Teaching for higher levels of thinking: developing quantitative and analytical skills in environmental science courses

Ecosphere

Donna Vogler¹  Ana Porzecanski²  Eleanor Sterling²  Adriana Bravo²  Stuart Ketcham³  Michelle Cawthorn⁴  Timothy Leslie⁵  Denny S. Fernandez⁶  Laurie Freeman⁷  John Mull⁸  Nora Bynum⁹ 

[1] Biology Department State University of New York College at Oneonta Oneonta New York 13820 USA;Center for Biodiversity and Conservation American Museum of Natural History New York New York 10024 USA;College of Science and Mathematics University of the Virgin Islands Kingshill Virgin Islands 00850 USA;Department of Biology Georgia Southern University Statesboro Georgia 30460 USA;Department of Biology Long Island University Brooklyn New York 11201 USA;Department of Biology University of Puerto Rico at Humacao Humacao PR 00792‐4300 Puerto Rico;Department of Science Fulton‐Montgomery Community College Johnstown New York 12095 USA;Department of Zoology Weber State University Ogden Utah 84408 USA;Keller Science Action Center The Field Museum Chicago Illinois 60605 USA;

关键词: data analysis;    ecology;    process skills;    science courses;    undergraduate students;   

DOI  :  10.1002/ecs2.1290

来源: DOAJ

【 摘 要 】

Abstract Professionals with strong quantitative and analytical skills are essential to understanding and responding to current environmental challenges. The goal of this study was to promote and evaluate the development of data analysis (DA) skills in undergraduate students through targeted interventions in environmental science courses. We developed materials to promote practice, instruction, and assessment of four core DA dimensions: the ability to make appropriate calculations, convert data to graphical representations, interpret graphical or mathematical information, and draw conclusions based on the analysis of data. We integrated two conservation exercises as pre/post assessment tools, flanking differentiated teaching interventions, into selected science courses and used a standardized rubric to measure students' performance level. We found that students improved their DA skills in a single semester, but the level of improvement varied across skill dimensions. Students struggled with dimensions that require higher levels of thinking such as data interpretation and drawing conclusions. The use of additional exercises targeting these dimensions and alternative practices might enhance gains. Importantly, students also gained content knowledge in ecological principles while developing skills, and demonstrated an increase in self‐confidence with their DA skills. Our approach and open‐access materials can be integrated into existing courses to develop and assess data skills in undergraduate learners.

【 授权许可】

Unknown