ELEKTROTEHNI ˇ SKI VESTNIK 92(3): 77–85, 2025
OVERVIEW SCIENTIFIC PAPER
Scientific Landscape of Inclusive Knowledge
Management Processes in STEM Education: A
Clustering Approach
Damjan Fujs
1, † , Anna Becevel
2
, Paul Doyle
2
, Brian Gillespie
2
, Tomaˇ z Hovelja
1
, Mariana
Rocha
2
, Claudia Rivera
2
1
University of Ljubljana, Faculty of Computer and Information Science, Veˇ cna pot 113, 1000 Ljubljana,
Slovenia
2
Technological University Dublin, School of Computer Science, Central Quad, CQ-214, Grangegorman
Campus, D07 ADY7, Dublin 7, Ireland,
† E-mail: damjan.fujs@fri.uni-lj.si
Abstract. Education is one of the fundamental societal values. Therefore, it is crucial that every student,
including those with learning difficulties and disabilities, has access to quality education. This paper aims to
provide a comprehensive visual overview of scientific publications focused on inclusivity in STEM education.
A total of N = 1263 records were retrieved from the Scopus academic database on 16 July 2024. Data were
analyzed using VOSviewer software, employing bibliometric mapping techniques such as co-occurrence and
co-citation analysis. The co-occurrence analysis (all keywords) revealed seven distinct clusters, while the
co-citation analysis (journal publications) identified four. Our approach uses both quantitative and qualitative
methods and is also applicable to other areas.
Keywords: text mining, clustering approach, teaching, learning, inclusion, bibliometric analysis
Znanstvena krajina vkljuˇ cujoˇ cih procesov upravljanja
znanja na podroˇ cju STEM izobraˇ zevanja: pristop na
podlagi gruˇ cenja
Izobraˇ zevanje je ena izmed temeljnih druˇ zbenih vrednot, zato
je kljuˇ cnega pomena, da ima vsak uˇ cenec, vkljuˇ cno s tistimi
ki imajo uˇ cne teˇ zave ali so drugaˇ ce omejeni, dostop do
kakovostnega izobraˇ zevanja. Namen tega prispevka je po-
dati vizualni pregled znanstvenih publikacij, osredotoˇ cenih
na inkluzivnost v STEM izobraˇ zevanju. Iz akademske baze
podatkov Scopus je 16. julija 2024 bilo pridobljenih 1263
zapisov. Podatki so bili analizirani z uporabo programske
opreme VOSviewer, ki uporablja bibliometriˇ cne tehnike, kot
sta analiza pojavnosti kljuˇ cnih besed in citiranosti. Analiza
kljuˇ cnih besed je razkrila sedem razliˇ cnih gruˇ c, medtem ko
je analiza citiranja (znanstvene revije) identificirala ˇ stiri. Naˇ s
pristop uporablja tako kvantitativne kot kvalitativne metode in
je prav tako uporaben tudi na drugih podroˇ cjih.
Kljuˇ cne besede: tekstovno rudarjenje, gruˇ cenje, pouˇ cevanje,
uˇ cenje, inkluzivnost, bibliometriˇ cna analiza
Received 9 September 2024
Accepted 22 April 2025
Copyright: © 2025 by the authors.
Creative Commons Attribution 4.0
International License
1 INTRODUCTION
STEM is an abbreviation for science, technology,
engineering, and mathematics [21]. Moreover, according
to Leporini and Buzzi [18], STEM is a key catalyst in
a growing economy. Therefore, it is important to make
it accessible to the entire student population - also
for those with disabilities. While inclusivity is often
associated with providing broad access to education,
the term encompasses much more. Inclusivity is not
limited to accommodating students with disabilities;
it asserts that all educational institutions should strive
to create optimal learning environments for every
student, regardless of their abilities or disabilities [28].
Nevertheless, there is evidence that the dropout rate can
be higher for people with disabilities [27]. Therefore, it
is important to pay more attention to this issue.
