Internet of Things: Opportunities for Vocational
Education and Training
Presentation of the Pilot Project
Juha Vihervaara and Teemu Alapaholuoma
Pori Campus, Tampere University of Technology, Pohjoisranta 11, 28100 Pori, Finland
Keywords: Vocational Education, Internet of Things, Pilot.
Abstract: In the Internet of Things (IoT), machines and devices are equipped with sensors and Internet connections that
makes it possible to collect data and store this data to cloud services. In vocational education and training, the
stored data can be used to improve decision-making processes. With the help of this data, a teacher can also
get a more accurate picture of the current state of the education environment than before. IoT should be
integrated into vocational education and training because IoT will help to achieve important educational
objectives. IoT is able to promote students' preparation for working life, the safety of education environment,
self-directed learning, and effective learning. It can also improve the efficient use of educational resources.
In additional, IoT based solutions should be introduced so that students would have a vision of new types of
IoT skill requirements before they enter the labour market. In this paper, we presents IoT related aspects that
enable to meet the above-mentioned educational objectives. By implementing a pilot project, we aim to
concretise IoT’s possibilities in the education sector.
1 INTRODUCTION
The Internet of Things (IoT) (Whitmore et al. 2014)
allows enhanced interaction between the physical
world and computer-based systems. It makes possible
for devices, machines, people and other types of
physical objects to collect data and transmit this data
to cloud services. This data can be utilized in
decision-making processes to improve accuracy and
efficiency. It also makes sense to integrate IoT more
closely into vocational education and training
because IoT will help to achieve the important
objectives of vocational education. IoT is able to
promote students' preparation for working life, the
safety of education environment, self-directed
learning, and effective learning. It can also improve
the efficient use of educational resources.
Working life is going to be even more technical in
the future when IoT connects physical machines and
devices to the digital world. This means that machines
and devices are equipped with sensors that send state
information to the data warehouses of cloud services
over the Internet. This will change working life and
place new demands on employees' know-how.
Education should prepare future employees for these
changes. These changes should also be taken into
account in curriculums. Above all, vocational
education and training should introduce IoT based
solutions so that students would have a vision of new
types of IoT skill requirements before they enter the
labour market.
A safe school environment is an important issue
for students to make them feel safe (Young et al.
2016). However, there are some physical school
environments, especially in vocational education,
which set a number of challenges for a safe
schoolwork. There are often machines and equipment
whose correct and safe use can be challenging for
novice users. Therefore, it is important to ensure that
students can use these machines and equipment in a
safe and skilled way so that any kinds of physical
hazards can be avoided. New instruction and
oversight methods are always welcome to better
prepare new and young workers to occupational
safety. The results of the studies show that the young
participating in the labour market often have a higher
incidence of accidents and other negative work
effects then the workforce in general (Andersson et
al. 2014; Laberge et al. 2014; Schulte et al. 2005).
In recent years, there has been a growing interest
in self-directed learning (SDL). SDL promotes the
476
Vihervaara, J. and Alapaholuoma, T.
Internet of Things: Opportunities for Vocational Education and Training - Presentation of the Pilot Project.
DOI: 10.5220/0006353204760480
In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 476-480
ISBN: 978-989-758-239-4
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
role of the learner. The aim of SDL is that learning
happens when the individual learner takes ownership
for his own learning. It is important to cerate the
learning environment where SDL can concretise in a
proper way. For self-directed learning, there must be
tools to keep students on the right learning path.
Feedback is among the most important features of
SDL (Embo 2010). Students need feedback on the
development of their professional skills.
So, self-directed learning creates new challenging
requirements for learning technologies. Teo et al.
(2010) mentioned that technology may have direct
impact on self-directed learning because it has greatly
facilitated access both to information resources and to
online expertises. Related to self-directed learning,
being able to access a wide and unlimited range of
information to serve learning needs and interests are
important for students. This includes capturing,
storing, manipulating and displaying information as
well as making contact with fellow learners and
experts around the world. Jossberger et al. (2010)
noted that educational environments should be
adaptive to the learners' needs to achieve individual
learning. Furthermore, teachers should support
learners to become competent in the domain but also
guide them to become selfdirected learners. The
interaction between the student, teacher, and
environment is important.
New technical solutions are always welcome to
improve educational outcomes. Nowadays in
vocational education, teachers can use a wide range
of multi technical resources to enhance learning. For
example, simulators and video-based approaches can
be utilized. However, these technical resources must
be able to combine to an integrated adaptable solution
in order to promote effective learning. Lowman
(1996) expended the scope of effective teaching by
defining it as the process of selecting the materials,
resources, teaching strategies, and assignments that
have the greatest potential to contribute to student
learning.
