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Educational Robotics for All: Gender, Diversity, and Inclusion in STEAM

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Robotics and computational thinking are valuable tools for developing the pedagogy of science, technology, engineering, arts, and mathematics (STEAM), and for promoting the inclusion and integration of diverse groups of students. There is a multitude of robotic teaching tools at our disposal intended to promote innovation and motivation of students during the learning process, once robots are increasingly common in our world it is important to integrate them into our daily lives. There are already cooking robots, autonomous cars, vacuum cleaner robots in houses and gardens, or prostheses. This paper describes a course focused on a combination of teaching methodologies, educational robotics tools, and a student learning management methodology, all within an inclusive framework to strengthen the presence of women and other under-represented groups in engineering.
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Educational Robotics for All: Gender, Diversity,
and Inclusion in STEAM
Pedro Plaza
DIEECTQAI, UNED
Madrid, Spain
pplaza@ieec.uned.es
Teresa Restivo
University of Porto
Porto, Portugal
trestivo@gcloud.fe.up.pt
Antonio Menacho
DIEECTQAI, UNED
Madrid, Spain
amenacho@ieec.uned.es
Manuel Blazquez
DIEECTQAI, UNED
Madrid, Spain
mblazquez@ieec.uned.es
Irene Fondón
Universidad de Sevilla
Sevilla, Spain
irenef@us.es
Cristina Fernandez
Universidad Carlos III de Madrid
Madrid, Spain
cristina.fernandez@uc3m.es
Rosana Chan
Chinese University of Hong Kong
Hong Kong, Hong Kong
yychan@ie.cuhk.edu.hk
Edmundo Tovar
Universidad Politécnica de Madrid
Madrid, Spain
edmundo.tovar@upm.es
Jose A. Ruipérez-Valiente
Massachusetts Institute of Technology
Massachusetts, USA
jruipere@mit.edu
Guillermo Botella
DIEECTQAI, UNED
Madrid, Spain
gbotella@madrid.uned.es
Paulo Abreu
University of Porto
Porto, Portugal
pabreu@fe.up.pt
Manuel Castro
DIEECTQAI, UNED
Madrid, Spain
mcastro@ieec.uned.es
Aruquia Peixoto
CODIB CEFET/RJ
Rio de Janeiro, Brazil
aruquia@gmail.com
Félix García-Loro
DIEECTQAI, UNED
Madrid, Spain
fgarcialoro@ieec.uned.es
Paloma Diaz
Universidad Carlos III de Madrid
Madrid, Spain
pdp@inf.uc3m.es
Auxiliadora Sarmiento
Universidad de Sevilla
Sevilla, Spain
sarmiento@us.es
Susan Lord
University of San Diego
San Diego, United States
slord@sandiego.edu
Melany Ciampi
COPEC
Science and Education Research
drciampi@copec.eu
Magdalena Salazar
Universidad Carlos III de Madrid
Madrid, Spain
m.salazar-palma@ieee.org
Blanca Quintana
DIEECTQAI, UNED
Madrid, Spain
bquintana@ieec.uned.es
África López-Rey
DIEECTQAI, UNED
Madrid, Spain
alopez@ieec.uned.es
Diana Urbano
University of Porto
Porto, Portugal
urbano@fe.up.pt
Julia Merino
Tecnalia Innovation
Bizkaia, Spain
julia.merino@ieee.org
Carina Gonzalez
Universidad de La Laguna
Santa Cruz de Tenerife, Spain
cjgonza@ull.es
Elio Sancristobal
DIEECTQAI, UNED
Madrid, Spain
elio@ieec.uned.es
Inmaculada Plaza
Universidad de Zaragoza
Zaragoza, Spain
inmap@unizar.es
Iciar Civantos
Telefónica
Madrid, Spain
iciar.civantosgomez@telefonica.com
Diane Rover
Iowa State University
Iowa, United States
drover@iastate.edu
Russ Meier
Milwaukee School of Engineering
Milwaukee, United States
meier@msoe.edu
Susan Zvacek
Consultant Teacher
USA
smzvacek@comcast.net
Sergio Martín
DIEECTQAI, UNED
Madrid, Spain
smartin@ieec.uned.es
Myriam Guedey
University of Applied Sciences
Stuttgart, German
myriam.guedey@hft-stuttgart.de
Rebecca Strachan
Northumbria University
Northumbria, United Kingdom
rebecca.strachan@northumbria.ac.uk
Abstract Robotics and computational thinking are
valuable tools for developing the pedagogy of science,
technology, engineering, arts, and mathematics (STEAM),
and for promoting the inclusion and integration of diverse
groups of students. There is a multitude of robotic teaching
tools at our disposal intended to promote innovation and
motivation of students during the learning process, once
robots are increasingly common in our world it is important
to integrate them into our daily lives. There are already
cooking robots, autonomous cars, vacuum cleaner robots in
houses and gardens, or prostheses. This paper describes a
course focused on a combination of teaching methodologies,
educational robotics tools, and a student learning
management methodology, all within an inclusive framework
to strengthen the presence of women and other under-
represented groups in engineering.
