Increasing motivation in social exercise games: personalising gamification elements to player type
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
Fun and social affiliation are good predictors of long-term intention to use game-based interventions including those for motivating physical activity, yet current player matching algorithms are poor at facilitating social connectedness. In this paper, we report on the results of a study investigating how different player traits are associated with interest in social features of an exercise game for improving player experience through better player matching using common and complementary characteristics. Twelve conceptual scenarios were illustrated using storyboards and data was collected from 196 respondents who rated their attitudes and preferences towards gamification elements. Correlational results showed that all scenarios, except for cutting corners, were perceived as persuasive, enjoyable, engaging and are likely to increase future exercise intention for players who score high on Philanthropist- and Socialiser-oriented traits. Results also showed that many players favour the altruistic donation feature. Furthermore, qualitative results underscore that it is the player's partner that matters more than the players’ personalities. We conclude with practical recommendations for designing more personalised exercise games that can include more socially engaging game mechanics in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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