Korean College EFL Learners’ Task Motivation in Written Language Production
Bibliographic record
Abstract
The aims of the present study were to explore the effects of two different types of task conditions (topic choice vs. no choice) on the quality of written production in a second language (lexical complexity, syntactic complexity, and cohesion) and to investigate the effects of these two different task conditions on task motivation. This research was conducted by means of a task motivation questionnaire and a collation of the writings of 31 Korean college students learning English as a foreign language. The data was analyzed using Coh-Metrix 3.0. The major findings were as follows: 1) The writings of participants in the topic choice condition were better than those in the no-choice condition in terms of lexical sophistication and temporal cohesion. However, participants’ written production in the no-choice condition was better than that in the topic choice condition in terms of syntactic complexity. 2) The participants’ task motivation levels were higher for the perceived choice domain in the topic choice condition than in the no-choice condition. These findings should help L2 writing instructors, materials developers, and researchers to design L2 writing instruction with a focus on written production specifically for Korean college-level learners.
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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".