Usability in Software for People with Disabilities: Systematic Mapping
Luiz Felipe Cirqueira dos Santos
1 a
, Edmir Queiroz
2 b
, Igor Rafael Eloi dos Santos
2 c
,
Elisrenan Barbosa da Silva
2 d
, Mariano Florencio Mendonc¸a
1 e
and Fabio Gomes Rocha
1 f
1
Postgraduate Program in Computer Science - PROCC/UFS, Federal University of Sergipe, Av. Mal. C
ˆ
andido Rondon,
Rosa Elze, 1861, S
˜
ao Cristpv
˜
ao, Brazil
2
IT Courses, Est
´
acio University Center, R. Teixeira de Freitas, Salgado Filho, 10, Aracaju, Brazil
Keywords:
Usability, Accessibility, Analysis Tools, Digital Inclusion, Inclusive Interfaces.
Abstract:
This study presents a systematic mapping of usability analysis tools focused on accessibility, aiming to
identify technologies, methods, and challenges related to improving inclusive interfaces. Tools such as
DUXAIT-NG, Guideliner, and MUSE were analyzed, standing out for integrating automated evaluations
and specific adaptations. However, they exhibited technical limitations in customization and application to
different contexts and types of disabilities. The results demonstrated the positive impact of these tools on the
development of accessible software while also highlighting research gaps, such as the lack of empirical studies
and the absence of real-time dynamic analyses. Based on this analysis, the study contributes by organizing and
systematizing knowledge on accessibility tools, identifying research gaps that emphasize the need for greater
flexibility in solutions and validations, and suggesting technological and methodological advancements. It
reinforces the importance of expanding research to other databases and developing more robust and dynamic
tools.
1 INTRODUCTION
With the growing popularity of mobile devices, the
alignment between web applications and usability
guidelines has become one of the key factors for
user satisfaction and the success of an application
(Marenkov et al., 2018).
However, manual usability evaluation is
time-consuming and resource-intensive, making
automated evaluation a promising alternative to
overcome these limitations (Marenkov et al., 2018).
Evaluating user satisfaction with user interfaces (UIs)
presents additional challenges due to the dynamic
nature of UIs and the constant movement within
the usage context (Yigitbas et al., 2019). These
challenges are even more evident in mobile devices,
where developers have focused on creating interfaces
that are intuitive and easy to use (Bessghaier et al.,
a
https://orcid.org/0000-0003-4538-5410
b
https://orcid.org/0009-0004-6930-3031
c
https://orcid.org/0009-0000-9106-2896
d
https://orcid.org/0000-0001-8890-9718
e
https://orcid.org/0000-0003-0732-3980
f
https://orcid.org/0000-0002-0512-5406
2021).
Achieving these qualities requires an iterative
process of evaluating mobile interfaces and
eliminating structural flaws that could compromise
visual and functional consistency—crucial factors for
the user experience.
At the same time, technological advancements
have driven the development of smart cities, which
demand the creation of applications that meet
rigorous usability criteria, such as the ten usability
heuristics and the principles of usability analysis
(Adinda and Suzianti, 2018). This scenario
underscores the importance of continually evaluating
software usability to ensure its effectiveness and
accessibility, particularly for people with disabilities.
In this context, the present study systematically
maps the literature to identify and evaluate usability
analysis tools focused on software accessibility. To
this end, articles in the selected databases were
analyzed, providing a broad understanding of the
available tools and methods.
The remainder of this article is structured as
follows: Section 2 provides the theoretical context,
addressing concepts of usability and accessibility in
Santos, L. F. C., Queiroz, E., Santos, I. R. E., Barbosa da Silva, E., Mendoncça, M. F. and Rocha, F. G.
Usability in Software for People with Disabilities: Systematic Mapping.
DOI: 10.5220/0013393600003929
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 597-604
ISBN: 978-989-758-749-8; ISSN: 2184-4992
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
597

software. Section 3 presents related work. Section
4 details the methodology used. Section 5 describes
the results, while Section 6 discusses the analyzed
issues. Finally, Section 7 presents the conclusions of
this study.
