Community Building and Computer-Mediated Conferencing
Bibliographic record
Abstract
This study examined the relationship between community cohesion and \ncomputer-mediated conferencing (CMC), as well as other variables potentially \nassociated with the development of a learning community. Within the context of \na graduate-level course in instructional design (a core course in the Masters of \nDistance Education program at Athabasca University) students participated in \nasynchronous online discussion groups as an integral part of their course \nactivities. Upon completion of the course, a questionnaire based on Rovai's (2002) \nClassroom Cohesion Scale (CSS) was administered to examine the relationship \nbetween community cohesion and students' perception of their CMC \nparticipation as well as other selected variables. The CSS was comprised of two \nsubscales: the Connectedness subscale and the Learning Community subscale. \nResults revealed a significant positive correlation between community cohesion \nand passive CMC involvement (i.e., reading postings) but not with more active \nCMC involvement (e.g., making postings, replying to others' postings). Significant \npositive correlations were also found between course satisfaction and community \ncohesion (both the Learning Community and Connectedness subscales) and \nbetween program satisfaction and community cohesion (only the Connectedness \nsubscale).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".