Presentation on theme: "David P. Ellis University of Maryland"— Presentation transcript:
David P. Ellis University of Maryland
Research Motivation 1. Need for a reliable, transparent means of grading and sequencing tasks for both TBLT and TBA 2. Research findings to date on the linguistic influences of task complexity are ambiguous 3. Belief that the underlying assumption of both existing task complexity models is misguided
Model 1 - Skehan (1998) Three components Code Complexity (language required) Code Complexity (language required) Cognitive Complexity (thinking required) Cognitive Complexity (thinking required) Cognitive Familiarity Cognitive Familiarity Cognitive Processing Cognitive Processing Communicative Stress (performance conditions) 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)
Model 2 – Robinson (2007) Triadic Componential Framework Task Complexity Task Complexity (cognitive factors) Resource-directing (developmental) Resource-dispersing (performative) Task Conditions Task Conditions (interactional factors) Task Difficulty Task Difficulty (learner factors)Prediction An increase in task complexity along resource-directing dimensions increases both linguistic complexity and accuracy An increase in task complexity along resource-directing dimensions decreases fluency, but increases both linguistic complexity and accuracy.
Research Questions 1.How does task complexity influence the linguistic complexity of speaker output? 2.Should linguistic complexity be the primary dependent variable of task complexity studies?
Method Participants: 24 native speakers of English (12 males, 12 females) 24 native speakers of English (12 males, 12 females) TasksTasks: Tasks Map directions task (adapted from Robinson, 2001) Map directions task (adapted from Robinson, 2001) Car accident reporting task (Lee, YG, unpublished) Car accident reporting task (Lee, YG, unpublished)
Procedure Repeated measures design with full counterbalancing Repeated measures design with full counterbalancingcounterbalancing Participant responses: Participant responses: Recorded digitally and uploaded to PC Recorded digitally and uploaded to PC Transcribed into MS Word using Express Scribe Transcribed into MS Word using Express Scribe Parsed by clause and input into MS Excel Parsed by clause and input into MS Excel Coded for Linguistic Complexity Coded for Linguistic Complexity Clauses per C-unit (Robinson, 2001) Clauses per C-unit (Robinson, 2001) Clauses per AS-unit (Foster et al, 2000) Clauses per AS-unit (Foster et al, 2000) Lexical complexity not measured Lexical complexity not measured Two independent coders; discrepancies resolved 100% Two independent coders; discrepancies resolved 100% Data analyzed using SPSS Data analyzed using SPSS
Discussion Possible reasons for null findings: 1. Common measures of linguistic complexity are not appropriate operationalizations of the construct 2. Linguistic complexity does correlate well with task complexity, but task complexity is not operationalized well 3. Linguistic complexity simply does not correlate with task complexity, at least within tasks
Discussion (contd) Reasons to abandon linguistic complexity as the dependent variable of interest: 1. Not a reliable correlate of task complexity within tasks 2. Even if it were, it would say nothing about L2 development, only L2 production 3. Teachers/Raters cannot reliably assess the linguistic complexity of a speakers performance, especially in real time
Discussion (contd) Alternative to linguistic complexity as the DV : Amount of production (i.e., word count) Amount of production (i.e., word count)word countword count
Preliminary Conclusion Increase the complexity of tasks to induce linguistically complex output. 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.