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Approaches to illuminate content-specific gameplay decisions using open-ended game data

Abstract

Games can be rich environments for learning and can elicit evidence of students’ conceptual understanding and inquiry processes. Illuminating students’ content-specific gameplay decisions, or methods of completing game tasks related to a certain domain, requires a context that is open-ended enough for students to make choices that demonstrate their thinking. Doing this also requires rich log data and methods of Game Learning Analytics (GLA) that are granular enough to look at the specific choices most relevant to that context and domain. This paper presents research done on student exploration of high school level Mendelian genetics in a multiplayer online game called The Radix Endeavor. The study uses three approaches to identify content-specific gameplay decisions and distinguish players utilizing different methods, looking at actions and tool use, play patterns and player types, and tool input patterns. In the context of the selected game quest, the three approaches were found to yield insights into different ways that students complete tasks in genetics, suggesting the potential for a set of more generalized guiding questions in the GLA field that could be adopted by learning games designers and data scientists to convey information about content-specific gameplay decisions in learning games.

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Acknowledgement

We would like to thank the members of the Radix team at MIT who made this game and research possible, including Angie Tung, Susannah Gordon-Messer, and Jody Clarke-Midura.

Funding

This research was funded by the Bill and Melinda Gates Foundation and the Ministry of Science and Technology (MOST), Taiwan, under grant contract no. 104-2918-I-018-008 and 105-2511-S-018-015-MY5.

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Correspondence to Meng-Tzu Cheng.

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Rosenheck, L., Cheng, MT., Lin, CY. et al. Approaches to illuminate content-specific gameplay decisions using open-ended game data. Education Tech Research Dev 69, 1135–1154 (2021). https://doi.org/10.1007/s11423-021-09989-0

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Keywords

  • Games
  • Science learning
  • Gameplay patterns
  • Game learning analytics