Learning to Teach Global Competencies in a Transforming Digital World
Bibliographic record
Abstract
In our globally connected, ever-changing society, the ability to adapt to new environments and technologies can greatly enhance success. In Canada,“21st century skills” are being prioritized in the education system so that young children can develop skills to thrive in our technologically advanced world. However, current teaching practices do not always appear to include 21st century skills in the curriculum. This chapter seeks to examine Canadian university Bachelor of Education programs to gather information about where, and how often, 21st century skill training occurs in pre-service teacher education. A keyword search was conducted on program and course descriptions from 45 Canadian university websites to determine where 21st century skill terminology was present. Next, a more in-depth examination of one specific teacher education program in a consecutive, pre-service program in a mid-sized urban centre in Ontario was conducted. Recommendations are discussed for pre-service education in support of integrating 21st century skills in teacher preparation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".