A Usability Evaluation of a Software Framework for Designing Persuasive Games.
Bibliographic record
Abstract
With the rise of persuasive game design for health, there is a need for easy, quick, and effective ways of developing and testing out persuasive games across multiple domains. This paper presents the design and usability evaluation of P-Gamer – a software framework for developing persuasive games and evaluating the effectiveness of various strategies. In line with the user-centred design approach, we designed a prototype of the system and conducted a usability evaluation with six persuasive system designers to (a) understand how usable the P-Gamer platform is, (b) understand the ease of use for each of the six major features in the platform, and (c) identify and correct the design issues existing in the platform. Our results showed that the overall system was perceived to be useable. The system had an overall system usability score of 87.92, which is within the excellent score range in the SUS scale. Our results also showed that five of the six major sections of the platform were significantly easy to use. We reflect on the results and also discuss the design issues and insights into addressing them.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".