In the framework of this paper, we focus on the subject
of inclusive knowledge management process in the field
of STEM. The knowledge management process should
be an integrated process that involves the interaction
between information technology (IT), people, and
techniques to utilize knowledge effectively [29]. In this
paper, we use the term knowledge management process,
as we want to emphasize that learning is essentially
acquiring/giving/organizing/sharing etc. knowledge.
Therefore, it is not a one-time event but a process

78 FUJS ET AL.
that may last for a long period of time. The research
objectives of the study are as follows:
• To provide an overview of the scientific landscape
concerning inclusive knowledge management in
STEM education (main topics).
• To analyze bibliometric networks to identify influ-
ential topics and journals.
• To develop a comprehensive understanding of
the impact of technology on inclusive STEM
education by integrating qualitative insights and
quantitative data to identify key implications that
can inform future inclusion educational practices.
The remainder of this paper is structured as follows.
Following this introduction (Section 1), we present a
literature review (Section 2) on inclusivity and biblio-
metric analysis in the field of STEM. In Section 3, we
describe the steps of the approach and the methods used.
Section 4 describes the results and discusses them, while
Section 5 draws the key conclusions and provides future
research directions and some limitations.
2 RELATED WORKS AND BACKGROUND
In the existing literature, several papers that refer
to inclusivity in STEM can be found. For example,
Sahara et al. [8] dealt with the possibility of supporting
deaf students in elementary school, where the key
is understanding the special needs of these students.
Guralp, McHugh and Hayes [9] researched informal
science learning/teaching in socio-economically
deprived areas. Savonen et al. [10] provide guidelines
for inclusive classrooms where they point out that it
is necessary to create a pleasant working environment
(e.g., use of interactive quizzes, curriculum testing
for color vision compatibility with tools such as
ColorOracle
*
, use of inclusive language as not
everyone necessarily knows the basics in STEM,
etc.). Ramirez-Montoya et al. [11] mention that open
education can enhance inclusive education, helping
achieve international goals such as the United Nations’
Sustainable Development Goals specified in the 2030
Agenda. Lilly et al. [12] address teachers’ challenges
in implementing Next Generation Science Standards
†
in STEM when dealing with both general education
classes and inclusive classes. Bertram and Rolka [14]
are engaged in inclusive mathematics education. They
point out that a typical school with an inclusive setting
creates heterogeneous groups in the classroom, so it
is typically necessary to take care of differentiation to
improve education for all students.
∗ https://colororacle.org/
† https://www.nextgenscience.org/
In this research, bibliometric analysis was used, which
has many advantages in analyzing large quantities
of bibliometric data. Donthu et al. [20] mention that
bibliometric analysis is suitable for analyzing large
quantities of bibliometric data, i.e. when dealing with
a broad-scope review. Bibliometric analysis with the
VOSviewer software tool has been used several times
to analyze the field of STEM education. Gil-Domenech
et al. [21] conducted a co-citation analysis of journals
among STEM publications. They also researched the
most productive institutions that publish research in the
field. In doing so, they analyzed the resources indexed
in the Web of Science academic database. In addition,
it is possible to find many publications that analyze
the STEM field over a certain period. For example,
Thu et al. [22] analyzed STEM education research in
middle school over a period of time (in 2000− 2020).
Similarly, Zhan et al. [23] analyzed STEM education
publications indexed in the Web of Science database
in 2004− 2021. Hsu et al. [24] analyzed hot topics in
STEM education in 2011− 2020. Hernandez-Torrano et
al. [30] provide a bibliometric analysis of the literature
regarding inclusive education in the years 1994− 2019.
In the past, bibliometric analysis was also applied to
gain insight into the knowledge management process.
For example, Mufutumari [25] provided a bibliometric
analysis of articles on the knowledge management
process in higher education, namely in the years from
2019 to 2024. Their results showed that knowledge
management process terminology is not widely used
in the existing literature on knowledge management
in higher education institutions. Haque et al. [26]
analyze the literature on knowledge sharing and
student development. Their study demonstrated how the
relationship between knowledge sharing and student
development has evolved and how it may influence
student performance. However, bibliometric analysis
for the inclusive knowledge management process in the
context of STEM education, at least according to our
knowledge, cannot be tracked. In addition, the studies
mentioned above mostly use a quantitative approach.
