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Examining the Digital Disability Div...

Arias, Meghan.

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Examining the Digital Disability Divide in Higher Education.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Examining the Digital Disability Divide in Higher Education./
Author:	Arias, Meghan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	142 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-05, Section: A.
Contained By:	Dissertations Abstracts International81-05A.
Subject:	Educational technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22618433
ISBN:	9781687907806

Examining the Digital Disability Divide in Higher Education.
Arias, Meghan.

Examining the Digital Disability Divide in Higher Education. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 142 p.

Source: Dissertations Abstracts International, Volume: 81-05, Section: A.

Thesis (Ph.D.)--George Mason University, 2019.

This item must not be sold to any third party vendors.

The number of students with disabilities pursuing higher education is increasing (Madaus, Kowitt, & Lalor, 2012; Sachs & Schreuer, 2011). However, the research related to this population is sparse (Pena, 2014), particularly regarding technology (L. Newman, Browne-Yung, Raghavendra, Wood & Grace, 2017). The current study examines the technology attitudes, technology usage, and learning environment preferences of students with disabilities, as well as their perception of institutional support for accessible technology. This study was a secondary analysis using data from the 2016 Educause Center for Analysis and Research (ECAR) Student and Technology survey. A point biserial correlation analysis was conducted to establish the relationships between technology attitude, usage, and learning environment preference. A discriminant analysis was used to compare students with and without disabilities on these variables. Finally, a hierarchical regression was used to examine which student characteristics contribute to the ratings of institutional support for accessible technology by students with disabilities. Results of the study showed that students with disabilities differ from students without disabilities in terms of learning environment preferences, the use of technology as a distraction, and attitude toward technology. The hierarchical regression indicated that gender, ethnicity, declared major, technology attitude, social and academic technology use, and learning environment preference contribute to predicting students' ratings of institutional technology support.

ISBN: 9781687907806Subjects--Topical Terms:

517670
Educational technology.
Subjects--Index Terms:

Digital divide

Examining the Digital Disability Divide in Higher Education.
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500 $a Source: Dissertations Abstracts International, Volume: 81-05, Section: A.
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520 $a The number of students with disabilities pursuing higher education is increasing (Madaus, Kowitt, & Lalor, 2012; Sachs & Schreuer, 2011). However, the research related to this population is sparse (Pena, 2014), particularly regarding technology (L. Newman, Browne-Yung, Raghavendra, Wood & Grace, 2017). The current study examines the technology attitudes, technology usage, and learning environment preferences of students with disabilities, as well as their perception of institutional support for accessible technology. This study was a secondary analysis using data from the 2016 Educause Center for Analysis and Research (ECAR) Student and Technology survey. A point biserial correlation analysis was conducted to establish the relationships between technology attitude, usage, and learning environment preference. A discriminant analysis was used to compare students with and without disabilities on these variables. Finally, a hierarchical regression was used to examine which student characteristics contribute to the ratings of institutional support for accessible technology by students with disabilities. Results of the study showed that students with disabilities differ from students without disabilities in terms of learning environment preferences, the use of technology as a distraction, and attitude toward technology. The hierarchical regression indicated that gender, ethnicity, declared major, technology attitude, social and academic technology use, and learning environment preference contribute to predicting students' ratings of institutional technology support.
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