東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Toward Leveraging Artificial Intelli...

Aljedaani, Wajdi Mohammed R., Sr.

Linked to FindBook

Google Book

Amazon

博客來

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges./
Author:	Aljedaani, Wajdi Mohammed R., Sr.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	227 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
Contained By:	Dissertations Abstracts International85-02A.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30671567
ISBN:	9798380070065

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.
Aljedaani, Wajdi Mohammed R., Sr.

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 227 p.

Source: Dissertations Abstracts International, Volume: 85-02, Section: A.

Thesis (Ph.D.)--University of North Texas, 2023.

This item is not available from ProQuest Dissertations & Theses.

The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

ISBN: 9798380070065Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Accessibility

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.
LDR:02812nmm a2200433 4500 001 2393236
005 20240311061618.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380070065
035 $a (MiAaPQ)AAI30671567
035 $a (MiAaPQ)0158vireo3295AljedaaniSr
035 $a AAI30671567
040 $a MiAaPQ $c MiAaPQ
100 1 $a Aljedaani, Wajdi Mohammed R., Sr. $3 3762683
245 1 0 $a Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 227 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
500 $a Advisor: Ludi, Stephanie.
502 $a Thesis (Ph.D.)--University of North Texas, 2023.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.
590 $a School code: 0158.
650 4 $a Computer science. $3 523869
650 4 $a Audiology. $3 537237
650 4 $a Disability studies. $3 543687
653 $a Accessibility
653 $a User reviews
653 $a Machine learning
653 $a Deaf students
690 $a 0984
690 $a 0800
690 $a 0201
690 $a 0300
710 2 $a University of North Texas. $b Department of Computer Science and Engineering. $3 3287906
773 0 $t Dissertations Abstracts International $g 85-02A.
790 $a 0158
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30671567