Keywords diversity, inclusion, educational robotics,
instrumentation, STEAM.
I. INTRODUCTION
Gender inequality and exclusion vary from place to
place, but we still find, in all countries, different forms of
gender discrimination, gender stereotypes, and an unequal
distribution of power between women, men, girls, and boys.
Not only does this type of discrimination exist, but there is
also exclusion based on race, class, ethnicity, ability,
language, sexual orientation, and gender identity. We
recognize that people have multiple identities that shape
their experiences and believe that addressing gender
inequality can be strengthened by examining how these
identities are intertwined; this knowledge can then inform
programmers and advocacy. Gender inequality intensifies
the negative effects of other forms of exclusion, leading to
a different and, in many cases, worse environment for girls
and women [1]. Among excluded groups, girls often face
the greatest obstacles to exercising their rights and,
therefore, gender equality and girls' rights must become a
clear priority for education.
Educational robotics is one resource to promote
inclusion, interaction, interdisciplinarity, problem solving,
and collaborative work. Teaching and learning that
incorporates these characteristics has the potential to
achieve important skills in a motivating, dynamic, and
enjoyable way that can build confidence and self-esteem. In
this paper, we present an initiative in this direction, offering
a completely free Massive Open Online Course (MOOC) on
educational robotics that emphasizes the need for inclusion
in STEAM education.
II. STATE OF THE ART
Educational inclusion is understood as a set of actions
and measures aimed at identifying and overcoming barriers
to learning and participation of all students, as well as
promoting the educational progress of all [1]. Inclusion
should consider different learner strengths, preferences,
motivations and interests, in addition to their personal,
social, economic, cultural, and linguistic situations. This
must be accomplished without equating difference with
inferiority so that all students can fulfill their potential to the
best of their abilities.
When it comes to gender, the field of software
engineering is heavily skewed toward men. As one example,
Izquierdo et al. [2] identified multiple studies indicating
gender imbalance in the open source environment.
There are also studies demonstrating a global shortage
of knowledge in STEM (Science, Technology, Engineering
and Mathematics) areas that can be addressed by
diversifying the field [4]. In addition, many studies have
analyzed the perception of gender in the field of
engineering, such as the work described by the authors in
[5]. As an example, a study on MOOC registrations across
twelve MOOC providers analyzed the important disparities
in MOOC enrolment by gender across these platforms [6].
This gender gap is even more evident in developing regions,
more specifically, previous work analyzed this issue with
Arab learners in edX and Edraak (an Arabic MOOC
provider) environments [7]. They found that the gender gap
was more acute in the global setting than in the regional
setting of Edraak with courses in Arabic.
In recent years, STEAM (Science, Technology,
Engineering, Arts and Mathematics) has become part of the
training and education offered in centers where students
have a leading role, sometimes taking advantage of this to
work on equality plans and to promote women in science.
