

computer-mediated communication. Ph.D. dissertation, The University of Texas at Austin, United States-Texas.

Levy, M., & Stockwell, G. (2006). CALL dimensions: Options and issues in computer- assisted language learning. Mahwah, NJ: Lawrence Erlbaum Associates.

Loewen, S., & Erlam, R. (2006). Corrective feedback in the chatroom: An experimental study. Computer Assisted Language Learning, 19(1), 1-14.

References cont.

Pellettieri, J. (2000). Negotiation in cyberspace: The role of chatting in the development of grammatical competence. In M. Warschauer, & R. Kern (Eds.), Network-based language teaching: Concepts and practice (pp. 59-86). Cambridge: Cambridge University Press.

Sachs, R., & Suh, B., (2007). Textually enhanced recasts, learner awareness, and L2 outcomes in synchronous computer-mediated interaction. In. A. Mackey (Ed.), Conversational interaction in second language acquisition: A collection of empirical studies (pp. 197-227). Oxford: Oxford University Press.

Sauro, S. (2009). Computer-mediated corrective feedback and the development of L2 grammar. Language Learning and Technology, 13(1) 96-120. Available from http://llt.msu.edu/vol13num1/sauro.pdf

Smith, B. (2003). Computer-mediated negotiated interaction: An expanded model. Modern Language Journal, 87(1), 38-57.

Smith, B. (2008). Methodological hurdles in capturing CMC data: The case of the missing self-repair. Language Learning & Technology, 12, 85-103. Available from http://llt.msu.edu/vol12num1/smith/default.html

Smith, B., & Sauro, S. (2009). Interruptions in chat. Computer Assisted Language Learning, 22(3), 229-247.Sotillo, S. (2000). Discourse functions and syntactic complexity in synchronous and asynchronous

communication. Language Learning & Technology, 4(1), 82-119. Available from http://llt.msu.edu/vol4num1/sotillo/default.html

Werry , C.C. (1996). Linguistic and interactional features of Internet Relay Chat. In S. Herring (Ed.), Computer mediated communication: Linguistic, social, and cross-cultural perspectives (pp. 47-64). Amsterdam: John Benjamins.