Student teacher learning with Ozobots and Makey Makeys during a workshop and field experience
Bibliographic record
Abstract
This pilot project examines the experiences of a small sample (n = 4) of elementary pre-service teachers (PSTs) as they designed and attempted to implement a series of short lessons, or mini-units, using Ozobots or Makey Makeys during their field experience. Results indicated that, despite the fact that none of the PSTs were able to deliver their mini-units as originally planned, all were able to gain comfort with the tools, recognise their adaptability and articulate how they might be used in future practice. An unanticipated finding, PSTs also reported on cooperating teachers’ (CTs) technological learning, with CTs relying heavily on PSTs for general technological training during the COVID-19 pandemic. These results have important implications regarding PST training and field experiences, namely, providing PSTs with the opportunity to see technology use enacted during their field experiences and matching PSTs with CTs interested in and trained on technological integration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".