Among workshop participants, the three questionnaire dimensions were positively and significantly intercorrelated, with the highest association between block-based programming familiarity and active-learning perceptions¶

Claim · All claims
Evidence · 1 study · causal r1 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 Self-reported block-based programming and game-engine familiarity correlated highly with active-learning perceptions (Spearman's ρ = 0.699, p <.001). → Susana Sánchez Castro 2026

Evidence¶

Susana Sánchez Castro 2026¶

Susana Sánchez Castro, Mª Ángeles Pascual Sevillano, Rogelio Buceta Otero, Javier Fombona Cadavieco. (2026). Developing computational thinking in teacher education: a brief experience with coding, robotics, and machine learning. Education Tech Research Dev. https://doi.org/10.1007/s11423-026-10744-6

q2 · i3 · causal · r1

Spearman correlation analysis within the experimental group (n = 105) between dimension mean scores. The article also reports ρ = 0.521 (knowledge–block-based) and ρ = 0.602 (knowledge–active learning), both p <.001, and notes the associations "should not be interpreted as directional or causal relationships."

"Block-based programming and game engines – Active learning 0.699 <.001 High"

Discussion¶

Related Claims¶

• Favorable overall self-reported perceptions coexisted with lower self-reported familiarity in specific technical areas: machine learning, prior block-based programming experience, and game-engine programming — related
• Exploratory regression models showed both technical dimensions were significantly associated with active-learning perceptions, while only active learning was significantly associated with self-reported knowledge of programming and machine learning — related