Prof. James Slotta
University of Toronto, Canada
Jim Slotta is Professor and President's Chair in Knowledge Technologies at the Ontario Institute for Studies in Education, University of Toronto. Since 2005, he
has led a team of students, designers and developers to
investigate new models of collaborative and collective inquiry in K-12 science, including powerful new roles for technology enhanced learning environments.
Building on a background in physics and cognitive psychology, he has developed a pedagogical model known as Knowledge Community and Inquiry, in which students
explore investigate a phenomenon or issue within a carefully scripted and orchestrated sequence of learning activities. Their inquiry is situated within smart
classrooms and distributed learning environments, featuring user-contributed content, aggregated and emergent forms of knowledge, and a variety of scaffolds for
the orchestration of individual, small group, and whole class activities.
From 2006 - 2011, Slotta served as Canada Research Chair in Education and Technology. He has served as PI or co-I on more than 30 funded projects
totalling more than $30 Million, supervised 20 doctoral and post-doctoral researchers, and co-directed the NSF-funded Center for Technology-Enhanced Learning (2003-2008).
Prof. Chee-Kit Looi
Education University of Hong Kong, Hong Kong
& National Institute of Education, Nanyang Technological University, Singapore
Professor Looi Chee Kit is Research Chair Professor in the Education
University of Hong Kong. He is Emeritus Professor at National
Institute of Education (NIE) of Nanyang Technological University
(NTU).
During his stint in Singapore, Professor Looi Chee-Kit was with the
Learning Sciences and Assessment Academic Group in the National
Institute of Education (NIE) of Nanyang Technological University
(NTU). He was the Co-Director of the Center for Research and
Development into Learning (CRDL) of NIE, NTU.
Professor Looi’s research focuses on learning sciences,
computer-supported collaborative learning, mobile learning, AI &
Education, and computational thinking. Professor Looi is a Fellow of
the International Society of Learning Sciences, and a Fellow of the
Asia-Pacific Society for Computers in Education. He was the founding
member of the Global Chinese Society of Computers in Education, and
served as its President (2017-2019). He was a member of the Core
Expert Group for developing the framework for assessing
Collaborative Problem Solving in OECD PISA 2015, and a special
consultant to the Smart Learning Institute of Beijing Normal
University. Since 2021, Professor Looi is listed in the annual list
of the Top 2% of Career Scientists Worldwide, compiled by Stanford
University and Elsevier.
Prof. Longkai Wu
Central China Normal University, China
Longkai Wu is professor and Ph.D. supervisor at Faculty of
Artificial Intelligence in Education at Central China Normal
University, National Engineering Research Center for
E-Learning, and MOE Educational Informatization Strategy
Research Base (Central China). His primary research
interests include artificial intelligence in education,
virtual reality and augmented reality, formal and informal
learning, STEM, inquiry-based learning, and educational
information technology and policy. He has led tens of
educational research projects and is currently the lead
principal investigator of a key project funded by the
National Social Science Fund of China. He has published over
100 articles and has delivered talks across 14 countries and
regions.
Prof. Dave Towey
University of Nottingham Ningbo China, China
Professor Dave Towey received the BA and MA degrees in
computer science, linguistics, and languages from the University of
Dublin, Trinity College, Ireland; the MEd degree in education
leadership from the University of Bristol, U.K.; and the PhD degree
in computer science from The University of Hong Kong, Hong Kong
S.A.R., China. He has been with University of Nottingham Ningbo
China (UNNC) since 2013, where he serves as the associate dean for
education and student experience, the deputy head of the School of
Computer Science, and as deputy director of the International
Doctoral Innovation Centre. His current research interests include
software testing (especially adaptive random testing, for which he
was amongst the earliest researchers who established the field, and
metamorphic testing) and technology-enhanced education. He
co-founded the ICSE International Workshop on Metamorphic Testing in
2016. He is a fellow of the HEA, and a senior member of the ACM and
IEEE.
Speech Title" Moving beyond the Oracle: Doing and Teaching Software Engineering Differently with Metamorphic Thinking"
Abstract: Drawing on the
traditions of Autoethnography and reflective practice, this paper
explores the experiences and reflections of one of the longest
serving foreign academics in China's Sino-foreign higher education
system. Taking software engineering, and in particular software
testing, as the main topic for examination, the paper explores how
recent advances in computing technology --- including machine
learning, generative AI, big data, and others --- have exposed
inadequacies in the software quality assurance (SQA) of modern
software. SQA encompasses many activities, including software
testing, where the software under test (the SUT) is executed with
test inputs, and its output or behavior is checked for errors. The
mechanism for this checking is called an oracle. The lack of an
oracle, or a practical inability to use it, is called the oracle
problem. Software facing the oracle problem has been called
untestable: This is a major challenge for SQA, making many
traditional software testing methods unusable. Metamorphic testing
(MT), and its associated concepts of metamorphic exploration and
metamorphic thinking, has provided a new perspective on software
testing: Instead of focusing on individual SUT correctness, it looks
at relationships and relations that should hold if the SUT has been
properly implemented. A violation of such relations indicates a flaw
in the SUT. Using these, a new and effective approach for testing
software has become applicable for untestable software. In addition
to introducing the principles of MT, and some of its significant
success, this paper also explores application of metamorphic
exploration and metamorphic thinking more generally to the teaching
and learning of software engineering, and more broadly in other
educational settings.