JOURNAL ARTICLE
Teaching Mammalogy in the 21st century: advances in undergraduate education.
Published In: Journal of Mammalogy, 2023, v. 104, n. 4. P. 655 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Flaherty, Elizabeth A; Lanier, Hayley C; Varner, Johanna; Duggan, Jennifer M; Beckmann, Sean; Yahnke, Christopher J; Erb, Liesl P; Patrick, Lorelei E; Dizney, Laurie; Munroe, Karen E; Connors, Patrice K 3 of 3
Abstract
This article focuses on current teaching practices and pedagogical approaches in undergraduate Mammalogy courses, based on a 2021 survey of instructors who are members of the American Society of Mammalogists (ASM). It finds that while traditional lecturing remains the dominant instructional method (averaging 65% of class time), many instructors incorporate active learning strategies, skill development, and inclusive teaching practices, with 64% expressing a desire to update their courses. The study highlights barriers to adopting active learning, such as time constraints and institutional challenges, and emphasizes the importance of integrating professional skill development, diversity, equity, and inclusion (DEI) content, and student-centered approaches to better prepare students for STEM careers. Recommendations include gradually incorporating active learning, leveraging shared teaching resources, fostering inclusive classroom climates, and linking course activities to career skills to enhance student engagement and retention in Mammalogy and related STEM fields.
Additional Information
- Source:Journal of Mammalogy. 2023/08, Vol. 104, Issue 4, p655
- Document Type:Article
- Subject Area:Education
- Publication Date:2023
- ISSN:0022-2372
- DOI:10.1093/jmammal/gyac121
- Accession Number:169828202
- Copyright Statement:Copyright of Journal of Mammalogy is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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