The Effects of Genre Writing on Korean High School Learners’ English Writing Abilities and Learning Motivation according to the Degree of English Writing Anxiety
Bibliographic record
Abstract
English writing education is crucial for ESL/EFL learners, and the significance of affective factors, such as anxiety, has been emphasized. However, there are limitations in the school field that prevent a balanced English learning environment. Moreover, longitudinal studies are lacking to track learners’ progress over time. Thus, this study aimed to investigate the effects of genre writing on Korean high school learners’ English writing abilities and learning motivation according to the degree of English writing anxiety. The study engaged twelve first-year (Grade 10) high school students who participated in genre writing for a three-year longitudinal study from mid-March 2020 to December 2022. The results of this study are as follows. First, it confirmed that the English writing anxiety of all learners who participated in genre writing was significantly alleviated. However, the higher the level of English writing anxiety, the more significant the alleviation. Second, it was found that the English writing abilities of all students who participated in genre writing was significantly improved in most period sections, regardless of their level of English writing anxiety. However, the lower the level of English writing anxiety, the greater the improvement in the English writing abilities. Finally, the learning motivation of all students who participated in genre writing significantly improved, regardless of their level of English writing anxiety. The findings have useful pedagogical implications for teachers to develop English writing abilities while considering learners’ affective factors.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".