Dimensions of Interactive Pervasive Game Design: Systematic Review
Bibliographic record
Abstract
BACKGROUND: As the gaming industry grows around the world, playing pervasive games is becoming an important mode of entertainment. A pervasive game is one in which the game experience extends into the actual world or where the fictive world of the game merges with the physical world. How pervasive games can adapt to the ever-changing nature of technology and design in current society requires a comprehensive review. OBJECTIVE: In this systematic review, we aimed to measure and analyze 4 dimensions of pervasive games through development, technology, experience, and evaluation. Moreover, we also aimed to discover and interpret their relationship with game, interaction, experience, and service design. METHODS: We first chose 3 well-known databases, Web of Science, Scopus, and EBSCO, and searched from 2013 to April 2022. A strictly thorough Boolean search for research keywords such as "pervasive game," "design," and "interactive" resulted in 394 relevant articles. These articles were identified, screened, and checked for eligibility to find valid and useful articles, which were then categorized and analyzed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. RESULTS: The systematic selection was finally left with 40 valid and valuable articles. After categorization and analysis, all articles were classified according to 4 main themes, which were design and development (11/40, 28%), interaction and technology (15/40, 38%), users and experience (9/40, 23%), and evaluation and service (5/40, 13%). These 4 main areas can be subdivided into several smaller areas. CONCLUSIONS: In the 4 areas of game design, interaction design, experience design, and service design, many scholars have studied pervasive games and made contributions. Although the development and technology of pervasive games have evolved with the times, there is still a need to strengthen emerging design concepts within pervasive games.
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 | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".