Administrators’ professional learning via Twitter: the dissonance between beliefs and actions
Bibliographic record
Abstract
Purpose Although there has been increasing optimism about the potential for social media platforms such as Twitter to support educators’ professional learning, it is yet unclear whether such promises hold true. Accordingly, the purpose of this study is to explore school administrators’ use of Twitter for professional learning. Design/methodology/approach This qualitative case study draws data collected from 17 school administrators from throughout the United States and Canada. In addition to individual, semi-structured interviews, administrators’ tweets were collected for two weeks. This resulted in 1460 tweets. Analyses were aimed at perceptions about Twitter, the knowledge shared, and its impact on practice. Findings Findings presented a paradox: although administrators were enthusiastic about the social and professional benefits associated with Twitter, they did not share or apply much knowledge commonly associated with administrator work. Topically, administrators’ tweets tended to focus on technology, rather than other leadership issues. Also, administrators’ informal tweets focused on norms and relationships in the online community, rather than other dimensions to leadership craft. What’s more, leaders were rarely able to point to direct changes in their school policies or practices resulting from Twitter. Research limitations/implications The present study raises issues for future research, including: How do administrators evaluate the expertise of peers or other resources online? How do leaders negotiate conflict or dialogue online? How might leaders leverage social media as public relations tools? Practical implications Whereas popular media have described the benefits of platforms like Twitter in broad strokes, the present study provides a detailed account of the practitioner experience. This account includes not only descriptions of what leaders might (or might not) be learning via Twitter, but also some of the benefits of being able to socialize with colleagues online. Originality/value As social media use has grown, so has interest in using such platforms for professional learning. However, there is a gap in knowledge regarding the strengths and shortfalls facing administrators. This study breaks new ground by comparing Twitter's purported benefits to user's tweets and outcomes.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".