3 METHODOLOGY
A mixed-methods approach was employed to
comprehensively address the research objectives,
integrating qualitative and quantitative steps. This
approach allows for analysis, leveraging the strengths
of both qualitative insights and quantitative rigor.
Mixed-method research integrates both qualitative and
quantitative approaches, either iterative or concurrently,
to produce a research outcome that is more robust
than using either method alone [2]. Figure 1 shows
the individual steps of conducting our research.

INCLUSIVE KNOWLEDGE MANAGEMENT PROCESSES IN STEM 79
Note, that the approach provided here is a novel
methodology developed as part of this research. The
approach is divided into a qualitative (grey color) and
a quantitative part (blue color). Moreover, in Figure
1, E stands for search engine (in our case, it was
the Scopus academic database), and S stands for
software (in our case, we used VOSviewer software).
The approach can also be applied with the help of
other academic databases (search engines) and software.
The Approach (Figure 1) consists of ten steps, which
can be briefly summarized as follows:
• Definition: reach a consensus regarding the re-
search scope and query that will be used for the
literature search.
• Source mining: reach consensus on the use of
academic databases for literature search.
• Final set of literature: download full bibliometric
data to get a dataset in a data format suitable for
analysis (depending on the software).
• Clustering: identify suitable attributes or methods
and conduct clustering. This method is exploratory
and requires several iterations.
• Visualization: analyze different solutions in the
clustering framework.
• Mapping: choose the most suitable visualization
and report the appropriate type.
• Explanation: describe the individual clusters and
add descriptions of the methods used (such as
parameters, number of attributes, etc.).
• Statistics: extract more detailed statistics not visi-
ble on cluster visualizations (such as all items in a
certain cluster, occurrence of keywords, etc.).
• Sandpit: integrate findings from the sandpit to lit-
erature analysis solutions using clustering. Sandpit
is a term used in the European project (Global
Entrepreneurial Talent Management 4, GETM4)
and represents co-creative workshops where par-
ticipants exchange views, knowledge, experiences,
etc. on predefined topics.
• Implications: provide practical and theoretical
implications (if possible) and guidelines for further
work.
In this study, we decided to use the Scopus academic
database because it includes multidisciplinary scientific
literature and provides more coverage than the Web of
Science academic database [1]. In addition, it should
be noted that all the main metadata (e.g., citation
information, bibliographical information, abstract &
keywords, funding details, and other information) had to
be exported for the analysis using VOSviewer software.
VOSviewer software version 1. 6. 30 [3] was used to
analyze these data. VOSviewer enables the visualization
of bibliometric data from scientific databases and, in
this way, provides an insight into the comprehensive
picture of the scientific landscape of the researched
field. In addition, the added value of using VOSviewer
concerning the analysis of bibliometric data is the
visual perception of the results and insight into possible
connections between sources/authors/keywords, etc. [4].
Namely, the acronym VOS denotes ”visualization of
similarities” [5].
To obtain the broadest overview of inclusive teaching
in STEM, the following query to search for scientific
publications in the Scopus academic database is used:
(ALL("teaching approach" OR
"teaching approaches" OR
"knowledge management") AND
ALL(inclusivity OR
inclusion OR inclusive) AND
ALL("STEM"))
Based on this query results (N = 1263), two methods
were used to analyze the final data, namely (1) journal
co-citation analysis and (2) keyword co-occurrence
analysis. The methodology of the mapping technique
is described in more detail by van Eck and Waltman
[5], but the two methods can be briefly interpreted as
follows:
• Co-citation method (unit of analysis = cited
sources) was used to identify the most influential
scientific journals. As a unit of analysis, instead
of all keywords, is it possible to include only
author keywords or index keywords. In addition,
we also wanted to gain insight into the dynamics
of citations between journals. Sources with at least
100 citations were included in the unit of analysis.
