Available via license: CC BY 4.0
Content may be subject to copyright.
Received November 30, 2020, accepted December 2, 2020, date of publication December 4, 2020,
date of current version December 17, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.3042555
Educational Robotics: Platforms, Competitions
and Expected Learning Outcomes
SALOMI EVRIPIDOU1, KYRIAKOULA GEORGIOU1, LEFTERIS DOITSIDIS2,
ANGELOS A. AMANATIADIS 3, (Senior Member, IEEE), ZINON ZINONOS 1,
AND SAVVAS A. CHATZICHRISTOFIS 1, (Member, IEEE)
1Intelligent Systems Laboratory, Department of Computer Science, Neapolis University Pafos, 8042 Paphos, Cyprus
2School of Production Engineering and Management, Technical University of Crete, 731 00 Chania, Greece
3Department of Production and Management Engineering, Democritus University of Thrace, 671 00 Xanthi, Greece
Corresponding author: Savvas A. Chatzichristofis (s.chatzichristofis@nup.ac.cy)
ABSTRACT Motivated by the recent explosion of interest around Educational Robotics (ER), this paper
attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The
contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring
the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains,
we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of
robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the
growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose
a new way to categorize the platforms, free from their manufacturers’ vague age boundaries. The proposed
categories, including No Code,Basic Code, and Advanced Code, are derived from the prior knowledge and
the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions,
and tournaments increases in parallel with ER platforms’ increase, the paper presents and analyses the most
popular robotic events. Robotics competitions encourage participants to develop and showcase their skills
while promoting specific learning outcomes. The paper aims to provide an overview of those structures and
discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions
and their correlation with the six proposed learning outcomes. This raises the question of which primary
features compose a competition and achieve its’ pedagogical goals. This paper is the first study that correlates
potential learning gains with ER competitions to the best of our knowledge.
INDEX TERMS Educational robotics, educational robotics learning outcomes, educational robotics
competitions, educational platforms.
I. INTRODUCTION
Educational Robotics (ER) is defined as a ‘‘research field
aimed at promoting active, engaging learning through the arti-
facts students create and the phenomena they simulate’’ [1].
More specifically, ER is a field of study that aims to improve
the learning experience of students through the creation,
implementation, improvement, and validation of pedagogical
activities, tools (e.g., guidelines and templates), and tech-
nologies, where robots play an active role, and pedagogical
methods inform each decision [2]. ER has emerged as a
unique learning tool that can offer hands-on, fun activities in
The associate editor coordinating the review of this manuscript and
approving it for publication was Byoung Wook Choi .
an attractive learning environment feeding students interest
and curiosity [3].
Over the years, several robot construction kits have been
specifically designed for educational and special education
use [4], [5]. The robots’ morphology may be static or vari-
able, allowing the student to build, plan, and program dif-
ferent kinds of robotics artifacts that have been designed
to follow the learning principles derived from Piaget and
Papert’s theories [6]. Constructivism and constructionism
theories are particularly bearing for the field of educational
exploitation of robotics. According to Piaget, in the construc-
tivist approach, learning is a result of mental construction
by the learner [7], [8]. Papert [9] extended Piaget’s the-
ory of constructionism by creating the learning theory of
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S. Evripidou et al.: ER: Platforms, Competitions, and Expected Learning Outcomes
constructionist learning and the creation and development
of the LOGO programming language and the floor robotic
device he named ‘Turtle’. Papert argued that students learn by
doing that allows them to construct their knowledge by inter-
acting with objects. Analytically, students construct their con-
ceptualizations through their experiences gained from the real
world. Besides, he argued that learning is accomplished when
the child builds a robotic structure. The construction process
allows the child to invent from the beginning techniques
and ways of solving problems that enhance problem-solving
and reasoning skills [10]. Through constructionism theory,
Papert gave a slightly different perception of learning, where
learners construct knowledge and meaning through making
or tinkering with a tangible object or an entity [11].
Robotics has been endorsed by many researchers as an
innovative learning tool, able to transform education and
support students in many learning contexts. Many studies
indicate that robotics is a supporting tool for teaching sub-
jects related to the robotics fields, such as programming,
construction, or mechatronics [12], [13]. Moreover, the inte-
gration of artificial intelligence in educational robots results
to intelligent teacher assistants which can be used to under-
take different teaching tasks, such as teaching students to
read and pronounce words [14], [15]. ER involve a synthe-
sis of many interdisciplinary activities from various areas,
including mathematics and physics, design and innova-
tion, electronics, computer science and programming, and
psychology [6]. With robotics, students work on real-world
applications of engineering and technology concepts, and the
abstractness of science and mathematics is removed [16].
Thus, according to researchers, robotics is introduced as spe-
cial educational leverage as they mitigate the lack of students’
interest in STEAM (Science, Technology, Engineering, Art,
and Mathematics) topics. At the same time, they motivate
them to pursue a career in one of these fields [16]–[18].
Moreover, several studies show that, even when students are
not interested in robotics or technology, they are motivated
when robotics are used as a teaching tool [12], [13].
Through hands-on robotics activities, students are trans-
formed into active learners able to develop essential skills
by acting as researchers. They explore, make hypotheses,
conduct experiments, and receive feedback from their phys-
ical work [18] increasing their critical thinking, problem
solving and meta-cognitive skills [13], [17], [19], [20].
The hands-on nature of ER constructs a fun, playful, and
exciting learning environment that motivates students to
engage in learning. As a result, students advance their self-
confidence, decision-making, self-direction, creativity, and
innovation [13]. Research also reports a positive impact
on students’ social learning when robotics are used in the
classroom [6], [20], [21].
Robotics exists in education since the late 80s, but they
have only gained so much attention from educators due
to a combination of factors. First, constant technologi-
cal advances accelerated the speed of innovation faster
than ever before. Eguchi [22] characterized students as
’digital natives’ who are growing using technology. As the
modern technology environment must be reflected in the
content of school education, education has reformed to keep
up with the societal and technological changes [23], [24].
Educators boost their teaching, with new features and ideas
such as game-based learning, interactive methods, and virtual
classes. Bragg [25] in his research, found that game-based
lessons led to 93% of class time spent on class tasks. He also
pointed out that 34 % of the conversation time was ded-
icated to math when games were used, compared to 11%
when they were not [26]. Takeuchi and Vaala [27] surveyed
700 teachers to found that 74% of them have used digital
game-based learning to enhance their lessons. Barth [28]
research exposed a 38% increase in virtual school enrolment
in only two years between 2011 and 2012, and 47% of the stu-
dents engaged in online courses according to research in the
period 2007-2009 [29]. Also, several educational movements,
such as the Hour of Code, strengthen educational innovation.
During the first Hour of Code event in 2013, 15 million
students from 170 countries participated in online program-
ming activities. Educational programs had to adapt to the
changes [22]. Simultaneously, robotics has been integrated
into all society levels and has become a benchmark of science
and technology. As technology shapes learning and teaching
processes, educators utilized robotics as a useful add-on to
learning.
Besides, the young generation should stay competitive by
effectively forming the knowledge, abilities, and competen-
cies to participate in society. Eguchi and Uribe [24], in their
research, pointed out the need for STEAM education to meet
the needs of a STEAM-educated workforce and the devel-
opment of a STEAM literate public. Additionally, the Office
of the Chief Economist’s research presents faster-growing
employment in STEAM occupations than in non-STEAM
occupations over the last decade [30].
Moreover, ER has been attracting more attention because
of the increased availability of robotics platforms and pro-
grams suitable for students of different ages and intellectual
levels [31], [32]. During the early 2000s, students had very
few available options for robotics kits. However, with the
development of less expensive robotics kits and devices like
Arduino and Raspberry Pi, more students had access to more
advanced tools [24]. As a result, the robotics kits’ cost has
dropped exponentially, making them accessible to schools
with even modest budgets [33]. Almost all of the available
robotics kits offer different options of programming through
free applications [34].
Another reason influencing the trend mentioned above is
the growing number of robotics competitions, tournaments,
and events [35], [36]. Over the years, the number of par-
ticipants in the robotics competitions has grown as well.
Today, hundreds of thousands of students participate in a
wide variety of educational robotic competition programs.
For example, in 2017, 584 teams participated in the World
Robot Olympiad (WRO) in Germany, comparing to 32 teams
who participated five years earlier [36]. In the same year,
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S. Evripidou et al.: ER: Platforms, Competitions, and Expected Learning Outcomes
18,000 teams from 40 countries competed in virus robotics
challenges during the VEX competition [37]. Each competi-
tion has unique features with a variety of activities. Compe-
titions’ differences are mainly found in the target audience,
the pedagogical goals, the organizational background, and the
target region [38].
Despite their differences, robotic competitions bring
together researchers, students, and robot enthusiasts. By pur-
suing a technological challenge, robotic competitions can
benefit both the research community and education [13].
Those events foster or initiate research to STEAM relevant
topics, and under some conditions, they can also consider
as benchmarks for objective performance assessment. More-
over, to support research, many competitions require the
winning teams to share their systems’ technical details of
their systems publicly [39], [40]. Some robotic competitions
are embedded in technical conferences where participants
and researchers can present and discuss related ideas and
methods [39], [41].
Based on the literature, educational robotics competitions
positively impact education for all the participants, including
students, teachers, and mentors [36], [42]. ER competitions
as a goal-oriented approach to teaching, impact education
on various levels [11], [31], [43], [44]. Most of the com-
petitions primary goal is to promote students’ interest in
STEAM domains and increase their likelihood of considering
STEAM professions later in life [37], [45]–[47]. As preparing
future STEAM professionals has become a growing con-
cern for educators and researchers, robotics competitions
are integrated into classrooms as an educational activity or
as a part of the curricula [48]–[51]. According to a survey
from the FIRST robotic competition, 69% of the students
who participated in the competition from 2002 to 2005 were
interested in pursuing a career in science and technology [35].
Simultaneously, students become confident in using technol-
ogy and widening their knowledge of physics, programming,
mechanical engineering, electronics, and science [42]. Stud-
ies on educational robotics competitions also highlight that a
well-designed challenge provides an environment for learn-
ing problem-solving techniques, promoting creative thinking,
brainstorming, critical thinking, and creativity [52].
Robotics competition have proven to increase motiva-
tion, engagement, self-determination, self-confidence and
self-efficacy [13], [42], [49], [52], [53]. Murphy [54], in her
article, supported that a competition must be seen as an oppor-
tunity for intellectual growth, according to Perry’s model of
intellectual development. Students within a competition can
mature, through the nine stages of increasing complex reason-
ing based on Perry’s model, to finally accept that there may
be more than one right answer to a given problem. However,
some education psychologists suggested that competitions
may sometimes be harmful to many students’ self-esteem.
In most cases, there is only one winner in the competitions
and several losers [39].
Robotics contests, support team-based learning and
enhance skills of communication and personal development,
as they require from students to work in teams or allow
inter-team alliances [16], [42], [48], [50]. Additionally to
the results of the FIRST survey mentioned above, concern-
ing team working, results showed that 95% of the students
recognized the value of working on a team, and 83% of
them realized the importance of grate professionalism [35].
Competitions that require alliances between the teams help
participants enhance their leadership skills and develop
responsibility and strategy making skills. Even though col-
laboration allows individuals with different skills to work
together, to achieve a larger goal, a careful balance of
competition and collaboration must be achieved to be
effective [16], [55].
Competitions are a tool to support and strengthen edu-
cation, especially in concepts related to technology such as
robotics. While competitions are closely related to winning,
when it comes to education, the focus is concentrated on
teaching the methods that ultimately lead to success [54].
Thus, some characteristics of the robotics competitions such
as the competition design, the competitive nature of the
activities, the teacher’s role, and the applied teaching ped-
agogy are crucial for a competition to be beneficial for the
students [13], [56].
It is worth noticing that this work does not constitute a
systematic review of the related literature, rather an attempt,
to propose an updated definition and re-approach the field
of ER. This paper takes into account the current state of the
art, the continuously evolved platforms and investigates the
correlation of the learning outcomes that a student is expected
to gain from an educational robotics-related activity such as
the robotics competitions, a subject of growing interest.
More precisely, Section III proposes a set of 6 key learning
outcomes that a student is expected to gain upon completion
of an educational robotics-related activity. Section IV catego-
rizes the most recent ER kits based on three new categories,
the first time proposed in this paper: No Code, Basic Code,
and Advanced Code. In Section V, the most popular robotic
events and competitions are overviewed, followed by the cor-
relation of the competitions with the proposed set of expected
learning outcomes. In Section VI, the correlation of the learn-
ing outcomes with the competitions is being presented based
on each competitor’s characteristics, rules, and goals. Finally,
in Section VII correlates the proposed learning outcomes with
the ER competitions.
II. METHODOLOGY
This section briefly presents the adopted methodology and
procedures, highlighting how these contribute to the aims and
objectives of the study. Even if the relevant literature was
selected using systematic techniques, the work described in
this paper is not intended to serve as a systematic review
of the ER literature; but rather as a compilation of evidence
that educational robotics can contribute to the educational
procedure.
The literature review in the domain under study involves
a keyword-based search for a peer-reviewed journal and
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S. Evripidou et al.: ER: Platforms, Competitions, and Expected Learning Outcomes
conference articles which was performed from the scientific
databases IEEE Xplore, Scopus and Google Scholar. Google
Scholar was also used to evaluate the impact of each article,
taking into account the number of citations is has.
A. PURPOSE OF THE STUDY
This paper aims to re-approach the continually evolving field
of ER, through the analysis of its related concepts. More
particularly, our study focuses on the learning outcomes that
a student is expected to develop by engaging in educational
robotics-related topics, the available ER platforms, the most
popular robotics events and their educational aspect. This
paper can be considered as an essential guide for future
readers that wish to use robotics in the education sector.
Through the study of an extensive list of learning outcomes
found in the literature, the paper proposes a set of six learning
outcomes that a student expects to gain by participating in an
ER activity. The proposed learning outcomes can be used as
the basis for the design of practical robotic related courses.
At the same time, the paper offers a detailed overview of
the most recent ER platforms. It proposes three new cate-
gories to differentiate them, based on students’ prior knowl-
edge and skills in programming. Students’ interests, motiva-
tion and involvement in learning can be influenced by the
difficulties faced in the teaching process. This categorization
can support ER educators in choosing the most appropriate
robotic tools for teaching their students efficiently.
Also, through robotic competitions, students can develop
or improve specific skills. While perceiving robotic events as
an additional teaching method, the paper presents some of the
most popular robotic events and describes their challenges
and characteristics. The paper also discusses their efficacy
by evaluating competitions concerning the proposed learning
outcomes. To the best of our knowledge, this is the first
approach that aims to generalize the correlation between the
learning outcomes that a student is expected to gain from an
educational robotics-related activity with the robotics com-
petitions. This is an attempt to explore the primary features
that compose a competition, able to achieve its’ pedagogical
goals. This will benefit ER educators and ER competition
organizers who aim to promote specific learning outcomes,
as they can embrace the corresponding practices proposed in
this paper.
