Inquiring tweets want to know: #Edchat supports for #RemoteTeaching during COVID‐19
Bibliographic record
Abstract
Abstract Social media use has spiked around the world during the COVID‐19 global pandemic as people reach out for news, information, social connections, and support in their daily lives. Past work on professional learning networks (PLNs) has shown that teachers also use social media to find supports for their teaching and ongoing professional development. This paper offers quantitative analysis of over a half million Twitter #Edchat tweets as well as qualitative content analysis of teachers’ question tweets ( n = 1054) and teacher interviews ( n = 4). These data and analyses provide evidence of the kinds of supports that teachers in the United States and Canada sought on social media during the rapid transition to emergency remote teaching in Spring 2020 and how these supports informed teaching practices. These results provide insights into PLN theory and teachers’ social media use during times of disruption and crisis. Practitioner notes What is already known about this topic Prior to the spring 2020 pandemic, teachers turned to social media to find supports for teaching and just‐in‐time professional development (PD). #Edchat, one of the oldest and most used educational hashtags on Twitter, supports education‐related conversations, frequently self‐promotional rather than collaborative. The COVID‐19 pandemic disrupted educational systems globally and created new demands on teacher PD during transitions to emergency remote teaching and learning. What this paper adds Teachers’ professional learning networks (PLN) on social media can be flexible around contextual circumstances and users’ needs. #Edchat discourse can move beyond self‐promotion to inquiry with benefits for professional learning. Education‐related response networks on social media are useful to teachers in emergency situations (and beyond them) where just‐in‐time professional learning needs and questions surpass local PD capacity. Implications for practice and/or policy Teachers should increase capacities inquiring discourses on Twitter. Education stakeholders should increase support for teachers’ agency and advocate for broader conceptions and approaches to PD that incorporate PLNs spanning social media.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".