For course content selection, to establish a measure of consensus on topics of relevance, and to compare sources of content, a novel quantitative method was developed and applied to a medical school course. Sources included texts, board review books, and a listing of topics currently taught in the course. Data mining of topics from sources developed data as binary encoded lists of what was present (among 350 topics) before two classical similarity measures were used to compute relatedness in pairwise comparisons of 13 sources. Relatedness was not always as expected. Total topics included ranged from highest in the course handouts, to lower in all other sources. This quantitative indication is opposed to reliance on subjective impressions, and can help faculty make better choices on content topics to include in a course and to compare texts.
|Original language||American English|
|State||Published - 4 Sep 2020|
|Event||Oklahoma State University Center for Health Sciences Research Day 2020 - Oklahoma State University Center for Health Sciences College of Osteopathic Medicine, Tulsa, United States|
Duration: 27 Feb 2020 → 28 Feb 2020
|Conference||Oklahoma State University Center for Health Sciences Research Day 2020|
|Period||27/02/20 → 28/02/20|