Bibliographic record
Abstract
Computer conferencing is one of the more useful parts of computer-mediated communications (CMC), and is virtually ubiquitous in distance education. The temptation to analyze the resulting interaction has resulted in only partial success, however (Henri, 1992; Kanuka and Anderson, 1998; Rourke, Anderson, Garrison and Archer, 1999; Fahy, Crawford, Ally, Cookson, Keller and Prosser, 2000). Some suggest the problem is made more complex by failings of both technique and, more seriously, theory capable of guiding transcript analysis research (Gunawardena, Lowe and Anderson, 1997). We have previously described development and pilot-testing of an instrument and a process for transcript analysis, call the the TAT (Transcript Analysis Tool), based on a model originally developed by Zhu (1996). We found that the instrument and coding procedures used provided acceptable "sometimes excellent" levels of interrater reliability (varying from 70 percent to 94 percent in pilot applications, depending upon user training and practice with the instrument), and that results of pilots indicated the TAT discriminated well among the various types of statements found in online conferences (Fahy, et al., 2000).
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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 | 0.321 | 0.613 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".