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Collecting Big Data Through Citizen Science: Gamification and Game-based Approaches to Data Collection in Applied Linguistics

Kim, Yoolim; Kogan, Vita V; Zhang, Cong; (2024) Collecting Big Data Through Citizen Science: Gamification and Game-based Approaches to Data Collection in Applied Linguistics. Applied Linguistics , 45 (1) pp. 198-205. 10.1093/applin/amad039. Green open access

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Abstract

Gamification of behavioral experiments has been applied successfully to research in a number of disciplines, including linguistics. We believe that these methods have been underutilized in applied linguistics, in particular second-language acquisition research. The incorporation of games and gaming elements (gamification) in behavioral experiments has been shown to mitigate many of the practical constraints characteristic of lab settings, such as limited recruitment or only achieving small-scale data. However, such constraints are no longer an issue with gamified and game-based experiments, and as a result, data collection can occur remotely with greater ease and on a much wider scale, yielding data that are ecologically valid and robust. These methods enable the collection of data that are comparable in quality to the data collected in more traditional settings while engaging far more diverse participants with different language backgrounds that are more representative of the greater population. We highlight three successful applications of using games and gamification with applied linguistic experiments to illustrate the effectiveness of such approaches in a greater effort to invite other applied linguists to do the same.

Type: Article
Title: Collecting Big Data Through Citizen Science: Gamification and Game-based Approaches to Data Collection in Applied Linguistics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/applin/amad039
Publisher version: https://doi.org/10.1093/applin/amad039
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10176563
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