Bibliographic record
Abstract
This paper explores graduate students’ experiences and perceptions of using discussion forums to build a sense of community in an accelerated online Master of Library and Information Studies program. The Classroom Community Short Form survey was adapted to include short answer questions. The results suggest that while students feel supported and that they care about each other, they still feel isolated. Discussion forums that were informal, provide peer to peer interaction and participation by the instructor were more likely to create a sense of community. Supportés mais isolés : Perspectives d'étudiants diplômés sur la construction de communauté par le biais de discussions dans les forums dans le contexte d'un programme de MBSI en ligne RésuméCet article explore les expériences et les perceptions des étudiants diplômés par rapport à l'utilisation de forums de discussion pour bâtir un sentiment de communauté dans un programme en ligne accéléré de maîtrise en bibliothéconomie et sciences de l'information. Le court sondage sur la communauté de classe a été adapté pour y inclure des questions à réponse courte. Les résultats suggèrent que bien que les étudiants se sentent supportés et qu'ils se soucient les uns des autres, ils se sentent tout de même isolés. Les forums de discussion qui étaient informels, qui apportaient des interactions entre les pairs et dans lesquels les formateurs participaients, avaient plus de chances de créer un sentiment de communauté.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.011 |
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".