EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
Unfolding the Curriculum: Physical Computing,
Computational Thinking and Computational Experiment
in STEM’s Transdisciplinary Approach
Sarantos Psycharis, Konstantinos Kalovrektis, Eva Sakellaridi, Konstantinos Korres, and Dimitrios
Mastorodimos
Abstract—The aim of the present article is to analyze the
relation of physical computing with the computational thinking
dimensions and the transdisciplinary approach of STEM
epistemology in inquiry-based learning environments, when
the methodology of the computational experiment is
implemented. We argue that computational science and
computational experiment can be applied in connection with
STEM epistemology, when physical computing activities are
embedded in the curriculum for Higher Education students. In
order to implement this connection, we present software
applications that combine algorithms and physical computing.
We believe that engaging students through their existing
STEM courses in physical computing - in the form of the
computational experiment methodology- is a strategy that is
much more likely to succeed in increasing the interest and
appeal of STEM epistemology. Different learning modules
were designed, which covered the combination of easy java
simulations (Ejs) with Arduino and Raspberry pi.
Index Terms—Computational Experiment; Computational
Thinking; STEM Epistemology; Physical Computing.
I. INTRODUCTION
A. Computational Science (CS)
Computational Science (CS) is the integration of
Mathematics, Computer Science and any other discipline to
explore “real world” problems. It is defined as a
multidisciplinary activity, which brings together concepts
from a variety of cognitive subjects [12] and is considered to
be part of the Computational Science-Engineering (CSE)
community. CS helps learners solve a STEM problem using
computational models and this includes tasks like:
formulating the problem in a way proper for development of
models of simulations, selecting an efficient algorithm;
collecting numerical data; analyzing the data obtained;
discovering the solution of the problem. One of the essential
components of CS is the transformation of a real
phenomenon to an abstract model and its execution as a
computational model. This leads us to the notion of the
computational experiment (CE), where the model and its
Published on February, 2018
S. Psycharis is Professor of ASPETE and Visiting Professor at
Huddersfield University (e-mail: spsycharis@gmail.com).
K. Kalovrektis is a School Teacher (e-mail: kkalovr@gmail.com).
E. Sakellaridi is a Doctoral Researcher at UCL, Institute of Education
(e-mail: evangelia.sakellaridi.15@ucl.ac.uk).
K.
Korres
is
a
School
Teacher
(e-mail:
korres.konstantinos@gmail.com).
D.
Mastorodimos
is
a
School
Teacher
(e-mail:
mastorodimos@gmail.com)
DOI: http://dx.doi.org/10.24018/ejers.2018.0.CIE.639
corresponding simulation “substitute” the “hands-on”
experiment [18], [19]. According to Landau et.al. [12], CS
suggests the following steps for problem–solving a.
Definition of the Problem (from science/real world); b.
Modeling the problem (introduction of the mathematical
relations between selected variables); c. Selection of the
Simulation Method (determination of time dependence of
the state variables, selection of discrete, continuous or
stochastic processes); d. Creation of the algorithm based on
selected numerical analysis methods; e. Implementation of
the algorithm in source code (using computer languages);
and f. Evaluation of the results and comparison with real
data.. Later on, when we will discuss computational thinking
(CT), it will be quite apparent that CS shares many
commonalities with CT and may serve as the background
platform to implement didactic models that include the
dimensions of CT and STEM epistemology.
B. Computational Thinking (CT)
Jeanette Wing [24] introduced the term 'computational
thinking' (CT) in an article published in Communications of
the ACM. CT involves solving authentic problems,
designing systems and understanding human behavior by
drawing on the concepts fundamental to computer science.
Despite this extensive interest, successful CT integration in
education still faces unresolved issues and challenges [9].
According to the literature, CT includes: abstraction,
algorithmic thinking, decomposition, debugging, pattern
recognition and generalization [1], [3].