Complex problems are worked on by students from different
backgrounds and different disciplines resulting in creative
and innovative solutions using the possible technologies [8].
These types of projects and educational experiences help to
minimize the digital gap and social segregation and increase
access to technology [12].
Diversity has always been present in schools, in
educational communities, and in all activities, providing an
environment where diversity is the natural thing. Despite
this, there are many places where diversity is not the norm.
In recent years, it has gained prominence in educational
projects and teaching practices, due to a demand for
visibility by various institutions and above all, by society in
general. Some examples can be found in [15].
These reasons all led to the decision to design a MOOC
that promotes diversity and inclusion, specifically related to
gender, in STEAM through the use of educational robotics.
III. MOOC INTRODUCTION
Educational robotics is a valuable tool for developing
the pedagogy of STEAM and for promoting the inclusion
and integration of students. There are currently a multitude
of robotics teaching tools at our disposal. These tools are
designed to promote innovation and motivation of students
during the learning process. Because robots are increasingly
common in our world it is important to integrate them into
our daily lives. This course focuses on a combination of
teaching methodologies, educational robotics tools, and a
student learning management methodology, all within an
inclusive framework to strengthen the presence of women
and other under-represented groups in engineering.
For the development of the MOOC content, we have
taken advantage of experiences related to four tools for
working STEAM and educational robotics: Electronic
Instrumentation together with tools as Scratch, Crumble and
Arduino microcontroller.
Electronic Instrumentation is the science and technology
of measurement. Instruments that measure non-electrical
magnitudes use sensors that provide electrical signals from
other magnitudes (mechanical, thermal, magnetic,
chemical, radiation, etc.). Therefore, the first module in this
area is dedicated to the Electronic Instrumentation, drawing
on the experiences described in [19].
The use of a virtual tool can be a positive first contact
with computational thinking and educational robotics,
therefore Scratch has been chosen for this purpose
(https://scratch.mit.edu/). The results of the experiences
indicated in [21] have been built on and integrated as part of
the course. Figure 1 shows the aspect of the Scratch tool.
Fig. 1. Interface example of Scratch tool.
In a next step, the use of a tool that allows simple
programming and simple hardware connections is then used
so that students can experiment with the hardware elements
progressively. Crumble was chosen because it allows
students to work on concepts related to STEAM and
educational robotics (https://redfernelectronics.co.uk/), as
detailed in [24]. Figure 2 shows the aspect of the Crumble
tool.
Fig. 2. Interface and hardware examples of Crumble tool.
Finally, the Arduino microcontroller, which emerged as
a project for students at the Ivrea Institute (Italy), is used
because it is a free hardware platform consisting of a board
with a microprocessor and a development environment
(https://www.arduino.cc/). It has also been chosen because
it allows students to develop more complex applications
with more functionalities than those that can be achieved
with the two tools mentioned above. Some application
examples are detailed in [27], and these have been used as a
reference. Figure 3 shows the aspect of the Arduino tool.
Fig. 3. Interface and board examples of Arduino tool.
One of goals for this MOOC is to provide a set of tools
which allow to build learning scenarios with different tools.
This strategy has been made satisfactorily along the works
[30].
There are two types of course objectives in the MOOC:
(1) what the student will learn about; and (2) what the
student will learn how to do.
For the first set of course objectives, the student will
learn about:
The value and relevance of including women and
other underrepresented groups in engineering
education and STEAM careers;
Active teaching methods and project-based
learning focused on robotics and related STEAM
activities;
The relevance of the Electronic Instrumentation
for Robotics;
Robotics tools, including the user interface
environments of Scratch, Crumble, and Arduino;
Concepts that facilitate the deployment of robotics
tools in an educational environment; and
Methods for student learning management.
For the second set of course objectives, the student will
learn how to:
Create simple robotics applications using a variety
of instruments;
Integrate robotics and related STEAM activities
into the classroom or online instruction; and
Manage this same instruction to present concepts
effectively, offer constructive feedback, monitor
student progress, and motivate students from
underrepresented populations.
