JOURNAL ARTICLE

Bringing Autoethnography to Undergraduates: An Interdisciplinary Course-Cluster and Lab at Oberlin College and Conservatory.

  • Published In: Journal of Autoethnography, 2023, v. 4, n. 2. P. 236 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Evangelista, Al; Hutchins, KG; Ragon, Kathleen 3 of 3

Abstract

The label "autoethnography" has been applied to a wide range of knowledge-producing practices, from what might be considered "normal" science to narrative-driven writing to performance. These debates highlight some of the most fundamental tensions about legitimate ways of knowing/knowledge production in the contemporary world. Further, one strength of autoethnography as a method lies in situating personal experience within broader political, social, and cultural events, which can create new opportunities in academia for voices often silenced. With these elements of autoethnography in mind, and in response to the COVID-19 pandemic, the authors founded an interdisciplinary autoethnography course cluster and lab at Oberlin College and Conservatory. In this essay, we describe the course cluster, lab, and successes and challenges of each. We also discuss the strategies and innovations of introducing undergraduate students to autoethnography. We hope that our model will be instructive for colleagues with similar goals at their institutions. Through the cross-course workshops and collaborative exercises of the autoethnography lab, our students had the opportunity to use autoethnography not just to analyze their communities but also to build a community of practice. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Autoethnography. 2023/04, Vol. 4, Issue 2, p236
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:2637-5192
  • DOI:10.1525/joae.2023.4.2.236
  • Accession Number:164202291
  • Copyright Statement:Copyright of Journal of Autoethnography is the property of University of California Press 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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