Effects of Mastery and Performance Goals on the Composition Strategy Use of Adult EFL Writers
Bibliographic record
Abstract
This study examines the combined effects of contrasting mastery and performance goals on the use of composition strategies by adult writers of English as a Foreign Language (EFL). Thirty-eight Taiwanese English-major college seniors of homogeneous writing proficiency consented to participate in the study. Based on responses on a goal scale, 19 participants were assigned to the high-mastery-low-performance (HMLP) group, and 19 were assigned to the low-mastery-high-performance (LMHP) group. Participants in the HMLP group were diagnosed as having stronger mastery but weaker performance goal orientations, whereas those in the LMHP group demonstrated the opposite tendency. Evidence from think-aloud protocols indicated that (a) participants used 20 distinctive strategies classified into five categories; (b) the HMLP group used monitoring/evaluating, revising, and compensating strategies significantly more often than the LMHP group; and (c) the frequency of revising strategies and mastery orientations served as two significant positive predictors for better writing outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".