The effects of task complexity on L2 English rapport-building language use and its relationship with paired speaking test task performance
Bibliographic record
Abstract
Abstract Although task complexity effects on L2 oral production have been widely studied in teaching contexts, their application to task-based language assessment and pragmatic language use remains underexplored. Since pragmatic competence is part of effective communication and interactional abilities and is context-dependent, an investigation of the relationships among task complexity, pragmatic language use, and paired speaking test performance is needed. This study examined the effect of the resource-directing and resource-dispersing dimensions (the number of elements, causal reasoning demand, and planning time) of Robinson’s task complexity construct on fifty-two intermediate-level English as a Second Language (ESL) learners’ rapport-building language use during two decision-making tasks as the achievement test in an EAP program, and the relationships between rapport language use and three dimensions of paired speaking test performance: collaboration, task completion, and style. The results showed that frequency and variety of rapport-building language use did not significantly differ between the two tasks. However, the study found that only in the simple task did different types of rapport-building language have statistically significant positive or negative relationships with different dimensions of paired speaking test scores. Specifically, greeting language use had a strong or close to strong positive relationship with collaboration and style scores, whereas agreeing language had a strong negative relationship with collaboration scores. Additionally, thanking language had a strong negative association with task completion scores. The findings further suggest that task complexity effects learners’ production of rapport-building language in terms of alignment with their peer interlocutors and formality of style, and also impacts raters’ perceptions of paired speaking task performance. The findings highlight the importance of task effects, paired oral assessment rubrics development, and rapport-building language instruction.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".