C. The inquiry-based teaching and learning approach
Inquiry-based learning is considered as a didactic model
for improving the teaching and learning of STEM
disciplines [6] and can be defined as the process of
identifying problems, reviewing experiments, selecting
alternatives,
designing
investigations,
developing
conjectures, searching for data, developing models,
communicating with peers and constructing consistent
arguments [5]. Bell et al. [4], identified nine main science
inquiry processes, that could be used in inquiry-based
STEM disciplines, namely: orienting and asking questions;
generating hypotheses; planning; investigating; analyzing
and interpreting; exploring and creating models; evaluating
and concluding; communicating; predicting. The nine
inquiry tools of Bell et al. [4] are connected to the essential
features of Inquiry [2], namely: Question (Learner engages
in scientifically oriented questions); Evidence (Learner
gives priority to data collection); Analysis (Learner analyses
the data to form evidence); Explain (Learner develops
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EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
explanations from evidence); Connect (Learner connects
explanations to scientific knowledge); Communicate
(Learner communicates and justifies explanations); and
Reflection (Learner engages in metacognitive experiences).
D. The Computational Experiment (CE) approach
combined with Inquiry-Based teaching and learning
approach
In order to effectively implement the inquiry-based
learning as a didactic model, Klahr & Dunbar [11],
introduced two “spaces”, the hypothesis and the
experimental spaces. Psycharis [20] added one more space,
the “prediction space”, in order to introduce the process of
modeling and the comparison of data produced by the model
with real data taken from a simulation or from a physical
computing activity. In the “prediction space”, the CS
methodology is implemented through the development of
models of simulations that include all or some of the
dimensions of the “Computational Thinking (CT)”.
According to Psycharis [18],[19]), the three spaces of the CS
methodology should include dimensions from CT, namely:
logically collecting, organizing and analyzing data;
representation of data in forms suitable for analysis and
exploration , development of abstract models, creation of
simulations; and algorithmic thinking (a series of ordered
steps) as they are also suggested by the International Society
for Technology in Education (ISTE) and the Computer
Science
Teachers
Association
(CTSA)
(http://www.iste.org/docs/ct-documents/computationalthinking-operational-definition-flyer.pdf?sfvrsn=2,
Last
Access, 31 May, 2016).
In this context, the three spaces of the CS methodology
include:
1) The hypotheses space, where the students –usually
under the guidance of the teacher-state the
hypotheses of the problem to be studied, as well as
the variables included in the problem and the possible
relations between the variables.
2) The experimental space, which includes the
“numerical” model and the method of simulation for
the problems under study. In this space, the learners
are engaged in the scientific method writing their
models, according to the variables selected and the
interaction laws (e.g. from Physics) that govern the
phenomenon. In this space, students collect the data
from their model and analyze them; while they
attempt to connect their explanations with the theory
they have been taught.
3) The prediction space, where the results, conclusions
or solutions formulated in the experimental space, are
compared with the analytical (Mathematical)
solution, as well as with data from the real world.
Students would also make logical arguments about
the generalization of their results and whether the
results have been impacted by their hypothesis and
the variables they have chosen. Generalization is
closely linked to one of the dimensions of CT.
In Table 1I, we propose an interrelation between the
spaces of the CE and the features and the tools of inquiry.
E. STEM epistemology
STEM
methodology
follows
DOI: http://dx.doi.org/10.24018/ejers.2018.0.CIE.639
the
so-called
transdisciplinary approach, which focus on the “integrated”
approach to teach the four disciplines included in the STEM
cognitive areas. According to Kelley & Knowles [10],
STEM methodology includes: Situated Learning; the
Engineering Design and Making; the Scientific Inquiry and
the Mathematical Thinking and Logical Thinking, as an
integrated system. A pedagogy referred to as “Purposeful
Design and Inquiry” (PD&I), is considered as an
important component of integrative STEM education. PD&I
pedagogy combines technological design and making with
the inquiry based learning, engaging students in inquiry,
situated in the context of the problem-solving process [22].