In the following sections, the different topics, target
audience, and syllabus related to these objectives are
discussed in more detail.
IV. MOOC TOPICS AND CONTENT
The MOOC consists of a total of eight modules:
Presentation of and Introduction to the Course.
Special Interventions: Success stories and
leadership in the STEAM area.
Gender and STEAM: Active methodologies and
project-based learning used in STEAM and
educational robotics.
Electronic Instrumentation in Learning
Environments
Educational Robotics Tools: Scratch, Crumble,
and Arduino tools.
Deploying Educational Content with Scratch,
Crumble, and Arduino.
Student Learning Management Methodologies.
Lessons Learned and Conclusions.
The content areas are: Science, Teaching, Education,
Engineering, Mathematics, Teaching Methodologies,
Student Management Methodology, Women and Inclusion,
Programming, Electronic Instrumentation, Robotics,
STEAM, Technology. No previous knowledge is required.
The target audience of the course is people without
previous experience in robotics who want to get into
educational robotics and use what they have learnt at home,
in their institution, or as part of their research. It may also
be of interest to people who want to be more inclusive and
promote more women in STEAM fields.
V. MOOC SYLLABUS
Throughout the Presentation of and Introduction to the
Course, the content and topics are introduced. It is estimated
that this requires a dedication of less than one hour by
students.
The Special Interventions topic includes several
interviews from women who have reached success in
STEAM and leadership. It is estimated that this part will
require less than one hour.
Next, during the topic Gender and STEAM, there is an
introduction to gender, diversity, and inclusion in STEAM.
Furthermore, this topic includes design through co-
educational practices for STEAM and details of inclusive
design and project-based learning in STEAM projects.
Finally, the application of gender and value-sensitive design
in STEAM projects is demonstrated. This should require an
estimated dedication of four hours.
As part of the Electronic Instrumentation in Learning
Environments topic, different types of instrumentation and
sensors/transducers commonly used in the industry are
discussed, This is followed by a look at how these can be
integrated into the classroom and the elements users can
employ to make applications in the context of STEAM and
educational robotics. Once this topic is finished, it should be
possible to make simple applications with any of the
instrumentation dealt with throughout this topic. Students
will need an estimated four hours to complete this module.
Within the content of the Educational Robotics Tools
module, the Scratch, Crumble, and Arduino tools are
introduced. This is the student’s first contact with these
tools. Part of the topic describes the user interface
environments and provides a series of elements that can be
used to make applications in the context of STEAM and
educational robotics. Once this topic is finished, it is
possible to make simple applications with any of the three
educational robotics tools. An estimated dedication of four
hours is required for this topic.
Deploying Educational Content with Scratch, Crumble,
and Arduino will teach students about the use of Scratch,
Crumble, and Arduino. Throughout this topic, examples of
educational applications created with Scratch, Crumble, and
Arduino are shown. Examples of educational content
display related to STEAM and educational robotics in the
classroom are also described. At the end of this topic, it is
possible to create educational content related to STEAM
and educational robotics to be presented in the classroom.
An estimated dedication of four hours is required for this
topic.
The final main topic is focused on Student Learning
Management Methodologies. This module details how to
manage the learning process of students, how to manage the
educational content deployed in the classroom, and how to
improve the educational content already deployed, using the
information acquired from the educational results. At the
end of this topic it will be possible to manage the learning
process of the students and to manage the educational
contents and life cycle from one course to the next. It is
estimated that this will take students four hours to complete.
At the end of the course, the Lessons Learned and
Conclusions module addresses what was learned in this
course and summarizes the conclusions. This requires an
approximately two hours.
VI. MOOC MATERIALS AND ACTIVITIES
The first module includes a set of one document, one
video, and one quiz. The other modules are built on a set of
four documents, four videos, four activities, and three
quizzes.
