Promising practices for online professional learning
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 it