Co-citation of cited sources (unit of analysis) shows
the relatedness of items based on the number of
times they are cited together. This co-citation map
is built on co-citation links. co-citation link is a link
between two items that are both cited by the same
document [3].
• Co-occurrence method (unit of analysis = all key-
words) was used to identify the most frequently
used keywords in scientific publications and to
identify how keywords relate to each other. Sources
whose keywords appear at least five times were
included in the unit of analysis. The relatedness
of keywords is determined based on the number of
documents in which they occur together [3] for a
given dataset.

80 FUJS ET AL.
Figure 1. An approach showing the individual steps of conducting our research. The approach is divided into a qualitative (grey
color) and a quantitative part (blue color).
4 RESULTS AND DISCUSSION
The used query in the Scopus academic database
returned N = 1263 results (sources). The search
was conducted on 16. 07. 2024. However, for the
co-occurrence analysis, only 256 keywords met
the threshold (i.e., min. number of occurrences
of a keyword = 5). Figure 2 is the result of this
analysis and shows a bibliometric analysis of the
co-occurrence keywords where seven clusters can
be seen. To achieve this result, several iterations
of mapping or clustering were conducted. Due to
the interdisciplinarity of the topic, it is difficult to
determine the content of the clusters (based on keyword
mapping). Therefore, based on intuition, we selected a
map that is sufficiently homogeneous in general, and
the clusters are sufficiently heterogeneous. The dataset,
additional images and tables with journal citation
counts and keyword occurrence can be found here:
https://github.com/M16Nebula/Inclusivity-in-STEM.
Each of the clusters is presented in a different color and
can be described as follows, from largest to smallest
cluster [C
KR
> C
KG
> C
KB
> C
KY
> C
KV
>
C
KT
>C
KO
] (numbers in brackets represents keyword
occurrence):
The Red cluster (C
KR
) contains 65 keywords,
with ”higher education” (59), ”innovation” (29) and
”sustainability” (26) appearing most often. In C
KR
,
some other terms that can be interesting from the point
of view of inclusivity in STEM can be observed, such
as leadership (16), creativity (13), entrepreneurship (8),
industry 4. 0 (8), etc. Ahmad et al. [7] state that with
the help of technologies used in Industry 4. 0, it makes
sense to adapt education according to different student
profiles (e.g., different motivations, disabilities, cultural
environments, etc.). In this context, they list several
supporting technologies such as blockchain (for record
of students accomplishments), AI (for establishing
intelligent tutoring systems), augmented reality (for
people with autism spectrum), etc., to name just a few.
Savonen et al. [10] mention that inclusivity improves
innovation in data science. Moreover, innovation also
means striving for continuous improvement, as changes
occur yearly and new educational challenges and
opportunities arise with this [13].
The Green cluster (C
KG
) consists of 57 keywords. The
keywords ”students” (142), ”engineering education”
(73), and ”knowledge management” (60) appear most
often. The terms curricula (52), artificial intelligence
(37), computer aided instructions (30), and gamification
(10) are also worth mentioning. Mann et al. [6]
mention that curricula should be adapted to the needs
of students. In this context, it is desirable that the
curriculum be revised and that the students’ voices
be considered. In this case, it makes sense to use
peer review because, in this way, it is possible to
encourage collegial discussions and improve inclusivity.
In addition, it is desirable to use the so-called ”smart
education approaches” meaningfully, taking advantage
of the possibilities offered by technology [15].
The Blue cluster (C
KB
) contains 54 keywords,
with ”education computing” (42), ”stem” (33) and
”science education” (29) being the most frequently
mentioned. Other notable keywords include: ”equity”
(22), ”professional development” (15), ”personnel
training” (13), and ”differentiated instructions” (7).

INCLUSIVE KNOWLEDGE MANAGEMENT PROCESSES IN STEM 81
Figure 2. Bibliometric analysis of the co-occurrence keywords in selected publications. The shorter the distance between two
nodes, the larger the co-occurrences of the two keywords. Note: N = the number of items in a particular cluster, C = cluster,
K = Keyword, letters next to ”K” represent colors: B (blue), G (green), O (orange), R (red), Y (yellow), T (turquoise), and V
(violet).