TABLE I: THE INTERRELATION OF THE CE SPACES, THE INQUIRY METHOD FEATURES AND THE INQUIRY TOOLS
Spaces of the
Essential Features
Computational
Inquiry tools [4]
of Inquiry [2])
Experiment [20]
Orienting and asking
questions; generating
Hypotheses space
Question
hypotheses
Planning-Investigating
Evidence Analyze
Experimental space
Explain
Prediction Space
Connect
Communicate
Analysis and
interpretation
Modeling
Conclusion-EvaluationPrediction
F. Physical Computing
Recently, education researchers have adapted the term
physical computing and they are now using it in a wider
meaning. Specifically, they consider physical computing as
a way to use computers to collect data received by the
physical/real world [13]. Physical computing can be
implemented in computer science by using it in order to
teach computer science concepts [16]. Physical computing is
considered as a proper tool in order to combine digital
elements with the real world, as it develops a
communication between the physical world and the digital
world of the computer [23]. Physical computing takes the
computational concepts into the real phenomenon so that the
student can use them in an authentic environment Physical
computing activities are strongly connected to the
dimensions of CT, namely: abstraction; algorithmic
thinking; automation; decomposition; debugging; and
generalization.
G. Physical Computing: unplugged, making, tinkering and
remixing
According to Namukasa et al. [14], there are four
pedagogical stages of learning in order to think
computationally when learners are engaged in physical
computing,
namely: (1) unplugged; (2) making; (3)
tinkering; and (4) remixing (or “hacking”). Unplugged
activities can be implemented without the use of computers
and can be used to engage in CT dimensions (e.g.
abstraction), as well as to enhance subject knowledge, often
embedding and augmenting computer science concepts into
the curriculum [8]. These constructivist activities are often
kinesthetic in nature and make abstract concepts both
tangible and visible [8].
Making is a technical term used to refer to activities of
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EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
individuals or groups of people who, inspired by
technology, “work” with things, make things, take things
apart and want to develop things that solve problems. In
making, they engage in practices, such as prototyping and
testing products, methodologies used by engineers. Digital
making can be used to engage students in CT dimensions,
such as sequencing, recursion, decomposition and
debugging [17].
When mentioning about tinkering, Papert [15] described
learning as consisting of building up a set of materials and
tools that one can handle and manipulate. The main idea of
tinkering is to encourage students to use materials as tools to
represent/implement the dimensions of CT. Commonly and
widely used and known computer programming software for
children and novices, useful for tinkering, are: the Scratch
(https://scratch.mit.edu/),
Scratch
for
Arduino
(http://s4a.cat/), Ardublock (http://blog.ardublock.com/) and
Easy Java Simulations (Ejs). Arduino and Raspberry pi
platforms are also considered as essential tools to implement
tinkering. These can enable students to easily see the
connection between changes in the program and the
corresponding changes in the data received from the
physical model/real world. Activities can be created that
need the implementation of an algorithm and the
development of programming in order to guide a robot
through an obstacle course or programming the Arduino or
Raspberry pi to reveal specific patterns.
TABLE II: A MODEL FOR THE CONNECTION BETWEEN CT, CE AND
PHYSICAL COMPUTING
Spaces of the
Essential Features of
Inquiry tools
Computational
Inquiry –
Experiment
Physical ComputingDimensions of CT
Hypotheses space
Essential Features of
Orienting
Inquiry
and asking
Question
questions;
Physical Computing
generating
Unplugged activities
hypotheses
Dimensions of CT
Abstraction,
decomposition
Lastly, Remixing involves proficiency in examining a
source code with a critical eye, as well as modifying,
debugging and manipulating the code to adjust it to new
situations. When students are engaged in coding, they
develop their creative thinking by proposing new solutions
and alternatives. Scratch can be used for remixing, since,
according to Resnick et al. [21], community members are
constantly borrowing, adapting and building on one
another's ideas, images, and software programs. Physical
computing covers the design, making and implementation of
interactive objects and allows students to develop concrete
products and artefacts of the real world, which are in
alignment with the dimensions of CT. Physical computing
can be used in STEM education as an educational activity,
grounded in students’ interests and creativity.