2 THEORETICAL FRAMEWORK
Including all students in education, particularly in
higher education, has become a global concern, with
efforts focused on meeting the learning needs of
students with disabilities (Ndlovu, 2021). In this
context, assistive technologies (AT) play a crucial
role in providing academic, social, and physical
support to promote the well-being and independence
of these students (McNicholl et al., 2021). AT
encompasses devices such as iPods, iPads, computers,
and software that help overcome barriers imposed by
the environment or the disability itself (McNicholl
et al., 2021).
Providing AT in higher education is a fundamental
strategy to equalize learning opportunities for
students with and without disabilities. While all
students at this level meet the required academic
standards, specific limitations can impact the
performance of students with disabilities due to
environmental or individual factors (Ndlovu, 2021).
Equipping these students with appropriate devices
and technologies helps eliminate barriers and create
a more inclusive and accessible learning environment
(Mji et al., 2009; Tony, 2019; Lyner-Cleophas, 2019;
Alnahdi, 2014).
In this scenario, mobile learning emerges as
an opportunity to make the educational process
more flexible, allowing students to learn anytime
and anywhere (Kumar and Mohite, 2018). Mobile
learning provides adaptable and collaborative
environments, extending teaching possibilities
beyond the traditional classroom (Nedungadi and
Raman, 2012). However, the design of mobile
educational applications faces significant challenges,
such as adapting to small screens, input method
limitations, and the ever-changing context of use
(Kumar and Mohite, 2018). These factors highlight
the importance of usability testing to ensure that
applications are practical, effective, and easy to use
(Harrison et al., 2013).
The usability of mobile applications, especially
those designed for learning, is an emerging and
essential area in human-computer interaction (HCI).
Considered a determining factor for technological
success and adoption, usability is associated with
ease of use, perceived utility, and a satisfying
user experience (Nielsen, 1994; Cole et al., 2008).
Despite its importance, few studies are systematically
evaluating the usability of mobile educational
applications, representing a critical gap in research
and development (Zhang and Adipat, 2005).
Finally, the rapid technological evolution and the
introduction of more sophisticated mobile devices
continue to challenge developers to create interfaces
that meet user expectations while overcoming
technical limitations. Usability studies are an
indispensable tool to ensure that mobile learning
applications achieve not only a high level of user
satisfaction but also contribute to a more inclusive and
accessible education (Kumar and Mohite, 2016).
3 RELATED WORK
The work by Falconi et al. (Falconi et al.,
2023) presents an integrated usability evaluation
tool that combines heuristic evaluation methods
and tree testing. The goal is to automate the
usability evaluation process, allowing evaluators of
different experience levels to visualize the evaluation
process and its associated tasks. DUXAIT-NG
was developed in response to the need for tools
that support standardized usability evaluations,
especially in contexts where conducting in-person
tests is challenging, such as during the COVID-19
pandemic. The paper also discusses the importance
of conducting case studies in different domains to
validate the tool. It plans to expand its functionalities
to include other types of usability evaluations.
Paltern
`
o et al. (Patern
`
o et al., 2016) presents a
system based on timelines for visualizing interactive
events, which is used in a usability evaluation tool
for mobile web applications. The proposal is to
collect user interaction data while performing tasks
in web applications, allowing usability experts to
analyze this data to identify problems. The system
is designed to record various interaction events, such
as taps and gestures, and offers visualizations that
help compare the actual user behavior with an ideal
behavior. The paper concludes that improvements in
future visualization and data analysis are needed to
support better identification of usability issues.
Vasconcelos and Baldochi (de Vasconcelos and
Baldochi Jr, 2012) discuss the USABILICS system,
which aims to facilitate the usability evaluation of
web applications through an automated approach.
The main focus of the system is the analysis of
user interactions related to defined tasks, allowing
the identification of incorrect actions during task
execution. The paper also reports experiments
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
598

conducted with two web applications, an e-learning
system, and a technology article publishing website,
where several tasks were monitored. The results
showed that usability scores significantly improved
after implementing recommendations based on
USABILICS analyses, and incorrect actions were
reduced.
Zhang (Zhang et al., 2009) investigates the
usability of three digital libraries: ACM Digital
Library, IEEE Computer Society Digital Library, and
IEEE Xplore. The research was conducted with 36
participants who performed search and navigation
tasks. The results revealed several difficulties users
face, especially those with less experience. The study
used objective and subjective measures to assess
usability, including the number of queries, search
time, and user satisfaction. The authors discuss the
study’s limitations and suggest that future research
should include a more diverse sample and a wider
range of tasks to validate the results.
