Invited Speakers
Invited Speakers
Assoc. Prof. Rubing Huang (IEEE/CCF Senior Member)
Macau University of Science and Technology, China
Rubing received his
M.Eng and Ph.D. degree
in Computer Science and
Technology from Huazhong
University of Science
and Technology. After
that, Rubing spent one
year at Monash
University as an
academic visitor advised
by Dr. Xin Xia and Dr.
Robert G. Merkel and
three months at
Swinburne University of
Technology as an
academic visitor advised
by Prof. T. Y. Chen.
Currently, he is an
associate professor in
the School of Computer
Science and Engineering,
Faculty of Innovation
Engineering, Macau
University of Science
and Technology (MUST).
Before joining MUST, he
was a faculty member at
Jiangsu University,
China.
He has more than 80
publications in journals
and proceedings,
including IEEE
Transactions on Software
Engineering (IEEE TSE),
IEEE Transactions on
Reliability (IEEE TRel),
Journal of Systems and
Software (JSS),
Information and Software
Technology (IST),
Software: Practice and
Experience (SPE),
Science of Computer
Programming (SCP), IET
Software, International
Journal of Software
Engineering and
Knowledge Engineering
(IJSEKE), IEEE
Transactions on Emerging
Topics in Computational
Intelligence (IEEE
TETCI), Expert Systems
with Applications
(ESWA), The Computer
Journal, Tsinghua
Science and Technology
(TST), ICSE, ISSRE,
ICST, and COMPSAC. He
has served as the
program committee member
of ICST2020 (poster
track), APSEC2023-2024,
and AI Testing
2019-2021. He has served
as the reviewer for many
journals such as IEEE
TSE, ACM TOSEM, IEEE TC,
IEEE TIFS, IEEE TAI,
IEEE TBD, ACM TWEB, IEEE
TRel, IEEE TFS, IEEE
TNSE, IEEE T-ASE, IEEE
TCAD, ACM CSUR, STVR,
JSS, IST, SPE, JSEP,
ASEJ, SCP, EAAI, FCS,
IEEE Software, etc. He
is a senior member of
the IEEE and the China
Computer Federation.
His current research
interests include: 1) AI
for Software Engineering
(AI4SE), i.e., adopting
AI knowledge and
techniques such as
Machine Learning (ML),
Deep Learning (DL), and
Large Language Model
(LLM), to support the
development of software
engineering (including
software requirements,
testing, debugging, and
maintenance); 2)
Software Engineering for
AI (SE4AI), i.e.,
applying the principles
of software engineering
to AI systems (such as
AI systems testing,
debugging, and
maintenance); 3)
Failure-Based Testing
(FBT), i.e., taking
advantage of the
knowledge of software
failures to support the
testing process; and 4)
Testing of Complex
Software Systems such as
IoT systems, intelligent
systems, and autonomous
driving systems.
Prof. Dave Towey
University of Nottingham Ningbo China, China
Dave Towey received his
BA and MA degrees from
The University of
Dublin, Trinity College;
PgCHE from University of
Nottingham; PgCertTESOL
from The Open University
of Hong Kong; MEd from
The University of
Bristol; and PhD from
The University of Hong
Kong. He was the first
foreign academic
recipient of the Zhuhai
municipal outstanding
teacher award, in 2007,
and he received the Lord
Dearing award in 2017
for his outstanding
contribution to the
development of teaching
and student learning. He
is a full professor, and
the associate dean for
education and student
experience for the
Faculty of Science and
Engineering. He is the
deputy head of the
School of Computer
Science, which he joined
in September 2013. He
also serves as deputy
director of the
International Doctoral
Innovation Centre
(IDIC).
After graduating from
The University of
Dublin, Trinity College,
Dave worked in Japan in
the late 1990s, helping
develop a breast cancer
screening tool using
ultrasound imaging
technology and fuzzy
reasoning.
After this, he lived in
Hong Kong from 2000 to
2005/2006, where, as
well as completing his
PhD, he worked at The
University of Hong Kong,
and as a teacher and
teacher trainer in the
local school system.
In 2005, he became
involved in a newly
created liberal arts
college in Zhuhai,
Mainland China, the
Beijing Normal
University – Hong Kong
Baptist University
United International
College (UIC), where he
remained until 2013.
