Evaluating the Onboarding Phase of Free-toPlay Mobile Games
Bibliographic record
Abstract
The first few minutes of play, commonly referred to as the onboarding phase, of Free-to-Play mobile games typically display a substantial churn rate among new players. It is therefore vital for designers to effectively evaluate this phase to investigate its satisfaction of player expectations. This paper presents a study utilizing a lab-based mixed-methods approach in providing insights for evaluating the user experience of onboarding phases in mobile games. This includes an investigation into the contribution of physiological measures (Heart-Rate Variability and Galvanic Skin Conductance) as well as a range of self-reported proxy measures including: a) stimulated recall, engagement graphs, b) flow state survey and c) post-game experience questionnaire. These techniques were applied across 28 participants using three mobile Free-to-Play titles from different genres. This paper makes two important contributions to the games user research (GUR) domain: 1) evaluates different research techniques (e.g. physiological measures and experience graphs) in the context of mobile games; 2) provides an empirically based recommendation for design elements that result in high arousal.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".