Maritime instructors resisted automated assessment of simulator performance, preferring learning analytics that support their professional judgment and use existing simulator data¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Instructor focus groups found that any use of learning analytics in simulator training should support instructors' professional expertise, use available simulator data, and allow reasoning about student performance rather than providing an assessment. → Susan Harrington and Charlott Sellberg 2026 q2 i? Instructors expressed significant concern about automated assessment and MMLA monitoring because assessment involves grey areas requiring professional judgment about the correctness of student decisions. → Susan Harrington and Charlott Sellberg 2026 (2)

Evidence¶

Susan Harrington and Charlott Sellberg 2026¶

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

q2 · i? · design · r2

Thematic analysis of focus groups with instructors at three Nordic universities on core concerns around MMLA in the simulator. The article reports themes of wants, needs, and resistances, including support for instructors' practice and use of available simulator data.

"Key themes of wants, needs, and resistances to MMLA in the simulator. Any use of LA in this context should: Support instructors' practice and the fundamental role of their professional expertise"

Susan Harrington and Charlott Sellberg 2026 (2)¶

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

q2 · i? · design · r2

In the discussion of focus group findings, the article reports that instructors described grey areas in assessment where a student's explanation could render a seemingly incorrect response acceptable, and that appropriate feedback timing and level vary individually, underpinning "instructors express significant concern about any automated assessment".

"Instructors also spoke of providing feedback at the correct time —which varies from bridge to bridge, even during the same simulator session—and at a level appropriate for the students in question—again, a factor which varies individually."

Discussion¶

Related Claims¶

• Expert evaluation refined the draft heuristics around language clarity, usability-accessibility separation, and meaningful data interpretation — related
• The heuristic framework deliberately retains automatic performance checks opposed by instructors and students, as context-appropriate measure selection matters — related
• Maritime students supported MMLA but preferred on-demand post-simulation access via mobile application over real-time in-simulation dashboards — related
• AI-driven situated learning reshapes education by enhancing authenticity and transforming evaluation into assessment woven into the learning process — related
• Framing instructors as part of the solution shifted the design toward supporting instructor judgment and co-design — related
• Low-level feedback automation is appropriate only for sentence-level clarity; argumentative unclarity should trigger human review — related
• Feedback in full-mission simulator training is delivered through recurring, multidimensional, multimodal feedback cycles built on instructors' embedded assessments of the fit between learning objectives and the ongoing task — a broader claim this one bears on
• Recurring movements between professional (in-role) and educational (out-of-role) contexts during simulation are important for simulator training to fulfil its pedagogical potential — related