Three-dimension sociotechnical-pedagogical usability framework for learning experience design¶

Theory · All theories
Evidence · 4 claims (4 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 4 claims rest on one study

Description¶

The article organizes its heuristic framework around three usability dimensions drawn from Jahnke et al. (2020): "Socio-Pedagogical Usability: How social interactions and pedagogical practices are supported by the learning design", "Socio-Technical Usability: How the technical features of the system or tool facilitate or hinder social interactions among users", and "Technical-Pedagogical Usability: How well the technical features support the pedagogical objectives of the learning environment". These dimensions structure both the draft and final heuristic sets and are used to ensure evaluation captures pedagogical and social concerns, not only technical usability.

Design Implications¶

Context¶

Requirements¶

• Heuristic evaluation of learning technologies must go beyond usability-only heuristics and address pedagogical values and learning objectives

Constraints¶

• The framework was developed to address contexts whose complexity is not captured by current heuristic frameworks, specifically simulation-based professional learning

Target Learners¶

• professional learners in simulation-based training (e.g., maritime students)

Target Learning Objectives¶

• professional competence development including collaboration, communication, and decision-making

Claims¶

• Expert evaluation refined the draft heuristics around language clarity, usability-accessibility separation, and meaningful data interpretation [+W]
• The heuristic framework deliberately retains automatic performance checks opposed by instructors and students, as context-appropriate measure selection matters [+W]
• Maritime instructors resisted automated assessment of simulator performance, preferring learning analytics that support their professional judgment and use existing simulator data [+W]
• Maritime students supported MMLA but preferred on-demand post-simulation access via mobile application over real-time in-simulation dashboards [+W]

Related Theories¶

Examples¶

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Key Sources¶

• Susan Harrington and Charlott Sellberg. (2026). 12 Heuristics for Learning Analytics in Simulation-Based Professional Learning. Journal of Learning Analytics, 13(1), 130-142. https://doi.org/10.18608/jla.2026.9141