Empirical development of a heuristic evaluation methodology for shared workspace groupware
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it