Exploring the Strength of the Process Writing Approach as a Pedagogy for Fostering Learner Autonomy in Writing Among Young Learners
Bibliographic record
Abstract
Learner autonomy is widely recognized as a desirable educational goal in second or foreign language learning. However, the generality of the concept often makes it difficult to either nurture or measure the related traits. The present study focused on learner autonomy in the area of writing, exploring the use of the process approach as a means to foster its development in terms of students’ emerging writing skills. The study was conducted in the naturalistic settings of three secondary school ESL writing classrooms in Hong Kong involving 70 student participants. Data gathered quantitatively with a questionnaire and qualitatively through self-assessment forms, learners’ journals and case studies suggest that the process approach can reduce students’ reliance on the teacher and their tendency to seek help from others, while leading to growth in their metacognitive knowledge about writing and their knowledge of themselves as writers. These developments are all signs of the emergence of learner autonomy in these young ESL writers. Overall, the findings suggest that the process approach can bring about similar changes in young writers despite variations in the cultural backgrounds and teaching beliefs of its implementers. It is argued that the strength of the process approach may lie in the stimulation of the growth of autonomous skills and attitudes in writing in young learners, and such a strength should be recognized by language educators who view learner autonomy as a major educational goal.
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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 | 0.008 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".