B. ANALYSIS STRATEGY
To examine the parameters that would lead to robust conclu-
sions and support the purpose of the study, we have adopted
a four-dimensional analysis procedure.
Aiming to reformulate the definition of Educational
Robotics, we have adopted a meta-analysis research
approach. This approach takes into account keywords from
ER related papers and produces a correlation network and
a bibliometric map of them. As search keywords, we used
the following query on Scopus digital library, consider-
ing three metadata fields: title, abstract and keywords:
‘Educational Robots’ OR ‘Educational Robotics’ OR
‘Robotics Education’ OR ‘Robotics Learning’ OR ‘Robotics
Teaching’ (hereafter will be referred to the query as Q1). The
2078 resulted articles were analysed within the framework
of a classification scheme, taking into account the keywords
given by the authors. This procedure constitutes the first
dimension of our adopted analysis procedure. More details
are given in Section III.
To investigate the expected learning outcomes we used a
meta-analysis of the literature on ER for education that was
framed by the following question ‘‘What are the learning
outcomes when ER is used?’’ following the paradigm of
Belpaeme et al. [57]. Analytically, for the meta-analysis,
we used published studies extracted from the scientific
databases IEEE Xplore, Scopus and Google Scholar by using
the following search terms: Q2=((‘Educational Robots’
OR ‘Educational Robotics’ OR ’Robotics Education’
OR ‘Robotics Learning’ OR ‘Robotics Teaching’ OR
‘Competition’ (with manual filtering of those relevant to
education)) AND ‘Outcomes’). The selection of papers was
based on specific including and excluding criteria. The
including criteria were articles reporting evidence from
empirical research. Therefore, studies reporting qualitative
or quantitative data were included in the literature. Arti-
cles that were reporting participants’ belief of what they
learned from their experience with ER and did not contain
a comparative experiment or evidence on learning outcomes
were excluded. In addition, extended abstracts were omitted
since they usually contained preliminary findings and not
complete results. At first, a total of 57 articles, published
in the last five years were selected based on the afore-
mentioned criteria; matching the search keywords. A sup-
plemental review and analysis of these articles, identifying
articles that focused on the benefits and effectiveness of the
interaction of educational robotics and students from various
educational levels and settings resulted in only 14 articles
with either qualitative or quantitative information. The
learning outcomes of the different studies included both
cognitive and affective outcomes. Cognitive outcomes are
regularly measured through pre- and posttests of student
knowledge whereas affective outcomes are typically mea-
sured include self-reported measures and observations by the
experimenters [57]. This procedure constitutes the second
dimension of our adopted investigation procedure. The result
of the analyses of the educational robotics literature is the
classification of learning outcomes and is being described
in the Section III. It is worth noting that the results of the
first dimension of the proposed analysis, in several cases,
reinforce and confirm the results of the second dimension
of the proposed framework. For example, the meta-analysis
of the keywords clearly indicates that there is a strong cor-
relation between ER and ‘Computational Thinking’. In the
sequel, the meta-analysis of the articles clearly shows that
‘Computational Thinking’ is one of the expected learning
outcomes.
Our third goal was to identify and describe the most com-
monly used or unique ER platforms. To achieve this goal,
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S. Evripidou et al.: ER: Platforms, Competitions, and Expected Learning Outcomes
we have followed a specific research approach, which con-
sists of a combination of specific queries in scientific and
non-scientific databases. This approach shapes the second
dimension of our analysis procedure. Initially, this approach
takes into account the Scopus digital library results of
the query: Q3=((’Educational Robots’ OR ’Educational
Robotics’ OR ’Robotics Education’ OR ’Robotics Learn-
ing’ OR ’Robotics Teaching’) AND (’Platform’ OR ’Kits’)),
considering only keywords of metadata fields. The original
search identified 197 English language papers that were fur-
ther narrowed down to 110, by limiting the search to the last
five years. Additional criteria for selecting robotic platforms
to be introduced in this paper included their commercial
availability, the existence of recent versions that are still
available in the market and whether these tools were used as
learning platforms. It is worth noting that our objective was
to identify and record the ER platforms and not to analyse
the methods, the results and the findings of the retrieved
articles critically. At the same time, we conducted a similar
study on the conventional search engines to discover more
ER platforms that the initial investigation on Scopus did not
reveal. For those platforms, in the sequel, a more extensive
search in other scientific databases (IEEE Xplore, Google
Scholar) was done to identify the existence of published
studies on them. This paper presents and describes only the
platforms that are cited in peer-reviewed published works.
Also, with specifically formulated queries in Google Scholar,
we have extracted the number of articles referring to each ER
platform. More details are given in Section IV, Table 1.
The same research approach was used to gather the most
popular robotic events and competitions. The initial query
used on Scopus was Q4=(’Educational Robots’ OR ’Edu-
cational Robotics’ OR ’Robotics Education’ OR ’Robotics
Learning’ OR ’Robotics Teaching’) AND ’Competition’)
considering only the keywords of the articles. Searches were
restricted to peer-reviewed articles written in English and
resulted in a list of 81 papers. This number was reduced to 46,
as only the papers that were published over the last five years
were selected. In parallel, information about robotics com-
petitions was achieved from non-scientific sources and cor-
related with relevant scientific studies. Again, the retrieved
documents were used only as a means of recording and
identifying robotics competitions. The analysis, findings, and
conclusions of the articles were not the subject of this study.
The criteria by which the competitions will be presented in
this study include the following: There should be a relevant
reference to either the competition or the platforms used by
the competition in a scientific article, the participation on
the competition should be open, and the competition should
be active. Another criterion was their popularity, which is
evaluated by the number of participants they have each year.
The forth dimension of the utilised analysis procedure
engages a critical analysis of the robotics competitions aim-
ing to highlight their educational aspects. To correlate the
proposed learning outcomes (Section III) with the ER com-
petitions (Section V), we estimated the efficacy of each
expected learning outcome according to the characteristics,
rules, and goals by exhaustively analysing the parameters and
the objectives of each competition.
III. EDUCATIONAL ROBOTICS AND EXPECTED LEARNING
OUTCOMES
This section attempts to present an updated definition of
the scientific field of Educational Robotics is, as well
as to present the expected learning outcomes that a stu-
dent is expected to gain upon completion of educational
robotics-related activities.
A. REFORMULATING THE DEFINITION OF EDUCATIONAL
ROBOTICS
The 2078 results of the query Q1on Scopus revealed that
ER is a growing field with the potential to significantly impact
the nature of science and technology education at all levels,
from kindergarten to university. Figure 1depicts the number
of publications per year that use the terms of the specific
query. The reader can easily observe that more than 50% of
those articles have been published in the last five years. This
observation underscores the growing interest of researchers
in the field of educational robotics. Moreover, it is essential
to highlight that the articles originate or combine different
research areas such as computer science, engineering, social
sciences, mathematics, and art & humanities.
FIGURE 1. Number of Publications Related to Query Q1Per Year, Based
on Scopus Data.
It is interesting to observe the co-occurrence of the key-
words used to index scientific and technical Educational
Robotics related articles on Scopus. By adopting the method
proposed in [58], and taking into account the keyword
co-occurrence analysis, a graphical representation of the links
between the keywords was produced. This representation is
also known as a bibliometric map. Figure 2illustrates the
bibliometric graph representing each keyword, located at a
point on a 2-Dimensional plane. The keywords found to
co-occur are linked through a line, with width proportional to
the co-occurrences, that is to say, the similarity (link strength)
between the terms. The distances between the objects are
indicators of their dissimilarity.
Based on the bibliometric map, this paper attempts to refor-
mulate the definition of Educational Robotics. By observing
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FIGURE 2. Overview of ‘Educational Robotics’ - (Q1) Index Term
Bibliometric Map based on Scopus data.
the connections between the keywords, the authors concluded
that:
The Educational Robotics field of study was born, evolved,
and flourished at the intersection of educational science
and computer science, intending to serve and contribute to
both scientific areas. Considering the social nature of the
student-robot interaction, the research questions posed by
Educational Robotics, implemented by activities designed
by the theory of constructionism, focus on the develop-
ment of computational thinking skills, collaborative learning,
and project-based learning. ER, primarily, aims at teaching
programming skills, sequencing, coding, and algorithmic
thinking. Moreover, as an essential branch of educational
technology, the ER field of study seeks to increase traditional
teaching practices’ efficiency and effectiveness while simul-
taneously intends to bring pedagogical changes to enhance
education.
B. INVESTIGATING THE EXPECTED LEARNING OUTCOMES
The integration of educational robotics in educational settings
has been noted both in and out of school environments to
enhance K-12 students‘ engagement and academic achieve-
ment in various fields of STEAM [59] and non-STEAM edu-
cation [60]. Many systematic reviews on educational robotics
in diverse educational settings highlight their potential learn-
ing gains [61].
Benitti, in her systematic review [12], reported that the
learning gains deriving from the use of robotics could be sum-
marized into two categories: (i) the learning concepts/subjects
and (ii) the development of skills. The first category describes
the acquisition and construction of knowledge from various
domains (e.g., mathematics) being taught in multiple edu-
cational settings, whereas the second category the develop-
ment of skills (e.g., communication, collaboration), which are
highly appreciated by employers [62].
A number of studies is documenting that the use of edu-
cational robotics as a pedagogical tool in curriculum courses
or after-school programs promotes the better understanding
of abstract concepts from various fields (e.g. [63]–[66]).
For example, the interactive nature of educational robotics
can aid learners to construct mathematical knowledge
through hands-on experience [67]–[69]. Williams et al. [70]
measured the effectiveness of an afterschool program
in implementing hands-on LEGO Mindstorms-based lab
robotics activities. Their results documented that learners
improved their conceptual understanding of the content in
science and mathematics subjects after participating in the
activity based on pre- and post-evaluation surveys.
In addition, educational robotics can also be utilized for
fostering and promoting the development of skills. These
skills vary from thinking skills [71] to problem-solving
skills [72] to creativity (e.g., [73]) and teamwork [74]. Also
researchers documented that educational robotics enhance
motivation [75]–[77], promote collaboration [78], [79] and
foster computational thinking [80], [81].
Having thoroughly studied the educational robotics’ recent
literature, this paper proposes a set of learning outcomes (LO)
that should be included and achieved in both formal
(e.g., classroom) and informal (e.g., contests and events)
K-12 learning environments. In general, learning outcomes
represent what is formally assessed and accredited to the
student. They offer a starting point for a viable model for the
design of activities and courses, which shifts the emphasis
from input and process to the celebration of student learn-
ing. In other words, the proposed learning outcomes are
statements that describe what a student expects to know or
what they will be able to do upon completion of an educa-
tional robotics’ related course or activity. We have identified
six learning outcomes. It is worth noting that, although the
bibliographic map aimed to reformulate the definition of
Educational Robotics, the output in several cases reinforce
and confirm the proposed learning outcomes. The proposed
learning outcomes are formally analyzed as follows:
•LO1: Problem-solving skills
Researchers have reported that educational robotics
can constitute an effective instructional tool for the
development of problem solving skills [72], [82]–[84].
Problem-solving skills empower learners to search for a
solution for a given problem; therefore, they are consid-
ered important cognitive activities. Students are asked
to apply knowledge and monitor their understanding
of [85]. Atmatzidou et al. [72] revealed that students,
who were provided strong guidance in solving prob-
lems regarding their activities with educational robotics,
obtained greater problem-solving skills with the stu-
dents belonging in the control group. Castledine and
Chalmers [82] suggested that educational robotics can
be used as a useful problem-solving tool conducting a
qualitative study with twenty-three grade six students
participating in LEGO robotics activities. Their study
included data collected from researcher observations of
student problem-solving discussions, collected software
programs, and data from a completed questionnaire.
The study indicated that the robotic activities helped
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FIGURE 3. The Expected Learning Outcomes.
students reflect on the problem-solving decisions they
made. Besides, the authors documented that students
with robotic activities could relate their problem-solving
strategies to real-world contexts.
At the same time, by observing the terms of the bib-
liographic map illustrated in Figure 2, one can easily
recognise terms, directly or indirectly, related to the
‘Problem Solving’ definition. For example, the terms
‘STEM’, ‘STEAM’, ‘Engineering Education’ and ‘Pro-
gramming’ indicate related to the specific learning out-
come activities [86]–[88]. This fact demonstrates that
much of the ER literature focuses on aims and objectives
that are within the scope of the specific LO.
•LO2: Self-efficacy
Self-efficacy is considered to be among the guiding
factors of human activity since it allows a person to
estimates what he can accomplish with his skills in a par-
ticular task [89]. By studying the relationship between
self-efficacy and educational robotics as an instructional
approach, Stewardson et al. [37] designed research that
used robotics and game design to develop middle school
students’ computational thinking strategies. Their
participants were one hundred and twenty-four stu-
dents that used LEGO EV3 robotics and created games
using Scalable Game Design software. The study
results revealed students’ self-efficacy on video gaming
increased significantly in the combined robotics/gaming
environment compared with the gaming-only context.
Meanwhile, trying to investigate correlations between
the bibliographic map and the learning outcomes, one
could reasonably associate the ‘Self-efficacy’ with the
term ‘Constructivism.’ Constructivism is a theory of
learning according to which, that learners actively con-
struct their knowledge and meaning from their experi-
ences [90]. Educational efforts based on constructivist
theory are associated with the self-efficacy beliefs of the
students [90].
•LO3: Computational thinking
Computational thinking is being defined as ‘‘the process
of recognizing aspects of computation in the world that
surrounds us, and applying tools and techniques from
computer science to understand and reason about nat-
ural and artificial systems and processes’’ [91](p. 29).
A large body of literature emphasizes the importance
of effective integration of the development of computa-
tional thinking in education (e.g. [92]–[94]) since com-
putational thinking is important for educating the next
generation of computationally literature students [95]
and is enlisted among the 21st century skills [96], [97].
Studies in computational thinking have concluded that
educational robotics represent an effective instructional
tool for developing the skills of computational think-
ing. Atmatzidou and Demetriadis [17] in their research
used Lego Mindstorms NXT 2.0 educational robotics
kit for their training robotics seminars four Junior high
and four High vocational schools. Their interventions
were conducted during the typical school schedule, and
the class teachers helped students with the implemen-
tation of their robotics activities. Their results showed
that although computational thinking skills need time to
develop fully, educational robotics constitute a fruitful
and meaningful teaching tool.
