The Effect of Modelling Innovative Technology Use in STEM Teacher Education on Teacher-Candidates’ Knowledge for Teaching and Views on the Nature of Science
Bibliographic record
Abstract
The Science Education Videos for All project engages secondary science, technology, engineering and mathematics (STEM) teacher-candidates (TCs) in designing short educational videos of hands-on science experiments relevant to British Columbia (BC) curriculum. TCs contribute to the development of the resource through providing feedback on the existing videos, designing their own, and using this resource during their school practicum and Faculty-wide STEM open house event for the general public. The goal of the current study is to investigate the effect of the project on the TCs’ technological pedagogical and content knowledge (TPACK) (Koehler & Mishra, 2015) and TCs’ views on the nature of science (Adams et al., 2006). The preliminary findings of the study indicate that active engagement of TCs in designing technology rich education resources has a potential to affect their knowledge for teaching, their views on the nature of science, and their willingness to use these resources in their teaching.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".