Smartphone Games Heuristics (SmGH) – Towards a Standard Set of Platform-Centric Heuristic for Smartphone Games Evaluation
Bibliographic record
Abstract
The assessment of software application usability typically relies on a predefined set of general principles known as heuristics. However, these heuristics are often used interchangeably to evaluate games across different platforms such as smartphones, tablets, and desktops, potentially leading to inconsistent or inaccurate evaluations. Hence, there is a notable absence of a standard platform-centric heuristics to evaluate games for a particular platform. In this paper, we address this gap by developing 144 smartphone game heuristics (SmGH), spanning across six categories and accounting for technical, non-technical, and gameplay aspects. Further, we compared our proposed SmGH with four mobile game heuristics published in the literature. The aim of the comparison was to identify the overlaps and differences between SmGH and the existing heuristics in mobile game literature. Lastly, we conducted a preliminarily assessment of the utility of SmGH using gameplay analysis of 5 popular smartphone games (from 2017 to 2021, having 4.5+ average rating and 100 M + downloads) and 12 recent smartphone games (released in 2022). We obtained two important findings. First, there is a limited overlap among various mobile game heuristics in the literature. The first finding highlights an important takeaway to establish a standard set of platform-specific heuristics in both game user research and the industry. Second, popular games tend to incorporate a larger proportion of heuristics compared to recently released games. The second finding provide insights into the number and distribution of heuristics across all six categories within smartphone games, which will be beneficial for future evaluations of new and unseen games using SmGH. The second finding also suggest that adherence to platform-centric game heuristics may contribute to a game’s popularity on a particular platform and could be a factor considered by game developers. This work contributes to the field of Human Computer Interaction (HCI) and smartphone games by advancing our understanding and application of platform-centric game heuristics and highlights the significance of SmGH as a standard and reliable set of heuristics in the design and evaluation of smartphone games.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".