The possibilities of transforming learning: a practitioner research study of a pilot alternative learning program
Bibliographic record
Abstract
In this study, I examine the pilot year of an alternative learning environment in which I, as a practitioner, explored the possibilities for transforming learning for a small class of Grade 11 and 12 students. Drawing on a pedagogy of care, a constructivist model of learning and a student-centered approach to learning, the students and I negotiated new curriculum, combining regular classroom courses with courses constructed by their own learning interests. In this case study, a rhizomatic analysis of student and practitioner data, collected both during and after students’ graduation from high school, showed that students were highly engaged with learning when guided by their personal interests. In the study, I also found, however, that students struggled to fully embrace the potential of their own interests, held back by the ambiguity of self study and the clear metrics of the regular school system to which they were accustomed. As practitioner, I struggled to meet the demands of the prescribed curriculum and those of the curriculum that constantly evolved and changed according to students’ interests. The study also speaks to the tensions in defining the role of a teacher in this alternative learning environment. In conclusion, I suggest we seek to make possible an alternative high school learning environment that more closely resembles free schooling (i.e., learn what you want, where and when you want) within a public school that would, combined with a traditional course of study, meet the provincial criteria for graduation accreditation.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".