Bibliographic record
Abstract
This paper reports a classroom study in which group learners brainstormed ideas in virtual group space and justified their ideas through articulating their rationales in the shared rationale space. The investigation focused on the learners' practices of articulating and sharing rationales. The results suggest that group members would brainstorm the ideas and generate rationales to justify the ideas before reading the others' ideas and rationales. Also, the members in general brainstormed all the ideas first and then elaborated the rationales to justify the ideas; and grouped the shared rationales according to their authors. The group members' reasoning styles were examined by using Rhetorical Structure Theory to analyze the shared rationales. It was found that similar reasoning styles existed across the groups. Additionally, the group context seemed to have affected the members' strategies of using contextual and additional information to justify their ideas. Several design implications are presented to support the practices of articulating and sharing rationales in virtual group workspace. The authors also articulate how their work contributes to other research areas such as project management, crowdsourcing, and online deliberation. Based on their study, the authors argue for a rationale-based knowledge management approach to complex collective activities in the online environment.
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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 | 0.078 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| 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".