Selecting Effective Strategies for Tailoring Persuasive Health Games to Gamer Types
Bibliographic record
Abstract
Persuasive games can be effective tools for motivating healthy behaviors and/or attitudes, and so recent years have witnessed an increasing number of persuasive games. However, most games adopt a one-size-fits-all approach to persuasion in their design. Studies on gameplay and player motivation have shown that treating gamers as a monolithic group is a bad design approach because a motivational approach that works for one individual may actually demotivate the desired behavior in others. To correct this problem, we conducted a large-scale study on 1108 gamers, which examined the persuasiveness of ten Persuasive Technology (PT) strategies, and the receptiveness of seven gamer types identified by BrianHex to the strategies most commonly used in PT design. We developed models showing the receptiveness of the gamer types to the ten strategies and created persuasive profiles, which are lists of strategies that can be employed to motivate behavior for each gamer type. Although we studied and created our models using ten strategies, in this paper, we report results of five strategies. Keywords Persuasive game; gamer types; persuasive strategies; health;
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".