Theall (1999) viewed effective teaching as a
complex, multidimensional, and dynamic process
affected by the individuals who involved in the
process as well as by the circumstances in the
classroom. IoT is a tool that, in many cases, enables
to integrate various technical tools to a workable
educational entity. Mohd (2016) mentioned that
students in vocational education viewed their
instructor as an effective educator when they are
accessible, fair in testing and grading, technology
competent, and manage class time. IoT can promote
education with respect to all these factors.
In Section 2, we bring out IoT related things that
enable to meet the above-mentioned challenges.
Section 3 introduces our pilot project related to the
IoT opportunities in education. Section 4 concludes
the paper.
2 IoT IN VOCATIONAL
EDUCATION
Due to IoT the information and communication
technology (ICT) penetrates deeper into vocational
education and training. ICT spreads to places where
it has not been utilized on a large scale in the past.
Trough IoT, the physical and digital world are linked
more closely to each other. Machines and things
equipped with sensors represent the physical world.
The digital world includes cloud services, data
analytics, intelligent algorithms, and user terminals.
The digital world also includes software-based
intelligent services and user friendly interfaces built
on top of the above-mentioned components.
IoT enables that data can flow from the physical
world to the digital world through data networks.
With the help of this data, a person can get a more
accurate picture of the current state of machines and
systems. As a result, control operations and decision-
making processes will become more data-driven
because the collected data enables fast and clever
conclusions for control functions. In addition, the
actions of control functions can be executed more
quickly because data can also be transmitted from the
digital world to the actuators of the physical world in
the form of control commands. IoT will make the
machines and services of vocational education more
intelligent in the future. Of course, it can be said that
IoT is already integrated into education on a small
scale (Pruet et al. 2015; Selinger et al. 2013; Wang
2010).
2.1 Students' Preparation for Working
Life
Many countries and companies have identified IoT as
one of the key factors when trying to guarantee the
future competitiveness of the industry. Vocational
education institutions play a key role in these
competitiveness strategies because these institutions
educate future workers. Therefore, IoT must be
integrated into vocational education more widely.
Relating to the machinery and equipment, ICT
technology is becoming more and more a tool of
general use. Younger generations even expect that
ICT technology will facilitate working life (Jones et
Internet of Things: Opportunities for Vocational Education and Training - Presentation of the Pilot Project
477
al. 2010). In the future, the software will play the
more important role everywhere compared to the
physical iron. A machine or a thing equipped with the
sophisticated software will observe its condition and
its environment better than before. In addition,
devices will send information to cloud services that
allows the remote monitoring of devices, and even the
remote use of devices. Remote monitoring has been
commonplace at least the past twenty years.
However, IoT is changing remote monitoring because
cloud services and data analytics come into play
(Mohammed et al. 2014). These changes set new
requirements for future employees.
2.2 Safe Education Environment
ICT and IoT solutions often improve the safety of
working environment (Carbonari et al. 2011). Related
to vocational education and training, the collected
data can be analysed to detect such wrong ways of
using the machine, which may create a hazardous
situation. After the findings, the student can be guided
relating to the safe work procedures. Data analytics
can be used to detect data anomalies compared to the
data representing the normal operating machine
(Wang et al. 2015). This allows predict machine
failures that can be dangerous for students. The
machine can be stopped before a hazardous situation.
The teacher can also define the students
authorized to use a specific device based on the
required skill level. The student must always log in
the device, for example, using fingerprint
authentication. Safety is also increased by the fact that
the machines equipped with the right kind of sensors
are able to recognize human limbs, and therefore,
these machines do not pose a danger to humans. On
the contrary, the intelligent machines are capable to
prevent the dangerous situations that careless people
cause themselves. For example, the machine can
automatically stop the movement of the actuator
when a dangerous situation arises.
If an accident occurs for some reason, the
collected data can be used to analyse the accident.
The course of events can be analysed afterwards, for
example, by using the stored video. The stored data
may indicate that, for example, the student has had
sufficient capacity for independent use of the device.
2.3 Self-directed Learning
IoT is able to promote the independent work of
students. Intelligent devices are able to guide the
students and give feedback to students. IoT enables
advanced remote monitoring which enables the
teacher to monitor the work of several students at the
same time. In real time, as well as afterwards, it is
possible to find out who are using a certain device.
With the help of collected data and advanced data
analytics, it is possible to evaluate how well the
students have performed with their exercise works
independently. For the reason that this evaluation
may be carried out automatically by an impartial
system, IoT is able to promote the equal treatment of
students. The collected data also enables the
automatic documentation of students’ progresses.