In this case, relating the specific learning outcome to
the terms of the bibliometric map is relatively easy,
as the term ‘Computational Thinking’ matches one
of the listed keywords. Moreover, the term appears
geometrically-related close to the core of the map, indi-
cating a strong correlation.
•LO4: Creativity
ER is an innovative educational technology proven to
strengthen student’s creativity. Creativity is believed to
be directly connected with the mental procedure that
permits people to generate mental products such as use-
ful and novel ideas or solutions to problems [98]–[102].
Research testimonies document that robotics’ training
impacts students’ creativity. Badeleh [103] conducted
quasi-experimental research with one control and one
treatment group with the administration of pretest and
posttest. The participants of the study were 120 stu-
dents from 11th grade. The research data were collected
after an eight-session treatment period with a ques-
tionnaire. The findings indicated that Robotics training
improved creativity and learning in physics among the
participants.
The term ‘Creativity’ may not be explicitly referred in
the bibliometric map as a keyword but is directly related
to the terms ‘STEAM’, ‘STEM’ and the objectives of
the term ‘Project-Based Learning’ [104]. However, there
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are keywords that their connection to the specific LO is
not so obvious. For example, the terms ‘Human-Robot
Interaction’, ‘Social Robot’ and ‘Child-Robot Interac-
tion’ also contribute to this LO. Several studies highlight
that children interacting with the highly creative robot
formed more ideas, explored more themes of ideas, and
generated more creative ideas [105]. Although social
robots are not the only way to provide this creativity
support through behavioural modelling, they certainly
are a compelling way [105].
•LO5: Motivation
Motivation refers to an individual’s choice to devote
effort to, engage in, and persist at a particular
activity [106], [107] as an exemplary study driven by the
idea that educational robotics can be a tool to encour-
age and enhance students‘ motivation is the empirical
study of Aris and Orcos [74]. More specifically, par-
ticipants in their research were 158 students from the
secondary education level and 61 teachers from several
schools who participated in the FIRST LEGO League
tournaments in 2017-2018. They spent approximately
five hours developing robotics projects 12 to 20 weeks.
Data consisted of both students’ and teachers’ answers
to questionnaires that documented their perceptions and
assessments after participating in the tournament. The
researchers concluded that educational robotics pro-
motes high motivation in students and autonomy in
decision-making. The findings report that with educa-
tional robotics in the learning procedure, it is possible
to achieve students’ motivation because of mutual inter-
action with classmates and teachers positively impacts
performing practical activities.
Again, this LO does not match with any of the key-
words of the bibliometric map. However, it is directly
connected with several included terms such as the
‘STEM’ and ‘STEAM’. STEM and STEAM activi-
ties help students to become motivated independent
learners-one of the main goals of education [108], [109].
However, the absence of the terms ‘Motivation’ and
‘Creativity’ may reveal that researchers take for granted
that these learning outcomes arise from their actions and
do not focus their efforts to achieve them.
•LO6: Collaboration
Collaboration is being recognized as an essential skill
for 21st-century students in working and communica-
tion [22]. It is an interpersonal attitude and the most
common component that almost every STEM disci-
pline stressed [110], [111]. Collaboration is defined as
the process that enables people from the same work-
ing environment to complete a task or achieve a pre-
defined goal. In educational settings, collaboration is
essential to the fostering of a student’s capacity for
social interaction. Hwang and Wu [78] designed a study
with three different scenarios were students used con-
trolled robots to move dice. The plans were: three stu-
dents to three robots, three students to two robots, and
two students to three robots. The experimental samples
comprised sixth-grade students in elementary schools,
16 groups in total, and each group formed three students.
The researchers investigated the students’ collaborative
strategies engaged in the three different scenarios and
their behavioral interactions. The results revealed three
joint plans the independent-control, the mutual-control,
and the coordinator-directed. The study also reported
that the students completed a task better with the least
required time to adopt the mutual-control strategy.
The correlation of the specific learning outcome with the
terms of the bibliometric map, in this case, is compar-
atively straightforward, since among the keywords set,
the term ‘Collaborative Learning’ occurs.
IV. EDUCATIONAL PLATFORMS
This section presents and categorizes the most common Edu-
cational Robotics kits and platforms. Most manufacturers
recommend using their educational robotics tools based on
students’ ages and the capabilities and difficulties an age
group will face when building or programming them. How-
ever, when implementing them, those age boundaries are
vague as most of the ER kits offer more than one option
of programming, like onboard buttons and visual or textual
programming, making them suitable for more age groups.
Moreover, the programming background of students and their
general cognitive skills, in combination with an ER kit that
can maintain their interest, can affect their motivation and
engagement in learning [17], [112], [113].
Following the above, we have chosen to categorize the ER
kits based on the prior knowledge and programming skills
a student must have to use them efficiently. In this paper,
we propose three categories of robotics: No Code,Basic
Code, and Advanced Code.
The ‘No Code’ category includes all the educational
robotic kits programmed with a Tangible Programming Lan-
guage (TaPL). The program flow can be specified by haptic
programmable onboard buttons or physical code cards or
bricks that correspond to programming elements and com-
mands. Although no specific programming language or plat-
form is used, students can compose instructions and learn
basic programming concepts [114]. Tangible programming
languages rely on real-world interaction where students don’t
use a computer, a mouse, or a keyboard to create their pro-
gram. In this way, tangible programming languages evoke
growth in students’ intuitive, everyday knowledge and human
abilities to manipulate physical materials to combine objects
and program their robot [114], [115].
In the ‘Basic Code’ category, we categorize the robotics
platforms that can be programmed through a Visual Pro-
gramming Language (VPL). With the VPL, the amount of
traditional hand-code writing is reduced as pictures replace
the commands. The robotic kits can be programmed through
a friendly graphical user interface, with visual programming
blocks, a student can drag and drop to compose a program.
While VPLs are free of language syntax and semantics,
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students must still follow some visual form rules. More-
over, students can experiment with their program by merely
changing the order or the parameters’ values in the graphical
blocks [115], [116].
‘Advanced Code’ Robotics consist of robotics kits that
can be programmed with Textual Programming Languages
(TPLs). In TPLs, students use linear sequences of text, num-
bers, and punctuations to create their program [116]. Some
of the robotics kits found in this category use interfaces that
support professional programming languages, like Java, C,
C++, C++, Python, or custom-made text-based applications
developed for educational purposes. To create a textual rep-
resentation of a program follow, students must learn to cope
with a broad set of language concepts.
Researchers support that the physical form of the tan-
gible programming languages is perceived as an engag-
ing, easy, and apprehendable way of learning, especially
by younger students and novice programmers [114], [115],
[117]. In Sapounidis and Demetriadis [117] work, it is high-
lighted that between the three different age groups (i.e., 5−6,
7−8 and 11−12 years old) which were used to compare
two operationally equivalent interfaces - one tangible and
one graphical - only the younger students found the tangible
interface to be easier. The older students who had more expe-
rience characterized the graphical interface as easier. More-
over, studies that compared tangible with visual programming
interfaces concluded that even though both are perceived
as equally easy, tangible prevails over visual as it fosters
collaboration and is found to be more interesting [115], [118],
[119]. Regarding gender, Horn et al. [120] supported that
boys favor graphical interfaces. However, tangible interfaces
seem to be equally accepted by both genders, while according
to Sapanoudis et al. [119] girls were more fascinated by
tangible interfaces.
Researchers also compared VPLs with Textual Program-
ming Languages (TPL), and they agree that VPLs are more
suitable for beginners as they positively influence their moti-
vation and productivity [112], [113], [116]. VPLs requires
less background knowledge of programming while providing
an environment with immediate visual feedback that they let
the user incrementally and interactively create the program
flow [116], [121]. On the other hand, TPLs are more suitable
for large scale and complex tasks [112], [116]. With TPLs,
advanced students have more opportunities to develop their
programming skills and knowledge. Moreover, to advance to
professional programmers, they must familiarise themselves
with the programming formalism of professional languages
that use textual programming languages [112], [116], [121].
Finally, many researchers agree that visual programming
languages can be seen as the pathway to textual program-
ming languages [112]. Similarly, the proposed categories ‘No
Code’, ‘Basic Code’, ‘Advanced Code’ can correlate with the
stages a student must go through to learn to program. Using
this distinction and recognizing the students’ prior knowledge
and programming skills, an educator may select the most
appropriate robotic kit for them.
A. ’No CODE’ ROBOTICS
Terrapin offers various educational floor robots suitable
for three to fourteen-year-old students, including Bee-Bot,
Blue-Bot, Pro-Bot, InO-Bot, and Tuff-Bot. Those robots are
designed to introduce kindergarten and lower primary school
children to basic programming, directional language, and
mapping skills [122], [123]. The programming of the robots
mentioned above is based on the LOGO programming lan-
guage. Research testimonies on the LOGO programming lan-
guage have shown that programming, when introduced with
a structured framework, can help students to develop a wide
range of cognitive skills, including basic math and language
skills, the development of their visual memory [124], and the
development of computational thinking. Programming with
robots offers a range of observable cause-and-effect actions;
it can be used as a platform for engaging children with
abstract ideas [125]. Simultaneously, it allows students to
develop concepts related to sequence, classification, and logic
in accessible ways. In this way, they can apply fundamental
concepts of technology and information technology in their
contact with the real world [115].
Bee-bot is a robot designed to resemble a yellow bee with
black stripes and has seven haptic programming buttons on
its surface used to enter up to forty commands. Four of
them serve for a backward/forward motion and rotation to
the left/right, while the central command key ‘Go’ can start
executing the commands entered by the student. The other
two buttons correspond to the ‘Clear’ command that can clear
the robot’s memory and the ‘Pause’ command that can pause
the robot for a second while executing commands. Students
can enter commands to Bee-bot to make it move on prepared
story mats or move through designed routes made with build-
ing blocks to reach specific destinations [126]. The length of
the robot’s step is fixed to 15 cm, and the size of the angle
rotation is 90 degrees. Bee-bot notifies the students that it
has completed the given sequence of instructions by blinking
its eyes and beeping, providing playful and straightforward
feedback to the students [127].
Blue-Bot is an advanced version of Bee-Bot that introduces
new features such as remote control with Bluetooth connec-
tion. It has the same shape and buttons as the Bee-Bot and
is transparent so that its components can be seen. It can be
programmed by pressing the buttons on the robot’s back -
like Bee-Bot- or using a suitable application. Its’ Bluetooth
technology allows students to program the robot through a
computer or a tablet or with the use of its custom-made
programming bar and the sequential instruction cards [128].
Bee-Bot and Blue-Bot are accompanied by their download-
able tablet apps that enable students to create a program on
screen and send it to the robots via Bluetooth. The Blue-Bot
TacTile Reader is a hands-on programming device to control
Blue-Bot offering extended commands for Blue-Bot, includ-
ing 45-degree angles and repeat sequences.
Pro-Bot expands learning opportunities provided by the
Blue-Bot and Bee-Bot, and it is specially designed for kids
from 8 to 10 years old. Unlike the two previous floor robots,
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Pro-Bot looks like a race car and has a built-in LCD screen
and several built-in sensors like touch, sound, and light sen-
sors. Students can enter commands via a set of arrows and
number keys mounted on the robot’s back. Unlike Bee-Bot
and Blue-Bot in Pro-Bot, arrow keys can be combined with
the number buttons with distances entered for movement and
degrees entered for turns. Students transition smoothly from
one mode to another as their skills develop. They also can
program the robot with the Terrapin Logo coding application
based on the Logo language [129].
Tuff-Bot (the Rugged Robot) is a robot that differs from the
previously mentioned, It also has multiple speeds that make it
operationally adaptable in a range of environments with 20cm
travel distance for each step and can store up to 256 steps.
It can be programmed via onboard buttons and remotely via
a free downloadable tablet app or the TacTile Reader. Finally,
it includes recordable messages to confirm when commands
have been entered and a hole to insert the camera mount.
Due to their ease of use and the fact that they promote stu-
dents’ effective engagement, more educational robots func-
tionally equivalent to Bee-Bot were proposed. Colby is an
automated mouse-like educational floor robot with tangible
buttons on its surface for programming. It comes with its
Code & Go Robot Mouse activity set, consisting of maze
grids, parts to create walls and tunnels, 30 double-sided
coding cards, ten double-sided activity cards, and a cheese
wedge [53].
More advanced programming concepts (e.g., loops, events,
conditionals, functions, and variables) suitable for young ages
are introduced with similar robots, like Botley The Coding
Robot and Sammy Kids First Coding & Robotics. Sammy,
peanut butter and jelly sandwich shape robot, uses an opti-
cal scanner to read the program through the corresponding
physical code cards as it drives over them. Botley can be
programmed by entering commands on its remote control
Remote Programmer. It has an object detection sensor at the
front and a line-following the sensor at the bottom. It can
help students follow and remember their program’s sequence
by using the forty coding cards that mirror each step in their
program [130].
Another concept is proposed with Cubetto and KIBO,
which are made of tactile and hard-wearing wood. Cubetto
robot is a robotic set that includes a wooden cubic device with
wheels, sixteen coding blocks (four forward, four right, four
left, four-function). A programming table was the sequence of
commands being displaced. Cubetto programming is based
on lucid language, which is a functional dataflow program-
ming language. Children with ages from three to nine years
old use iteration and recursion to navigate Cubetto by plac-
ing the coding blocks in the programming table’s command
lines.
KIBO was created by the Developmental Technologies
Research Group at Tufts University and became commer-
cially available through KinderLab Robotics in 2014. KIBO
is a robotics construction kit that contains the KIBO robot,
tangible programming blocks, and mechanical components
such as wheels, motors, light output, and a variety of sen-
sors. The robot contains an embedded scanner than scan the
barcodes on the programming blocks. Each programming
block is color-coded and represents an action or instruction.
Programming is accomplished by connecting interlocking
wooden blocks that comprise a sequence of commands
followed by the KIBO robot. After a sequence is built,
starting with a ‘Begin’ block and ending with an ‘End’
block, children scan the set of blocks in sequence
using the KIBO’s barcode scanner and push a button to
see the robot perform the sequence of commands they
created [131].
All the aforementioned robots are characterized by
programming-learning tools without using screens; therefore,
they do not require screen time on a separate computer.
Consequently, they innately minimize both the complexi-
ties of manipulation and coding comprehension, resulting in
reduced cognitive load. Also, because they include a visual
interface, face-to-face interactions with teachers and peers
can be promoted. This is aligned with the American Academy
of Pediatrics’ recommendation that young children have a
limited amount of screen time per day.
