Mind wandering detection is more accurate for female than male learners, with the gender gap larger for temporal models¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Across both prediction scenarios and all latent-feature models, mind wandering instances are predicted more accurately for females than for males. → Bühler 2025 q2 i? The gender difference becomes larger with temporal models, especially in within-dataset prediction, which the authors attribute to the increased parameter count requiring large training data. → Bühler 2025

Evidence¶

Bühler 2025¶

Bühler, B., Bozkir, E., Goldberg, P., Sümer, Ö., D'Mello, S., Gerjets, P., Trautwein, U., Kasneci, E. (2025). From the Lab to the Wild: Examining Generalizability of Video-based Mind Wandering Detection. Int J Artif Intell Educ 35, 823–857. https://doi.org/10.1007/s40593-024-00412-2

q2 · i? · design · r2

Gender comparison of all latent-feature models (Table 6) in within- and cross-dataset prediction; e.g., LSTM within-dataset AUC-PR 0.458 (female) vs 0.328 (male). The article attributes the gap to dataset imbalance in overall gender rates and gender-specific mind wandering rates, giving fewer male mind wandering instances in training.

"In general, mind wandering instances are predicted more accurately for females than for males."

Discussion¶

Related Claims¶

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