The learning ecology of Web 2.0 tool use by teacher librarian candidates
Bibliographic record
Abstract
The learning ecology captured by this survey of Web 2.0 tool usage among teacher librarian candidates at Western Kentucky University paints a narrow landscape of Social networking and communication tools used for personal activities, which are used to a lesser degree in the professional and academic spheres. Results indicate that the Top five Web 2.0 tools (Social Networking, Communication, Photo/Video Sharing, Blogs, and Productivity Tools) are being used more often in professional settings than previously reported in other national and state studies. Age does not appear to be significant in the learning ecology of teacher librarian candidates' Web 2.0 tool use, indicating that an overall enthusiasm for technology among teacher librarian candidates putthem in a special category of user. Suggestions for further study include exploring how ―gateway‖ Web 2.0 tools such as collaboration and networking tools used by teacher librarian candidates in their personal lives could transfer to the professional and academic spheres, and spur motivation to use other less commonly used Web 2.0 tools such as social gaming, pod-casting or virtual environments. This study confirms that the potential for educators to integrate Web 2.0 tools into all aspects of their lives are currently hampered by limited bandwidth at home, and the use of restrictive acceptable use policies and filters in schools.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".