Expert evaluation refined the draft heuristics around language clarity, usability-accessibility separation, and meaningful data interpretation¶

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

Subclaims¶

q2 i? Think-aloud expert evaluation of draft LXD heuristics surfaced three key improvement areas: clarity of language, differentiating usability from accessibility, and meaningful interpretation of data. → Susan Harrington and Charlott Sellberg 2026 q2 i? Experts positively received the draft heuristics, validating their suitability for the MET context while highlighting the need for evaluation by experts with diverse backgrounds. → Susan Harrington and Charlott Sellberg 2026

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

Think-aloud sessions with four experts (in gamification/LXD, HCI/diversity, nautical operations, and UX/usability) evaluated draft heuristics against paper prototypes in roughly 45-minute audio-recorded sessions. The article reports these three feedback themes and that "the heuristics were well received, with experts offering feedback to aid the design process".

"Key findings from this phase included feedback around (1) clarity of language, (2) differentiating between usability and accessibility, and (3) meaningful interpretation of data."

Discussion¶

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