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Special Track—Preparing the workforce for
Industry 4.0: Robotics, automation, and ubiquitous
smart technologies in education
Zilu Liang
Faculty of Engineering
Kyoto University of Advanced Science
(KUAS)
Kyoto, Japan
liang.zilu@kuas.ac.jp
Usman Naeem
School of Electronic Engineering and
Computer Science
Queen Mary University of London
London, UK
u.naeem@qmul.ac.uk
Ian Piumarta
Faculty of Engineering
Kyoto University of Advanced Science
(KUAS)
Kyoto, Japan
ian.piumarta@kuas.ac.jp
Osamu Tabata
Faculty of Engineering
Kyoto University of Advanced Science
(KUAS)
Kyoto, Japan
tabata.osamu@kuas.ac.jp
Takuichi Nishimura
National Institute of Advanced
Industrial Science and Technology
(AIST)
Tokyo, Japan
taku@ni.aist.go.jp
Akihiro Kashihara
Graduate School of Informatics and
Engineering
The University of Electro-
Communications (UEC)
Tokyo, Japan
akihiro.kashihara@inf.uec.ac.jp
Abstract—This special track was focused on how robotics,
automation, and ubiquitous smart technologies can be leveraged
to enhance learning and to better prepare students for work and
life in an uncertain future. This special track provided a
platform for teachers, educational researchers, and technology
developers to share their insights on the trends and issues in
RAU learning technologies.
Keywords—learning technologies, robotics, artificial
intelligence (AI), mobile learning, blended learning, ubiquitous
computing, on-the-job training, learning design
I. INTRODUCTION AND AIMS OF THE SPECIAL TRACK
The Industry 4.0 is transforming the world with
technologies like autonomous robots, artificial intelligence,
and Internet of Things (IoT). In relation to this, technology
literacy has gradually become an important prerequisite in
employability. Hence, the higher education sector must win
the race between technology and education to prepare students
for career readiness. Robotics, Automation and Ubiquitous
(RAU) smart technologies are not only important subject
matters for STEM students, they can also be leveraged to
amplify and transform learning and teaching. Nevertheless,
many challenges remain as to how these technologies can
mend gaps in equity, engage students as unique individuals
and prepare them for work and life in an uncertain future. This
special track aims to provide a platform for teachers,
educational researchers, and technology developers to discuss
and share their insights on the trends and issues in the field of
RAU learning technologies.
II. DESCRIPTION OF THE TOPICS
This special track was open to contributions of rigorous
educational research, experience report as well as the
development and evaluation of specific technological tool
related to the following topics.
• Design principles, frameworks and standards for RAU
learning technologies
• Learning design for integrating RAU learning
technology (curriculum development, pedagogical
practice, classroom management, reflected teaching
and learning, etc.)
• Application of knowledge graph and ontology
engineering in automated learning and teaching
• Integrating RAU learning technologies and student
modelling for personalized education
• Human-computer interaction and user experience
study of RAU learning technologies
• Application of RAU learning technologies in on-the-
job training, casual learning and lifelong learning
• Application of RAU learning technologies in faculty
development
• Models, strategies, and standards for evaluating
educational benefits of RAU learning technologies
• Case studies and challenges of adopting RAU learning
technologies at scale
• Ethical, social and policy considerations related to
RAU learning technologies
TABLE I. STATISTICS OF PRESENTATIONS IN SPECIAL TRACK 3
Oral Presentation
Poster Presentation
Full papers
Short
papers
Short
papers
Work-in-progress
paper
3
5
1
2
This special track featured 11 presentations, including 3
full papers and 5 short papers for oral presentation, and 1 short
papers and 2 work-in-progress papers for poster presentation.
The presentations mainly covered three themes: teaching and
learning with RAU in online/blended learning, RAU for
programming education and RAU for workplace training.
A. Teaching and Learning with RAU in Online/Blended
Learning
The COVID-19 pandemic triggered unprecedented
adoption of online learning and blended learning. Four papers
presented studies on the teaching and learning experience with
RAU learning technologies in either fully online environment
or blended learning settings. Liang, et al. [1] investigated the
landscape of online education during the COVID-19
lockdown by analyzing 8 online courses provided at the Kyoto
University of Advanced Science in Japan and at the University
of Macau in China. They identified several benefits of online
learning over face-to-face learning, including self-paced
learning, increased opportunities to gain procedural
knowledge, diversified communication channels, and greater
opportunities for learning analytics. However, their findings
also highlighted several drawbacks of virtual classrooms,
including the lack of timely access to all information and the
lack of interpersonal interaction. The authors proposed several
recommendations to mitigate the drawbacks of online learning.
Liu, et al. [2] explored the experiences and requirements of
university students in online learning using an original
questionnaire. The findings highlighted the importance of
leveraging offline social communication channels to enhance
interaction and supporting physical activities during lock-
down. Liu, et al. [3] provided a survey study on the application
of blended learning in the entrepreneurship education in China.