‘Cubelets’ were manufactured by Modrobotics and are
a modular robotic construction kit consisting of various
cubes connected via magnets designed to create tangible
Microworlds outside of the computer screen. There differ-
ent categories of cubes implementing different function-
alities. Actuation Cubelets include Cubelets with a single
wheel, a rotating face, and a lamp. Sense Cubelets include
brightness, temperature sensors, a potentiometer (Knob),
an infrared distance sensor. Think Cubelets include Inverter,
which performs a mathematical operation equivalent to
1-value, a Maximum Cubelet, which forwards only the max-
imum value that it receives on any of its faces, as well
as the Blocker, which only forwards energy, and the pas-
sive Cubelet, which forwards both energy and power it
receives [132]. Cubelets exchange sensor information and
transmit data and power between the blocks [133].
Another straightforward option for young children who
don’t yet know how to write is Ozobot robots. Ozobot offers
two versions for robots, the Bit 2.0 and the Evo. Both of
them are compact (2.5 cm tall and 17g weight). While they
look alike, Evo features more sensors and technologies than
Bit. Ozobots have a polycarbonate shell, two micro motors,
a micro USB Port, optical sensors for navigation purpose,
color sensors, and LEDs diodes [134]. Evo also includes a
speaker, a proximity sensor for detecting objects, and allows
Bluetooth connection [135]. Primary students can start pro-
gramming Ozobots by drawing lines and color codes, called
OzoCodes, that Ozobots detect with their sensors. Those
drawings are combinations of Black, Blue, Red, and Green
color lines that correspond to commands for adjusting their
speed direction and timing. Besides drawing on a paper, stu-
dents can draw their programs or experiment with OzoCodes
on a tablet, using the official Ozobot applications. Stu-
dents can advance their skills by programming their Ozobots
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with Ozoblocky, a visual programming language similar to
Scratch [136], [137].
Edison is an educational robot that can teach programming
and robotics to students of any age and skill level. It is
small and can be used as a base for building engineering
and STEAM projects using its construction kit or any other
Lego brick. The robot’s sensors can react to sound and light
while also following a line or avoiding obstacles. Students,
with ages ranging from 4 to 16 years, can program Edison
using a variety of programming environments. Younger stu-
dents can program Edison using a remote control, bar-codes
printed on paper, or with the use of the graphic language
EdWare, which is a drag and drop programming interface.
EdScratch an alternative for older students, is a block-based
programming language, like Scratch, which is offered for
more complex programming structures. Advanced program-
mers can use EdPy, a python-like text-based programming
language [137].
Lego education, a department in the Lego Group, designed
educational robotic kits for different age groups - early learn-
ing, primary and secondary school. Starting from preschool,
the Lego Education Coding Express uses action bricks to
teach young learners coding concepts such as sequencing,
looping, and conditional statements. Children can build a
train, combine tracks in various shapes and process differ-
ent exercises in the form of a story, based on their skills
and knowledge (beginner, intermediate, or advanced level).
By positioning the action bricks on the tracks, they can
change the train’s behavior, including making it turn the
lights on and off or change direction. There is a compatible
application and the physical set, allowing the user to further
explore in learning through other areas such as music, char-
acter, journey, and math. As a result, children can improve
their problem-solving skills and their computational think-
ing. At the same time, they can develop their interpersonal
skills, such as collaboration, to advance their confidence and
creativity.
B. ’Basic CODE’ ROBOTICS
InO-Bot (Input-Output-Bot) is suitable for kids up to 14 years
old, and it can be programmed via Scratch programming
language. It has two LED headlights, eight multicolor RGB
running lights, sounds, and builds in sensors like light sensor,
sound sensor, range finder sensor, proximity sensors, and a
line follower.
Another family of robots that are ready to program is Dash,
Dot, and Cue. Dash and Dot can be used by children as young
as six, while for Cue, the age range is 10 to 15 years old.
Dash has an infrared (IR) sensor, three proximity sensors,
a gyroscope, an accelerometer, and three microphones. Dot is
also equipped with an accelerometer that helps detect Dot’s
movement. Their compatible interface app uses drag and
drops code blocks that fit together like puzzle pieces. Cue has
the same sensors as Dash, but it can be programmed either
with a drag-and-drop block interface using Block or Wonder
app or with text coding using JavaScript editor [130].
For primary students ages seven and up, Lego Education
designed Wedo 2.0 robotics kit. This kit consists of classic
Lego bricks, several mechanical parts, a Lego USB hub,
two sensors (one motion sensor and one tilt sensor), and
a motor. Students are familiarizing with scientific subjects
such as engineering, physics, earth and space science, and
life sciences through the available guided and open projects.
Students can follow instructions or use their fantasy to create
different robots [138]. They can then intuitively program
them using the original Lego software or third party coding
platforms like Tickle, Tynker, Open Roberta and Scratch. All
software solutions use graphical programming blocks repre-
senting instructions and helping students program their robot
by drag and drop the coding blocks. The WeDo programming
environment is a blank canvas with a palette of pictorial
coding blocks on the bottom. Students can drag and drop the
instructions and combine them to make their robots interact
with their environment. Third-party options help students
shift from the horizontal icon-based block coding of the
LEGO Education WeDo 2.0 app to vertical text-based block
coding. However, this combination of text commands, even
if they are in an intuitive puzzle piece shape, is more difficult
for novice programmers. It is more suitable for students who
have used the Lego WeDo software before. Scratch also offers
students the opportunity to use extra elements such as the
if-then-else statement while only implementing if-statement
on the official Lego WeDo software. Students can also cre-
ate interactive animated stories or games and control them
with their physical Lego WeDo build. With all these oppor-
tunities, Lego WeDo can be used for different age groups
students from novice to more advanced programmers in pri-
mary school, to familiarize themselves with computer science
concepts, develop critical thinking and problem-solving skills
and learn how to collaborate [139].
Following the Lego WeDo concept, Lego Education cre-
ated its newest robotic kit Lego Spike Prime suitable for
grades 6-8. Students on the Lego Spike Prime box can find
a set of Lego building elements, including the programmable
hub, one large angular motor, two medium angular motors,
and three sensors - an ultrasonic sensor, a color, and a touch
sensor. Lego Spike Prime hub is more advanced than the
Lego WeDo hub. It features six input/output ports, a 5 ×5
light matrix, Bluetooth connectivity, a speaker, a 6-axis
gyro, and a rechargeable battery. Students can program it
through the Lego Education Spike App, which is based on
the Scratch programming language. Focusing on STEAM
learning combined with critical thinking and problem-solving
skills, Lego Spike Prime offers the teacher a set of lesson units
focusing on different subjects. Unit plants include Invention
Squad, Kickstart A Business, Life Hacks, and Competition
Ready. Invention Squad aims to teach students the engineer-
ing design process, including finding solutions for a problem,
making prototypes, testing, and evaluating their solutions.
Kickstart A Business helps students develop computational
thinking skills and teach them how to decompose a prob-
lem, create algorithms, and debug their codes. Life Hacks
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familiarize students with computer science features like
working with variables, operations, arrays, and qualitative
and quantitative data. Finally, the Competition Ready unit
helps students implement all the knowledge conquered from
the previous units by building and programming robots for
competitions. This unit also teaches the students how to
collaborate with others while working in teams to complete
their missions.
Lego Mindstorms EV3 is one of the most popular and
widely used robotics kits in education for grades 9-12. Lego
Mindstorms EV3 is the third generation robotic kit in Lego’s
Mindstorms line, following the two previous versions of the
programmable LEGO brick, the RCX released in 1998 and
NXT released in 2007 [140]. It includes elements from
the Lego Technic series, three servo motors, five Sensors
(Gyro, Ultrasonic, Color, and 2 Touch), and a programmable
brick through which all the parts can be controlled. Along
with the kit, Lego offers a set of lesson plans separated into
five segments, Coding, Engineering, Technology, Science,
and Maker, with exercises of different difficulty levels. This
curriculum aims to prepare students for higher education
and future jobs by building skills such as creativity, critical
thinking, collaboration, and communication.
The EV3 intelligent brick can be programmed through
different platforms offering different age groups and different
learning level learners to use the robotic kit. The first option
is the intuitive Lego official software, where students can
drag and drop visual programming tiles to compose different
instructions and specify a program flow. Students also have
the opportunity to program their Ev3 brick through other
programming environments like Swift Playgrounds, CoderZ,
Microsoft MakeCode, Scratch 3.0, ROBOTC, Open Roberta,
and Python. Swift Playgrounds uses Apple’s programming
language Swift and is suitable for beginner programmers.
CoderZ offers an online 3D simulation environment and the
two programming options, Blocky for beginners and Java
code for more experienced programmers. Microsoft Make-
Code and Open Roberta combine a 2D simulator and different
programming environments, making it easier for teachers
to integrate coding in their classrooms. Through Microsoft
MakeCode, students can use a drag and drop workspace or a
JavaScript editor for novice and expert programmers, respec-
tively, while Open Roberta is based on NEPO language.
Students can also use Ev3 brick with Scratch 3.0 to create
their own interactive stories, games, and animations based
on the drag and drop programming. RobotC is based on the
C programming language, offering learners the opportunity
to work with text-based programming. Finally, Ev3 Micro
Python programming language lets high school students learn
Python programming language using the Visual Studio Code
from Microsoft coding editor.
Engino is a versatile three-dimensional construction sys-
tem that proposes a new modular connector system to simul-
taneously connect up to 6 sides or extend at any length.
Through its Engino STEM and Robotics education series,
Engino offers robotics kits for all levels of education, based
on STEM and robotics principles. Starting from preschool,
students aged 3-6 can use the STEM Qboidz to develop
fundamental cognitive abilities, social and sufficient motor
skills. Through a set of activities, Qboidz helps students
learn about animals, vehicles, technology, airplanes, and sea
exploration. For Kindergarten and Early Primary, school
Engino proposes the starter robotic set Junior Robotics.
It includes Engino and Qboids connecting parts, a mini
controller, a touch sensor, one motor, and one LED. Stu-
dents can build the proposed models through instructions or
make their builds, and program the mini controller either
manually, using onboard buttons, or through its official
programming software KEIRO. Based on the Scratch idea
of drag and drop programming, KEIRO has action blocks
combined by the student to create the program and learn
about inputs/outputs, sensors, and flow diagrams. For primary
students (6-9 years old) STEM and Robotics Mini is the most
suitable solution.
Along with the Engino constructional parts, the students
can also find in the box the mini controller, 2 Infrared sen-
sors, 1 Touch sensor, 1 LED, and two motors. As for the
previous set, students can use the mini controllers’ buttons
for manual programming or KEIRO software with more
advanced features such as functions and live readings. Late
Primary and Secondary students can use the STEM and
Robotics PRO kit, including Engino and robotics parts such
as the PRO controller, 1 Touch sensor, 2 Infrared sensors,
3 Motors, and 5 LED lights. Instructions for creating up
to 34 models are given with the set, while students can
learn more complex programming concepts like conditional
statements, variables, and operators by using KEIRO soft-
ware. Finally, STEM and Robotics Produino is designed for
students of ages 14 and up. Produino is the most advanced
educational solution of Engino. It includes the Produino con-
troller, which has Bluetooth and Wi-Fi wireless connectiv-
ity, a USB port, a Display with six programming buttons,
and a Rechargeable battery. The set also includes a touch
sensor, a color sensor, infrared sensors, an ultrasonic sensor,
a Compass/Magnetometer, DC motors, and a servo motor.
The Produino controller integrates the open-source Arduino
platforms, connecting and using more sensors and shields.
The Scratch-like environment of KEIRO is available for pro-
gramming, while students can switch to Arduino mode for
textual programming in C++.
Thymio is a white, small shaped, and differential wheeled
robot, suitable all students’ ages. Thymio’s shape can be
expanded with Lego components, as it has compatible fits
on its surface and wheels. The robot has a lot of built-in
sensors and actuators. There are nine infrared proximity sen-
sors, 7 of them on the front and the back of the robot to
detect obstacles and two on the bottom to help the robot
detect the ground. It also has a 3-axis accelerometer, a micro-
phone, a temperature sensor, and an IR sensor for remote
control. Thymio also holds five capacitive touch buttons on
the top, a secure-digital SD card slot, two motors, a loud-
speaker, and 39 RGB LEDs. Thymio can be programmed
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with Aseba open-source programming environment using a
VPL or a scripting language. Users may also program Thymio
with Blockly, which offers a combination of a VPL and a
TPL [141], [142].
C. ’Advanced CODE’ ROBOTICS
A more flexible solution to education is Arduino, an open-
source electronic platform mainly used to construct and
programming electronics. The boards have a set of digital
and analog pins configured as either inputs or outputs. The
boards’ capabilities can be extended by plugging in various
expansion shields (boards), breadboards, or other circuits.
Thus it can use many sensors to sense the environment
and affect its surroundings by controlling a set of actuators
like lights and motors. The microcontroller on the board
is programmed using the Arduino programming language
(based on Wiring) and the Arduino development environment
(based on Processing). It uses a simplified version of the
C++ programming language, making it easier to learn to
program [143]. This flexibility of the Arduino board made
it widely used in computer programming and in creating
custom educational robots with different behaviors [144].
Students of different ages and intellectual levels can choose
between the official Arduino kit, and a series of custom made
robotic kits based on the Arduino board. Arduino Education
offers a series of different kits, covering students from mid-
dle school to university, covering different subjects such as
programming, physics, electronics, engineering, and mecha-
tronics [145], [146]. With the increasing complexity of the
kits, students can develop their critical thinking, collaborative
learning, and problem-solving skills. All kits include Arduino
programmable boards, sensors, accessories, and mechanical
parts while being programmed with open-source software.
Apart from the official Arduino kits, many third-party com-
panies created Arduino based educational robots like Make-
block and Pololu, allowing younger students to work with the
Arduino board.
Makeblock uses the Arduino board on its Mbot series,
including Mbot, Mbot Ranger, and Ultimate 2.0. Mbot is
an entry-level educational robotic kit suitable for elementary
and secondary education students, starting from 8 years and
up. It consists of the mainboard mCore based on Arduino
Uno, which can connect with various onboard sensors, such
as a buzzer, a light sensor, an RGB led, a button, an IR
Receiver, an ultrasonic sensor, and a line follower sensor.
Moreover, when working with this robot, students have all
the advantages of working with the Arduino board [147].
This robot can easily be assembled or modified to create
robots of different shapes, and it can be programmed with
software -like mBlock, Makeblock app, and mBlock [148].
mBlock is both a block-based and text-based programming
language developed after Scratch 3.0 and Arduino code.
