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ProBee: A Provenance-based Design for an Educational Game Analytics Model

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Abstract

Educational games help reinforce educational concepts. They help students learn through hypothesizing, probing, and reflecting upon the game environment. Understanding the impact of a game is important before deploying it in a class. Recent studies in learning analysis describe methodologies and approaches for analyzing educational games. However, richer and deeper analyses are underexplored, especially in games with various decisions and gameplay. Designing games based on provenance appears to be a great approach, but not deeply explored in the educational game environment. In this article, we present ProBee, a provenance-based model for educational game analytics. ProBee uses a provenance data-driven approach that allows for extracting all actions performed by the player, including temporal aspects. The method traces the game’s time series, which indicates the player’s behavior and adopted strategy during the game session. In addition, we evaluate our approach in a case study with the Control Harvest educational game, which helps teachers present the Biological Control subject

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Data Availability

Enquiries about data availability should be directed to the authors

Notes

  1. Globo Rural, from Globo Television Network https://tinyurl.com/yckcwjdu

  2. http://www.biologico.sp.gov.br

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Funding

This study was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

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Authors and Affiliations

  1. Cefet/RJ, Rio de Janeiro, Brazil

    Flávio Marques, Leonardo Lignani, João Quadros, Myrna Amorim, Eduardo Ogasawara & Joel dos Santos

  2. Federal University of Ceará, Ceará, Brazil

    Windson Viana

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  1. Flávio Marques
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  2. Leonardo Lignani
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  4. Myrna Amorim
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Correspondence to Joel dos Santos.

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CAPES, CNPq, and FAPERJ partially supported this work. The authors have no relevant financial or non-financial interests to disclose.

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Marques, F., Lignani, L., Quadros, J. et al. ProBee: A Provenance-based Design for an Educational Game Analytics Model. Tech Know Learn (2024). https://doi.org/10.1007/s10758-024-09758-x

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