While at UIC, he taught
modules related to
computer science,
linguistics, and
education. He also held
several roles, including
deputy director of the
English Language Centre,
and coordinator
(director) of the
Teaching English as a
Second Language (TESL)
degree programme. In
these roles, he oversaw
delivery of a large
number of pre-service
and in-service training
courses and workshops.
Dave's research
interests span a number
of areas, including
technology-enhanced
teaching and learning,
and software testing,
especially metamorphic
testing and adaptive
random testing.
Title: Metamorphic Thinking: Teaching and Doing Software Engineering Differently
Abstract: Focusing on software engineering, and in particular software testing, this presentation explores how recent advances in computing technology (including generative AI) have exposed inadequacies in how we ensure the quality of modern software. Metamorphic testing (and the related concepts of metamorphic exploration and metamorphic thinking) provides a different perspective on software testing: Instead of focusing on individual output correctness, it looks at relationships that should hold if the software has been properly implemented: A violation of such relations indicates a fault in the software. In addition to introducing the principles of metamorphic testing, and some of its significant success, this presentation will also explore the application of metamorphic exploration and metamorphic thinking more generally to the teaching and learning of software engineering, and beyond, in broader educational settings.
Assoc. Prof. Jining Han
Southwest University, China
Dr. Jining Han is an associate professor in the Faculty of Education at Southwest University. He earned his Ph.D. in Second Language Acquisition and Educational Technology from the University of South Florida and holds a Master’s degree in Pedagogy from Arizona State University. He also received a postdoctoral fellowship position at the Georgia Institute of Technology. He conducts research in applying AI-supported learning, virtual reality in education, and smart learning environment.
Title: Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for middle school students
Abstract: This study investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in middle school settings.
Prof. Yanjie Song
Education University of Hong Kong, China
Professor Song Yanjie is
Associate Director of
the Centre for
Excellence in Learning
and Teaching (CELT), and
Associate Director of
the Centre for Immersive
Learning and Metaverse
in Education (CILME).
She obtained her PhD in
Educational Technology
at the Faculty of
Education, The
University of Hong Kong,
and her MEd in IT,
Multimedia and Education
at Faculty of Education,
at the University of
Leeds, UK.
Professor Song has
successfully led her
team in developing
several award-winning
applications, including
the AR and VR-supported
vocabulary learning app,
‘VocabGo’; the
collaborative,
inquiry-based science
learning app,
‘m-Orchestrate’; and the
commercialised metaverse
platform,
‘Learningverse’. Her
team has secured several
patents for
technological
innovations. She has a
proven track record of
obtaining competitive
external research grants
and other funding that
have impacted education.
Additionally, she was
ranked among the top 2%
of the world’s
most-cited scientists in
education in recent
years, according to a
list compiled by
Stanford University.
Title: Leveraging AI and 3D Metaverse – “Learningverse” to Scaffold Students in Immersive and Collaborative Learning
Abstract: Generative artificial intelligence (AI) has been adopted in various fields. However, few studies have integrated generative AI into the metaverse environments to enhance students’ learning. In this talk, I will share my experiences in how to leverage generative AI in the 3D metaverse platform “Learningverse" to scaffold students for immersive and collaborative learning. Advantages and pitfalls are explored.
Assoc. Prof. Zigang Ge
Beijing University of Posts and Telecommunications, China
Zigang Ge is an associate professor and academic adviser for MA and MS programs at the School of Humanities, BUPT. He was honored with the title of "Beijing Higher Education Young Elite Teacher" by the Beijing Municipal Government. has authored over 30 peer-reviewed papers in prestigious journals, both domestically and internationally, including Computers & Education, Computer Assisted Language Learning, International Journal of Computer-Supported Collaborative Learning, Interactive Learning Environments, and the Journal of Educational & Society. He has served on the editorial board of the Journal of Asia TEFL and has been a technical committee member for several international conferences, such as the International Conference on Signal and Image Processing (ICSIP) since 2019, and the International Conference on Digital Technology in Education (ICDTE) since 2022. In addition, He is an external reviewer for academic papers at the China Academic Degrees and Graduate Education Development Center.