This offers users the opportunity to see and edit the code
with Arduino IDE with the C++ language. Using the mBot
series, students can learn basic programming concepts such as
loops, conditions structure, functions, procedures, variables,
lists, and sequences. At the same time, they can develop
their critical thinking and problem-solving skills [148]. Stu-
dents of 11 years and older can go further with Halocode
Board, a small-sized programmable computer board with
many sensors. They can start with graphical programming
using the mBlock software and move to textual programming
with Python. Makeblok also offers educational robotics for
students from early childhood to primary education. Young
learners at the age group 4-7 years old can work with mTiny,
a programmable robot with a screen-free coding tool. In con-
trast, students of the next level age group can use Codey
Rocky to learn more about computer science concepts and
programming skills.
Pololu 3pi platform is a small size robot, commonly used
for line-following. The robot core is a C-programmable
ATmega328 AVR microcontroller, where two micro gear
motors, five IR reflectance sensors, an 8 ×2 character
LCD, a buzzer, and three user push-buttons are connected.
The robot can also expand its abilities by adding acces-
sories, such as avoiding obstacles, following walls, solving
a maze, or turning into radio-controlled [149]. Pololu 3pi
can be programmed using the Pololu AVR C/C++ Studio is
combined with many libraries for controlling the integrated
hardware [150]. Users can also use Arduino IDE for
programming, as Pololu 3pi is compatible with the Arduino
platform [149].
BBC micro: bit is another educational board that can be
used to create different robotics projects. It is a pocket-sized
programmable computer, consisting of 25 red LEDs, two
pushbuttons, an accelerometer, a compass, a radio, and a
Bluetooth antenna. Besides the on board features, it’s possible
to connect various accessories like a joystick and color dis-
play board to advance it. Microbit can be used with different
age groups and educational levels as it can be programmed
with various software and programming languages. Start-
ing with blocks and JavaScript, students can proceed with
MakeCode editor or Scratch and then go further with more
advanced programming with Python editor [151].
A more advanced option when using boards in educational
robotics is Raspberry Pi, a fully-featured credit-card sized
computer. Today they are several models of Raspberry Pi,
from the Raspberry Pi Zero, a single board computer to the
4th generation dual-display desktop computer Raspberry Pi
4 Model B. All boards include a processor, a graphics chip
and a RAM, HDMI, and USB ports. Users can add peripherals
through USB or using the discrete input and output connector
ports. Raspberry Pi’s initial purpose was to help students of all
ages learn programming by using Scratch and Python. How-
ever, Raspberry Pi is now used as a universal programmable
control unit for many machines and applications, including
robotics.
Tetrix robotics system from Pitsco Education consists of
two robotics kits, Tetrix Prime for middle school and Tetrix
Max for high school. They both have aluminum and plas-
tic pieces, including structural elements, connectors, hubs,
brackets, wheels, gears, and sensors. They also include
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robotics controllers called TETRIX PULSE for Tetrix Prime
and TETRIX PRIZM for Tetrix Max, both Arduino compati-
ble. Thus students can use Arduino Software (IDE) to control
their robots. Tetrix Prime and its Tetrix pulse controller can
be programmed with the drag-and-drop block-based graphic
coding software TETRIX Ardublockly developed using the
Google interface called Blockly. Tetrix also offers a connec-
tion with Lego education robots. Today there is a TETRIX
PRIME Robotics Set for EV3 that allows students to build
larger, more powerful, and more complex robots. This set
includes a module attached to a sensor port on the EV3 brick
and connects up to six TETRIX PRIME servo motors and two
TETRIX PRIME DC Motors.
Nao and Pepper are two autonomous and programmable
humanoid robots offered by SoftBank robotics. They can be
used on various occasions like entertainment, therapy, human
assistance, and education. Nao is 58cm in height, and it has
25 degrees of freedom, allowing it to perform various motor
actions. Nao can interact with humans through a friendly
voice in 20 languages and proper vocabulary and grammar
using its four directional microphones and speakers. It also
has two 2D cameras for image recognition. Pepper is a 120 cm
tall, humanoid robot that moves on three multi-dimensional
wheels, enabling it to move around 360 degrees. It also has
two arms for object handling and a touch screen for users to
control the robot through various applications. It can com-
municate with users in 15 languages, through its four direc-
tional microphones [152]. Pepper is equipped with sensors
like infrared sensors, an inertial unit, 2D and 3D cameras,
and sonars for omnidirectional and autonomous navigation.
Both Nao and Pepper can be programmed with Choregraphe
IDE or the Software Development Kit (SDK). With Chore-
graphe IDE, students can program Nao and Pepper with a
graphic-based programming software using drag and drop
blocks or using Python. More access to robot features can
be achieved using SDK, which is available in Python and
C++ [153]. Pepper users can also use the Pepper SDK
plugin for Android Studio and program in Java or Kotlin.
Both robots can be used in education and assist educators in
different aspects of teaching. They can help students develop
problem-solving and analytical skills while at the same time,
they improve self-motivation in learning STEAM. Nao is suit-
able for primary to higher education, while Pepper is suitable
for higher education. Their friendly appearance and ability
to detect human emotions make them suitable for students
with disabilities and emotional or behavioral disorders. They
can help develop social communication skills, self-esteem,
reduce shyness, reluctance, un-confidence, and frustration in
individuals in special education [154].
E-puck is a small-scale robotic platform based on
open-source hardware/software. It has a straightforward
structure consisting of plastic parts, including the main
body, the light ring, and two wheels. E-puck uses an
STM32F4 microcontroller and is equipped with a wide range
of sensors for communicating with its environment. More
precisely, on the robot body are 8 IR proximity sensors,
9 IMU sensors, a 3D accelerometer, a CMOS camera, a ToF
distance sensor, an SD storage, and four digital microphones.
Also, the e-puck supports Bluetooth, WiFi, and USB con-
nectivity. E-puck actuators consist of two stepper motors,
a speaker, and a ring of 8 LEDs. Users can also extend e-puck
possibilities with additional sensors and actuators. As an
open-source project, besides its embedded bootloader, many
software and libraries are available for programming the
e-puck platform, including the ASEBA tool, Matlab, Python,
and C++ libraries, and the Player driver. Additionally, sim-
ulation programs like Webots and Enki are available to test
and verify users’ theoretical concepts for e-puck [155].
Table 1presents the number of articles that appear in the
Google Scholar web scientific indexing service when specific
and relevant keywords are used. As one can easily observe,
in the case of ‘No Code Robotics’ category, the vast majority
of the articles utilise the Edison robot. In the case of ‘Basic
Code Robotics’ category, most of the scientific community
adopts Lego Mindstorms - EV3. Its ease of programming,
TABLE 1. Number of articles that appear in Google Scholar when specific
queries are used. [Data Last Accessed on 2020 November 1st].
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low cost, and scalability make Arduino-based robots the most
common choice in ‘Advanced Code Robotics’ category and
the preferred microcontroller for teaching.
V. EVENTS AND COMPETITIONS
This section briefly describes some of the most noted
educational robotics competitions today. Robotics competi-
tions consist of various challenges, including project-based
tasks, team games, fighting challenges or solving a generic
task. In addition to the different challenges, most of the
competitions engage a variety of robotic platforms. Thus,
the presented competitions are not classified based on the
robotic platforms’ categories of the previous section. Robotic
competitions are aimed at a national or international audi-
ence. In this section, we categorize the competitions by con-
tinent, based on the countries in which they are allowed to
participate in the competition.
A. INTERNATIONAL COMPETITIONS
The Robotics Education & Competition Foundation offers
VEX IQ Challenge, VEX Robotics Competition, and VEX
U events to inspire and motivate students in STEAM edu-
cation. Participants in all VEX events are able to use only
VEX Robotics and use components from the VEX product
line. Moreover, as all VEX events require alliances between
the teams, students develop essential skills like teamwork,
leadership, and communication. VEX IQ Challenge is a com-
petition for elementary and middle school students. Students
use the VEX IQ robotic kit to build their robotic solutions
and compete in 3 challenges, Teamwork, Driving Skills, and
Programming Skills. In a Teamwork challenge, two teams
must cooperate to maximize their score. Scoring objects of
different colors are randomly placed inside a field, and teams
must place those objects inside the predefined positions to
earn points. For the other two challenges, teams work individ-
ually to collect points. In the Programming skills challenge,
the robot is in the autonomous driving mode, while in the
Driving Skills challenge, the robot is remotely controlled
by a team member. In the VEX Robotics Competition, two
teams ally and compete against other alliances. Each alliance
tries to score the most points by accomplishing a variety of
tasks. Every game has two periods, the autonomous driving
period, followed by the driver control period. The teams of
the alliance with the top-scoring points win the tournament
championship [156]. The VEX U event follows the same
rules and objectives of the VEX Robotics Competition but is
dedicated only to college and university students. In this level,
more customization and flexibility is allowed for the teams.
Besides the winning teams, in VEX competitions, special
awards are given to the teams based on their performance in
a particular aspect of the competition, such as programming.
World Robot Olympiad (WRO) is a global robotics com-
petition for students aged 6 - 25 years old. It was founded
in 2004 and aimed to develop students’ creativity, design,
and problem-solving skills through original robotic struc-
tures. Each country organizes a local WRO tournament.
The winners of each category, except the WeDo age group,
can participate in the final international competition hosted
by a different country every year.
Each year WRO has a new theme drawn from essential
aspects such as ‘Smart Cities’, ‘Food Matters’, ‘Robots for
sustainability’, and ‘Robots for life improvement’. WRO
consists of 4 categories with different age group competi-
tions: Regular Category, Open Category, WRO Football, and
Advanced Robotics Challenge (ARC).
The Regular category is a challenge-based competition.
The competition is separated into four subcategories based
on students’ age, WeDo for younger students up to ten years
old, Elementary for students ten up to twelve years old, Junior
for students thirteen up to fifteen years old, and Senior for stu-
dents sixteen up to nineteen years old. For the WeDo category,
only the Lego WeDo kit can be used, and teams must bring
their robots assembled to the competition. All other age group
teams can use one of the Lego Education Robotics platforms;
Mindstorms sets NXT or EV3 and Spike Prime, while beside
the HiTechnic Color Sensor, no other third-party elements
are allowed. All age groups can use any compatible software
or firmware to program their robots. The aim is to assemble
and program their robots on the competition day, without any
instructions. On some occasions, a surprise rule or task is
revealed on the competition day to boost creativity. The teams
achieving the surprise task are awarded extra points.
The Open Category is a project-based competition where
participants can create an innovative robotic solution based
on the season’s theme and present it to the judges. The project
is supported by a short video demonstrating the robot’s func-
tionalities and a written and illustrated report, summarizing
what the robot can do. According to this category regulations,
there are specific criteria (e.g. quality of the solution, pro-
gramming, engineering design, presentation and teamkork)
which groups must meet to collects points. The Open Cat-
egory is divided - like the Regular Category- into four sub-
categories according to age: WeDo, Elementary, Junior, and
Senior. For mechanical construction, a Lego WeDo kit can
be used by the WeDo group. Simultaneously, there is no
restriction on the robot’s size and the use of controllers,
elements, and materials for all the other age groups. They
are also free to use any software they prefer to program their
solution.
The WRO Football Category is a gameplay competition
inspired by human soccer. Two teams compete using two
autonomous robots: either a goalie and a forward player or
two forward players. The two robots chase an infra-red trans-
mitting ball, aiming to score the most goals and win the game.
To encourages students to develop their robots, the game
differentiates a little every year by the organizers. Unlike the
other two categories, only one age group can participate in
this category: the student 10-19 years old. The controller,
the motors, and the sensors used to assemble the robot must
be from LEGO MINDSTORMS sets and HiTechnic, and only
Lego brand pieces are allowed. Robots are assembled on
the assigned assembly time on the day of the competition.
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The program can be prepared in advance in any soft-
ware and any firmware on NXT / EV3 controllers. The
participants must also explain their robots’ operation and
answer questions for their construction and programming
procedure.
Robotex is an international robotics competition organized
since 2001 in Tallinn, Estonia. Teams selected as the 1st to
3rd place winners of each category at the national competition
have the right to participate in the International Robotex tour-
nament. During the international competition, various expo-
sitions, technology exhibitions, and workshops for young
people to take place, making Robotex a technology festival.
The International Robotex’s competitions are separated into
five categories, including Beginners, Intermediate, Advances,
Entrepreneurial challenge, and Girls, reflecting the age and
degree of difficulty of the tasks.
The Beginners’ category consists of four competitions with
two main subjects, the line-following (line-following and the
Makeblock line-following) and the Lego Sumo (Lego Sumo
and 3kg Lego Sumo). It also includes a non-competitive
robotics exhibition called ‘Insplay Robo League’. The Inter-
mediate category includes two Sumo challenges (Micro and
Mini Sumo), two line-following challenges (Enhanced and
Arduino line-following), a Maze Solving challenge, and two
race challenges (Folkrace and Water Rally). The Advanced
category consists of 6 challenges, three of which invite con-
testants to build robotic solutions for a given problem (City
Kratt, Animal Rescue, and Robotics Drone Race). The other
three challenges are Mega Sumo, Basketball, and Mind Con-
trol, where the challenge is to solve a problem by controlling
the robot’s movement with your mind.
The Insplay Robo League, the non-competitive exhibition
for the Beginners category, is a themed based challenge for
kindergarten and elementary school students. Each year, par-
ticipants are invited to build a robotic project based on the
given theme and present it to the mentors on the competition
day. Following the project’s presentation, teams get feedback
from the mentors on the idea and its execution, the program,
and the teamwork.
For the line-following challenge, teams must construct and
program a robot that will autonomously drive through a track,
marked with a black line on a white surface, as fast as pos-
sible. Line-following has various versions as a contest in the
Robotex tournament, based on the robotics platforms allowed
or variations in the rules. Lego line-following, Makeblock
line-following, and Line-following correspond to the use
of Lego, Makeblock, and Arduino based robotic platforms,
respectively. The Makeblock challenge increases difficulty
as the robot must also avoid obstacles and compete with
various challenges as it follows the track. The Enhanced
line-following is another variation with increased difficulty,
introduced by the addition of obstacles or changes of the
line thickness or coherence of the track that the robot must
traverse to complete the race. The line-following challenge is
exceptionally competitive and attracts the interest of schools
and universities around the world [157].
The Sumo challenge in Robotex resembles the human
sumo wrestle where a wrestler attempts to force his opponent
out of a circular ring. In the robotic competition, a sumo
robot competes against a robot opponent, aiming to push its
opponent out of the ring. Teams must develop a robot and
program it to locate the opponent, attack or resist an attack,
and avoid falling out of the game field. The Sumo challenge
has four versions in Robotex. Lego Sumo and 3kg Lego
Sumo challenges are organized only for the Lego Education
Mindstorms EV3/NXT and Spike robotic platforms. In the
Mini Sumo and Micro Sumo challenges of the Intermediate
level, participants must build their robotic solutions using
Arduino based platforms. The Mega Sumo challenge found
on the advanced category, allows teams to use any robotic
platform for their solution.
