Supporting Teachers’ Understanding of Innovative Maker Pedagogies During a Pandemic Through the Design of Ethical and Relational Online Professional Learning
Bibliographic record
Abstract
This qualitative research explores the challenges involved in designing online professional learning (OPL) for teachers with a focus on innovative pedagogies, specifically maker-centred practices. This OPL was designed in response to teachers’ expressed need for support to the government mandated pivot to emergency remote teaching (ERT) during the 2020 pandemic. The research question addressed is: What are the many ways in which we create the conditions for meaningful, authentic, and respectful professional learning focused on innovative practices, such as making, in an online environment? In this study, the conceptual model considers human-centred design and Nodding’s (2013) relational practice in the context of the Ontario College of Teachers’ (OCT) four-part conception of professional ethics. Implications include that designers: (a) can enhance teacher learning by highlighting the connection between empathy, perspective-taking, and techno-pedagogical competence with making; (b) should focus the sessions on common tools, as well as transferable activities and curriculum, to support early success; and (c) design with teachers, which requires the intentional design of conditions for teacher learning, targeted supports and scaffolds for learning, awareness of resources needed, and provision of appropriate instructional guidance and expertise.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".