There are many challenges when teaching STEM in
inclusive classrooms, as the teams are typically mixed
(i.e. students with/without disabilities) [12], requiring
special support and teacher training.
The Yellow cluster (C
KY
) includes 38 keywords.
Generally, they are related to so-called human aspects.
The most frequently mentioned keywords are ”human”
and ”humans” (total N = 124). Additional keywords that
we want to mention are: ”adult” (22), perception (13),
and visual impairment (5). Typically, the population
of higher education is diverse, which means that it
is necessary to ensure that all students can reach
their potential. In the context of inclusivity, especially
those with specific learning difficulties [17]. Visual
impairment is only one of the disabilities that can
cause students learning difficulties. Leporini ad Buzzi
[18] mention a few software tools that help the
blind in learning mathematics, such as Lambda
*
and
MathSpeak
†
. Other forms of learning disabilities in
STEM include but are not limited to: hearing loss,
learning difficulties (such as dyslexia, dyscalculia, etc.)
∗ https://www.lambdaproject.org/
† https://www.seewritehear.com/accessible-mathml/mathspeak/
[18], etc. In general, it is necessary to pay attention
to each person individually. Students with learning
difficulties perceive the course of lectures differently
than those with no disclosed learning difficulties [17].
The Violet cluster (C
KV
) contains keywords generally
associated with learning. The most frequently mentioned
keywords are: ”teaching” (74), ”education” (68), and
”e-learning” (55). Other terms that are in the cluster and
that would be interesting are: ”blended learning” (18),
”academic achievement” (13), and ”learning style” (6).
Audette et al. [13] define the term ”culture of care”,
stating that the educational process should be carried
out in such a way as to support each other since a
particularly big challenge in STEM courses is that they
are typically taught in large lecture courses - which can
negatively affect academic performance and retention.
Additionally, they state that it is possible to achieve a
community that cares through employee training. Also,
within the framework of Sandpit, which took place in
South Korea, the focus was on dyslexia as one of the
forms of learning difficulties. Preliminary guidelines
were drawn up for how processes and materials should
be adapted for such students (e.g., instructions for

82 FUJS ET AL.
the slide composition of, organization of the virtual
learning environment, etc.). In addition, in practice,
other examples from the industry can be found that
provide instructions for the development of so-called
inclusive graphical interfaces, such as Apple
‡
, IBM
§
,
SAP
¶
, etc.
In the Turquoise cluster (C
KB
), there are 15 keywords
in total. The most frequently mentioned keywords
are ”curriculum” (24), ”flipped classroom” (16), and
”training” (15). Terms like ”problem-based learning”
(14) and ”procedures” (11) are also worth noting.
Training regarding technological accessibility (i.e.,
technology must be accessible) is also mentioned by
Ramirez-Montoya et al. [11] and states that open and
distance education through technological solutions
can help people with disabilities overcome educational
barriers. The discussion within Sandpit also showed that
it makes sense to educate teachers about various aspects
of learning difficulties. As part of this, guidelines
were also created for assessing such students (e.g.,
students with dyslexia are not penalized for spelling,
and teachers should avoid similar-looking letters on
exams such as ”p” or ”q”, etc.). Asghar et al. [19]
mention scaffolding as one of the strategies in the field
of STEM education - to break a certain topic into
smaller parts and, in this way, possibly facilitate the
understanding of the study material.
The Orange cluster (C
KO
) contains only four
keywords: ”feedback” (7), ”educational experiences”
(5), ”scaffolds” (5), and ”universal design” (5).
Feedback has a dual role in education, namely, firstly,
as a feedback mechanism from students to course
providers to improve the course and secondly, as
feedback from the course provider to the student so
that the students can improve [13]. Universal Design
for Learning (UDL) is an approach that can maximize
learning for all students and prescribes multiple learning
resources to motivate students to learn. Originally, the
UDL
||
was intended for students with disabilities [16].
