The task is not enough: Processing approaches to task-based performance

Peter SKEHAN, Xiaoyue BEI, Qian LI, Zhan WANG

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

This article reports on three research studies, all of which concern second language task performance. The first focuses on planning, and compares on-line and strategic planning as well as task repetition. The second study examines the role of familiarity on task performance, and compares this with conventional strategic planning. The third study examines the effect on task performance of different types of post-task transcription. The three studies are also examined in relation to one another for the broader generalizations that they permit. These suggest that repetition can be stronger in its effects than on-line or strategic planning, but that planning is more potent in its effects than simply familiarity with the material being spoken about. In addition, what is termed supported on-line planning and post-task transcription are associated with less error in performance. The three studies are discussed in terms of the wider second language performance processes of complexifying, rehearsing and monitoring. These processes are linked to the Levelt model of speaking, and applied to the need to analyse tasks in a manner consistent with pedagogic goals. Copyright © 2012 The Author(s).
Original languageEnglish
Pages (from-to)170-187
JournalLanguage Teaching Research
Volume16
Issue number2
DOIs
Publication statusPublished - Apr 2012

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strategic planning
performance
planning
pedagogics
language
speaking
monitoring
Planning
Strategic Planning
Transcription
Familiarity
Language

Citation

Skehan, P., Bei, X., Li, Q., & Wang, Z. (2012). The task is not enough: Processing approaches to task-based performance. Language Teaching Research, 16(2), 170-187.

Keywords

  • TBLT (task-based language teaching)
  • Language learning task
  • Task performance
  • Task conditions
  • Repetition
  • Planning
  • Accuracy
  • Fluency
  • Complexity