For the Maze Solving challenge, an autonomous robot
drives through a maze, starting at a predefined corner and
moving towards its center in the shortest possible time. For
the robot construction, teams can choose between Arduino,
Raspberry, Pi, ARM, ESP, Engino, Lego EV3, or Lego Spike
robotics platforms. The robot cannot jump over, fly over,
or climb the maze walls to reach the destination square; it can
only drive through the paths. The maze map remains secret
until the day of the competition, and each team has to prepare
by developing a generic code that can perform successfully in
any maze. Robots are ranked based on the minimum official
time taken to reach their final position and the minimum
distance of this final position from the target.
Folkrace simulates rallycross, where up to five robots com-
pete against each other on the same track. The objective is to
complete the field in the correct direction as many times as
possible. The winner is the robot that earns the most points
within a three-minute time frame. Teams are free to choose
between the available platforms and adjust their robots based
on the competition’s needs. Extra features might be intro-
duced to make the race more enjoyable, including simple
obstacles like hills, holes and loose materials. Water Rally is
similar to Folkrace with the track placed in the water. More
precisely, autonomous robot boats must complete laps in a
small pool filled with obstacles.
In addition to the ground and water races, Robotex also
offers an air race called the Robotics Drone Race challenge.
This race is considered to be more advanced than the other
two. The goal is to build an unmanned aerial robot (drone)
that flies an eight shaped figure around two poles. The fastest
robot to complete the task and reach the landing point wins
the competition.
The Animal Rescue and the City Kratt are two advanced
challenges aiming to inspire teams to create autonomous
robots for specific purposes. In the Animal Rescue chal-
lenge, the robots must find and rescue animals lost in the
city. To develop the required robots, the participants apply
machine learning and object recognition skills. Unlike the
other Robotex games, the focus of the Animal Rescue chal-
lenge is on software development. Thus the participants are
entitled to use prebuild hardware platforms for their project.
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For the City Kratt competition, teams must create an inter-
active Kratt, a character of Estonian legends, a house servant
built from hay or old household items. Inspired by the myth,
each team has the task of building an artificially intelligent
house manager who can welcome visitors, direct people
around the building, and entertain them while they wait for the
host. The winning teams are encouraged to enter the market
with their product.
The Basketball competition imitates a real basketball
game. Two robots compete against each other, trying to
score as many balls into the opponent’s basket as pos-
sible within 60 seconds. Robots must be programmed to
be autonomous, recognize the green squash balls randomly
placed on the floor, collect them, and put them into the
baskets. To win, a robot must throw more baskets than its
opponent.
For the Entrepreneurial Challenge, the participating indi-
viduals or teams have to create an innovative working robotic
prototype. The robotic product can be applied in any area,
such as health or engineering. The prototype must include
electronic components and solve a real-world problem. The
teams must present their prototype during the competition,
to the visitors, other participants, potential investors, and the
press. They will receive real-world feedback, and at the same
time, they will compete with each other to get the most votes
from the visitors of Robotex and win.
The Girls firefighting challenge was founded to encourage
girls from all over the world to participate and engage with
the world of technology and engineering. The competition’s
objective is to create and program a firefighter robot to locate
and extinguish four randomly placed candles, without touch-
ing them. All the four candles stand at the center of a white
circle, and 3 of them are blocked by walls. Teams can use any
Arduino, Engino ERP, Engino Produino, Lego EV3, and Lego
SPIKE platforms to create their solution. Points are given
based on the number of candles extinguished by each team.
FIRST (For Inspiration and Recognition of Science and
Technology) is a STEAM engagement organization aiming
to encourage kids to engage with engineering, science, and
technology. It consists of three programs, the FIRST Lego
League, the FIRST Tech Challenge, and the FIRST Robotics
Challenge. Through these programs, FIRST also aims to help
children build self-confidence, knowledge, and life skills.
The FIRST Robotics Competition (FRC) is an interna-
tional high school robotics competition. FRC is the final
event of the season where the winners of each regional FRC
competition can participate. FRC allows students to work
on a real-world like engineering projects through engaging
challenges while volunteer professional mentors guide them.
This procedure inspires students to pursue careers in science
and technology. The robot challenge changes every season.
Students must create teams of 10 or more, raise funds to
support their effort, and build and program an industrial-size
robot, using a standard ‘kit of parts’, to play a sophisti-
cated field game against their competitors. In addition to
on-field competition winners, there are other awards for the
participating teams recognizing the critical features for
designing, building, and programming a robotic solu-
tion and teamwork skills, such as Digital Animation
Award,Engineering Inspiration Award, e.t.c.
FIRST LEGO League (FLL) is a partnership between
FIRST and Lego Corporation. The FLL extends the FIRST
concept to promote young people’s interest in STEAM by
using Lego Robotics to children ages 4-16. FLL has three
divisions based on students’ age, including the FLL Discover
for ages 4-6, the FLL Explore for ages 6-10, and the FLL
Challenge for ages 9-10. Every year, FLL releases a new
Season Topic based on a real-world theme like, e.g., City
Shaper for 2019. In all divisions, besides completing their
project, the students are expected to familiarise themselves
with the Core Values of the FLL. Those are Teamwork,
Inclusion, Discovery, Innovation, Fun, and Impact.
The FLL Discover and FLL Explore are non-competitive
events. In the FLL Discover, students work in teams of 4 with
an exclusive LEGO Education DUPLO set to create solutions
for the given challenge. While they explore the given theme,
they are introduced to the fundamentals of STEAM. Partic-
ipants in the Explore category work in teams of 2 to 6 chil-
dren, with LEGO Education WeDo 2.0 kit to design, build
and program robots based on the seasons’ challenge. They
must also create a team poster to present their findings and
their learning journey through this process. In both divisions,
teams meet up to present their projects, meet other teams,
and celebrate what they have learned during the season at a
celebration event. The FLL Challenge has three aspects: the
Robot Game, the Innovation Project, and the FIRST Core Val-
ues. Teams may have up to 10 children, and they must use the
Lego Mindstorms set to build and program a robot that will
complete specific missions in the Robot Game table. The aim
is to collect as many points as possible in the allotted time.
Besides completing the challenge, teams must also present to
the judges the innovation of their solution, answer questions
about their code and robot, and present their knowledge from
the preparation phase.
FIRST Tech Challenge is addressed to grades 7-12, and
team members can be up to 15 students. In this challenge,
the team’s goal is to design, build, and code robots to compete
in an alliance format against other teams. Along with the
robotic game, teams must also create, promote, and raise
funds for their team brand. FLL offers a specific robotic kit
to the members to create a remotely operated vehicle. The
FLL is controlled by an Android-based platform. The robot
can be programmed with a variety of levels of Java-based
programming. In addition to the Robot Game, teams can also
win judges’ awards line FIRST Dean’s List, Inspire Award
and Think Award e.t.c.
The Robot World Cup (RoboCup) is an annual event cre-
ated to promote robotics and AI research by setting a common
challenge. The main task of RoboCup is robot soccer, where
autonomous robots play soccer in a dynamic environment.
This task was chosen as soccer is a popular, beloved activity
and a complex, real-world problem that raises researchers’
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interest. Starting from 1997, researchers worldwide meet in
RoboCup competitions and scientific meetings to integrate,
test, present, and discuss their solutions, theories, and algo-
rithms [158]. RoboCup ambition is by the middle of the 21st
century, a team of fully autonomous soccer robots to win a
soccer game against the World Cup’s latest winner, accord-
ing to FIFA rules [43]. RoboCup has continuously evolved
through the progression of research and technology. Today,
robot soccer remains RoboCup’s main event, while four
more research-oriented leagues were added: RoboCup Res-
cue League, RoboCup@Home, RoboCup Industrial Leagues,
and RoboCupJunior [43].
RoboCup Soccer is divided into five robotic chal-
lenges: the Soccer Simulation League, Small-Size League,
the Middle-Size League, the Standard Platform League, and
the Humanoid League. Simulation League addresses research
questions in high-level decision making and team coordi-
nation. The games may be played in a 2D or a 3D vir-
tual soccer pitch, constructed within a computer. In the
2D environment, physics rules and agent representation are
simplified [159], [160]. The 2D simulator is a useful
research tool for autonomous decision making, formulating
team strategies and opponent modeling and adaptation [43],
[160]. Extra realism and more complex rules and physics
are included in the 3D League. In the 3D field, players
are simulated as NAO robots with articulated bodies. The
3D simulation environment is built with SimSpark, a multi-
agent system simulator [159]. Unlike 2D League, the research
interest in 3D simulation is not the design of the agents’
strategic behaviors when playing soccer. For 3D League,
the aim is the low-level control of the simulated humanoid
robots and the realistic simulation of robot behaviors like
walking, kicking, turning, and standing up [43], [160].
For both Leagues, the team’s simulators and binaries are
publicly available, making it easier for the community to
expand its solutions [160]. In the Small-Size League (SSL),
a fast-paced robot game takes place between teams of
semi-autonomous robots. Players are cylinder-shaped robots
of maximum 180 mm diameter and 15cm height that move
omni-directionally [43], [161]. Robots also have a sin-
gle kicker and spinning dribbler bars for controlling the
ball [160], [161]. The players’ position is tracked by a global
overhead vision system that helps teams focus on software
algorithms, hardware, and control engineering instead of a
ball and robot localization and mapping [161]. Teams use
an off-site computer to create and send commands to the
agents/robots according to the information received from
the vision system [43], [161]. Middle-Size League (MSL)
is the closest League to the real soccer while it encloses
mechatronics design and multi-agent coordination. In MSL,
five fully autonomous robots play soccer with a regular size
FIFA soccer ball on an 18m*12m field. Teams can design and
build their robot, ensuring that all the sensors and computing
power on-board. The most challenging task for a robot in
MLS is to pass the ball to its team players while passing
through the defense on the opponent team [43], [162]. In both
SSL and MSL participating teams are free to design their
own custom-made robots that satisfy each challenge rules. In
the Humanoid League (HL), soccer players are autonomous
robots with human-like bodies and senses. Teams must build
the mechanical and electronic parts of the robot and develop
its software. While playing soccer, robots must walk steadily,
visually perceive the ball, the players, and the field limits,
kick the ball, and self-locate in a spatial environment [163].
In the HL, there are 3 different size categories: KidSize,
TeenSize and AdultSize [164]. For KidSize and TeenSize
leagues, every match is played by two teams, each consisting
of field players and a goalkeeper. In an AdultSize league,
a team consists only of one field player. To meet the official
FIFA rules, the game rules of the HL are adapted every
year [165]. Standard Platform League Unlike HL, in Standard
Platform League (SPL), all teams use the same humanoid
robot platform, the Nao robot. Teams are not allowed to mod-
ify NAO’s hardware; thus, they focus on designing software
solutions and improving robot movements. Robots must play
completely autonomously, while they can communicate with
their team players [166].
RoboCup Rescue League comprises two Leagues: the Res-
cue Robot League and the Rescue Simulation League. In the
RoboCup Rescue Robot League, participants must develop
and demonstrate advanced robotic capabilities for emergency
responders in a hostile environment [167]. The League uses
realistic scenarios such as an earthquake, a flood, or a
fire [166]. In the rescue missions, robots face various chal-
lenges, including mobility, mapping, sensing, manipulation,
communications, and confined space operations. Teams may
use standardized robot platforms or create their rescue robots.
The League also created the Open Academic Robot Kit, a set
of open-source licensed resources online, where teams can
find instructions of robot designs created by 3D printable
mechanical parts and source code [167]. Teams are allowed
to use teleoperated robots with some autonomous capabilities
as assistance functions for the operator [43], [166]. Rescue
Simulation League’s objective is to develop simulators to
realistically represent natural disaster scenarios and develop
virtual emergency response robots or agents. This League is
separated into two sub-leagues, the Virtual Robot Simulation
competition, and the Agent Simulation competition [167].
RoboCup@Home intends to develop service and assistive
robots that can perform everyday tasks in dynamic home
environments [160], [166]. This category’s domestic service
robot has to cope with challenging tasks such as localization,
speech recognition, or grabbing and manipulating objects.
The robot abilities and performance are evaluated based on
a set of benchmark tests in a dynamic home environment
setting [160]. Therefore, League’s researcher interest focus
on a combination of domains: human-robot-interaction and
cooperation, navigation and mapping in dynamic environ-
ments, computer vision and object recognition under natu-
ral light conditions, object manipulation, adaptive behaviors,
behavior integration, ambient intelligence, standardization,
and system integration. RoboCup@Home is separated into
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three categories based on the allowed platforms. The open
Platform category use custom platforms and two standard
platforms the Toyota Human Support Robot (HSR) and
Pepper from SoftBank Robotics are used for the Domestic
Standard Platform League and the Social Standard Platform
League, respectively [166].
RoboCupIndustrial focuses on the industrial domain,
where mobile robots are deployed to perform several
industrially-relevant tasks. Two sub leagues, the RoboCup@
Work and the RoboCupLogistics League (RCLL) are avail-
able from the RoboCupIndustrial [166], [168]. RoboCup@
Work, robots equipped with advanced manipulators and sen-
sors, cooperate with human workers for complex tasks in
work-related scenarios [168]. RCLL is inspired by the indus-
trial scenario of a smart factory. According to dynamic orders,
multiple mobile robots must plan, create, and adjust a produc-
tion plan. In RCLL, a mobile robot from Festo, the Robotino
is used as the standard platform of the competition [169].
RoboCup Junior (RCJ) is an entry-level League for the inter-
national RoboCup initiative, where young students under
19 years old are introduced to STEM education. Students
in the RCJ have to design, build, and program autonomous
robots in a team setting. They are free to develop their
robotics platforms using any robotic kit or material to create
custom-made 3D printed or laser cut parts. Three Leagues
are available in RoboCup Junior: Soccer, Rescue, and Dance.
Two of the activities were created after the RoboCup main
events, the RoboCup Soccer and the RoboCup Rescue. The
third competition, RCJ Dance, was created to integrate arts
and edutainment into STEM [31]. While the final goal
remains the same as the significant events, in junior versions
of the soccer and rescue leagues, rules and regulations are
slightly simplified. In the dance competition, teams dance on
stage with their robots, a creative choreography they have
developed, and compete against other teams [166]. A lot of
researchers highlight the benefits that students gain from par-
ticipating in RCJ. Most of them emphasize social teamwork,
programming, involving in technology and robotics, develop-
ing problem-solving skills, and having the opportunity for a
possible career in STEM fields [31], [43].
The Federation of International Sports Association (FIRA)
is a robotic competition, which uses sports as benchmarks for
AI and robotics research. FIRA consists of 4 main categories
FIRA Sports, FIRA Youth, FIRA Challenges, and FIRA AIR
and encourages students to create their own custom-made
robotic solutions, avoiding the use of any commercially avail-
able ER platform.
