Bibliographic record
Abstract
BackgroundComputer Mediated Conferencing (CMC) provides the opportunity for interaction in distance education courses.Successful asynchronous text-based conferencing overcomes transactional distance (Moore, 1991), permitting student-student as well as instructor-student communication.This interaction is thought to foster the development of an on-line learning community.Strategic initial messages, triggers, in asynchronous text conferencing can lead to rich cognitive discussions.Such initiating messages or triggers have been reported in previous literature, defined either in relation to their effects (number of actual responses received), or their intentions (the writer's evident purpose of evoking responses by being in some way provocative).In Zhu's (1996) study, a good student starter usually pointed to a few major discussion themes for a weekly discussion.Fahy (2001) defined "response triggers" as messages that generated large numbers of subsequent postings.Triggers in the Community of Inquiry model are defined more in the latter sense, as messages that are intended by the writer to evoke discussion, whether or not they actually succeed in doing so (Garrison, 2002;Garrison, Anderson, and Archer, 2000;Garrison, Anderson, and Archer, 2001).The characteristics of postings which succeed in triggering responses, as compared with those which fail to do so, was the focus of this inquiry. PurposeThis report briefly summarizes the findings from the thesis Trigger Analysis in Computer Mediated Conferencing (Poscente, 2003).This study explored the frequencies and characteristics of trigger postings in asynchronous CMC conferences in a moderated, graduate-level, online course environment.The study focused on observing, identifying, and describing patterns in true triggers and true duds.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".