Unraveling the Effects of Task Sequencing on the Syntactic Complexity, Accuracy, Lexical Complexity, and Fluency of L2 Written Production
Bibliographic record
Abstract
Although several studies have explored the effects of task sequencing on second language (L2) production, there is no established set of criteria to sequence tasks for learners in L2 writing classrooms. This study examined the effect of simple ̶ complex task sequencing manipulated along both resource-directing (± number of elements) and resource-dispersing (± planning time) factors on L2 writing compared to individual task performance using Robinson’s (2010) SSARC model of task sequencing. Upper-intermediate L2 learners (N = 90) were randomly divided into two groups: (1) Participants who performed three writing tasks in a simple–complex sequence, and (2) participants who performed either the simple, less complex, or complex task. Findings revealed that simple-complex task sequencing led to increases in syntactic complexity, accuracy, lexical complexity, and fluency, as compared to individual task performance. Results are discussed in light of the SSARC model, and theoretical and pedagogical implications are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".