Paper No. 259-7
Presentation Time: 11:45 AM
CHARACTERIZING INSTRUCTIONAL PRACTICES WITH CLUSTER ANALYSIS: AN EXERCISE IN REDUCING UNCERTAINTY (Invited Presentation)
HARSHMAN, Jordan1, STAINS, Marilyne2, BARKER, Megan3, CHASTEEN, Stephanie4, COLE, Renee5, DECHENNE-PETERS, Sue Ellen6, EAGAN Jr., M. Kevin7, ESSON, Joan8, KNIGHT, Jennifer9, LASKI, Frank A.10, LEVIS-FITZGERALD, Marc11, LEE, Christopher12, LO, Stanley13, MCDONNELL, Lisa10, MCKAY, Timothy14, MICHELOTTI, Nicole15, MUSGROVE, Amanda16, PALMER, Michael17, PLANK, Kathryn18, RODELA, Tamara19, SCHIMPF, Natalie18, SCHULTE, Patricia18, SMITH, Michelle20, STETZER, MacKenzie21, STEWART, Jackie22, VAN VALKENBURGH, Blaire23, VINSON, Erin20, WEIR, Laura24, WENDEL, Paul J.25, WHEELER, Lindsay17 and YOUNG, Anna18, (1)Chemistry and Biochemistry, Auburn University, Auburn, AL 36849, (2)Department of Chemistry, University of Virginia, Charlottesville, VA 22904, (3)Department of Biological Sciences, Simon Fraser University, Bunaby, BC B6T 1Z4, Canada, (4)Center for STEM Learning, University of Colorado Boulder, Boulder, CO 80309, (5)Department of Chemistry, University of Iowa, Iowa City, IA 52242, (6)Department of Biology, Armstrong State University, Savannah, GA 31419, (7)Graduate School of Education & Information Studies, University of California Los Angeles, Los Angeles, CA 90095, (8)Department of Chemistry, Otterbein University, Westerville, OH 43081, (9)Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, (10)Departments of Life Sciences Core Education and Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095, (11)Center for Educational Assessment, University of California Los Angeles, Los Angeles, CA 90095, (12)Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA 90095, (13)Section of Cell and Developmental Biology, Program in Mathematics and Science Education, University of California San Diego, La Jolla, CA 92093, (14)University of Michigan, Ann Arbor, MI 48109; Physics Department, University of Michigan, Ann Arbor, MI 48109, (15)University of Michigan, Ann Arbor, MI 48109, (16)Chemistry Department, University of Calgary, Calgary, AB AB T2N 1N4, Canada, (17)Center for Teaching Excellence, University of Virginia, Charlottesville, VA 22903, (18)Department of Education, Director of the Center for Teaching and Learning, Otterbein University, Westerville, OH 43081, (19)Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, (20)School of Biology and Ecology & Maine Center for Research in STEM Education, University of Maine, Orono, ME 04469, (21)Department of Physics and Astronomy & Maine Center for Research in STEM Education, University of Maine, Orono, ME 04469, (22)Department of Chemistry, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada, (23)University of California, Los Angeles, 610 Charles Young Dr., S, Ecology and Evolutionary Biology, Los, CA 90095-7239, (24)Department of Biology, Saint Mary’s University, Halifax, NS B3H 3C3, Canada, (25)Department of Education, Otterbein University, 1 South Grove Street, Westerville, OH 43081
Researchers have been interested in characterizing instructional practices for a variety of reasons. These classroom observations have become a go-to method for those in discipline-based education research. Because of the variety of instructional practices, researchers have struggled to generally characterize classroom practices. Throughout this presentation, it will be demonstrated how the Classroom Observation Protocol for Undergraduate Science (COPUS), in tandem with cluster analysis, was used to characterize the teaching practices of over 2,000 classroom observations. Seven instructional profiles were identified and later reduced to three: Didactic, Interactive Lecture, and Student-Centered. Clustering on variables that show such wide distributions comes at the cost of certainty. Therefore, this talk focuses on the issue of reducing uncertainty in these classifications despite the large variety demonstrated. This was done through model-based clustering algorithms that allow for specific fit parameters as well as probabilistic profile assignments that serve as evidence to support the final classifications. Lessons learned for researchers using COPUS and/or cluster analysis algorithms will be provided.
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