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Gender Differences in Learning Game Preferences: Results Using a Multi-dimensional Gender Framework

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Artificial Intelligence in Education (AIED 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13916))

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Abstract

Prompted by findings of gender differences in learning game preferences and outcomes, education researchers have proposed adapting games by gender to foster learning and engagement. However, such recommendations typically rely on intuition, rather than empirical data, and are rooted in a binary representation of gender. On the other hand, recent evidence from several disciplines indicates that gender is best understood through multiple dimensions, including gender-typed occupational interests, activities, and traits. Our research seeks to provide learning game designers with empirical guidance incorporating this framework in developing digital learning games that are inclusive, equitable, and effective for all students. To this end, we conducted a survey study among 333 5th and 6th grade students in five urban and suburban schools in a mid-sized U.S. city, with the goal of investigating how game preferences differ by gender identity or gender-typed measures. Our findings uncovered consistent differences in game preferences from both a binary and multi-dimensional gender perspective, with gender-typed measures being more predictive of game preferences than binary gender identity. We also report on preference trends for different game genres and discuss their implications on learning game design. Ultimately, this work supports using multiple dimensions of gender to inform the customization of learning games that meet individual students’ interests and preferences, instead of relying on vague gender stereotypes.

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Acknowledgements

This work was supported by NSF Award #DRL-2201796. The opinions expressed are those of the authors and do not represent the views of NSF.

Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Huy A. Nguyen, Jessica Hammer, Sarah Di & Bruce M. McLaren

  2. University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Nicole Else-Quest

  3. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    J. Elizabeth Richey

Authors
  1. Huy A. Nguyen
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  2. Nicole Else-Quest
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  3. J. Elizabeth Richey
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  4. Jessica Hammer
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  5. Sarah Di
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  6. Bruce M. McLaren
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Corresponding author

Correspondence to Huy A. Nguyen .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  4. Despacho 3.01, UNED-Grupo de InvestigaciĂłn aDeNu, Madrid, Spain

    Olga C. Santos

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Nguyen, H.A., Else-Quest, N., Richey, J.E., Hammer, J., Di, S., McLaren, B.M. (2023). Gender Differences in Learning Game Preferences: Results Using a Multi-dimensional Gender Framework. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_45

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36271-2

  • Online ISBN: 978-3-031-36272-9

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