For the co-citation analysis, we included only sources
cited at least 100 times. This means that only 55
scientific journals that are connected are included
in the visualization (see Figure 3). Each of the
clusters is presented in a different color and can be
described as follows, from largest to smallest cluster
[C
JR
> C
JG
> C
JB
> C
JY
] (numbers in brackets
represents citation count).
‡ https://www.apple.com/accessibility/
§ https://www.ibm.com/able/toolkit/design/content/
¶ https://news.sap.com/2023/07/creating-accessible-content/
∥ https://udlguidelines.cast.org/
The Red cluster (C
JR
) mainly deals with journals
that cover the psychological and organizational field of
education. The three most frequently cited sources are
Sustainability (669), Frontiers in Psychology (250), and
Journal of Business Research (236).
In the Green cluster (C
JG
), journals relate mainly to
the broader field of education. The most frequently cited
sources are Journal of Research in Science Teaching
(410), International Journal of Science Education (393),
and Teaching and Teacher Education (290).
The Blue cluster (C
JB
) covers mostly journals related
to technical aspects of education. The three most
frequently cited sources are Computers & Education
(607), Education and Information Technologies (340),
and Computers in Human Behavior (276).
In the Yellow cluster (C
JY
), there are mainly
multidisciplinary journals that cover various research
disciplines. The most frequently cited sources are the
following: Plos ONE (279), Science (262), and Higher
Education (200).
These results of co-citation analysis can serve re-
searchers as a source of information - that is, to know
in which sources to look for relevant information related
to the topic under discussion. In addition, it enables an
insight into how certain disciplines are interconnected
(i.e., interdisciplinarity).
5 CONCLUSIONS, FUTURE WORKS AND
LIMITATIONS
This paper discusses selected aspects of inclusive
education in the field of STEM. A mixed-method
approach was used, meaning, that we combined
quantitative and qualitative aspects in researching the
topic. As part of the quantitative research approach, a
bibliographic analysis of keywords and a co-citation
analysis of the most influential journals was carried
out using the Scopus academic database search results.
A qualitative approach was used to complement the
quantitative results of the bibliometric analysis. This
means that with the help of existing studies, certain
keywords were additionally emphasized. Also within
this framework, certain findings from Sandpit were
integrated, the topic of which was the inclusivity of
STEM education for students with dyslexia as one of the
forms of learning difficulties. The whole approach gives
a comprehensive insight into the scientific landscape.
Future work may focus on developing a comprehensive
framework describing learning/teaching strategies
for existing learning difficulties and other forms
of disabilities. It would also make sense to get a

INCLUSIVE KNOWLEDGE MANAGEMENT PROCESSES IN STEM 83
Figure 3. Bibliometric analysis of the journal co-citation. The distance between the two journals in the visualization approximately
reflects their relatedness based on co-citation links, and lines represent the strongest co-citation links between journals. Typically,
journals that are closer together are more closely related [3]. Note: N = the number of items in a particular cluster, C = cluster,
J = journal. Letters next to ”J” represent colors: B (blue), R (red), Y (yellow), and G (green).
global insight into the extent to which providers of
STEM subjects are familiar with inclusivity/disability
concepts. Furthermore, future work may also explore
how supporting teachers in incorporating inclusivity
into their curriculum impacts their job satisfaction and
the academic performance of students.
Despite all the advantages of the mixed-method ap-
proach, there are certain limitations to the methods used
in this study. First, even though the Scopus academic
database includes a very large number of sources, it
would be reasonable to extract data from other databases
(e.g., Web of Science, IEEE Xplore, ACM DL) as well,
because a certain source may be indexed in one database,
but not in another. Third, interpretations of the results of
the bibliometric analysis are subjective. For this reason,
we tried to support the obtained results with scientific
literature (i.e., provide content analysis as suggested by
Donthu at al. [20]).
6 ACKNOWLEDGEMENTS
The Global Entrepreneurial Talent Management 4
(GETM4) project is funded by the European Union un-
der the Horizon 2022 Marie Sklodowska-Curie Staff Ex-
changes programme (Grant Agreement no. 101130572).
