Educating Culturally and Linguistically Diverse Learners (CLD) in United States Schools during COVID-19.

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Title: Educating Culturally and Linguistically Diverse Learners (CLD) in United States Schools during COVID-19.
Authors: Burgin, Ximena D.1 xrecald1@niu.edu, Daniel, Mayra C.1 mcdaniel@niu.edu, Riley, Carolyn F.1 cfriley3@gmail.com
Source: TESL-EJ. May2023, Vol. 27 Issue 1, p1-18. 18p.
Subject Terms: *COVID-19 pandemic, *Online education, *School year, *Teachers, *Academic achievement
Abstract: The COVID-19 pandemic has propelled educational communities across the world into emergency remote models of instruction. This study documented the perceptions of 11 teachers from the State of Illinois regarding the quality of online instruction in their schools and their unexpected challenges from March of 2019-2020 academic year. Three points of data collection documented the teachers' perceptions of the transition to online learning and subsequent difficulties. Recurrent themes evident in responses to interview questions (Stake, 2000) were examined using a constant comparison method (Glasser & Strauss, 1967). Three emerging themes were identified: increased job demands, need for educational supports, and educators' concerns about the quality of the delivery of online instruction for Culturally and Linguistically Diverse Learners during the pandemic. [ABSTRACT FROM AUTHOR]
Copyright of TESL-EJ is the property of TESL-EJ 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.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="AR" term="%22Burgin%2C+Ximena+D%2E%22">Burgin, Ximena D.</searchLink><relatesTo>1</relatesTo><i> xrecald1@niu.edu</i><br /><searchLink fieldCode="AR" term="%22Daniel%2C+Mayra+C%2E%22">Daniel, Mayra C.</searchLink><relatesTo>1</relatesTo><i> mcdaniel@niu.edu</i><br /><searchLink fieldCode="AR" term="%22Riley%2C+Carolyn+F%2E%22">Riley, Carolyn F.</searchLink><relatesTo>1</relatesTo><i> cfriley3@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22TESL-EJ%22">TESL-EJ</searchLink>. May2023, Vol. 27 Issue 1, p1-18. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22School+year%22">School year</searchLink><br />*<searchLink fieldCode="DE" term="%22Teachers%22">Teachers</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink>
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  Data: The COVID-19 pandemic has propelled educational communities across the world into emergency remote models of instruction. This study documented the perceptions of 11 teachers from the State of Illinois regarding the quality of online instruction in their schools and their unexpected challenges from March of 2019-2020 academic year. Three points of data collection documented the teachers' perceptions of the transition to online learning and subsequent difficulties. Recurrent themes evident in responses to interview questions (Stake, 2000) were examined using a constant comparison method (Glasser & Strauss, 1967). Three emerging themes were identified: increased job demands, need for educational supports, and educators' concerns about the quality of the delivery of online instruction for Culturally and Linguistically Diverse Learners during the pandemic. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of TESL-EJ is the property of TESL-EJ 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.</i> (Copyright applies to all Abstracts.)
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              Text: May2023
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