Marsh (Marsh, 1999) analyzes the complexity
of usability evaluation in virtual reality (VR),
highlighting the inadequacy of traditional 2D
interface methods for 3D environments. The author
explores the definition of VR, its differences from
GUIs, and the challenges in usability evaluation. He
emphasizes the need to develop new methodologies
adapted to the VR user experience and suggests
future research directions.
Marsh proposes the need to develop new
evaluation methods that consider the user experience
within the virtual environment and suggests future
research directions. The paper emphasizes the
importance of adapting evaluation methodologies to
meet the specific needs of VR to improve the user
experience. These studies show how automated and
innovative methods are essential for enhancing the
user experience and facilitating usability evaluation,
especially in specific environments such as VR
and digital libraries, while promoting inclusion and
accessibility.
4 METHODOLOGY
The goal of this article was to characterize, in a
structured manner, an initial perspective on tools
that perform usability analysis of software, with a
special focus on accessibility. A systematic mapping
was chosen as the research instrument to achieve
this. A systematic mapping is a review of a specific
topic or area, which allows the identification of
various approaches and their associated challenges
(Vel
´
asquez-Dur
´
an and Ram
´
ırez Montoya, 2018;
Keele et al., 2007). The systematic mapping in
this article follows the guidelines of Kitchenham and
Charters (Petersen et al., 2008), divided into three
phases: planning, execution, and communication
of results. We defined the research topic, applied
methodology, and selected the databases where the
articles were searched. Research questions were
prepared to be answered through this study, and the
search string related to the theme was used using a
tool called Parsifal.
A detailed protocol for article classification was
followed to ensure the study’s transparency and
reproducibility. This protocol included the following
steps:
• Definition of Inclusion and Exclusion Criteria
• Selection of Articles
• Classification and Analysis of Articles
• Definition of Research Questions
• Number of Publications per Year
In the definition of inclusion criteria, articles that
addressed, even partially, usability tools for software
and studies that included usability analysis for people
with disabilities were included. As for the exclusion
criteria, we removed duplicate articles, studies that
did not evaluate software or assistive technologies,
studies that did not focus on usability analysis, and
publications that did not address accessibility or tools
aimed at people with disabilities, as shown in Table 1.
Table 1: Inclusion and Exclusion Criteria.
Inclusion Articles that address, even partially,
usability tools for software
Exclusion
Duplicate articles
Studies that do not evaluate
software or assistive technology
Studies that do not directly address
usability analysis
Publications that do not address
accessibility or tools aimed at
people with disabilities
We conducted the search using the terms
(”usability analysis tools” OR ”usability evaluation
tools” OR ”usability testing software” OR ”software
usability assessment” OR ”usability metrics
software”) AND (”software usability evaluation”
OR ”user experience analysis” OR ”UX analysis”
OR ”human-computer interaction” OR ”HCI”
OR ”interface evaluation”) AND (”tools” OR
”frameworks” OR ”programs” OR ”systems”) AND
(”evaluation methods” OR ”heuristic evaluation” OR
”usability metrics” OR ”task analysis” OR ”cognitive
Usability in Software for People with Disabilities: Systematic Mapping
599

walkthrough” OR ”eye-tracking” OR ”heatmaps”),
with no period restriction, in the ACM and IEEE
databases on September 11, 2024. The selected
databases are hybrid, search engines, or bibliometric
databases, widely used in studies as they provide
good coverage for systematic reviews (Kitchenham
and Brereton, 2013). A total of 38 relevant articles
were identified, which met, even partially, the
inclusion criteria listed in Table 1.
We verified that there were no duplicate articles
concerning the search databases used. Of 38 articles,
26 were considered relevant to the research, as their
titles and abstracts addressed the inclusion criteria,
while 12 met the exclusion criteria, as shown in
Figure 1. As part of the study stages, we read the
articles that met the inclusion and exclusion criteria
to find answers to the research questions. Among
them, 13 provided answers using structured questions
that can assist in future research sequences and in
evaluating the research process (Kitchenham and
Brereton, 2013), as described in Table 2.
