Exploring the Linguistic Complexity of OnTask and Off-Task Interaction During Chat Shannon Sauro University of Texas at San Antonio [email protected].
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Exploring the Linguistic Complexity of OnTask and Off-Task Interaction During Chat Shannon Sauro University of Texas at San Antonio [email protected] 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 During the Task After the Task Mina Malmö: Mina Malmö: 1,8 kids Steve Penn: on average Mina Malmö: yes. Steve Penn: how is it in USA? Steve Penn: 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 good, but one of your nouns could use the zero article. 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: Carsten Malmö: What if a missile hits a nuclear power plant? What happens? Because of how the core of the powerplant is built Nothing would happen actually Carlos Penn: Is it stabalized or something? Carlos Penn: stabilized, I mean Carsten Malmö: Carlos Penn: I did study science, but I’m not sure how to explain it in english to be honest :) Is that all the words? Carsten Malmö: two to go Carsten Malmö: Repairing the Task Susan Penn: what’s it called in Swedish? MC Malmö: It’s okay. I don’t really know myself MC Malmö: Susan Penn: Wind maler perhaps haha… no I really don’t know 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 3851 160.46 54.55 53 307 Off-Task 3318 138.25 87.98 36 344 Total 7169 N= 24 MLT On-Task: 10.05 MLT Off-Task: 7.65 Results: Lexical Diversity Tokens Types Mean Index of G SD On-Task 3851 2244 7.38 0.83 Off-Task 3318 2125 7.47 1.56 Total 7169 4369 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 449 420 1.06 0.32 Off-Task 542 600 .93 .29 Total 991 1020 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 technologymediated 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 StatesTexas. 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). 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