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A Multi-Institution Mixed Methods Analysis of a Novel Acid-Base Mnemonic Algorithm

Camille Massaad,Harrison Howe,Meize Guo,Tyler Bland

Published 2025 in Multimodal Technologies and Interaction

ABSTRACT

Acid-base analysis is a high-load diagnostic skill that many medical students struggle to master when taught using traditional text-based flowcharts. This multi-institution mixed-methods study evaluated a novel visual mnemonic algorithm that integrated Medimon characters, symbolic imagery, and pop-culture references into the standard acid-base diagnostic framework. First-year medical students (n = 273) at six distributed WWAMI campuses attended an identical lecture on acid-base physiology. Students at five control campuses received the original text-based algorithm, while students at one experimental campus received the Medimon algorithm in addition. Achievement was measured with a unit exam (nine focal items, day 7) and a final exam (four focal items, day 11). A Differences-in-Differences approach compared performance on focal items versus baseline items across sites. Students at the experimental campus showed no significant advantage on the unit exam (DiD = +1.2%, g = 0.12) but demonstrated a larger, but still non-significant, medium-to-large effect on the final exam (DiD = +11.0%, g = 0.85). At the experimental site, 39 students completed the Situational Interest Survey for Multimedia (SIS-M), revealing significantly higher triggered, maintained-feeling, maintained-value, and overall situational interest scores for the Medimon algorithm (all p < 0.001). Thematic analysis of open-ended responses identified four themes: enhanced clarity, improved memorability, increased engagement, and barriers to interpretation. Collectively, the findings suggest that embedding visual mnemonics and serious-game characters into diagnostic algorithms can enhance learner interest and may improve long-term retention in preclinical medical education.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Multimodal Technologies and Interaction

  • Publication date

    2025-11-11

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.3390/mti9110113
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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