And then you hit play: Investigating players’ responses to wayfinding cues in 3D action-adventure games
Bibliographic record
Abstract
This research is concerned with wayfinding, one of the most basic interactions of 3D action-adventure games. Even though players are required to move from point A to point B to progress in games, there is little research on the difficulties, needs, and preferences of players regarding wayfinding in 3D game worlds. It is well known that to alleviate wayfinding issues, designers add wayfinding cues to the game world. However, little is known about how those cues affect players’ in-game behavior and, more importantly, the player experience. This research addresses those issues by investigating players’ responses to a variety of wayfinding cues. To this end, I developed two research tools resembling commercial 3D action-adventure games. Both games (i.e., The Lost Island and A Warrior’s Story) presented several wayfinding cues and tasks, purposefully designed to make players move from one space to the next. I investigate the player experience through mixed method and user-centered approaches, collecting and analysing quantitative and qualitative data. In the first study, all participants played the same version of The Lost Island, and I emphasized the differences between the experiences of more and less skilled players. For the second study, I categorized wayfinding cues into three groups that worked as my independent variables. Participants played one of the three versions of the game (i.e., experimental conditions) and reported on their experiences. Through concrete examples, this work demonstrated how wayfinding cues had an impact on players’ wayfinding behavior and attitude towards the games. Design implications are also discussed. I hope this work will assist wayfinding researchers in their future investigations, and assist wayfinding system designers in creating and ameliorating their systems for a more profound user experience.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".