1. David P. Ellis University of Maryland dpe@umd.edu
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Car accident reporting task (Lee, YG, unpublished)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 dpe@umd.edu
Thank you
David P. Ellis
University of Maryland