Each module lasts one week. At the beginning of each
module, students take a pre-test to assess their existing
knowledge on the topics. At the end of the module, a post-
test is taken that contains similar questions to the pre-test,
plus new desirable knowledge only obtained through the
module. Additionally, learners are also asked about their
satisfaction with the module and its topics.
Additionally, each module also includes a social activity
that involves students sharing some of the results in Twitter
using widely active hashtags related to education. The
objective is engaging other social media users that care
about gender, diversity, and inclusion issues in STEAM
education. This way, the social phenomenon of the MOOC
goes beyond the course participants, by inviting other
interested individuals to participate.
The main content in each module are videos of about 10-
15 minutes and short documents (typically less than five
pages) that include the information presented in the video,
plus recommended material to extend the information on the
topic. Furthermore, as complementary materials, there are
links to related open educational resources. As part of the
activities, the students are expected to participate in forums
by discussing three questions. Finally, the students are also
challenged to build a learning community in which to share
experiences and resources.
VII. CONCLUSIONS
This paper has presented the outline for a MOOC that
promotes gender diversity and inclusion in STEAM through
the use of educational robotics. This MOOC employs an
inclusive framework designed to be attractive to women and
other underrepresented groups in STEAM.
As part of this provision, there is a plan to investigate
how this type of learning allows students to achieve several
important skills in a more motivating, dynamic, and
enjoyable way, thus increasing student confidence and self-
esteem. Hence, the presented MOOC design is intended to
answer several questions to tackle and investigate the
inclusion problem:
How do students react cognitively and emotionally to
using the MOOC and to its contents?
Are there any differences in this reaction, depending on
race, gender, religion, and/or social environment?
How does the MOOC and its contents impact
knowledge gain?
Do students feel able in transferring the acquired
knowledge to practice?
Are there differences in knowledge gain based on
gender or other traits?
Which of the contents and activities provided by this
MOOC have more impact on knowledge gain and
motivation?
Therefore, future work will be the analysis about how
these questions are answered throughout the experiences
with students.
ACKNOWLEDGEMENTS
The authors acknowledge the support provided by the
UNED Industrial School of Engineering, the UNED
Doctoral School, and the project "Techno-Museum:
Discovering the ICTs for Humanity" (IEEE Foundation
Grant #2011-118LMF).
Authors also acknowledge the support of the e-LIVES.
e-Learning InnoVative Engineering Solutions- Erasmus+
Capacity Building in Higher Education 2017 - 585938-
EPP-12017-1-FR-EPPKA2-CBHE-J, IoE-EQ. Internet of
Energy - Education and Qualification, Erasmus+ -
Cooperation for Innovation and the Exchange of Good
Practices nº 2017-1-IT01-KA202-006251 and I4EU - Key
Competences for an European Model of Industry 4.0,
Erasmus+ Strategic Partnership nº 2019-1-FR01-KA202-
06296. As well as to the projects 2020-IEQ15, 2020-IEQ14
and 2020-IEQ13 from the Escuela Superior de Ingenieros
Industriales of UNED. The project GID2016-17
"Laboratorios de STEM y robótica educativa para la mejora
de la experiencia del estudiante STEMSEC" Proyecto
de Innovación Docente (PID) para Grupos de Innovación
Docente (GID). UNED and 2020-IEQ12 "Industria 4.0:
visión 3D y robótica". And finally, the Spanish Ministry of
Economy and Competitiveness through the Juan de la
Cierva Formación program (FJCI-2017-34926).
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... At times, unintentional discrimination (i.e., biased predictive accuracy against certain groups) may be happening due to factors like demographic imbalance, where certain demographic groups (e.g., gender, race) are under-represented in the dataset [19]. Gender imbalance (i.e., the data samples pertinent to students of different genders are unequal), for instance, is common across various courses and learning platforms [20], [17]. Previous research has shown that predictive models that were trained using datasets with a small sample size on distinct groups of people, such as those of different genders, race, or language groups, can cause and reinforce unintended bias [17], [19]. ...
