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David P. Ellis University of Maryland

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1 David P. Ellis University of Maryland
The Relationship between Task Complexity and Linguistic Complexity: An Analysis of L1 Speaker Production David P. Ellis University of Maryland

2 Research Motivation Need for a reliable, transparent means of grading and sequencing tasks for both TBLT and TBA Research findings to date on the linguistic influences of task complexity are ambiguous Belief that the underlying assumption of both existing task complexity models is misguided

3 Model 1 - Skehan (1998) Three components Prediction
Code Complexity (language required) Cognitive Complexity (thinking required) Cognitive Familiarity Cognitive Processing Communicative Stress (performance conditions) Prediction Attentional resources are finite, so an increase in one dimension comes at the expense of the other two (e.g., an increase in linguistic complexity will result in a decrease in fluency and accuracy)

4 Model 2 – Robinson (2007) Triadic Componential Framework Prediction
Task Complexity (cognitive factors) Resource-directing (developmental) Resource-dispersing (performative) Task Conditions (interactional factors) Task Difficulty (learner factors) Prediction An increase in task complexity along resource-directing dimensions decreases fluency, but increases both linguistic complexity and accuracy.

5 Research Questions How does task complexity influence the linguistic complexity of speaker output? Should linguistic complexity be the primary dependent variable of task complexity studies?

6 Method Participants: Tasks:
24 native speakers of English (12 males, 12 females) Tasks: Map directions task (adapted from Robinson, 2001) Car accident reporting task (Lee, YG, unpublished)

7 Procedure Repeated measures design with full counterbalancing
Participant responses: Recorded digitally and uploaded to PC Transcribed into MS Word using Express Scribe Parsed by clause and input into MS Excel Coded for Linguistic Complexity Clauses per C-unit (Robinson, 2001) Clauses per AS-unit (Foster et al, 2000) Lexical “complexity” not measured Two independent coders; discrepancies resolved 100% Data analyzed using SPSS

8 Results SPSS LC Output Excel Descriptive Statistics

9 Discussion Possible reasons for null findings:
Common measures of linguistic complexity are not appropriate operationalizations of the construct Linguistic complexity does correlate well with task complexity, but task complexity is not operationalized well Linguistic complexity simply does not correlate with task complexity, at least within tasks

10 Discussion (cont’d) Reasons to abandon linguistic complexity as the dependent variable of interest: Not a reliable correlate of task complexity within tasks Even if it were, it would say nothing about L2 development, only L2 production Teachers/Raters cannot reliably assess the linguistic complexity of a speaker’s performance, especially in real time

11 Discussion (cont’d) Alternative to linguistic complexity as the DV:
Amount of production (i.e., word count)

12 Preliminary Conclusion
Increase the complexity of tasks to induce more linguistic output, not necessarily more linguistically complex output. Rationale Doing so will not only create more opportunities for learners to consolidate, reorganize, and improve access to existing L2 knowledge, thereby improving fluency, but also spur L2 development by inducing a greater number of gaps (both lexical and morpho-syntactic), thereby improving accuracy and potentially complexity.

13 David P. Ellis University of Maryland
Thank you David P. Ellis University of Maryland

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