Challenging Students’ perspectives with Game Design for Older Adults
Bibliographic record
Abstract
Older adults are a group that is often overlooked by the game industry, even though they make up a reasonable portion of gamers. It is important for game designers to be able to consider different users and the needs involved. In this study, game design students were challenged with the task of making a video game for older adults that had some level of learning and social interaction. A total of sixty students, 13 older adults, an instructor, and the researchers were involved in the study. Seven games were created over two semesters. Older adults participated in the design by providing feedback. The students initially were disappointed with this assignment and resistant to the task, but as the project continued, they were able to embrace the challenge and see the benefits of designing for older adults. It challenged them to think from a different perspective and consider game design that is accessible to a wider audience. What they thought was intuitive (e.g. easy for the player to understand and use) did not always turn out to be so for the older cohort. This required the students adjust their design to suit a wider audience. User-centered design with a cohort different from their own was a beneficial approach to getting students to think of a broader audience.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".