... Previous research has shown that predictive models that were trained using datasets with a small sample size on distinct groups of people, such as those of different genders, race, or language groups, can cause and reinforce unintended bias [17], [19]. Unfortunately, samples from different groups rarely reach comparable quantity in most real-world data collections in education [20], [21], e.g., female students have been traditionally under-represented in STEM courses with only around 1 in 5 learners in a STEM MOOC being female [21]. Given that the predictive models have been widely adopted to empower teaching effectiveness -e.g., instructors may offer a timely intervention to students based on the results of dropout prediction and instructors may also provide forum responses to those classified as urgent posts -biased prediction resulted from under-represented demographic groups in training data may cause unintentional bias in teaching support. ...
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Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible. Class balancing, which aims to adjust the unbalanced data samples of different class labels before using them as input to train a predictive model, has been widely regarded as a powerful method for boosting prediction accuracy. However, its impact on algorithmic bias remains largely unexplored, i.e., whether the use of class balancing methods would alleviate or amplify the differentiated prediction accuracy received by different groups of students (e.g., female versus male). To fill this gap, our study selected three representative predictive tasks as the testbed, based on which we 1) applied two well known metrics (i.e., hardness bias and distribution bias) to measure data characteristics to which algorithmic bias might be attributed; and 2) investigated the impact of a total of eleven class balancing techniques on prediction fairness. Through extensive analysis and evaluation, we found that class balancing techniques, in general, tended to improve predictive fairness between different groups of students. Furthermore, class balancing techniques (e.g., SMOTE and ADASYN), which add samples to the minority group (i.e., oversampling) can enhance the predictive accuracy of the minority group while not negatively affecting the majority group. Consequently, both fairness and accuracy can be improved by applying these oversampling class balancing methods. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairCBT .
... STEAM robotics instruction gives imaginative challenges and openings for school-level learners to create one-of-a-kind concepts and advanced learning skills (Afari & Khine, 2017). STEAM with educational robotic kits is an efficient approach because they encourage the ease with which pupils can connect among STEAM disciplines (Plaza et al., 2020;Plaza et al., 2018). Robotic kits like LEGO's, connected with a block-based programming language like Scratch, can help the pupil's fundamental programming learning (Dorling & White, 2015;Ruzzenente et al., 2012). ...
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In STEAM education, Lego WeDo 2.0 robot kit is a well-known tool for introducing educational robotics in elementary schools. This kit teaches students the skills necessary for future success. It provides a wide array of educational opportunities across subjects, along with lessons and other digital resources. This article presents experimental commands/functions development in Python programming language through a Raspberry Pi, permitting a suitable connection to the Lego WeDo 2.0 robot based on Scratch WeDo 2.0 commands for STEAM robotics learning in advanced classes. The main reasons for developing the commands are that Scratch language is a novice programming, and students gain incorrect perceptions of programming behaviour. In contrast, Python is real-world programming, in which students can utilise the language in future careers, and students can also create dynamic programs in Python using WeDo 2.0. Additionally, in this study, some projects are presented using the constructed Python functions developed by us versus the same programs in Scratch as examples for activities in the STEAM classrooms using Lego WeDo 2.0 Robot Kit. The limitation of this study was the lack of testing functions in actual instructive practice for data collection about the effectiveness of Python WeDo 2.0 commands in the classroom. The contribution of this study lies in the novelty framework of the development of WeDo 2.0 Python functions, which can be utilised in STEAM robotics advanced classrooms for learning in the fields of science, technology, engineering, the arts and mathematics.
... The MOOC structure and design are described in [20]. In the following sections, we will present the main goals and the structure of the MOOC and, we will describe a Module 2, as example of the educational contents and instructional design. ...
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This paper presents findings on participants of a massive open online course named "Educational Robotics for all" developed under Open edX platform. The document describes the organization and structure of the MOOC and some of its preliminary results. As an example, Module 2 about Gender and STEAM is presented and discussed.
... In addition, several activities have been performed using a combination of these tools. For details on the activities, refer to [16][17][18][19][20][21][22][23][24][25]. ...
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