Game learning analytics showed significant age and gender differences in interests, skills, achievement, and progression through attempts¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · no dated study recorded · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Significant age and gender differences were found in interests, skills, achievement, and progression through attempts. → analytics-2

Evidence¶

Zapata-Cáceres 2021¶

M. Zapata-Cáceres and E. Martín-Barroso, "Applying Game Learning Analytics to a Voluntary Video Game: Intrinsic Motivation, Persistence, and Rewards in Learning to Program at an Early Age," in IEEE Access, vol. 9, pp. 123588-123602, 2021, doi: 10.1109/ACCESS.2021.3110475. https://doi.org/10.1109/access.2021.3110475

q2 · i? · associational · r2

Observational analysis of game learning analytics collected over 15 months, in which 4124 users played more than 28187 games. The article reports "significant age and gender differences in relation to interests, skills, achievement, and progression through attempts"; no effect sizes are printed in the available text.

"The analysis shows significant age and gender differences in relation to interests, skills, achievement, and progression through attempts."

Discussion¶

Related Claims¶

• Basic computational concepts in a voluntary video game were achievable by children aged 3–6, with full mastery possible by age 4 regardless of gender — related
• Persistence behaviors in the face of challenge differed significantly by age and gender — related