Wu, et al. [4] presented the design of an intelligent
questionnaire for automatic assessment of educational
information level, i.e., the effective application of information
technology to teaching.
B. RAU for Programming Education
Two papers focused on enhancing programming education
using RAU learning technologies. Kellermayer, et al. [5]
investigated the influence of the Anki Vector robots on the
motivation of programming novices in a controlled laboratory
environment. The results demonstrated positive influence of
the Vector robots on the motivation of students. The authors
pointed out two directions for future studies: to repeat the
experiment on a larger cohort and to investigate the influence
of the Vector robots on experienced programmers. Kim [6]
presented a work-in-progress work on the curriculum design
of a course integrating programming and math, which targets
students from all majors.
C. RAU for Workplace Training
Four papers fall into this category, and three of these
contributions applied artificial intelligence to facilitate
professional training. Casillo, et al. [7] proposed a framework
for developing chatbots that can be used to assist new
employees during the learning phase of new tasks. To validate
the proposed framework, the authors developed a prototype
and conducted an evaluation experiment. The results show
that the chatbots helped to simplify the learning process. M.
De Santo, et al. [8] proposed a multilevel recommendation
system that creates customized training paths to help
employees build their careers. The system utilizes graph-
based tools such as Ontologies and Context Dimension Tree
to generate tailor-made work and training paths that is adapted
to the context, characteristics and the needs of the users. A
prototype was developed and the evaluation demonstrated an
overall accuracy of 75.2%. Along the same line of AI-
enhanced workplace training, Nishimura, et al. [9] presented
a work-in-progress project that aimed to enable knowledge
sharing within organizations for co-education of both novices
and team experts. The proposed method built on ontology
engineering and used knowledge graph to facilitate
knowledge collection and sharing within organizations. The
method has been used at an elderly care facility and in training
professional dance athletes. Related to workplace training,
Gui, et al. [10] presented a curriculum design framework for a
higher vocational education under the background of the
Industry 4.0. The framework focused on the integration of
work, learning and business and emphasized the collaboration
between colleges and enterprises.
In addition to the above three themes, one paper focused
on applying gamification to enhance learning in lower
secondary school [11].
III. INTENDED PARTICIPANTS
The special track co-chairs and program committee
attended the special track sessions to facilitate discussions.
The target audience of this special track included: (1) teachers
and instructors who are interested in using RAU learning
technologies in teaching, (2) educational researchers who are
interested in investigating the impact of RAU learning
technologies on learning, and (3) technology developers who
are interested in designing and developing new RAU learning
technologies. We hope this special track continues in the
TALE conference in the coming years.
ACKNOWLEDGMENT
This special track was organized in collaboration with the
Kyoto University of Advanced Science (KUAS), Japan.
REFERENCES
[1] Z. Liang, M. G. da Costa Jr, I. Piumarta, “Opportunities for improving
the learning/teaching experience in a virtual online environment,” in
Proc. of the IEEE TALE, 2020.
[2] Z. Liu, Z. Han, “Exploring trends of potential user experience of online
classroom on virtual platform for higher education during COVID-19
Epidemic: A Case in China,” in Proc. of the IEEE TALE, 2020.
[3] Z. Liu, C. Xiong, D. Xie, “Application of the online and offline blended
learning mode in innovation and entrepreneurship education,” in Proc.
of the IEEE TALE, 2020.
[4] C. Wu, D. Wu, M. Chen, et al., “Design method of intelligent
questionnaire for the automatic assessment of educational
informatization level,” in Proc. of the IEEE TALE, 2020.
[5] B. Kellermayer, D. Meyer, M. Stirzel, et al., “Raising motivation of
programming novices? Findings from a controlled laboratory
experiment using Anki Vector™ Robots,” in Proc. of the IEEE in Proc.
of the IEEE TALE, 2020.
[6] S. Kim, “Kepler vs Newton: Teaching programming and math to
almost all-majors in a single classroom,” in Proc. of the IEEE TALE,
2020.
[7] M. Casillo, F. Colace, L. Fabbri, et al., “Chatbot in Industry 4.0: an
approach for training new employees,” in Proc. of the IEEE TALE,
2020.
[8] M. De Santo, L. Fabbri, R. Mosca, et al., “A multilevel approach to
recommend working paths in Industry 4.0,” in Proc. of the IEEE TALE,
2020.
[9] T. Nishimura, Y. Yoshida, C. Oshiyama, et al., “Interweaving data and
knowledge for co-education augmentation,” in Proc. of the IEEE TALE,
2020.
[10] Y. Gui, B.. Fu, Q. Pan, et al., “Research on the talent training model of
“integrateion of work, study and business” in China’s higher vocational
education under the background of Industry 4.0,” in Proc. of the IEEE
TALE, 2020.
[11] M. G. da Costa Jr, C. Teng, J.-Z. Zheng, et al., “A simple gamification
approach of reflected electron diffraction phenomenon for lower
secondary school students,” in Proc. of the IEEE TALE, 2020.
