A 21st Century Teacher’s Character Journey: An autoethnographic exploration and narrative retelling of ELA teachers’ perception and implementation of 21st century education
Bibliographic record
Abstract
Education is often prescriptive in nature and teachers within this institution are asked to interpret the government curriculum and use their own materials and methods to teach their disciplines. With the accelerated changes in educational devices and learning models, teachers have had to navigate the new 21st century learning space under a working definition of what it means to be a 21st century teacher in a 21st century classroom. In this auto-ethnography, the author explores what 21st century education means to in-the-field teachers, how it informs their pedagogy, and how it shapes their teaching identities, as well as her own, through interactive interviewing and storytelling. The author uses a narrative approach to construct a story in which five participating ELA teachers from a high school, in the Greater Montreal Area, reveal their subjective experiences and reflections as they learn to define and describe 21st century learning through their own words and interpretations. The literary technique of characterization allowed the author to understand her own role in the 21st century as an educator. Collaborative story-making, told through the eyes of a first-person narrative, has produced is a narrative that reveals how teachers’ unique perceptions and implementations of their crafts can contribute to a descriptive account of 21st century education that is as nuanced and diverse as those who teach within its framework
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".