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Understanding Gender Effects in Game-Based Learning: The Role of Self-Explanation

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Artificial Intelligence in Education (AIED 2024)

Abstract

We conducted a 2 × 2 study comparing the digital learning game Decimal Point to a comparable non-game tutor with or without self-explanation prompting. We expected to replicate previous studies showing the game improved learning compared to the non-game tutor, and that self-explanation prompting would enhance learning across platforms. Additionally, prior research with Decimal Point suggested that self-explanation was driving gender differences in which girls learned more than boys. To better understand these effects, we manipulated the presence of self-explanation prompts and incorporated a multidimensional gender measure. We hypothesized that girls and students with stronger feminine-typed characteristics would learn more than boys and students with stronger masculine-typed characteristics in the game with self-explanation condition, but not in the game without self-explanation or in the non-game conditions. Results showed no advantage for the game over the non-game or for including self-explanation, but an analysis of hint usage indicated that students in the game conditions used (and abused) hints more than in the non-game conditions, which in turn was associated with worse learning outcomes. When we controlled for hint use, students in the game conditions learned more than students in the non-game tutor. We replicated a gender effect favoring boys and students with masculine-typed characteristics on the pretest, but there were no gender differences on the posttests. Finally, results indicated that the multidimensional framework explained variance in pretest performance better than a binary gender measure, adding further evidence that this framework may be a more effective, inclusive approach to understanding gender effects in game-based learning.

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Acknowledgments

This work was supported by the National Science Foundation Award #DRL-2201796. The opinions expressed are those of the authors and do not represent the views of NSF. Thanks to Jimit Bhalani, John Choi, Kevin Dhou, Darlan Santana Farias, Rosta Farzan, Jodi Forlizzi, Craig Ganoe, Rick Henkel, Scott Herbst, Grace Kihumba, Kim Lister, Patrick Bruce Gonçalves McLaren, and Jon Star for important contributions to the development and early experimentation with Decimal Point.

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Correspondence to J. Elizabeth Richey .

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Richey, J.E. et al. (2024). Understanding Gender Effects in Game-Based Learning: The Role of Self-Explanation. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science(), vol 14829. Springer, Cham. https://doi.org/10.1007/978-3-031-64302-6_15

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  • DOI: https://doi.org/10.1007/978-3-031-64302-6_15

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