Gathering Self-Report Data in Games Through NPC Dialogues: Effects on Data Quality, Data Quantity, Player Experience, and Information Intimacy
Bibliographic record
Abstract
Self-report assessment is important for research and game development, e.g., to gather data during play. Games can use dialogues with non-player characters (NPCs) to gather self-report data; however, players might respond differently to dialogues than questionnaires. Without guidance on how in-game assessment affects player perceptions and experiences, designers and researchers are in danger of making decisions that harm data quantity and quality, and perceptions of privacy. We conducted a user study to understand self-report collection from NPC dialogues and traditional in-game overlay questionnaires. Data quality and player experience measures autonomy, curiosity, immersion, and mastery did not differ significantly, although NPC dialogues enhanced meaning. NPC dialogues supported an increase in data quantity through voluntary 5-point scales but not via open responses; however, they also increased the perceived intimacy of shared information despite comparable objective intimacy. NPC dialogues are useful to gather quantitative self-report data. They enable a meaningful play experience but could facilitate negative effects related to privacy.
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.
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 | 0.025 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".