The development of English 12 First Peoples as an online distance education course
Bibliographic record
Abstract
This autoethnographic study documented the development of the online version of English 12 First Peoples and investigated which of the course qualities needed to be developed and nurtured differently in an online format. Data were collected through a reflective journal maintained throughout the course development process and analyzed by examining themes which emerged. The main themes involved e-learning as an emergent field in education today, the advantages of e-learning, and strategies for building community in a virtual classroom. Subsequent themes which emerged were organizational considerations for online course development and the challenges of time and technology. The research determined that important course qualities such as the provision of reflective and experiential learning opportunities and respectful interactions could be developed in an online secondary school classroom if deliberate attention were paid to using technology effectively to build community. --P. ii.;" This research project traced the development of the face-to-face delivery model of the new English 12 First Peoples course into an e-learning format for the North Coast Distance Education School (NCDES). The goal was to create an on-line learning model that would reflect the First Peoples principles of learning outlined in the English 12 First Peoples IRP, but would use technology to support and enhance students' abilities to meet the prescribed learning outcomes. --P. 1."
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".