FIRA Sports focuses on soccer, a robotic challenge ideal
for finding solutions to the multi-agent automated system’s
problems. FIRA Sports has four sub-leagues: HuroCup,
RoboSot, SimuroSot, and AndroSot. HuroCup is a humanoid
robot competition, emphasizing the development of flexible,
robust, and versatile robots that can perform several tasks
in complex environments. HuroCup encourages research
into relevant areas of humanoid robotics, especially active
balancing, complex motion planning, and human-robot
interaction with the use of humanoid robots. HuroCup
includes seven events Basketball, Climbing, Lift and carry,
Long jump, Marathon, Obstacle run, and Sprint focusing
on object manipulation, complex motion planning, hand-eye
coordination, navigation skills or endurance [170]. Students
and researchers can participate in the corresponding cate-
gories HuroCup Kid and HuroCup Adults of the HuroCup
competition. In the RoboSot match, two autonomous intel-
ligent wheeled mobile robots play soccer against each other
in a specific game field. Robots must be fully autonomous,
while they can only communicate and interact with their
team’s other robots. Except for the main soccer game,
RoboSot consists of a series of additional challenges like
vision challenge, motion challenge, and race challenge [171].
The SimuroSot competition is a simulation league where
teams of simulated robots play soccer. This category aims
to help researchers and students focus on developing control
algorithms and team strategies without the need for compli-
cated and costly hardware setup [172]. Finally, in the AdroSot
challenge, humanoid robots play soccer while controlled by
a global vision system. In AndroSot, research is concentrated
on advancing the abilities of attack and defense in androids.
In AndroSot soccer, game robots must perform tasks like
dribbling, obstacle avoidance, shooting, trajectory detection,
goalkeeping, role arrangement, and positioning control.
The FIRA Youth League is a student-oriented (under
19 years old) event, offering a set of challenging events
including Sports Robots, Innovation and Business, HuroCup
Junior, CityRacer, DCR-Explorer, Cliff Hanger, and Mission
Impossible. The Youth category aims to allow the younger
researchers to develop their ideas and learn about robotics
inside an attractive environment. In Sports Robots, teams
must build and program a robot that will perform tasks rel-
evant to the sports field, like weightlifting, kicking the ball,
or pushing obstacles. The Innovation and Business League
is a call for inventors and students who want to better under-
stand starting a startup company. Teams must solve a real-life
problem through a project and demonstrate it to investors and
industry executives, medium and professional professors in
the exhibition venue. For a project to be complete, students
must create both the robot’s hardware and software and create
a business and marketing plan. The CityRacer is deigned to
challenge junior and high school students‘ problem-solving
skills. Teams must create a robot able to track and follow
a line on the floor and traverse the uneven terrain. The
robot must also lift and manipulate small items that are
randomly placed in the field. Students participating in the
DCR-Explorer league must create an autonomous explorer
robot to surpass obstacles in a disaster area, to deliver rescue
packs to victims. The Cliff Hanger challenge is a sumo fight,
where two robot opponents fight in a circular playing surface
with a cylinder fixed at the center. Based on the robot size,
there are two categories, Lightweight (<=1Kg) and Heavy-
weight (1Kg - 3Kg), while robots in both categories must
be autonomous. Through the Mission Impossible league, stu-
dents use their imagination and creativity to solve challenging
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tasks, such as collecting treasures. Teams must create their
robots with a limited set of materials during the construction
phase and compete in the game field.
To stimulate researchers’ interest, FIRA created
3 Challenges. One of those corresponds to the Innovation
and Business challenges in the FIRA Youth League. The goal
and objectives of this challenge are the same as the student’s
version. The other two Challenges are Autonomous Cars and
Warehouse Robots. With these challenges, FIRA encourages
researchers to develop robots for autonomous driving and
storage for two real-life tasks.
With FIRA AIR competitions, FIRA encourages students
and researchers to work with autonomous flying robots in
commercial and industrial applications. In all four events
included in FIRA AIR, Autonomous Race, Autonomous
Race U19, and Emergency Services Indoor and Outdoor, par-
ticipants have to develop efficient, robust, and autonomous
drones and cope with challenging tasks as localization, explo-
ration, and intelligent navigation in dynamic environments.
RoboGames (previously known as ROBOlympics) is an
annual robot contest with various challenges mimicking
the human Olympics. It is held in the United States, and
competitors from all over the world can participate. It is
known as the world’s largest open robot competition [173].
Over 70 different events are to participate, divided into
ten categories: Humanoid, Autonomous Humanoid Chal-
lenges, Sumo, Combat, Robot Soccer, Open, Jr. League,
Autonomous Autos, Art Bots, and BEAM. Thus, RoboGames
engages both custom robots and commercially available ER
kits, like Lego Mindstorms series. Most of the robots in
the events are autonomous, while only some are remotely
operated by the teams. RoboGames was founded to bring
together robot builders from different areas of interest and
professional formation to collaborate and exchange ideas.
Moreover, RoboGames is open to everyone, and thus partic-
ipants may be students, professionals, researchers, and hob-
byists, regardless of their age, affiliation, country of origin,
or gender [173], [174].
RoboMaster is a relatively new competition, started only
in 2015 from China, and expanded to International compe-
tition. It is powered by Da-Jiang Innovations (DJI) and is
addressed to college students and young engineers. Robo-
Master is a fighting robot competition divided into four
events, the Robotics Competition, the Technical Challenge,
the AI Challenge, and the RoboMaster Youth Tournament.
Participants can use only the official DJI RoboMaster robotic
kits, including the RoboMaster EP, the RoboMaster S1, and
the AI Robot. In the Robotics Competition, university stu-
dents must develop different robots, such as vehicles or aerial
robots that will cooperate in fighting an opponent team.
Robots can be fully-automated or remotely operated while
they will attack the opposing team’s robots with projectiles
to destroy their base. The Technical Challenge aims to attract
researchers‘ interest in a specific filed of robotics. Like in
the Robotics Competition, participants should be from higher
education. Teams in this competition must develop one robot
for one challenge. This challenge aims to motivate partici-
pants to research a specific technical field in robotics and
seek in-depth solutions to perfect their robots. The AI Chal-
lenge is co-sponsored by the DJI RoboMaster Organizing
Committee (RMOC) and the IEEE International Conference
on Robotics and Automation. In this event, university stu-
dents must develop algorithms for a given robotic platform
to enable robots to make independent decisions, move, and
fire in the field.
MakeX started in 2017 and is a global robotics competition
for students of different ages. The competition is driven by
the spirit of creativity, teamwork, fun, and sharing and aims
to introduce young learners in the STEAM fields. It pro-
vides four challenges: Spark, Starter, Challenge, and Premier,
and participants are allowed to use only official MakeBlock
robotics kits like mBot. The Spark program invites 6 to
13 years old students to create teams of 2-4 people and par-
ticipate in a project-based event. Participants must construct
and program their project within a specific time and present it
to the audience and the judges. Based on their demonstration,
teams will get feedback from the judges. Spark event aims to
promote students’ creativity, imagination and advance their
problem-solving and logical thinking skills. The Starter event
focuses on improving the social skills of students between
6-16 years old. Teams of a maximum of 2 students must
design and program a robot, to work both automatically and
manually. The competition requires a corporation between
two teams to complete several independent and alliance
missions. Young students between 11 to 18 years old can
participate in the MakeX Challenge program. In this pro-
gram, the competition is held between two unions. Each
alliance consists of two teams, and they must work together
to complete specific tasks. Following the Starter program,
the Challenge event is divided into the automatic face and the
remote-control face. Teams of 2-8 people have to use their
engineering knowledge to construct their robotic solutions
and employ their logical thinking skills and decision-making
ability in the game field. The Premier program encourages
students over the age of 14 to participate in an aggres-
sive robotics competition. Two alliances, each consisting
of two teams, play against each other to collect points
and win the game. Every coalition must pass through the
four stages of a match: Automatic, Manual, Modifica-
tion, and Final. For this program, participants build and
modified their robots during the game, making decisions
about their strategy and cooperating with their alliance
team.
B. EUROPE
RoboParty is a robotic three-day camp, organized at the
University of Minho in Portugal. This non-competitive event
aims to teach electronics, mechanical engineering, and pro-
gramming to school-age children [175]. Participants cre-
ate teams of 4 people, consisting of 3 students and one
adult. The educational event includes lectures, speeches,
hands-on classes, and robotic demonstrations. Lectures teach
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participants how to build the electronics, assemble the
mechanics, and program their robot. Moreover, two speakers
present their research area expertise to increase the students‘
knowledge of robotics and relevant scientific fields. Students
build their robot during the workshop using the Bot‘n Roll
One A robotic kit, which was developed especially for this
event. The construction face includes soldering the electronic
components on the electronic board, assembling the mechan-
ical components, and programming the robot. As Bot‘n Roll
One A platform is Arduino based, students use C language to
create their code. Finally, three challenges including avoiding
obstacles, following a line and dancing, are given to the
students to test their robots and algorithms [176].
C. ASIA
The Asia-Pacific Broadcast Union Robot Contest (ABU
Robocon) is a themed based robotic competition for higher
education. Since 2002, ABU Robocon is held among asso-
ciated member countries of the ABU. The host country,
inspired by its culture, defines the theme and the rules of the
competition [177]. The ABU contest’s main objective is to
develop multidisciplinary and multi-professional knowledge,
creativity, collaboration, and innovation among university
students. Participants must analyze the contest challenges
and work as a team to solve the given problems. They have
to design and build both the hardware and the software of
their robotic solution. ABU Robocon is characterized as a
high-level robot contest. Thus participants must have a strong
academic background to be competitive [177], [178].
ROBO-ONE is a robot fighting competition in Japan,
where two small-size humanoid robots do battle in a fighting
arena. The competition is open to public participation and
is favored mainly among armature hobbyists [179], [180].
Participants may be young students, university researchers,
hobbyists, or adults with engineering backgrounds and fam-
ilies. Five tournaments are included under the ROBO-ONE
umbrella. The primary ROBO-ONE game features two
bipedal walking robots that are remote-controlled by the par-
ticipants. The objective of the game is to take their opponents
down or force them outside the ring. Robots are built with
parts from hobby robot kits and are programmed to walk, run,
and perform gymnastics, dance routines, or combat move-
ments. Participants may dress up their robots as mechanical
warriors, animal-like characters, or fantasy figures. The Light
version of the competition follows the same rules as the main
game but is addressed to beginners. Participants are allowed
to use commercial robot kits certified by the ROBO-ONE
Committee.
In comparison to the original version of the competi-
tion, robots are fully autonomous anthropomorphized robots
in the ROBO-ONE auto challenge. ROBO-ONE Kendo
and ROBO-Ken Arm are two contests: bipedal robots and
one-armed robots perform ‘Kendo’ swordsmanship, a tradi-
tional Japanese martial art. Even though ROBO-ONE show-
cases humanoid combats, there is no sense of aggression
in the events [179]. Besides the competitions, participants
usually create local groups and meetings to exchange
information [180].
D. USA
Boosting Engineering, Science, and Technology (BEST) is a
national competition in the United States, where middle and
high school students can participate. BEST aims to increase
students’ interest in pursuing a degree or a career in STEM
fields. The competition lasts for 6-weeks, starting from the
Kick-Off Day where the game theme, the playing field, and
an overview of the game is revealed [47]. Every team receives
a kit of parts for their projects. The kit includes construction
materials such as plywood, fiberglass board, metal sheet,
and a box which is filled with raw materials, such as PVC
pipes, screws, valve cover, piano wire, aluminum paint grid,
a bicycle inner tube, rollerblade rollers, duck tapes, and a
micro-energy chain system. It also includes electrical com-
ponents such as the brain, controller, servers, DC motors,
and sensors [47], [181]. Teams can choose any software they
wish.
BEST, among other software, propose MathWorks, EasyC,
Robot C, Computer-Aided Drafting software of SolidWorks,
and HSMWorks; Training programs offered by InspirTech;
Computational Tool of Wolfram Mathematica; Control Sys-
tem provided by VEX. With the components, teams must
design and build a remote-controlled robot and complete
a set of tasks within a specific time. Before creating their
robots, teams can also create a 3D platform simulation
to test their ideas. Furthermore, teams can also participate
in additional events about their oral presentations, techni-
cal writing, web design, or video production. This moti-
vates students with different interests to work in smaller
groups focusing on a specific task. For example, a team
may consist of smaller groups, including an engineering
group, a marketing group, and a creative design group.
However, teams are not required to participate in all the
events. Except for the Engineering Notebook and the Robotic
Game, all the other events are mandatory. When a team
succeeds at a local hub Game Day, it can participate in
the regional competition and then proceed to the National
Championship [47].
Table 2presents the competitions in relation to the plat-
forms that the participants are allowed to use. Interna-
tional competitions offer the participants the opportunity
to use and experiment with a variety of robotic kits. It is
observed that most of the competitions use the ER kits pre-
sented in Section IV. Some challenges, in specific compe-
titions also allow the use of custom-made solutions, while
only a small number of the robotic events prohibit the
use of commercially available robotic kits. In their major-
ity, competitions engage platforms from the ’Basic Code’
and ’Advanced code’ category. Although tangible program-
ming is suitable for learning basic programming concepts
using ’No Code’ robots is not compelling in a competition
setting.
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TABLE 2. Approved ER Platforms in Competitions.
VI. MAPPING EXPECTED LEARNING OUTCOMES WITH
EVENTS AND COMPETITIONS
In the current section, we correlate the learning outcomes
of Section III with the competitions presented in Section V.
More precisely, we identify each competition’s six proposed
learning outcomes based on its characteristics, rules, and
goals. The efficacy of each expected learning outcome is
presented in Table 3, as Limited, Moderate, or Strong.
LO1: Problem-solving skills
Participants in robotics competitions face a problem-solving
process, during which they must define, examine, and find
solutions to scientific problems. In most competitions, this
process takes place before the day of the match as the chal-
lenges of the contest, the rules, and the game fields are given
to the teams before the competition. Even though participants
develop problem-solving skills, those competitions’ efficacy
is considered to be Moderate (Table 3). The problem-solving
process occurs before the competition day, and participants
can only prove their skills through their ready-made solu-
tions. Teams in these competitions have guidance from their
coaches, wide availability of resources, and sufficient time to
work and prepare their robotic solution in advance.
On the other hand, competitions that offer extra chal-
lenges on the day of the event, such as hidden parameters,
additional rules or restrictions, and secret fields, are eval-
uated as Strong. These competitions allow the participants
to implement and demonstrate their problem-solving skills.
