Linking Teacher Professionalism and Learner Autonomy Through Experiential Learning and Task Design
Bibliographic record
Abstract
Like others in the teaching profession, second language education (SLE) teachers have been subjected in recent years to a process of work intensification and accountability. This process makes use ofexternally developed sets of behavioral objectives, assessment instruments, commercially produced classroom materials, and externally controlled technologies. Taken together, these have resulted in a marked reduction in the freedom of dedicated SLE teachers to be inventive, flexible, adaptable, and responsive to students' needs. In short, teachers are losing professional autonomy. In this article, we argue that an experiential learning approach can help counter this trend. We describe and compare three theoretical frameworks and models in this orientation that have been developed specifically for SLE and draw out practical implications for classroom task design. We contend that experiential learning is more than a classroom management technique and argue the importance of linking learner autonomy and teacher professionalism through this approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".