Figure 2 illustrates the variation in research
involving the topic, showing that publications in
the searched databases have experienced significant
fluctuations in interest in the subject mentioned in
this research, with both increases and decreases in the
number of publications over time.
5 RESULTS
In this section, we present the results of our
systematic mapping. We divided our findings into
subtopics to facilitate understanding and visualization
of the information. In each subtopic, we will show the
answers to the specific questions.
5.1 RQ1. What Tools Are Available for
Software Usability Analysis?
Several software usability analysis tools exist,
such as DUXAIT-NG, MOBILICS, and Guideliner.
DUXAIT-NG is a heuristic evaluation tool that
inspects user interfaces, focusing on user experience
(Falconi et al., 2023). MOBILICS (Gonc¸alves et al.,
2016), which focuses on mobile interface analysis,
helps evaluate software on mobile devices. At the
same time, Guideliner automates the verification of
UI compliance with usability guidelines, such as
links, images, and buttons (Marenkov et al., 2018).
Figure 1: Selection of The Articles.
5.2 RQ2. What Usability Tools
Specifically Focus on Evaluating
Software Accessible to People with
Disabilities?
Tools such as Google’s Mobile Friendly Test Tool and
Bing Mobile Friendliness Test focus on evaluating
usability on mobile devices, considering access on
mobile platforms (Patern
`
o et al., 2017). Guideliner,
as described in the article (Marenkov et al., 2018),
includes specific accessibility guidelines, with 13
guidelines aimed at making the interface accessible,
addressing issues such as contrast and navigation on
mobile devices.
5.3 RQ3. How Do Usability Analysis
Tools Address Different Types of
Disabilities (Visual, Auditory,
Motor, Cognitive)?
Usability analysis tools adapt to handle different
disabilities. Guideliner, for example, includes
16 guidelines to ensure accessibility for visual
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
600

Table 2: Research Questions and Justifications.
Question
Number
Question Text Justification
Q1 What tools are available for software usability analysis? Identify the main tools used and their features, providing a
broad view of the current state of available technologies.
Q2 What usability tools specifically focus on evaluating software
accessible to people with disabilities?
Investigate the tools that meet accessibility needs, promoting
the development of more inclusive solutions.
Q3 How do usability analysis tools address different types of
disabilities (visual, auditory, motor, cognitive)?
Understand how different needs are addressed by the tools to
ensure that all disabilities are adequately considered.
Q4 What are inaccessible software’s most frequently used usability
evaluation methods?
Identify the most effective methods for evaluating accessibility
and guiding best development practices.
Q5 How are usability tools evaluated in terms of their effectiveness
in identifying accessibility barriers?
Evaluate the performance of tools in identifying barriers,
ensuring they meet accessibility goals.
Q6 Is there a correlation between the type of disability and the
preference for certain usability tools?
Explore user preferences to adapt tools to their specific needs
and improve their acceptance.
Q7 How do usability analysis tools handle accessible mobile and
web interfaces?
Verify the adequacy of the tools to modern technologies and
their impact on the usability of mobile and web interfaces.
Q8 What challenges are faced when adapting usability tools for
users with disabilities?
Identify the main obstacles in adapting tools to meet users’
needs better.
Q9 What is the impact of usability analysis tools on developing
more inclusive software?
Examine how these tools contribute to a more inclusive design
aligned with accessibility needs.
Q10 What are the gaps in research on usability tools focused on
accessibility?
Highlight underexplored areas to guide future research and
innovations in the field.
Figure 2: Articles per Year.
and auditory disabilities, as well as adjustments
for mobile interfaces (Marenkov et al., 2018).
DUXAIT-NG focuses on improving software
interfaces to ensure inclusion (Falconi et al., 2023),
and other tools like MUSE (Patern
`
o et al., 2017) offer
recommendations and perform heuristic evaluations
focusing on cognitive and motor needs.
5.4 RQ4. What Are the Most
Frequently Used Usability
Evaluation Methods Inaccessible
Software?
Traditional methods such as usability testing,
interviews, and heuristic evaluations are commonly
used (Falconi et al., 2023; Marsh, 1999). However,
new approaches are being developed, such as
automated testing with DUXAIT-NG (Falconi
et al., 2023), which allows continuous and real-time
usability evaluation. Other methods, such as
cognitive walkthroughs and user feedback, are
also essential to understanding how people with
disabilities interact with software and identifying
areas that need improvement (Yigitbas et al., 2019).
