Bibliographic record
Abstract
This study took place at the beginning of the COVID-19 pandemic when most schools worldwide were making the transition to online teaching and learning. Through this single-case study design, the study examined the learning experiences of a group of teachers engaged in interactive, inquiry-based professional learning focused on math, making and coding during a shift to emergency remote teaching. The primary objective was to identify promising practices for online professional learning (PL) focused on math and coding using a maker-pedagogies approach to teaching and learning, based on the teachers’ learning experiences. Study participants included 20 teachers from a rural school board in Northern Ontario, Canada. Findings indicated that the following may be considered as promising practices when developing and implementing virtual math and coding PL from a maker perspective. It is important to: a) balance sessions focused on specific math and coding content with more general sessions focused on learning the various maker-technology tools; b) include both synchronous and asynchronous learning opportunities for the variety of teachers involved in the learning; c) include collaborative learning in the teacher PL and a virtual platform that can support this type of social learning; d) ensure the PL sessions are on-going as opposed to one-off or isolated sessions. This research suggests that online professional learning sessions need to consider three elements: the teacher, the content, and the learning environment and offers important recommendations for future work in this area.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 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".