Title: Exploring the Impact of Different Types of E-learners’ Anonymity on Their Learning Engagement in Competitive Gamified Language Learning
Abstract: The present study aims to explore the impact of two types of e-learners’ anonymity on their language learning engagement in a 50-day competitive gamified English vocabulary learning process. The two anonymity types were full anonymity, which means the participants were anonymous to both their peers and the instructor in the learning process, and partial anonymity, which required the participants to be anonymous only to their peers. One hundred and eighty adult e-learners were recruited as the participants and randomly assigned into three groups, with one group adopting the full anonymity pattern, one group using the partial anonymity pattern, and a third group employing the non-anonymity pattern. An online pretest, an online posttest and two online questionnaires were used as the research instruments. The two tests were used to assess the participants’ learning performance. One questionnaire was to obtain the participants’ self-report values on three scales of learning engagement, namely behavioral engagement, emotional engagement, and cognitive engagement. The other questionnaire together with some interviews was applied to inquire about the participants’ attitudes towards anonymity in gamified learning. The results indicate that the full anonymity pattern exerted the most positive impact on the participants’ behavioral and emotional engagement, and this pattern also brought about the best learning performance on the posttest. However, the non-anonymity pattern outperformed the other two patterns on the cognitive engagement scale. Then the possible impact of anonymity on learning engagement and learning performance was discussed.
Senior Lecturer Dr. Mariam Mohamad
Universiti Sains Malaysia, Malaysia
Dr. Mariam Mohamad is a Senior Lecturer at Universiti Sains Malaysia, Penang Malaysia. Her research interest is in the field of mobile learning and mobile assistive technology. Dr Mariam was acknowledged as one of the pioneers from Malaysia in mobile learning research, in the book entitled Mobile Learning in Higher Education in the Asia-Pacific Region published by Springer. Her work also referred by UNESCO in the publication entitled; Turning on Mobile Learning: Illustrative Initiatives and Policy Implications. Dr. Mariam has extensive experience in disseminating her work internationally. She actively involves presenting her work in international conferences with ISI Web of Science and SCOPUS indexed status. She has shown her capability in her area as invited speakers. She was invited by UNESCO to present her work in Mobile Learning Week 2014 at UNESCO headquarters in Paris. Recent contribution was as invited speaker in ICEPS 2023, ICLLL 2023, AETS 2024, ICETM2024 and ICLLL 2024. As one of the renowned researchers in mobile learning in Malaysia, she was elected as an exco member for Mobile Learning Association Malaysia. For her success in the field, in 2021, she was awarded with international grants which include Sumitomo Foundation and Japan Foundation to support her research in mobile assistive technology. She also won various prestigious innovation competition and the latest achievements include; (i)Silver medal award from International Innovation Arsvot Malaysia (IAM 2021) entitled “Learning module for history subject via mobile based learning” and (ii)Bronze medal award from International Innovation Arsvot Malaysia (IAM 2021) entitled “Education in need and demand victory app: An innovation framework utilizing virtual reality to improve visual learning ability and students performance gain in history lessons contents”. For the community, she also devoted her time as the Advisor for Special Need Students Association, University Sains Malaysia and a Committee Member for Research & Development Unit, Malaysian Association for The Blind.
Title: A Bibliometric Review of Mobile Assistive Technology Research for Dyslexic Children: Mapping the Landscape of a Decade
Abstract: Dyslexia, a prevalent neurodevelopmental disorder affecting reading and writing skills, poses significant challenges to educational attainment. In recent years, mobile assistive technologies have emerged as promising tools to support dyslexic children in their journey. Despite the increasing focus on mobile assistive technology for dyslexic children, a comprehensive overview of the research landscape is lacking. The field is characterized by a proliferation of studies, diverse methodologies, and an expanding knowledge base. This bibliometric review leverages advanced analytical tools, with a primary focus on the VOS viewer software, to conduct a comprehensive analysis of the literature spanning from 2014 to 2024. A carefully curated dataset, comprising research articles sourced from reputable databases, forms the basis of the analysis. Anticipated outcomes include visually rich maps depicting the citation networks and keyword co-occurrence patterns within the realm of mobile assistive technology for dyslexic children. We expect to identify key articles shaping the field, prominent clusters of research, and evolving trends. This bibliometric review aspires to contribute a panoramic view of the last decade's research landscape in mobile assistive technology for dyslexic children. The anticipated insights hold the potential to guide future research directions, technological innovations, and educational interventions, ultimately enhancing the support available to dyslexic children through mobile assistive technologies.