Synthesising New Pedagogies for Deep Learning with Transformative Learning Theory and the QEP for Meaningful Practices in the Secondary English Language Arts Cycle 2 Program
Bibliographic record
Abstract
In this thesis I critically examine New Pedagogies for Deep Learning (NPDL) to assess its compatibility with Transformative Learning Theory and Constructivism, one of the grounding theories of the Quebec Education Plan (QEP). This thesis explores how NPDL and the QEP’s Secondary English Language Arts Cycle 2 (SELA2) program can be brought together to create deeper, transformative, student-centred learning experiences for students in Québec. This thesis aims to establish common themes of the aforementioned theories and to propose a conceptual framework and outline some practical applications for SELA2 teachers in order to address critical 21st Century skills as well as subject-specific competency development outlined in the SELA2 program. Furthermore, this thesis addresses the complexity of educational change and asserts that the proximity of Secondary English Language Arts Cycle 2 teachers affords them with the profound power to affect change in instruction. It is my stance that providing students with more opportunities to engage in the development of 21st Century skills grounded in the principles of NPDL can open the door for transformation. The proposed framework and the use of applications such as those elaborated on in this thesis, educators can promote deep and more meaningful learning that can lay the groundwork for transformative learning experiences.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".