Attitudes and perceived effectiveness among first-time online instructors during Covid-19

Fuente: arXiv
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Auteur principal: Zhang, Owen Xingjian
Format: Preprint
Publié: 2024
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author Zhang, Owen Xingjian
author_facet Zhang, Owen Xingjian
contents Online teaching has expanded access to education, offering flexibility compared to traditional face-to-face instruction. While early research has explored online teaching, it is important to understand the perspective of instructors who conducted their first online classes during the Covid-19 pandemic. This study focuses on instructors teaching online for the first time, regardless of whether they volunteered. Surveys were conducted when universities transitioned from in-person to online instruction in April 2020, with a follow-up survey after their first online teaching semester. The study investigated instructors' expectations of class success before their first online teaching experience. Using Bayesian modeling, we analyzed how these expectations varied based on instructors' characteristics (self-efficacy in online teaching, technological proficiency, and acceptance of technology) and course attributes (subject area, class size, and instructional design). Results showed that instructors' self-efficacy significantly impacted their expectations of success, while smaller class sizes were associated with lower expectations. Interestingly, factors like prior use of technology platforms and classroom design did not contribute significantly to expectations. The study offers practical recommendations to support online teaching. To improve self-efficacy, instructors should collaborate with colleagues and familiarize themselves with online platforms. Universities should provide workshops or training to enhance teaching skills. In small interactive classes, nonverbal communication should be emphasized, and institutions should establish support teams and feedback mechanisms to ensure quality and effectiveness in online education.
format Preprint
id arxiv_https___arxiv_org_abs_2409_17600
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Attitudes and perceived effectiveness among first-time online instructors during Covid-19
Zhang, Owen Xingjian
Human-Computer Interaction
Online teaching has expanded access to education, offering flexibility compared to traditional face-to-face instruction. While early research has explored online teaching, it is important to understand the perspective of instructors who conducted their first online classes during the Covid-19 pandemic. This study focuses on instructors teaching online for the first time, regardless of whether they volunteered. Surveys were conducted when universities transitioned from in-person to online instruction in April 2020, with a follow-up survey after their first online teaching semester. The study investigated instructors' expectations of class success before their first online teaching experience. Using Bayesian modeling, we analyzed how these expectations varied based on instructors' characteristics (self-efficacy in online teaching, technological proficiency, and acceptance of technology) and course attributes (subject area, class size, and instructional design). Results showed that instructors' self-efficacy significantly impacted their expectations of success, while smaller class sizes were associated with lower expectations. Interestingly, factors like prior use of technology platforms and classroom design did not contribute significantly to expectations. The study offers practical recommendations to support online teaching. To improve self-efficacy, instructors should collaborate with colleagues and familiarize themselves with online platforms. Universities should provide workshops or training to enhance teaching skills. In small interactive classes, nonverbal communication should be emphasized, and institutions should establish support teams and feedback mechanisms to ensure quality and effectiveness in online education.
title Attitudes and perceived effectiveness among first-time online instructors during Covid-19
topic Human-Computer Interaction
url https://arxiv.org/abs/2409.17600