5.5 RQ5. How Are Usability Tools
Evaluated in Terms of Their
Effectiveness in Identifying
Accessibility Barriers?
The effectiveness of tools is generally measured
by their ability to identify accessibility flaws and
the accuracy in adapting interfaces for specific
disabilities. Tools like MUSE (Patern
`
o et al., 2017)
are evaluated for their ability to detect flaws during
navigation and offer recommendations. Moreover, the
Usability in Software for People with Disabilities: Systematic Mapping
601

flexibility of tools like Guideliner (Marenkov et al.,
2018) and the Mobile Friendly Test Tool (Patern
`
o
et al., 2017) is crucial, as they can be applied in
different accessibility contexts for users with varying
needs.
5.6 RQ6. Is There a Correlation
Between the Type of Disability and
Preference for Specific Usability
Tools?
Yes, the preference for usability tools may vary
depending on the type of disability. For example,
people with visual impairments may benefit from
tools that adjust contrast and text readability, such
as Google’s Mobile Friendly Test Tool (Patern
`
o
et al., 2017). In contrast, people with motor
impairments prefer tools that adapt navigation and
facilitate interaction control (Yigitbas et al., 2019;
Jahan et al., 2019; Bessghaier et al., 2021).
5.7 RQ7. How Do Usability Analysis
Tools Handle Accessible Mobile and
Web Interfaces?
Tools like Google’s Mobile Friendly Test Tool and
MUSE are designed to evaluate mobile interfaces
and test navigability and accessibility on mobile
devices. These tools use contextual and behavioral
data to optimize design and ensure that interfaces are
accessible and functional on different devices. Tools
like Guideliner, in addition to evaluating usability
guidelines, also allow customization for accessing
content in an optimized way on mobile and web
platforms (Lettner and Holzmann, 2012; Patern
`
o
et al., 2017; Marenkov et al., 2018; Bessghaier et al.,
2021; Gonc¸alves et al., 2016; Zhang et al., 2009;
Baguma, 2018).
5.8 RQ8. What Challenges Are Faced in
Adapting Usability Tools for People
with Disabilities?
The main difficulty is personalizing the tools to fit
the specific needs of each type of disability. The
tools need to be adaptable enough to ensure
accessibility without losing effectiveness in
evaluation. Additionally, integrating automated
resources that provide continuous feedback tailored
to different types of disabilities continues to be an
essential technical challenge (Patern
`
o et al., 2017;
Bessghaier et al., 2021; Marenkov et al., 2018;
Marsh, 1999; Baguma, 2018; Holmes et al., 2019).
5.9 RQ9. What Is the Impact of
Usability Analysis Tools on
Developing More Inclusive
Software?
Usability analysis tools have a significant impact
as they help identify accessibility flaws and adjust
the software to serve users with disabilities better.
This contributes to digital inclusion, promoting better
experiences and greater functionality for all users
(Falconi et al., 2023; Jahan et al., 2019; Bessghaier
et al., 2021; Marsh, 1999; Baguma, 2018; Holmes
et al., 2019).
5.10 RQ10. What Are the Gaps in
Research on Usability Tools
Focused on Accessibility?
Research still faces gaps, especially in developing
tools that effectively integrate automated analysis
with accessibility. For example, the article by
Yigitbas (Yigitbas et al., 2019) highlights that
real-time customization of interfaces is still an open
area of research. Another need is empirical studies on
the impact of tools on users with specific disabilities
(Au et al., 2008; Patern
`
o et al., 2017; Jahan et al.,
2019; Yigitbas et al., 2019; Holmes et al., 2019).
6 DISCUSSION
The analysis of the results demonstrates significant
progress in developing tools focused on software
usability and accessibility, especially those that
integrate automated assessments with specific
guidelines, such as DUXAIT-NG, Guidelines, and
MUSE. These solutions are central to identifying
barriers and promoting inclusive interfaces for users
with different disabilities. There are still challenges
to be overcome, especially in customization to meet
the specific needs of each user group, considering
the technical limitations that hinder effective
universal adaptation. Although there are tools
focused on mobile devices and the web, such as
the Mobile-Friendly Test Tool and MUSE, the
restrictions of these environments, such as reduced
space and interaction limitations, continue to
represent substantial challenges for accessibility.
