Bibliographic record
Abstract
The Teacher Education Program at the University of Northern British Columbia (UNBC) implemented three initiatives in 2018 to improve the practicum experience for teacher candidates. One of these initiatives was to extend the use of e-Portfolios into final practicum. E-Portfolios are first developed by teacher candidates in EDUC 431, the Education Technology course, but they were asked to continue its use in the following term during final practicum. The extended use of e-Portfolios served as one response in the teacher education program to BC’s Curriculum (2021) and changes in the K-12 system, which in turn modelled several aspects of BC’s Curriculum such as personalization, Core Competencies, formative assessment, and the First Peoples Principles of Learning. Including final practicum as part of the e-Portfolio, teacher candidates were able to deepen their understanding of the Professional Standards for BC Educators (2019), reflect on their teaching experience, and conclude the program with a presentation at the Celebration of Learning. Teacher candidates were able to maintain an e-Portfolio during final practicum, identify additional artefacts to demonstrate their understanding of the professional standards, and create a digital narrative describing who they are as educators.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".