Selected Topics from a Matched Study between a Face-to-face section and a Real-time Online section of a University Course
Bibliographic record
Abstract
Two sections of an interpersonal skills building university course were observed for the purposes of this matched study. The face-to-face (F2F) section was in a classroom on the Concordia University campus in Montreal, Canada, while the non-turn-taking real-time online section used a Web application, LBD eClassroom© designed specifically for highly interactive large size classes and meetings. Two sections used the same instructor, facilitators, pedagogy, and course content. This study revealed a unique pattern of non-turn-taking synchronous interaction in the online section. Online students were found to be more likely to participate and express themselves. Interaction of online participants led to the creation of a group entity – a polis – a cornerstone for collaborative group learning. In contrast, in the F2F section, interaction followed the traditional classroom pattern – centered on the teacher or expert, resulting in fewer students interacting, and hence, lower interaction overall. In sum, during these three hour sessions, it was found that the nature of online non-turn-taking environment afforded online students more time to express themselves compared to students learning the same material F2F.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".