Qualitative study of five FSL teachers using constructivism to support second language learning / by Laura Hope Southcott. --
Bibliographic record
Abstract
thesis.I was first introduced to constructivism as a theory of learning in Dr. Puk's Master's course.During that course I learnt what it means to allow students to direct their own learning and to make their own meaning from the readings, discussions and reflections in class and outside the university.In the course of my thesis, I experienced constructivism firsthand as I created my questions, built on what I already knew, constructed new knowledge, reflected on what I was learning, and shared my ideas and thoughts with my committee and peers.Dr. Puk guided me through the process and was there to give me his comments and advice.I particularly appreciate the fact that much of this was done on-line and from a great distance.France and Canada seemed very close, despite the many miles between the two countries
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".