For instance, the WRO Regular Category is characterized as
Strong in Table 3since, although most of the challenge rules
are known, a surprise rule or task is given to the participants
on the competition day. Teams must solve the extra problem
by adapting their robotic solution, without help from their
coach within a specific time. In this way, teams evidence their
problem-solving ability. Finally, since all robotics competi-
tions involve applying problem-solving strategies to different
contexts, none of the competition appears as Limited.
LO2: Self-Efficacy
According to Banduras’ work, there are four self-efficacy
sources, including enactive mastery experiences, vicari-
ous experience, verbal/social persuasions, and emotional
arousal [182]. Mastery experience is considered the most
effective way of enhancing self-efficacy, and it has the most
significant correlation with robotic competitions. This means
that a person’s sense of efficacy is boost by successful accom-
plishments. Besides, the difficulty of a task and the required
amount of effort affects a person’s perceived effectiveness.
Students participating in robotics camps and competitions
increase their self-confidence in performing robotics tasks
as they experience designing and programming their robots.
To achieve this, they must have specific roles and respon-
sibilities while working in a team. Based on the above,
we classify as Moderate the competitions that even though
they require the contestants to work in small groups, their
role is not clearly defined. For example, in the Sumo Robotex
competition, teams consist of 2-5 students, with one member
acting as the leader who is the robot’s operator during the
game. Accordingly, a bigger group that would be unable to
offer sufficient time and space for its members to build their
self-efficacy would be categorized as Limited, even though
such a case has not been observed. Additionally, some of
the competitions are Strong, like RoboCup Rescue League,
FLL, or WRO. This category requires the participants to
have defined roles and prove their work by presenting their
robotic solution, answering clarifying questions, creating
their robotic solution on the day of the competition, or effec-
tively control their robot’s action to perform specific robotic
tasks.
LO3: Computational thinking
Decomposition, abstraction, algorithms, debugging, itera-
tion, and generalization are the most common components
of Computational Thinking based on the various definitions
found in the research literature [183]–[185]. This means
that participants in robotic competitions must systemati-
cally approach problems through a series of ordered steps.
They must break down the problem into smaller parts of
particular functionality and sequence the parts (decomposi-
tions), extract the most relevant information from a problem
(abstraction), represent their solution as ordered instructions
(algorithm), detect and fix possible errors in an incorrect
answer (debugging), systematically test and modify the solu-
tion to achieve the most efficient solution (iteration) and
quickly solve new problems based on previous solutions
to problems (generalization). These Computational think-
ing facets are expected to be applied by the participants in
robotics competitions’ challenges. Since those facets are used
in all competitors, none of them appears as Limited in Table 3.
Focusing on the lack of generalization on the Project-based
competitions, such as MakeX Spark, we categorize them
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TABLE 3. Correlating the proposed learning outcomes with the competitions: •••Strong, • • Moderate, •Limited.
as Moderate. On the contrary, on challenges like maze and
line-following, students are expected to develop a generic
code to perform successfully in any field. For instance, in a
maze competition, students need to decompose the problem
to understand how to exit the maze, grasp the major concepts
that define the problem, and work on an algorithm applied
to all mazes. In that sense, these competitions are Strongly
enhancing the Computational Thinking skills of the partici-
pants.
LO4: Creativity
Building and programming a robot to do a specific mission
is an intriguing task for the students’ creativity. Robotics
competitions promote students’ creativity by challenging
them to think of new solutions or recreate the existing ones
using an innovative method. Our evaluation argues that games
that use a standard ready to use the platform inhibit team
creativity. An example is the RoboCup@Home Social Stan-
dard Platform League, where the team’s invention is limited
to developing their algorithms. As the students cannot design
and construct their novel robotic structures, these competi-
tions are classified as Limited. The rest of the matches are
differentiated to Strong and Moderate based on the free-
doms and constraints of the creative process. Research on
creativity has revealed that putting limitations on the set
of possible methods or recourses available to an innovative
team can provide helpful boundaries to provoke and structure
the collective creative process [186]. From this perspective,
BEST competition is described as Strong since it allows for
participants with specific and limited materials to construct
their robotic structures. On the contrary, in a VEX challenge,
the participating teams may use any number of parts, as long
as they pick them exclusively from the original licensed parts.
L5: Motivation
Robotics competitions are motivational because they offer
an exciting and fun learning environment, thus among
40 Challenges, there is no Limited evaluation in Table 3.
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The nature of a challenge and the opportunities that com-
petition can offer to the participants are the criteria that
differentiate the Strong and Moderate effectiveness of games
on participants’ motivation. Competitiveness, demonstrated
by the desire to defeat others, is an essential aspect of compe-
tition. Fighting robot challenges, like sumo, and robot games,
like soccer, tend to attract and retain participants’ interest and
enjoyment. Two representative examples are the ROBO-ONE
and FIRA Sports competitions, characterized as Strong based
on the above criteria. Besides, prizes, travel grants, and the
opportunity to participate in worldwide contests increase par-
ticipants’ motivation. In this way, the Robotex line-following
challenge, which is neither fighting nor a gameplay chal-
lenge, is also evaluated as Strong, as it offers the winners of
the regional competitions the opportunity to participate in the
International Robotex contest.
L6: Collaboration
Collaboration allows individuals to work together to
achieve bigger goals. However, the team’s collaboration qual-
ity can affect its performance in a robotic competition. Also,
teamwork requires shared accountability between individu-
als, the interdependence between them, and clarity of roles
and goals. Thus, the absence of a team and the acceptance of
individual participation in a robotic competition is character-
ized as Limited. Notably, for all of the matches presented in
this paper, individuals are only allowed to participate as team
members. However, having team-based participation is not
the only criterion for a competition to be ranked as Strong
for promoting participants’ collaboration. Games without
clearly defined roles are evaluated as Moderate, while the
opposite leads to a Strong evaluation. Moreover, competitions
like VEX or MakeX are also characterized as Strong, since
they require participating teams to join forces and compete
against other alliances. In this way, participants demonstrate
their collaboration skills, as they must communicate, share
knowledge, and exchange ideas with strangers to accomplish
a common goal.
By observing the evaluation results presented in Table 3,
one concludes that all competitions contribute to every learn-
ing outcome. It is observed that some competitions promote
specific learning outcomes more than others. However, none
of the competitions seems to contribute to all of the defined
learning outcomes ‘Strongly’. Only the Regular category of
the WRO event and the BEST competition receive a ‘Strong’
rating in 5 out of 6 learning outcomes. The former has a
‘Moderate’ evaluation for the Creativity skill and the latter
receiving a ‘Moderate’ evaluation for the Problem-Solving
skill.
In regards to Problem Solving, all of the competitions
except for one were classified as ‘Moderate’. On the contrary,
most of the competitions reveal a ‘Strong’ enhancement of
the participants’ Computational Thinking skills. Even though
there is a ‘Strong’ link between Problem Solving and Compu-
tational Thinking, the different approaches to the evaluation
criteria, including extra challenges and generalization, led
to this result. The existence of a small team with defined
roles and responsibilities played a significant role in both
learning outcomes of Self-efficacy and Collaboration. Thus,
these two learning outcomes display related results. The only
difference is that the latter has an additional criterion for pro-
moting the participants’ Collaboration skills. The competi-
tions that require alliances between stranger teams encourage
more social interaction between them, as described above.
It is noteworthy that ‘Moderate’ evaluation prevails in the
Creativity’s column, although it is considered one of the
most common skills a student earns from activities involving
robotics. Besides, Creativity is the only learning outcome
where ‘Limited’ evaluation appears. Limitations on methods
or recourses are the point of comparison that stretches partic-
ipants’ Creativity. Concerning the Motivation factors set on
this paper, almost all of the competitions achieve to ‘Strong’ly
stimulate students’ interest, with few exceptions achieving it
to a lesser extent.
Overall, ‘Strong’ is the most frequent rating in both Com-
putational Thinking and Motivation. This implies that, in gen-
eral, competitions achieve to promote those skills. ‘Moderate’
assessment dominates in the Problem-Solving column, while
in the rest of the learning outcomes, the results vary.
VII. CONCLUSION
The principal aim of this paper is to investigate the potential
learning outcomes that a student is expected to develop by
engaging in an educational robotics-related activity. We con-
clude a set of six key learning outcomes through a com-
bination of the literature findings and the data taken from
the ’Educational Robotics’-related index term bibliographic
map (Figure 2). The proposed learning gains were adequately
analyzed in Section III.
Also, driven by the ever-increasing ER platforms, the paper
offers a thorough study of the commercially available ER kits.
Each ER platform has an advisable age group that defines
the difficulties an age group will face when using it. How-
ever, we consider the age criterion not efficient, as students’
interest in learning is affected by their prior knowledge and
programming skills. Based on these criteria, we propose three
new categories for the ER platforms: No Code, Basic Code,
and Advanced Code. Educators can consult this categoriza-
tion to select the most appropriate teaching tool based on
their educational background and interests. Similarly, new
ER platforms may follow the proposed categories to help
users choose the one that fits them best, based on their unique
profile.
As robotics is developing, more complicated and sophisti-
cated competitions are appearing. The most common robotics
competitions are described in this paper. Contrary to the
ER platforms, we do not classify the ER competition in the
proposed categories as all competitions offer various chal-
lenges and allow the participation of various ER platforms.
The final section explores the correlation between the six
proposed learning outcomes with the described ER compe-
titions. We identify the expected learning outcomes of each
competition based on its characteristics, rules, and goals.
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The competition’s efficacy on the six learning outcomes
was rated as Limited, Moderate, and Strong. In regards to
Problem-Solving, the nearly unanimous results in favor of
’Moderate’ show that there is room for improvement. For
instance, based on our criteria, a competition that wishes to
promote this skill can trigger the problem-solving process by
offering extra challenges to participants on the competition
day.
Furthermore, having team-based participation is not the
most effective way to develop Self-efficacy. Clearly defined
roles and responsibilities help participants to enhance their
sense of efficacy. Results also show that, in their majority,
competitions promote ’Strong’ Computational-thinking and
Motivation. Robotic challenges that employ generalization
are more likely to boost Computational-thinking. In contrast,
the nature of the robotic challenge, the award type, and
the opportunity to participate in worldwide contests increase
students’ Motivation. Moreover, for a competition that wants
to spark students’ creativity, rules must introduce some lim-
itations on the set of possible methods or recourses avail-
able. Finally, to support Collaboration among peers, besides
encouraging team-working, a competition may include the
concept of alliances between stranger teams.
From a pedagogical perspective, this paper aims at support-
ing robotic educators to design new or modify current robotic
activities to help students develop the proposed skills. Also,
we argue that the criteria set for evaluating each learning out-
come can be used as guidelines to design new competitions
that foster a more robust development of each skill.
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SALOMI EVRIPIDOU received the B.Sc. degree
in computer science from the Aristotle University
of Thessaloniki, and the M.Sc. degree in manage-
ment from the University of Leicester. She is cur-
rently pursuing the Ph.D. degree with the Neapolis
University Pafos, Cyprus. She is also a Research
Assistant with the Intelligent Systems Laboratory
(ISLab), Neapolis University Pafos. Her research
focuses on education robotics, algorithms, deep
neural networks, and intelligent tutoring systems.
KYRIAKOULA GEORGIOU received the B.Sc.
degree in mathematics from the University of
Crete, Greece, the master’s degree in computa-
tional mathematics and informatics in education
from the University of Patras, Greece, and the
Ph.D. degree in educational technology from the
Department of Education, University of Cyprus,
specialized in educational robotics. Her research
interests include educational robotics, learning
technologies, and instructional design models and
theories with the use of ICT. In addition, her scientific interest includes the
advancement of computational thinking in various educational settings and
the development of tools for assessing the development of computational
thinking.
LEFTERIS DOITSIDIS received the Diploma,
M.Sc., and Ph.D. degrees from the School of Pro-
duction Engineering and Management, Technical
University of Crete (TUC), Greece, in 2000, 2002,
and 2008, respectively. Prior to his appointment,
he was a Faculty Member at the Department of
Electronic Engineering, Hellenic Mediterranean
University, Greece. He was also a Visiting Scholar
at the Department of Computer Science and Engi-
neering, University of South Florida, Tampa, FL,
USA. He is currently an Assistant Professor with the School of Production
Engineering and Management, TUC. His research interests include multi-
robot teams, design of novel control systems for robotic applications and
autonomous operation, and navigation of unmanned vehicles. He has been
involved as a Senior Researcher, Technical Manager, and Project Coordinator
into numerous research projects.
ANGELOS A. AMANATIADIS (Senior Member,
IEEE) received the Diploma and Ph.D. (Hons.)
degrees from the Department of Electrical and
Computer Engineering, Democritus University of
Thrace (DUTH), Greece, in 2004 and 2009,
respectively. He is currently an Assistant Professor
with the Department of Production and Manage-
ment Engineering, DUTH. He has published more
than 70 scientific papers in the area of autonomous
robotic systems, computer vision, machine learn-
ing, and real-time embedded systems. He was awarded as one of the Transac-
tions ‘‘Outstanding Reviewers’’ in appreciation of outstanding service to the
IEEE Instrumentation and Measurement Society. He has received the Stavros
Niarchos Award for Promising Young Scientists and a State Scholarships
Foundation (IKY) Postdoc Scholarship.
ZINON ZINONOS received the Diploma degree
in computer engineering from the Computer
Engineering and Informatics Department (CEID),
University of Patras, Greece, in 2005, and
the M.Sc. and Ph.D. degrees from the Com-
puter Science Department, University of Cyprus,
in 2008 and 2013, respectively, all in computer
science. Since 2017, he has been a Lecturer with
the Computer Science Department, Neapolis Uni-
versity. He is also the Assistant Director of the
Intelligent Systems Laboratory (ISL), NUP. His research interests include the
Internet of Things (IoT), wireless, ad hoc and sensor networks, mobility man-
agement in low power devices, adaptive topology control, computer commu-
nication networks, blockchain for IoT,intelligent systems, implementation of
real-time monitoring and control systems, and energy efficiency.
SAVVAS A. CHATZICHRISTOFIS (Member,
IEEE) received the Diploma and Ph.D. degrees
(Hons.) from the Department of Electrical and
Computer Engineering, Democritus University of
Thrace, Greece. Since 2017, he has been a Fac-
ulty Member with the Department of Computer
Science, Neapolis University Pafos, where he is
currently an Associate Professor and the Director
of the Intelligent Systems Laboratory (ISLab). His
research focuses on the intersection of artificial
intelligence, computer vision, and robotics. His scientific interests include
visual feature extraction, image analysis, matching, indexing and retrieval,
SLAM, and educational robotics. He has over 13 years of solid experience
reporting more than 85 publications in these fields. For his research contri-
bution, he has received several distinctions, grants, scholarships, and awards.
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