Empirical development of a heuristic evaluation methodology for shared workspace groupware
Bibliographic record
Abstract
Good real time groupware products are hard to develop, in part because evaluating their support for basic teamwork activities is difficult and costly. To address this problem, we are developing discount evaluation methods that look for groupware-specific usability problems. In a previous paper, we detailed a new set of usability heuristics that evaluators can use to inspect shared workspace groupware to see how they support teamwork. We wanted to determine whether the new heuristics could be integrated into a low-cost methodology that parallels Nielsen's traditional heuristic evaluation (HE). To this end, we examined 27 evaluations of two shared workspace groupware systems and analysed the inspectors' relative performance and variability. Similar to Nielsen's findings for traditional HE, individual inspectors discovered about a fifth of the total known teamwork problems, and that there was only modest overlap in the problems they found. Groups of three to five inspectors would report about 40-60% of the total known teamwork problems. These results suggest that heuristic evaluation using our groupware heuristics can be an effective and efficient method for identifying teamwork problems in shared workspace groupware systems.
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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 | 0.110 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".