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Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning

Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning

Elizabeth Rowe, Jodi Asbell-Clarke, Erin Bardar, Ma. Victoria Almeda, Ryan S. Baker, Richard Scruggs, Santiago Gasca
Copyright: © 2020 |Pages: 25
ISBN13: 9781799811732|ISBN10: 1799811735|EISBN13: 9781799811756
DOI: 10.4018/978-1-7998-1173-2.ch006
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MLA

Rowe, Elizabeth, et al. "Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning." Advancing Educational Research With Emerging Technology, edited by Eugene Kennedy and Yufeng Qian, IGI Global, 2020, pp. 99-123. https://doi.org/10.4018/978-1-7998-1173-2.ch006

APA

Rowe, E., Asbell-Clarke, J., Bardar, E., Almeda, M. V., Baker, R. S., Scruggs, R., & Gasca, S. (2020). Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning. In E. Kennedy & Y. Qian (Eds.), Advancing Educational Research With Emerging Technology (pp. 99-123). IGI Global. https://doi.org/10.4018/978-1-7998-1173-2.ch006

Chicago

Rowe, Elizabeth, et al. "Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning." In Advancing Educational Research With Emerging Technology, edited by Eugene Kennedy and Yufeng Qian, 99-123. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1173-2.ch006

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Abstract

Digital games provide engaging opportunities to support and assess implicit learning—the development of tacit knowledge and practices that may not be explicitly articulated by the learner. The assessment of implicit learning reveals learning not captured by traditional tests and may be critical to meet the needs of a broad range of neurodiverse learners. This chapter describes tools and methods designed to build implicit game-based learning assessment (GBLA), where research-grounded automated detectors identify implicit learning in gameplay. The detectors are based upon theoretical and empirical underpinnings, including extensive hand-labeling. The authors present a detailed overview of a six-step process for emergent GBLA, which has been applied and refined across multiple game-based learning studies. This chapter also includes a description of the data architecture and tools the authors designed and developed specifically for this approach.

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