The impact of usability analysis tools on digital
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
602

inclusion is evident, promoting continuous interface
improvements and strengthening accessible design
practices. There are still gaps to be filled, such as
the lack of empirical studies validating these tools’
application in diverse contexts and in real-time. The
need for technological advances becomes crucial to
enable dynamic and continuous adaptations, ensuring
that tools not only identify problems but also assist
in implementing practical solutions for software
accessibility.
7 FINAL CONSIDERATIONS
The study provides a comprehensive overview of
how different technologies and methods have been
applied to identify and address accessibility barriers
in software. Tools such as DUXAIT-NG, Guideliner,
and MUSE demonstrate advances in integrating
automated assessments and specific adaptations for
different disabilities but still have limitations in
universally meeting user needs. This systematic
mapping highlights the importance of documenting
the state of the art in usability and accessibility,
providing a basis for future research and improving
practices in developing inclusive software. We
emphasize that accessibility is not just a technical
issue but an ethical and social commitment, requiring
collaborative efforts between researchers, developers,
and stakeholders.
For future work, we intend to expand the research
to include databases such as IEEE Xplore, Web
of Science, and Scopus, broadening the scope of
the studies analyzed. This approach will allow
for the identification of new methodologies and the
validation of the results obtained. Empirical studies
will be conducted to evaluate the effectiveness of
the tools analyzed in different scenarios and with
various audiences, deepening the understanding of
their limitations and potential in creating accessible
and inclusive software.
REFERENCES
Adinda, P. P. and Suzianti, A. (2018). Redesign of
user interface for e-government application using
usability testing method. In Proceedings of the
4th International Conference on Communication and
Information Processing, pages 145–149.
Alnahdi, G. (2014). Assistive technology in
special education and the universal design for
learning. Turkish Online Journal of Educational
Technology-TOJET, 13(2):18–23.
Au, F. T., Baker, S., Warren, I., and Dobbie, G.
(2008). Automated usability testing framework. In
Proceedings of the ninth conference on Australasian
user interface-Volume 76, pages 55–64.
Baguma, R. (2018). Usability evaluation of the etax portal
for uganda. In Proceedings of the 11th International
Conference on Theory and Practice of Electronic
Governance, pages 449–458.
Bessghaier, N., Soui, M., Kolski, C., and Chouchane, M.
(2021). On the detection of structural aesthetic defects
of android mobile user interfaces with a metrics-based
tool. ACM Transactions on Interactive Intelligent
Systems (TiiS), 11(1):1–27.
Cole, J. S., Bergin, D. A., and Whittaker, T. A. (2008).
Predicting student achievement for low stakes tests
with effort and task value. Contemporary Educational
Psychology, 33(4):609–624.
de Vasconcelos, L. G. and Baldochi Jr, L. A. (2012).
Towards an automatic evaluation of web applications.
In Proceedings of the 27th Annual ACM Symposium
on Applied Computing, pages 709–716.
Falconi, F., Moquillaza, A., Lecaros, A., Aguirre, J.,
Ramos, C., and Paz, F. (2023). Planning and
conducting heuristic evaluations with duxait-ng. In
Proceedings of the XI Latin American Conference on
Human Computer Interaction, pages 1–2.
Gonc¸alves, L. F., Vasconcelos, L. G., Munson, E. V.,
and Baldochi, L. A. (2016). Supporting adaptation
of web applications to the mobile environment with
automated usability evaluation. In Proceedings of the
31st Annual ACM Symposium on Applied Computing,
pages 787–794.
Harrison, R., Flood, D., and Duce, D. (2013). Usability
of mobile applications: literature review and rationale
for a new usability model. Journal of Interaction
Science, 1:1–16.
Holmes, S., Moorhead, A., Bond, R., Zheng, H., Coates,
V., and McTear, M. (2019). Usability testing
of a healthcare chatbot: Can we use conventional
methods to assess conversational user interfaces? In
Proceedings of the 31st European Conference on
Cognitive Ergonomics, pages 207–214.
