Perspectives on the Experience of the Learning Community Through Online Discussions
Bibliographic record
Abstract
In this article I discuss the findings from a student survey that examined the role of online discussion groups in maintaining a learning community when nursing students were separated by time and place during their last practicum before graduation. The learning community was created face-to-face and then maintained and enhanced through online discussions. Students gained skills and knowledge about nursing, teaching and learning, technology, time management, and critical thinking. Dans cet article, je discute des résultats d’une enquête, menée auprès d’étudiants en nursing, et qui examine le rôle des groupes de discussion en ligne dans le maintien d’une communauté d’apprenants lorsque les étudiants ont été séparés dans le temps et l’espace pendant leur dernier stage avant leur graduation. La communauté d’apprenants a été créée en face à face puis elle s’est maintenue et s’est développée par le biais des discussions en ligne. Les étudiants y ont acquis des habilités et des connaissances dans le domaine du nursing, ainsi que dans les domaines de l’enseignement et de l’apprentissage, des technologies, de la gestion du temps et de la pensée critique.
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 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".