Professional Development via Facebook Group: Perception of School Librarians
Bibliographic record
Abstract
Facebook offers the ability to its users to create a group on a specific subject or interest and ask friends and acquaintances to join and share information which is entirely driven by them. This study seeks to explore whether and how Facebook group adds value to the complex process of school librarians’ professional development. The successive objectives are to understand the current and future professional learning trends in school librarianship through Facebook group posts. An online questionnaire was posted to selected Facebook groups and sent to two school librarians’ listservs, and 404 usable responses were received. Study findings indicate that with the Facebook group, School Library Professionals (SLPs) are able to stay informed with new knowledge in their field by exchanging information, opportunities and ideas. The findings also confirm that more and more SLPs are using, or at the very least, experiencing Facebook groups as a Professional Development Tool (PDT). Study data shows that 78.4 percent of SLPs desire to see posts related to ‘teaching resources’ and 63.8 percent ‘how to use technology’ on the Facebook group and are also indicative of the current professional learning trends of SLPs. Research skills tips (67.9%), makerspace ideas (57%) and how to collaborate with classroom teachers (51.6%) are found to be the top future professional learning aspirations of SLPs. This paper provides valid empirical evidence and highlights that many SLPs are using Facebook groups and 38.4 percent regarded it as a ‘very useful tool’ for professional development. The discussions that take place on Facebook groups empower and enhance librarians’ professional practice and networking.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".