Teacher professional learning in online communities: toward existentially reflective practice
Bibliographic record
Abstract
In this article, I use autobiography as a method to recount my experiences of self-directed professional learning in an interest-driven online community of teachers. I draw on Søren Kierkegaard’s critique of the Press and his conception of three spheres of human existence as a conceptual framework. Although the online community in which I participated provided an unprecedented opportunity to exchange high volumes of information, it failed to create a platform where I could turn information into meaningful pedagogical knowledge. Moreover, the online community seemed to encourage members to take interest in everything and share opinions or resources without taking responsibility of their consequences. Based on the insights from this autobiographical reflection, I make a proposal for taking an existential approach to reflective practice with regards to teachers’ self-directed professional learning in online communities.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".