CREATIVE METHODS IN TRANSFORMING EDUCATION USING HUMAN RESOURCES / KŪRYBINIAI METODAI PERTVARKANT ŠVIETIMĄ PASITELKUS ŽMOGIŠKUOSIUS IŠTEKLIUS
Bibliographic record
Abstract
Language teaching has become more learner-centered for last three decades. Teachers with a potential of being change-agent received considerable attention of researchers. Teachers need to reconsider their traditional role in helping learners control their learning. Present study investigated viable autonomy-supportive teaching role and creative practices teachers can follow to aid their learners in becoming autonomous. Data were collected from 16 university teachers teaching English communication skills at Bachelor of Science level in public sector universities of the Punjab, Pakistan. Individual semi-structured interviews were conducted to gather teachers’ perceptions. Results revealed that teachers perceived their role vital in developing learner autonomy but they considered a meaningful change in present role with a gradual shift of learning responsibilities from teacher to learner. Moreover, 11 autonomy-supportive teaching practices were suggested to enable learners to control their learning. Study proposed teachers to be trained for their new role, and must have teaching autonomy to materialize autonomy-supportive practices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".