2.4 Efficient Use of Education
Resources
With the help of IoT, the teacher is able to see the
current state of the whole education environment
better than before. Sensors will be the extra eyes and
ears of the teacher that help to make the right
decisions at the right time. Efficiency improves when
information flows seamlessly from place to place. All
the information can be displayed in a centralized user
interface. Couple this with the results of real-time
data analytics, teachers are capable of faster and
better quality decision-making than without the help
of IoT. So, it possible to improve the quality of
teaching. IoT can offer autonomously working
solutions for teaching. Teachers will benefit from this
because they can manage better with their daily
routines. Intelligent systems assist to direct the
teaching to the right direction.
3 PILOT PROJECT
By implementing a pilot project, we aim to concretise
the above-mentioned possibilities. Figure 1 presents
the implementation solution of our pilot project. The
solution consists of three parts, which are the training
area, the cloud service, and the user interface
components of the teacher. Data flows from one part
to another over the Internet. Next, the logical
operation of the system is described and technical
implementation issues are mainly neglected.
3.1 Training Area
The training area consists of a machine, video camera
and control unit. If IoT is considered, the control unit
has an important role. In the pilot project, the control
unit is implemented using the Raspberry PI board
which is a credit card-sized single-board computer
(Raspberry Pi Foundation 2017). It is widely used in
IoT pilots.
CSEDU 2017 - 9th International Conference on Computer Supported Education
478
Figure 1: Three parts of the pilot project.
The control unit includes a user authentication unit so
that the machine can be used only by the students
having the required skills and knowledge. For
example, the power supply of the machine is not
switched on until the student’s access right is
authenticated. Such switching on can be done
wirelessly, for example, by using the Z-wave
technology (Gomez and Paradells, 2010). We use the
NFC reader for user authentication so that each
student has his own personal NFC tag. Another even
more trustworthy technique for this use would be the
fingerprint authentication. Immediately after
permitting the use of the machine, the control unit
switches on the video surveillance of the training
area.
A control unit button allows the student to send an
invitation to the teacher when the student needs
guidance. Another control unit button allows the
student to start the instruction and safety video of the
machine. With the help of this video, the student can
independently recall the issues related to the safe use
of the machine. Xeroulis et al. (2007) mentioned that
computer-based video instructions are able to serve as
a useful pedagogic adjunct for basic skills training.
3.2 Cloud Service
The system-related information is stored into the
cloud service. There are stored the instruction and
safety video, access rights and guidance requests. In
addition, all statistical data including the videos
relating to the video surveillance is stored there. All
this information not necessarily reside on the same
physical server. For example, videos may be stored
on different servers than other information.
3.3 Teacher’s Components
The user interface components offer a view of the
system data. With the help of the mobile application
and the web browser, the teacher can monitor the
training area in real-time. The guidance requests sent
by the students can also be viewed through these
components. They also offer a view of the statistical
data. For example, based on the stored data, the
teacher can examine how long each student has used
the machine. The stored videos can also be viewed
afterwards.
4 CONCLUSIONS
This paper has emphasized that IoT is capable of
improving vocational education and training in many
ways. There is also a need to integrate IoT into the
learning process because young people are
accustomed to find answers to open problems with
the help of digital devices. This paper points out that
IoT solutions are suitable for teaching the basic
technical skills and safety issues. When IoT is
incorporated into education in a reasonable way, it
can serve as a useful pedagogic adjunct for skills
training.
Companies have already taken the first steps of
IoT. It is expected that education will follow the
business with a slight delay after the IoT solutions
will stabilize and prices will fall. The changes will not
happen overnight. IoT's utilization can proceed in
small steps. The solutions implemented in a hurry and
their technical problems can be factors that can give
students a bad impression about IoT solutions. Once
the first steps have been taken, the workload of
offering it to additional devices is relatively small. In
many cases, it is possible that the manufacturer of
some technical device produces the IoT components
of that device. In addition, we have to remember that
IoT is only one tool among other tools to improve
educational activities. It cannot replace teachers but it
Internet of Things: Opportunities for Vocational Education and Training - Presentation of the Pilot Project
479
will certainly pose challenges to the teachers’
professional skills.
REFERENCES
Andersson I-M and et al. 2014. Knowledge and Experiences
of Risks among Pupils in Vocational Education. Safety
and Health at Work, (referred in 9.9.2016)
http://dx.doi.org/10.1016/j.shaw.2014.06.002.
Carbonari, A., Giretti, A. and Naticchia, B. 2011. A
proactive system for real-time safety management in
construction sites. Automation in Construction, 20(6),
pp. 686-698.
