Recognising and promoting collaboration in an online asynchronous discussion
Bibliographic record
Abstract
Abstract This paper reports on a study involving the identification and measurement of collaboration in an online asynchronous discussion (OAD). A conceptual framework served for the development of a model which conceptualises collaboration on a continuum of processes that move from social presence to production of an artefact. From this model, a preliminary instrument with six processes was developed. Through application of the instrument to an OAD, the instrument was further developed with indicators added for each process. Use of the instrument to analyse an OAD showed that it is effective for gaining insight into collaborative processes in which discussants in an OAD do or do not engage. Use of the instrument in other contexts would test and potentially strengthen its reliability and provide further insight into the collaborative processes in which individuals engage in OADs. Analysis of an OAD using the instrument revealed that participants engaged primarily in processes related to social presence and articulating individual perspectives, and did not reach a stage of sharing goals and producing shared artefacts. The results suggest that the higher‐level processes related to collaboration in an OAD may need to be more explicitly and effectively promoted in order to counteract a tendency on the part of participants to remain at the level of individual rather than group or collaborative effort.
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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".