Personalization in Serious and Persuasive Games and Gamified Interactions
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
Serious and persuasive games and gamified interactions have become popular in the last years, especially in the realm of behavior change support systems. They have been used as tools to support and influence human behavior in a variety of fields, such as health, sustainability, education, and security. It has been shown that personalized serious and persuasive games and gamified interactions can increase effectivity of supporting behavior change compared to "one-size-fits all"-systems. However, how serious games and gamified interactions can be personalized, which factors can be used to personalize (e.g. personality, gender, persuadability, player types, gamification user types, states, contextual/situational variables), what effect personalization has (e.g. on player/user experience) and whether there is any return on investment is still largely unexplored. This full-day workshop aims at bringing together the academic and industrial community as well as the gaming and gamification community to jointly explore these topics and define a future roadmap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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