How similar are sources of content for courses? ﻿

Joseph A. Price

How similar are sources of content for courses?

Joseph A. Price

Pathology

Research output: Contribution to conference › Poster › peer-review

Abstract

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

Conference	Oklahoma State University Center for Health Sciences Research Day 2020
Country/Territory	United States
City	Tulsa
Period	27/02/20 → 28/02/20

Access to Document

https://hdl.handle.net/20.500.14446/324236

Cite this

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title = "How similar are sources of content for courses? ",

abstract = "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.",

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PY - 2020/9/4

Y1 - 2020/9/4

N2 - 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.

AB - 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.

M3 - Poster

T2 - Oklahoma State University Center for Health Sciences Research Day 2020

Y2 - 27 February 2020 through 28 February 2020

ER -