Methods and Strategies for Exploring Engineering with Primary/Junior/Intermediate Teacher Candidates
Bibliographic record
Abstract
STEM (science, technology, engineering and mathematics) has become a common construct promoted by school boards but, as yet, our province has no Ministry of Education mandate to include it in the curriculum. Our presentation will describe a 4-year interfaculty STEM initiative between Faculties of Education and Engineering. The intention behind this collaboration was to design and deliver workshops to support our Teacher Candidates in developing their understandings of how the engineering component of STEM can be connected to the existing provincial curriculum. We leverage the strength of interdisciplinary collaboration, and the reciprocal learning that can occur when members of seemingly distinct disciplinary cultures work together, to develop the workshops. We view STEM as a common collaborative space or boundary object spanning many educational worlds (Shanahan et al., 2016). This perspective provides an opportunity for all those involved to work together to attain educational goals. As we continue to strive to invigorate Teacher Candidate understandings of the engineering component of STEM, key lessons learned from this collaboration will be presented.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".