Views and opinions expressed are however those of the
authors only and do not necessarily reflect those of
the European Union or the Research Executive Agency
(REA). Neither the European Union nor REA can be
held responsible for them. The work of Damjan Fujs
and Tomaˇ z Hovelja was also partially funded by the
Slovenian Research and Innovation Agency - ARIS
(Grant numbers P2-0426). The authors would also like
to thank the anonymous reviewers for their insightful
comments, which helped them to improve the paper.
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INCLUSIVE KNOWLEDGE MANAGEMENT PROCESSES IN STEM 85
Damjan Fujs received the Ph.D. degree in computer and information
science from the University of Ljubljana, Ljubljana, Slovenia, in 2024.
He is an Assistant Professor with the Faculty of Computer and Infor-
mation Science, University of Ljubljana. His research interests include
information and cyber security, security requirements engineering, and
software development methodologies. He has published in a range
of scientific journals such as Computers & Education, Computers &
Security, Education and Information Technologies, etc. Currently, he
is an Editorial Board Member of the International journal on advances
in security.
Anna Becevel holds a M.Sc. in Logic, Math and Philosophy of
Language from the University of Trento (Italy). Anna is the European
Research Manager at the School of Computer Science at TU Dublin
and acts as a Project Manager for most European projects led by the
School, such as Ethics4EU, GETM4, and Inclusion4EU.
Paul Doyle received the B.Sc. and M.Sc. degrees in the computer
applications from Dublin City University, Dublin in 1990 and 1992
respectively. Then he received his Ph.D. degree in computer science
from Technological University Dublin, Dublin in 2015. He is the
Head of the School of Computer Science at Technological University
Dublin and has over 30 years of experience as both an academic
and professional senior manager and director for companies such as
Sun Microsystems, CR2 and Critical Path. His research areas include
Big Data processing, Distributed Systems, Systems Infrastructure,
and international educational pedagogy. Focused on improving the
quality of education in Computer Science, he has been PI (Principal
Investigators) on several European and Asian funded projects in the
areas of technology enhanced learning.
Brian Gillespie holds a M.Sc. in Information Systems Management
from the University of Dublin and is a senior lecturer in the School
of Computer Science at TU Dublin. He has held senior technical
roles in several multinational technology companies leading solution
development in the areas of embedded systems, telecommunications,
networking and cloud computing. His current research interests include
software engineering processes, image processing, natural language
interfaces, and artificial intelligence. He is actively involved in several
EU funded research projects in the areas of entrepreneurship and
technology in education development.
Tomaˇ z Hovelja is a Professor at the Faculty of Computer and Informa-
tion Science, University of Ljubljana. He received a bachelor’s degree,
a master’s degree, and his PhD in Business Administration from the
School of Economics and Business at the University of Ljubljana. His
research areas include social, economic and organizational factors of
IT deployment in enterprises, strategic information system planning
and IT projects success criteria.
Mariana Rocha is a lecturer in the School of Computer Science at the
Technological University Dublin (TU Dublin). She received a Ph.D. in
Computer Science from TU Dublin, and is the co-PI in the Intelligent
Games Lab, a research group that studies digital games’ potential to
support learning engagement. Her research interests are related to the
potential of technology for making STEM education more inclusive.
Dr. Rocha designed games such as Once Upon a Maths, an award-
winning web application for primary school students. Mariana is also
a co-PI in the Access2CS, a European-funded project that aims to
transform the student experience for students with disabilities entering
a Computer Science degree in Ireland.
Claudia Rivera is an instructional designer and multidisciplinary
researcher specialising in accessibility and inclusive education. With
a background in architecture (BA) and advanced training in Human
Factors & Ergonomics (MSc) and project management (Professional
Diploma in Planning, Control, and Management of Construction
Projects), Claudia brings over a decade of experience in design
practice. Her work has evolved to focus on the design of accessi-
ble digital learning environments, inclusive curriculum development,
and collaborative educational research. Currently, she is involved in
European-funded projects that promote universal design for learning
and equitable access to computer science education for students with
disabilities. Claudia combines her design expertise with participatory
methods and project leadership to drive systemic change in higher
education.