H. Computational Science, Inquiry-based approach,
Computational Thinking and Physical Computing in STEM
epistemology. An integrated model
We will now propose an integration of the CE, the
physical computing, the STEM epistemology and the
DOI: http://dx.doi.org/10.24018/ejers.2018.0.CIE.639
dimensions of CT and present our results in a form of a
Table (Table II).
II. MATERIALS AND DIDACTIC ACTIVITIES
A. Software and Materials used
Here, we will present some experiments-activities that
connect the CE, CT and Physical computing using in some
examples
the
software
Ejs
(http://www.um.es/fem/EjsWiki/). Easy Java simulation
(Ejs) software does not demand knowledge of the
programming language Java and its interface is not difficult
for use, since it resembles the interface the students have
met in traditional lectures embedded with specific tools to
write
down
mathematical
expressions.
Arduino
(https://www.arduino.cc/) is an open hardware platform that
is becoming increasingly popular within the education
community. The creators of Arduino designed a very easyto-use platform and due to its open-source nature, it is
supported by a massive user community who share their
ideas, projects and solutions. Raspberry pi is an open
hardware platform that can be used to control physical
objects, while it has the advantage to operate as computer
and not only as controller (as Arduino does).
B. Didactic Activities
1) Visualization of Sorting algorithms (Activity 1)
Given that students face difficulties with the concepts of
the “variable” and the algorithm, researchers have turned
their focus on the “use” of CT to resolve such proplems. CT
poses an important question to researchers: What are the
proper ways to teach fundamental computing concepts to
students? Visualization is suggested as one way of
supporting student learning [7]. In this activity, we will use
Ejs to help students understand the sort algorithms using the
visualization capacity provided by the “view” element of
Ejs.
You
can
visit
the
link
(http://www.opendiscoveryspace.eu/edu-object/algorithmostaxinomisis-fysalidas-me-ti-hrisi-ejss-848384, Last Access
15 June 2017) to explore and download the algorithm and
the source code for the visualization of the bubble sort
algorithm. This application was developed by one of the
authors (Psycharis) and an MSc student (Mastorodimos
Dimitrios). Initially, we presented the video to students (as
an unplugged activity) about Hungarian dance and its
connection to bubble sort algorithms in order for them to
make hypotheses about the way the sorting algorithms work.
This activity also enhances the abstraction dimension of
students. At the next phase (the experimental phase), we
developed the source code in Ejs, in order to connect Ejs
with Arduino. The algorithm and part of the source code
developed are presented below. The reader can find the
algorithm and the source code at this link:
http://www.opendiscoveryspace.eu/en/eduobject/algorithmos-taxinomisis-fysalidas-me-ti-hrisi-ejss848384.
During the experimental phase of the CE, students used
the essentials features of inquiry-based teaching approach
(evidence, analysis, explanation), the algorithmic thinking of
CT and they participated in the development of the source
code. For the physical computing part, they had to classify
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EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
in order 20 CDs and they tried to reproduce the steps of the
algorithm, in order to sort the CDs according to the
algorithm. At the prediction phase, they communicated their
result and they tried to find examples to generalize their
code. They proposed examples of sorting from real life, like
the Hungarian dance (Fig. 1).
Fig. 3. Control of the LED from the keyboard using
Scratch4A(http://s4a.cat/)
Fig. 4. A simple video game using Raspberry pi
Fig. 1. The running of the model. During the running colors were changed
according to the comparison between them
2) Control of LED using Ejs and Arduino (Activity 2)
In this activity, students controlled a LED using Ejs (Fig.
2).
Fig. 5. An unplugged activity
Fig. 2. Connection of Ejs with Arduino
In this activity, students were engaged in many
dimensions of physical computing (i.e. design/making) and
in almost all dimensions of CT. For example, they used the
CT concept of abstraction when they thought about the
possibility to switch on a LED remotely, the design of the
circuit; the algorithmic thinking and the generalization (for
example, how we can extend this simple example in order to
control traffic lights). We also presented the same
application using the Scratch for Arduino, where the control
of the LED is done from the keyboard (Fig. 3).
