Bibliographic record
Abstract
This article explores the syntactic structure, cognitive functions, pedagogical features, and communicative characteristics of questions asked by participants in an asynchronous learning environment. Participants used fewer syntactic forms than observed in face-to-face postsecondary classrooms, but based on the Model of Productive Thinking (Barnes, 1983; Gallagher & Aschner, 1963), they exhibited higher levels of cognition. Questions at higher cognitive levels were found to stimulate more interaction than did those at lower levels. Compared with face-to-face interaction, students asked more rhetorical questions, using them to persuade, think aloud, and indirectly challenge other participants. Cet article explore la structure syntaxique, les fonctions cognitives, les particularites pedagogiques et les caracteristiques communicatives des questions posees par les participants dans un environnement d’apprentissage asynchrone. Les participants ont utilise moins de formes syntaxiques qu’observe dans des classes de cours de niveau post-secondaire, mais, base sur le modele de pensee productive (Barnes, 1983; Gallagher et Aschner, 1963), ils ont montre des degres plus eleves de cognition. Les questions aux degres cognitifs plus eleves se sont averees stimuler plus d’interaction que celles aux degres moindres. Comparativement a l’interaction en face a face, les etudiants ont pose plus de questions rhetoriques, les utilisant pour persuader, penser tout haut et, indirectement, mettre au defi les autres participants.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".