Towards Democratisation of Games User Research: Exploring Playtesting Challenges of Indie Video Game Developers
Bibliographic record
Abstract
Playtesting is a games user research (GUR) method used to evaluate design decisions based on feedback gathered from players with the goal to improve player experience. HCI games research has been actively working on and promoting best practices in GUR. However, these practices often require resources, knowledge and expertise, which are not readily available for indie video games developers. Thus, to better understand how GUR can support these developers, we conducted an interview study with 13 indie games professionals to learn about their practices and the challenges they face when doing playtesting. We report on the key findings from this study, including challenges with finding appropriate participants and handling the data from playtests. We provide a discussion of how existing GUR practices can be adapted and what HCI games research can do to help mitigate these challenges to make playtesting more accessible and impactful to indie video games developers.
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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 | 0.273 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".