Learning the Game: Decoding the Differences between Novice and Expert Players in a Citizen Science Game with Millions of Players
Open accessAbstract
In recent years, video games have surged in popularity, attracting millions of players across platforms. Citizen science games (CSGs) leverage the processing power of gamers to solve computational and scientific problems. Borderlands Science (BLS) is a mini-game within the mass market game Borderlands 3 that turns multiple sequence alignment (MSA) problems into puzzles. Parallel research demonstrated that BLS players outperformed classical approaches solving small sequence alignment tasks. This study aims to analyze the strategical differences in player solutions in BLS as they gain experience. Through the many collected player solutions from players of different experience level, we gained insights into players’ strategies, differences between expert and non-expert players, and how strategies evolve. We developed a Markov chain trained on solutions from players of different experience levels to understand their actions and outcomes. Results indicate that expert players utilize more gaps and achieve more matches, gradually improving and converging toward unique strategies. Our findings reveal distinct and evolving player strategies. For future citizen science projects, it will be important to consider the identification of player strategies and their evolution over time to improve the game design and data processing.
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Research works printed but not linked: 3
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Cited by
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- Authors
- Cai, E., Sarrazin-Gendron, R., Mutalova, R., Ghasemloo Gheidari, P., Butyaev, A., Richard, G., Caisse, S., Knight, R., Blanchette, M., Szantner, A., & Waldispühl, J.
- Affiliations
- McGill University (CA)
- School of Computer Science, McGill University, Canada
- Gearbox Studio Québec, Canada
- University of California San Diego (US)
- Massively Multiplayer Online Science, Switzerland
- ORCID
- Venue
- Proceedings of the 19th International Conference on the Foundations of Digital Games
- Publisher
- ACM
- Published
- 2024-05-21
- Pages
- 1-10
- OpenAlex
- W4400361140
- Semantic Scholar
- cdb2ca2eb0408ba091a02fe253962c7f06291688
- Cited by
- 0
- References
- 28
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on the Foundations of Digital Games (FDG) (publishedVersion, cc-by-nc-sa)
- eScholarship@McGill (McGill) (submittedVersion)
- McGill-DEV
- Funders
- Génome Québec
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- multiple sequence alignment, player strategies, strategy evolution, puzzle games
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