Awakeness, complexity and emergence: Learning through curriculum theory in teacher education
Bibliographic record
Abstract
In this self-study research, we explore how the work of significant and diverse curriculum scholars informed the learning of teacher candidates within an intensive summer semester that serves as the foundation for a Secondary Teacher Education Program (STEP) at a Canadian university. Questions that guided our inquiry include: How did teacher candidates take up and negotiate theory as part of their emerging professional identities? How did teacher candidates understand the relationship between pedagogy and their learning of/through curriculum theory? How did teacher candidates embody diverse theories and how did they understand the significance of this within and beyond this foundational semester? And finally, as teacher educators, how are our beliefs, understandings and practices developing through this self-study? We employed a qualitative, grounded theory approach and engaged in iterative cycles of analysis with learning artifacts and interview transcripts from 26 teacher candidates. We identified the rich and layered themes of emergence, complexity, and awakeness, which revealed shifts in teacher candidates’ awareness in relation to their evolving identities. We discuss these themes in relation to the above questions and locate the influences of the selected theorists. This research contributes to the field of curriculum studies through offering a living case that explores how taking up diverse and contemporary curriculum theorists has potential to both support teacher candidates to experience praxis and shift the ground of teacher education.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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 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".