Cluster analysis identified three exploratory response profiles among workshop participants — reluctant, intermediate, and enthusiastic — with the reluctant profile showing low technical familiarity alongside favorable perceptions of several pedagogical applications¶

Claim · All claims
Evidence · 1 study (2 entries) · causal r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A three-cluster K-means solution distinguished reluctant (n = 23; 21.9%), intermediate (n = 50; 47.6%), and enthusiastic (n = 32; 30.5%) profiles across the three dimensions. → Susana Sánchez Castro 2026 q2 i? Within the reluctant profile, lower scores concentrated in technical familiarity items while pedagogical-application items remained comparatively favorable. → Susana Sánchez Castro 2026 (2)

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 · i? · causal · r1

Hierarchical (Ward's method) and K-means cluster analysis on standardized dimension scores of the experimental group (n = 105); the article also reports intermediate (M = 3.00/3.14/3.25) and enthusiastic (M = 3.52/3.81/3.87) profiles. Descriptive, exploratory classification.

"The reluctant profile (n = 23; 21.9%) comprises students with comparatively lower scores across all three dimensions: knowledge of programming and machine learning (M = 2.32), block-based programming and game engines (M = 2.56), and active learning (M = 2.76)."

Susana Sánchez Castro 2026 (2)¶

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 · i? · causal · r1

Item-level inspection of the reluctant cluster (n = 23) showing lower scores were not uniform; favorable responses remained for mathematics support (C3; M = 3.04), student engagement (C8; M = 3.00), and art and music (C6; M = 2.96).

"The least favorable response patterns concerned previous university experience with visual block-based programming (A5; M = 2.00; 78.3% responses 1–2), self-reported knowledge of machine learning (A2; M = 2.22; 73.9% responses 1–2), and the ability to program in game engines for educational purposes (B5; M = 2.35; 65.2% responses 1–2)."

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
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