VIRTUAL COMMUNITIES AND NETWORKS OF PRACTISING MATHEMATICS TEACHERS:The Role of Technology in Collaboration
Bibliographic record
Abstract
The collaboration of practising teachers in a virtual environment introduces the technology tools themselves both as mediators and as participants – as co-actors – in the collaborative process. Although there is a growing literature on the collaboration of practising teachers, the role of virtual technology tools is typically not addressed. In this chapter, we turn our attention to two cases, one in Brazil and one in Canada, as we explore how tools mediate and interact in the way teachers collaborate and construct knowledge. A challenge in this exploration is that technological tools change dramatically over short periods of time. Some aspects of teachers’ online learning that are brought to light by the two cases from Brazil and Canada are: (1) virtual collaboration can happen in very different ways and using very different tools and methods; (2) online technology tools can transform abstract mathematics objects like polygons into tangible objects of communal attention and action; (3) collaborative knowledge construction tools like wikis help re-shape the collaborative process and transform roles played by teachers and instructors; and (4) multimodal communication through drawing tools, rich text, and video changes the “face” of mathematics. The virtual, non-human objects that are part of collaborative collectives of humans-with-media are not tools that we simply use for predetermined purposes. Humans-media interactions, which are quickly evolving with changes in the online world, are organic, reorganizing and restructuring our understanding of what it means for practising mathematics teachers to collaborate in a virtual environment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".