Jahan, M. R., Aziz, F. I., Ema, M. B. I., Islam, A. B.,
and Islam, M. N. (2019). A wearable system for
path finding to assist elderly people in an indoor
environment. In Proceedings of the XX International
Conference on Human Computer Interaction, pages
1–7.
Keele, S. et al. (2007). Guidelines for performing
systematic literature reviews in software engineering.
Technical report, Technical report, ver. 2.3 ebse
technical report. ebse.
Kitchenham, B. and Brereton, P. (2013). A systematic
review of systematic review process research in
software engineering. Information and software
technology, 55(12):2049–2075.
Kumar, B. A. and Mohite, P. (2016). Usability
guideline for mobile learning apps: An empirical
study. International Journal of Mobile Learning and
Organisation, 10(4):223–237.
Usability in Software for People with Disabilities: Systematic Mapping
603

Kumar, B. A. and Mohite, P. (2018). Usability of mobile
learning applications: a systematic literature review.
Journal of Computers in Education, 5:1–17.
Lettner, F. and Holzmann, C. (2012). Automated and
unsupervised user interaction logging as basis for
usability evaluation of mobile applications. In
Proceedings of the 10th International Conference on
Advances in Mobile Computing & Multimedia, pages
118–127.
Lyner-Cleophas, M. (2019). Assistive technology enables
inclusion in higher education: The role of higher
and further education disability services association.
African Journal of Disability, 8(1):1–6.
Marenkov, J., Robal, T., and Kalja, A. (2018). Guideliner:
A tool to improve web ui development for better
usability. In Proceedings of the 8th International
Conference on Web Intelligence, Mining and
Semantics, pages 1–9.
Marsh, T. (1999). Evaluation of virtual reality systems for
usability. In CHI’99 extended abstracts on Human
factors in computing systems, pages 61–62.
McNicholl, A., Casey, H., Desmond, D., and Gallagher,
P. (2021). The impact of assistive technology use
for students with disabilities in higher education:
a systematic review. Disability and rehabilitation:
assistive Technology, 16(2):130–143.
Mji, G., MacLachlan, M., Melling-Williams, N., and
Gcaza, S. (2009). Realising the rights of disabled
people in africa: An introduction to the special issue.
Disability and Rehabilitation, 31(1):1–6.
Ndlovu, S. (2021). Provision of assistive technology
for students with disabilities in south african higher
education. International Journal of Environmental
Research and Public Health, 18(8):3892.
Nedungadi, P. and Raman, R. (2012). A new
approach to personalization: integrating e-learning
and m-learning. Educational Technology Research
and Development, 60:659–678.
Nielsen, J. (1994). Usability engineering. Morgan
Kaufmann.
Patern
`
o, F., Schiavone, A. G., and Conti, A. (2017).
Customizable automatic detection of bad usability
smells in mobile accessed web applications. In
Proceedings of the 19th international conference on
human-computer interaction with mobile devices and
services, pages 1–11.
Patern
`
o, F., Schiavone, A. G., and Pitardi, P. (2016).
Timelines for mobile web usability evaluation. In
Proceedings of the International Working Conference
on Advanced Visual Interfaces, pages 88–91.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M.
(2008). Systematic mapping studies in software
engineering. In 12th international conference on
evaluation and assessment in software engineering
(EASE). BCS Learning & Development.
Tony, M. P. (2019). The effectiveness of assistive
technology to support children with specific learning
disabilities: Teacher perspectives.
Vel
´
asquez-Dur
´
an, A. and Ram
´
ırez Montoya, M. S. (2018).
Research management systems: Systematic mapping
of literature (2007-2017).
Yigitbas, E., Hottung, A., Rojas, S. M., Anjorin, A., Sauer,
S., and Engels, G. (2019). Context-and data-driven
satisfaction analysis of user interface adaptations
based on instant user feedback. Proceedings of the
ACM on human-computer interaction, 3(EICS):1–20.
Zhang, D. and Adipat, B. (2005). Challenges,
methodologies, and issues in the usability testing
of mobile applications. International journal of
human-computer interaction, 18(3):293–308.
Zhang, X., Liu, J., Li, Y., and Zhang, Y. (2009). How usable
are operational digital libraries: a usability evaluation
of system interactions. In Proceedings of the 1st
ACM SIGCHI symposium on Engineering interactive
computing systems, pages 177–186.
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
604