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Continuing medical education speakers with high evaluation scores use more image-based slides
Abstract
Introduction: Although continuing medical education (CME) presentations are common across health professions, it is unknown whether audience evaluations of the speaker is independently associated with slide design. Based on the conceptual framework of Mayer’s theory of multimedia learning, this study aimed to determine whether image use and text density in presentation slides are associated with overall speaker evaluations.
Methods: This retrospective analysis of six sequential CME conferences (two annual emergency medicine conferences over a three-year period) used a mixed linear regression model to assess whether post-conference speaker evaluations were associated with image fraction (percent of slides with at least one image) and text density (number of words per slide).
Results: A total of 105 lectures were given by 49 faculty members, and 1,179 evaluations (67.8% response rate) were available for analysis. On average, 47.4% (SD=25.36) of slides had at least one image (image fraction). Image fraction significantly predicted overall higher evaluation scores [F(1, 100.676)=6.158, p=0.015] in the mixed linear regression model. The mean (SD) text density was 25.61 (8.14) words/slide but was not a significant predictor [F(1, 86.293)=0.55, p=0.815]. Of note, the speaker [χ2(1)=2.952, p=0.003] and speaker seniority [F(3, 59.713)=4.083, p=0.011] significantly predicted higher scores.
Conclusion: This is the first published study to date assessing the linkage between slide design and CME speaker evaluations by an audience of practicing clinicians. The incorporation of images was associated with higher evaluation scores, in alignment with Mayer’s theory of multimedia learning. Contrary to this theory, however, text density showed no significant association, suggesting that these scores are multifactorial. Professional development efforts should focus on teaching best practices in both slide design and presentation skills.Main Content
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