From product to process data: Game mechanics for science learning

Game-based learning environments (GBLEs) supplement classroom instruction so students can demonstrate their scientific reasoning abilities and increase knowledge, providing a platform that promotes interest and engagement in science. The goal of this study was to examine the effectiveness of game mechanics for science learning. This study identifies how two types of game mechanics—learning and assessment mechanics—are used by high school participants (N = 137) as they learn about microbiology with Crystal Island, a game-based learning environment for science education. Participants’ learning outcomes were evaluated in two ways: learning gains, which assessed participants’ domain knowledge acquisition, and game completion, which assessed participants’ ability to successfully demonstrate scientific reasoning abilities. Results from this study showed that game completion is not related to learning gains. However, as participants engaged with increasingly more assessment mechanics, learning gains decreased. Further, profiles of learners were extracted to better understand the learning process that best supports greater learning outcomes. Results showed that learners who engaged in less recurrent transitions across assessment mechanics were more likely to successfully demonstrate scientific reasoning abilities. Implications for the design of games which provide scaffolding based on process data of learners’ game mechanic use are provided.

Authors

Institutions

Publication Details

Journal
International Journal of Serious Games
Published
2024-11-21
DOI
https://doi.org/10.17083/ijsg.v11i4.790
Citations
4
Primary Topic
Educational Games and Gamification
Type
article
Field-Weighted Citation Impact
3.16

Funders

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article

From product to process data: Game mechanics for science learning

Annamarie Brosnihan, Kevin A. Smith, Roger Azevedo, Daryn A. Dever et al.
4 citations
International Journal of Serious Games
Educational Games and Gamification
3.16
article

From product to process data: Game mechanics for science learning

Annamarie Brosnihan, Kevin A. Smith, Roger Azevedo, Daryn A. Dever, James C. Lester, Megan Wiedbusch, Cameron Marano, Milouni Patel, Tara Delgado
article en
4 citations

Abstract

Game-based learning environments (GBLEs) supplement classroom instruction so students can demonstrate their scientific reasoning abilities and increase knowledge, providing a platform that promotes interest and engagement in science. The goal of this study was to examine the effectiveness of game mechanics for science learning. This study identifies how two types of game mechanics—learning and assessment mechanics—are used by high school participants (N = 137) as they learn about microbiology with Crystal Island, a game-based learning environment for science education. Participants’ learning outcomes were evaluated in two ways: learning gains, which assessed participants’ domain knowledge acquisition, and game completion, which assessed participants’ ability to successfully demonstrate scientific reasoning abilities. Results from this study showed that game completion is not related to learning gains. However, as participants engaged with increasingly more assessment mechanics, learning gains decreased. Further, profiles of learners were extracted to better understand the learning process that best supports greater learning outcomes. Results showed that learners who engaged in less recurrent transitions across assessment mechanics were more likely to successfully demonstrate scientific reasoning abilities. Implications for the design of games which provide scaffolding based on process data of learners’ game mechanic use are provided.

International Journal of Serious GamesVol. 11(4)
University of Central Florida (US), North Carolina State University (US), University of Florida (US)
National Science Foundation, North Carolina State University, Social Sciences and Humanities Research Council of Canada
Quality Education
Openalex Percentile: Top 8%
Educational Games and Gamification
3.16
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