1) Activity 3
In this application, we used the Raspberry pi in order to
control the motion of four graphical elements using Scratch
(Fig. 4).
2) Activity 4
This activity is an unplugged activity. Students were
asked to construct (design and make) the following robotic
arm (Fig. 5).
DOI: http://dx.doi.org/10.24018/ejers.2018.0.CIE.639
During the activity, students had to be engaged in the
features of the inquiry-based teaching and learning approach
and the physical computing activities (design/make).
Students also had the chance to be engaged in CT
dimensions, as they had to think in abstraction for the
different parts of the arm, to decompose the system and use
logical reasoning to explain its motion.
III. PRELIMINARY RESULTS -DISCUSSION
We focused on physical computing, which involves
building interactive physical systems, which can sense and
respond to the measurements received from the Analog
world. We then developed applications created by
Raspberry Pi and Arduino and controlled by Ejs or Scratch.
This work proposes a model that combines the phases of the
CE with the dimensions of CT and the dimensions of
physical computing. We implemented –as a preliminary
work- the above activities to postgraduate students, who
study for their MSc degree in STEM, and prospective
trainee teachers. We followed the qualitative approach and
in the future, we aim to extend our research to a bigger
sample with quantitative tools like a questionnaire for CT.
After the instructional intervention, eight students were
selected for semi-structured interviews. The results showed
that the intervention based on the visualization method
resulted in significantly better acquisition of sorting
concepts. The qualitative data analysis indicated that
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EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
students constructed proper abstractions through their
engagement in visualization algorithmic activities, mainly
due to Activity 1. All students expressed the view that
framing the activities in the three phases of the
computational experiment and organizing the tasks in each
phase for CT and physical computing, made them more
organized and they considered that this connection was also
helpful in their preparation for organizing a lesson plan.
Students felt that in the experimental phase of the CE, they
had a lot of degrees of freedom to develop the dimensions of
CT (mainly the logical reasoning, he development of
algorithm and decomposition) and also that they could
propose the design of artefacts. Some students expressed the
view that sometimes unplugged activities can form a good
guideline to understand the physical process before they try
to transfer the problem in computing. All students
considered the above activities as a good starting point and
they continued their work developing new applications
connecting Scratch or Ejs with Arduino and Raspberry. For
example, one such activity can be found at:
http://portal.opendiscoveryspace.eu/edu-object/heattransfer-conduction-monitoring-tool-844601, which studies
the transfer of heat using Arduino and Ejs. Another artifact
was based on the Balak Ram theorem (http://page.mi.fuberlin.de/bhrnds/publ_papers/behrends_humble.pdf).
The authors of this study argue that students’ active
engagement in inquiry-based activities that combine the CT
and physical computing dimensions in a CE setting, could
help them increase their self-efficacy and internal motives
for STEM epistemology (work in progress). Another
research in progress is related to a quantitative research of:
“What effects does physical computing have on the
dimensions of computational thinking, when the
transdisciplinary approach is implemented in inquiry based
learning environments?”
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Sarantos Psycharis was born in Canada , 1961, He
holds a PhD in Computational Physics (University of
Glasgow,1989),
Msc
in
Information
Technology(University of Athens,2002) and Bsc in
Physics(University of Athes,1983) .His Scientific
interests focus on Applications of Computational
Science and STEM in Education/Didactics.
He is PROFESSOR at ASPETE (School of
Pedagogical and Technological Education, Athens,
Greece) and VISITING PROFESSOR
at
Huddersfield University, UK.
Currently he has published the articles
Psycharis, S., (2016).‘Inquiry Based- Computational Experiment,
Acquisition of Threshold Concepts and Argumentation in Science and
Mathematics Education”. Journal “Educational Technology & SocietyVolume 19, Issue 3, 2016.
Psycharis, S. (2016). ‘The Impact of Computational Experiment and
Formative Assessment in Inquiry Based Teaching and Learning Approach
in STEM Education” Journal of Science Education,25(2),316-326 and
Technology (JOST) DOI 10.1007/s10956-015-9595-z.
