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This paper describes the development of the ThinkPlay tool, designed to assist developers in applying Game Learning Analytics (GLA) indicators in Educational Digital Games (EDG). The tool aims to address the lack of standardization in the collection of data related to user learning in games, providing guidelines for implementing indicators that were identified through a review of the literature. The development process included a theoretical framework and involved the identification of GLA indicators from existing research. Employing the Interaction Design methodology, the project progressed from a validated low-fidelity prototype to a fully functional web-based platform. The tool was subsequently evaluated by experts in the fields of EDG and GLA using the Technology Acceptance Model (TAM) questionnaire. The results demonstrate high acceptance in terms of usefulness, ease of use, and intention of use, and it is expected that game developers can benefit from the tool to include the capture of learning data in their games.

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ThinkPlay: Integrating Learning Indicators in Educational Digital Games

  • 会议论文

  • 发表日期：

    2026年

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  • Eric Carvalho da Silveira,
  • Matheus Soppa Geremias,
  • Taynara Cerigueli Dutra,
  • Eleandro Maschio,
  • Isabela Gasparini

摘要

This paper describes the development of the ThinkPlay tool, designed to assist developers in applying Game Learning Analytics (GLA) indicators in Educational Digital Games (EDG). The tool aims to address the lack of standardization in the collection of data related to user learning in games, providing guidelines for implementing indicators that were identified through a review of the literature. The development process included a theoretical framework and involved the identification of GLA indicators from existing research. Employing the Interaction Design methodology, the project progressed from a validated low-fidelity prototype to a fully functional web-based platform. The tool was subsequently evaluated by experts in the fields of EDG and GLA using the Technology Acceptance Model (TAM) questionnaire. The results demonstrate high acceptance in terms of usefulness, ease of use, and intention of use, and it is expected that game developers can benefit from the tool to include the capture of learning data in their games.