Toward a CoI population parameter: The impact of unit (sentence vs. message) on the results of quantitative content analysis
Bibliographic record
Abstract
The goal of this study is to further corroborate a hypothesized population parameter for the frequencies of social presence versus the sum of teaching presence and cognitive presence as defined by the community of inquiry model in higher education asynchronous course forums. This parameter has been found across five variables: academic institution (distance university vs. campus-based college), academic discipline (exact sciences vs. humanities), academic level (graduate vs. undergraduate), course level (introduction vs. regular vs. advanced), and course size (small vs. medium vs. large), that is, the number of students enrolled. To date, the quantitative content analyses have been carried out using the syntactic unit of message. In an attempt to further establish the parameter’s goodness-of-fit, it is now tested across two different syntactic units, message versus sentence. To do so, the same three-week segments from 15 Open University undergraduate course forums (10 from humanities and five from exact sciences) were analyzed twice—once by the unit message and once by the unit sentence. We found that the hypothesized parameter remained viable when the unit of analysis is the sentence. We also found that the choice of syntactic unit is indeed a critical factor that determines the “content” of the transcript and its inferred meaning.
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 | 0.329 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".