PS 46-127
Student interpretation of conservation data: Does their reach exceed their grasp?

Wednesday, August 7, 2013
Exhibit Hall B, Minneapolis Convention Center
Donna W. Vogler, Biology, SUNY College at Oneonta, Oneonta, NY
Eleanor Sterling, Center for Biodiversity and Conservation, American Museum of Natural History, New York, NY
Ana Luz Porzecanski, Center for Biodiversity and Conservation, American Museum of Natural History, NY, NY
Adriana Bravo, Center for Biodiversity and Conservation, American Museum of Natural History, New York City, NY
Nora Bynum, Center for Biodiversity and Conservation, American Museum of Natural History, New York, NY
Michelle Cawthorn, Biology, Georgia Southern University
Laurie Freeman, Fulton Montgomery Community College
Stuart R. Ketcham, College of Science and Mathematics, University of the Virgin Islands, Kingshill, US Virgin Islands
Timothy W. Leslie, Department of Biology, Long Island University, Brooklyn, NY
John F. Mull, Weber State University
Terry Theodose, University of Southern Maine
Background/Question/Methods

The fast pace of biological data generated nowadays calls for our biology students to be proficient in quantitative skills such as data analysis. This study examined how well undergraduate students can develop data analysis skills relevant to ecology and conservation biology over the course of a single semester. Students completed two data analysis exercises, pre and post self-assessments of confidence in data analysis skills, a classroom discussion, and pre/post content assessments. The two data analysis exercises were adapted from the free online teaching modules on the Network of Conservation Educators and Practitioners website (www.ncep.amnh.org).  Between the first exercise (a demography problem involving palm harvests and parrots) and second exercise (calculating diversity indices for spider communities), a data analysis teaching intervention was administered in all classes. Instructional and assessment materials were created and validated by 24 conservation educators led by the Center for Biodiversity and Conservation at AMNH.

Results/Conclusions

Results from one semester show that students scored significantly higher on post-content assessments for both conservation exercises (N1 = 207 students; N2 = 199; P < 0.01 for both). We also found significant increases in student self-assessment of confidence in data analysis skills (N = 87). However, when evaluated at the level of different skill dimensions, students’ ability to represent and interpret data improved between exercises (N = 257; P < 0.01), but ability to complete calculations and draw conclusions was significantly worse on the second exercise (P < 0.01). While our study demonstrates that direct instruction in data analysis does improve student performance overall, there is a disconnect between student self-assessment of their data analysis skills and their actual ability. This indicates that some aspects of data analysis may require different teaching intervention approaches.