Preliminary results of measuring flow experience in a software modeling tool
Bibliographic record
Abstract
Weaknesses in the user experience (UX) provided by software modeling tools have been identified as important barriers reducing the uptake of such tools by developers. Emotional factors as essential parts of user experience have received little attention so far. Literature suggests that higher flow experience is associated with higher positive emotional state. Good flow experience means people feel they have clear goals and are focusing well on a task that they regard as enjoyable and are doing reasonably well at; furthermore, they do not feel a need to be concerned about time or what others are thinking and have a sense they are getting good feedback about their progress. Achieving flow is important for performance in creative tasks such as modeling. To learn more about flow we used a questionnaire-based empirical study to measure flow experience of UmpleOnline users. This paper reports preliminary results from 24 respondents, demonstrating a moderate experience of flow state in UmpleOnline. Our objective in this paper is to stimulate the research community to think about how flow can best be measured.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".