Teacher E-professionalism: An Examination of Western Canadian Pre-service Teachers’ Perceptions, Attitudes, and Facebook Behaviours
Bibliographic record
Abstract
This study addresses the pressing need for attending to teacher e-professionalism, that is, the appropriate application of information and communication strategies when using digital media. The authors examine data patterns related to 113 pre-service teachers’ perceptions of e-professionalism, attitudes towards existing technology-related professional guidelines, and current behaviours on Facebook. The results from the online questionnaire suggest that these Western Canadian pre-service teachers are uncertain about what online behaviours should be restricted and whether maintaining e-professionalism was possible. Integrated findings are discussed in light of communication privacy management theory. The paper concludes by advancing implications for informing e-professional education for teachers. Cette étude adresse le besoin pressant pour assister e-professionnalisme d'enseignant, qui est, l'application appropriée de stratégies d'information et de communication lors de l'utilisation des médias numériques. Les auteurs examinent les configurations de données de 113 enseignants en formation liées à leurs perceptions d’e-professionnalisme, attitudes à l'égard des directrices professionnelles environ la technologie, et comportements actuels sur Facebook. Les résultats du questionnaire suggèrent que ces enseignants en formation de ouest canadiens sont incertains quant à ce que les comportements basés sur le Web devraient être limités et si maintenir e-professionnalisme était même possible. Les conclusions sont discutées utilisant une théorie de la gestion de la confidentialité des communications. L’article conclut en avançant des implications pour informer l'éducation d' e-professionnel pour les enseignants.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".