The effect of collaborative knowledge modeling at a distance on performance and on learning
Bibliographic record
Abstract
This study examines the effect of co-elaborating a knowledge model in dyads at a distance on performance and on learning. Forty-eight adults participated in the study. Participants were trained to re-represent knowledge taken from a text, using an object-typed knowledge modeling editor software tool. Knowledge modeling is similar to concept mapping, except that the former is based on a typology of knowledge objects and a typology of links, and that the structure of the knowledge representation is not necessarily hierarchical. Immediately after the 75-minute training session, each participant constructed a knowledge model individually. The experimental session consisted of elaborating a knowledge model in dyads. In the first condition, participants constructed and shared the knowledge model at a distance, using a whiteboard and a chat tool (synchronous distance group). In the second condition, participants elaborated one knowledge model with a turn-taking approach; they used e-mail to communicate their work-in-progress to each other (asynchronous distance group). In the third condition, participants worked at the same computer (face-to-face group). Pre- and post-tests were administered to measure learning in the domain. Results show that the quality of the knowledge models was better for dyads in the face-to-face condition than for the ones in the asynchronous distance condition, but only for the score related to knowledge objects (and not to propositions). Results also indicate that working at a distance in a synchronous mode was more beneficial for learning than working face-to-face at the same computer. It may be that subjects in the face-to-face group focused more on the metalanguage used to construct the knowledge model. These results should be interpreted with caution considering the short duration of the experiment and the low familiarity of participants with the targeted domains and with knowledge modeling. (http://cmc.ihmc.us/papers/cmc2004-231.pdf)
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".