Exploring the Construct of Learner Autonomy in Writing: The Roles of Motivation and the Teacher
Bibliographic record
Abstract
Learner autonomy is widely recognized as a desirable educational goal in second language contexts. However, the lack of domain-specificity in research related to learner autonomy, compounded with the diverse views on its connotations, makes it difficult to either nurture or measure. This paper reports on a study that explored the construct of learner autonomy in the area of writing using quantitative data collected in the naturalistic settings of three secondary school classrooms in Hong Kong. In this study, learner autonomy was proposed as a construct consisting of autonomous attitudes including motivation, self-confidence and independence from the teacher, and autonomous skills embracing strategy use and metacognitive knowledge. A questionnaire was designed accordingly to measure changes in the participants after a writing programme that adopted the process writing approach, the potential of which in fostering traits of learner autonomy had been demonstrated in previous studies and was further explored in this study. Findings gathered through factor analysis on the questionnaire data, followed by a paired-sample t-test to investigate changes in the participants after the writing programme, suggest that a degree of independence from the teacher may possibly be a prerequisite for autonomy development in terms of writing skills, while motivation may have a more important role to play in its subsequent development.
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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 | 0.005 | 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".