Discourse analysis of computer-mediated conferencing in World Wide Web-based continuing medical education
Bibliographic record
Abstract
INTRODUCTION: Computer-mediated conferencing (CMC) is a computer messaging system that allows users to engage in asynchronous text-based communications that are independent of time and place. It has been suggested that CMC is an effective modality for facilitating constructivist learning environments that enable adult learners to engage in a continuous, collaborative process of building and reshaping knowledge and understanding. The goals of this exploratory study were to assess the nature of the interactions and collaborative learning characteristics exhibited in World Wide Web-based continuing medical education courseware programs that used CMC and to examine physicians' satisfaction with on-line CMC discussion as a planned learning activity of Web-based CME. METHOD: The Transcript Analysis Tool (TAT) was used to analyze the nature of the discourse that took place in four different Web-based CME courseware programs. Course evaluation surveys and interviews were also conducted with participants to evaluate their satisfaction with on-line CMC discussion. RESULTS: The results suggest that the nature of participation in the programs consisted primarily of independent messages with a minimal amount of learner-to-learner interaction. Elements of critical reflection, interaction, and debate between participants appeared to be missing from these discussions. As such, these discussions were not characteristic of the principles of constructivist learning environments. DISCUSSION: Interactive participation will not occur just because CMC is being used. The design of Web-based CME learning activities, participant characteristics, and facilitation are key factors that influence the effective use of CMC.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".