Bibliographic record
Abstract
New technologies expand the horizons of education, offering opportunities to explore practices based on collaboration and community rather than the individual teacher or learner. A social networking site was implemented in a university unit with the aim of progressing online participatory culture and increasing student engagement both online and in face-to-face classes. The challenge of engaging students and converting lurkers and stalkers into talkers is discussed.Analysis of linguistic features of blog and forum posts was undertaken and the findings were used to modify online instructor behaviour and presence, and to encourage student participation. The research found that posts with high response rates had short topic titles, used directives and lexical items suggesting immediacy: “Newest hottest topic”, while modalization and requests for help produced low or zero responses. Controversial topics received most responses. Gender was found to be a relevant factor, with blogs posted by males gaining higher response rates than those posted by females. The online site produced discursive shifts in “real life” interactions, and provided a speaking-space for quiet students. Students’ initial cynicism towards the site changed, and online social networking cultivated affinity groups and increased student participation in both online and face-to-face contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".