Embo, M,, Driessen, E. Valcke, M. and Van der Vleuten C.
2010. Assessment and feedback to facilitate
selfdirected learning in clinical practice of midwifery
students. Medical Teacher, 32 (7), pp 263–269.
Gomez, C. and Paradells, J. 2010. Wireless home
automation networks: A survey of architectures and
technologies. IEEE Communications Magazine, 48(6),
pp. 92-101.
Jones, C., Ramanau, R., Cross, S.J., and Healing, G. 2010.
Net generation or digital natives: Is there a distinct new
generation entering university?. Computers &
Education, 54 (3), pp 722-732.
Jossberger, H., Brand-Gruwel, S., Boshuizen, H. and van de
Wiel, M. 2010. The challenge of self-directed and self-
regulated learning in vocational education: a
theoretical analysis and synthesis of requirements.
Journal of Vocational Education & Training, 62 (4),
415-440.
Laberge M., MacEachen E. and Calvet B. 2014. Why are
occupational health and safety training approaches not
effective? Understanding young worker learning
processes using an ergonomic lens. Safety Science,
Vol. 68, October 2014, Pages 250–257.
Lowman, J. 1996. Characteristics of exemplary teachers.
New Directions for Teaching and Learning, 65, pp. 33-
40.
Mohammed, J., Thakral, A., Ocneanu, A., Jones, C., Lung,
C. and Adler, A. 2014. Internet of Things: Remote
patient monitoring using web services and cloud
computing. Proceedings of the 2014 IEEE International
Conference on Internet of Things (iThings), and IEEE
Green Computing and Communications (GreenCom),
and IEEE Cyber, Physical and Social Computing
(CPSCom), pp. 256–263.
Mohd, A. 2016. Effective Teaching Characteristics in
Vocational Education. A dissertation submitted to the
Graduate Faculty of Auburn University in partial
fulfillment of the requirements for the Degree of Doctor
of Philosophy. 173 Pages.
Pruet, P., Ang, C. S., Farzin, D. and Chaiwut, N. 2015.
Exploring the Internet of “Educational Things”(IoET)
in rural underprivileged areas. In Electrical
Engineering/Electronics, Computer,
Telecommunications and Information Technology
(ECTI-CON), pp. 1-5.
Raspberry Pi Foundation. 2017. Teach, Learn and Make
with Raspberry Pi. (referred in 2.1.2017) https://
www.raspberrypi.org/.
Teo, T., Tan, S. C., Lee, C. B., Chai, C. S., Koh, J. H. L.,
Chen, W. L., and Cheah, H. M. 2010. The self-directed
learning with technology scale (SDLTS) for young
students: an initial development and validation.
Computers & Education, 55 (4), pp. 1764–1771.
Schulte, P. and et al. 2005. Integrating occupational safety
and health information into vocational and technical
education and other workforce preparation programs.
American Journal of Public Health, 95(3), pp. 404-410.
Selinger, M., Sepulveda, A. and Buchan, J. 2013. Education
and the Internet of Everything.
http://www.cisco.com/c/dam/en_us/solutions/industrie
s/docs/education/education_internet.pdf.
Theall, M. 1999. New directions for theory and research on
teaching: A review of the past twenty years. New
Directions for Teaching and Learning, Vol. 80, pp. 29-
52.
Wang, C., Vo, H. T. and Ni, P. 2015. An IoT Application
for Fault Diagnosis and Prediction. In 2015 IEEE
International Conference on Data Science and Data
Intensive Systems, pp. 726-731.
Wang, Y. 2010. English interactive teaching model which
based upon Internet of Things. In Computer
Application and System Modeling (ICCASM), Vol. 13,
pp. 587-692.
Whitmore, A., Agarwal, A. and Da Xu, L. 2014. The
Internet of Things-A survey of topics and trends.
Information Systems Frontiers, pp. 1-14.
Xeroulis, G., Park, J., Moulton, C-A., Reznick, R.,
LeBlanc, V. and Dubrowski, A. 2007. Teaching
suturing and knot-tying skills to medical students: a
randomised controlled study comparing computer-
based video instruction and (concurrent and summary)
expert feedback. Surgery, 141(4), pp. 442–449.
Young, J., Williamson, M. and Egan, T. 2016. Students’
reflections on the relationships between safe learning
environments, learning challenge and positive
experiences of learning in a simulated GP clinic.
Advances in Health Sciences Education, 21 (1), pp 63–
77.
CSEDU 2017 - 9th International Conference on Computer Supported Education
480