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  • 标题:No Cameras, No Problem: Creating an Inclusive Research-Driven Classroom Environment Using Student-Generated Avatars
  • 本地全文:下载
  • 作者:Luzconsuelo Gavaldon ; Socorro Nieto-Gavaldon ; Christina E. D’Arcy
  • 期刊名称:Journal of Microbiology and Biology Education
  • 印刷版ISSN:1935-7877
  • 电子版ISSN:1935-7885
  • 出版年度:2022
  • 卷号:23
  • 期号:1
  • DOI:10.1128/jmbe.00251-21
  • 语种:English
  • 出版社:American Society for Microbiology
  • 摘要:Student involvement in distance education has continued to increase over the last decade and has been accelerated by the unplanned transition to remote instruction caused by the COVID-19 pandemic. Empirical data demonstrate variable levels of student learning and engagement in such environments, including limited use of technologies (e.g., laptop cameras) to interact with peers and course instructors. Consequently, creating accessible and inclusive virtual learning contexts that best promote collaboration and success – whether in lecture or laboratory spaces – can be a challenge. To address these concerns, we developed and evaluated the Researcher Avatar (RA) activity, a brief exercise designed to provide students with the opportunity to depict and describe their identity as a scientific researcher through creation of an avatar. Students then made use of their avatars as their profile pictures on the course videoconferencing platform throughout the duration of the term. Participants ( N = 24) who completed the RA activity as part of an online Research Foundations Course at our institution developed avatars that included both stereotypical imagery of scientists (e.g., lab coat) and personal attributes intended to reflect their dedication to science, collegiality, and future career interests. Collectively, participants reported that the activity was valuable in allowing them to “see” and get to know their classmates while likewise offering them the chance to reflect on their own goals as a researcher.
  • 关键词:active learninginclusive teachingonline teachingavatarresearcher identityremote instructionCOVID-19belongingness
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