Psycharis, S. ,Kalia, M.(2017). The Effects of Computer Programming
on high school students' problem solving, reasoning skills and self-efficacy
in Mathematics; ,Instructional Science, 45(5), 583-602 10.1007/s11251017-9421-5
Professor Psycharis is President of the Helelnic Education Society of
STEM Education(www.e3stem,edu.gr
23
EJERS, European Journal of Engineering Research and Science
Special Issue : CIE 2017
Konstantinos Kalovrketis holds a PhD in Computer
Science at Univestiry of Piraeus , MA in Education
and MSc in Embedded System. He is PostDoc at
University Of Thessaly, Department of Informatics
Science in subject ‘Stem for girls’. He works as
PD407 Lecturer at the University of Thessaly at
Department of Informatics Science. He has published
many research papers in international scientific
journals, and in international conferences, He is
author and co-athor in more than 10 academic books.
He is a reviewer in many international scientific journals and has
participated in many international conferences as a member of the
Scientific Committee. He is member editor on International Journal of
Research Studies in Science, Engineering and Technology [IJRSSET],
Education and Science (http://eduscience-uth.weebly.com/ ISSN 25852310), and more others. His research interests include; ICT in education;
STEAM education; development hardware/software IoT (Internet of
Thinks) for STEM, Pedagogical curriculum development and instruction.
Eva Sakellaridi holds an MA in Education from
the University of Bath, UK (2014) and is currently
a doctoral researcher at UCL, Institute of
Education, UK.
She works as an online Lecturer at the
University of South Wales, UK and as a freelance
Education Consultant.
Her research interests include leadership and
management; ICT in education; curriculum
development and instruction; STEM education.
Miss Sakellaridi
is a member of BERA and BELMAS
’’
(PESYP). He has also been teaching at the University of Athens in the
postgraduate program MSc in Counselling and Vocational Guidance and in
the Certificate of Pedagogical and Teaching Competence (PPDE). Also he
has been teaching in Secondary Education as a Mathematics teacher since
2005.
His research interests include amongst others Didactics of Mathematics
and Sciences, STEM Education, Research Methods in Education,
Quantitative research methods and methods of Statistical Analysis and
Psychometry. He has published many research papers at international
journals, he has presented many papers at international conferences and he
is the co-author of two books. He is a reviewer at many international
journals and he has participated in many international conferences as a
member of the scientific committee. He is a member of the board of the
Professional Body of Hellenic Education Society of STEM (E3STEM).
Dimitrios Mastorodimos received a BSc degree
in Computer Science from University of Ioannina,
Greece in 2000, a MSc degree in Computer
Science from Staffordshire University, UK, in
2007, and a MSc degree in STEM in Education
from A.S.PE.T.E., Greece in 2017.
He worked as an IT teacher in secondary
education in Greece for 11 years and he is
currently working as an IT teacher in Special
Primary School Herakleion Attica. His research
interests ’include algorithms; STEM education and Intelligent Tutoring
Systems.
Dr Korres Konstantinos is an Adjunct Lecturer at
the Department of Education of ASPETE and the
Department of Philosophy Pedagogy and Psychology
of the University of Athens .
Dr Korres Konstantinos has studied Mathematics at
the Department of Mathematics of the University of
Athens, holds an MSc in Didactics and Methodology
of Mathematics from the Department of Mathematics
of the University of Athens and a PhD in Didactics of
Mathematics and Sciences using New Technologies
from the Department of Statistics and Insurance Sciences of the University
of Piraeus.
He has been teaching in Higher Education as an Adjunct Lecturer since
2008. He has been teaching courses at ASPETE in the postgraduate
programs MSc in Science, Technology, Engineering and Mathematics
(STEM), MA in Education, MA in Education Sciences, in the Pedagogical
Training Program (EPPAIK) and in the Counselling and Guidance Program
DOI: http://dx.doi.org/10.24018/ejers.2018.0.CIE.639
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