Factors of Participants and Blogs that Predict Blogging Activeness During Teaching Practice and Induction Year
Bibliographic record
Abstract
The blog as a type of social software has been used in education for several years, and its positive effect in the field has been asserted in many studies. This study presents the factors of participants and blogs that predict blogging activeness during teaching practice and induction year. During the teaching practice and induction year all participants could use a blog to share experiences, ask questions, receive support and so on. The blogs were supported by the tutors and all blogs were collective blogs, but they were organised differently and used differently. The data collected included 1,137 blog posts from 192 participants using 13 collective blogs along with their self-reports via pre- and post-period questionnaires. Correlation analysis as the first stage and regression analysis as the second stage was used in this study. The results showed several relationships and the regression analysis indicated that the most influential positive factor predicting participant activeness on the blog was the strength of social relationships between the participants.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".