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

Conference Paper/Proceedings
Understanding Gender Effects in Game-Based Learning: The Role of Self-Explanation
Publication Year:
2024
Publication Source:
Artificial Intelligence In Education, Pt I, Aied 2024
Volume:
14829
Funding Type:
ECR:Core
Author(s):
Richey, J. Elizabeth; Nguyen, Huy A.; Mehrvarz, Mahboobeh; Else-Quest, Nicole; Arroyo, Ivon; Baker, Ryan S.; Stec, Hayden; Hammer, Jessica; McLaren, Bruce M.
Supporting Project(s):

We conducted a 2 x 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.