Measuring Fun
Bibliographic record
Abstract
The gaming industry and the concept of gamification have altered the way many developers and users approach interactive products. As social gaming demographics expand to what was previously considered “casual” audiences, more users expect an enjoyable experience from their digital applications and games. Developers now request more detailed subjective descriptions of satisfaction and the player experience from user-experience (UX) practitioners. Focusing on how fun a product is for users/players requires subjective, situationally dependent metrics rather than traditional UX efficiency metrics. The UX discipline is still constructing a comprehensive ecology of the player experience and how to measure it. This article contributes to that ecology by detailing a case in which our team conducted a usability test on a new video game peripheral. Our client’s primary concern dealt with how fun experienced gamers found the device. As our test progressed, we encountered a number of fun-related participant behaviors that led us to develop new metrics beyond our initial planned metrics. These new metrics helped us and our client better define and discuss enjoyability. Our case, in conjunction with a detailed definition and review of player experience and UX scholarship, shows the importance of adopting metrics contextually specific to the video-game product and player group when measuring fun is the primary goal.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".