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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The relationship between technological and pedagogical innovation has recently created a new field of research at the crossroads between Psychology, Educational Sciences and Artificial Intelligence: Educational Robotics (ER).
Through analysis of the achievable educational goals based on the technological status and specific learning modes of different types of robots, it is possible to define three pedagogical paradigms: learning robotics, learning with robotics, and learning by robotics.
In this book we address these three paradigms through three themes: human representations of robots, the acceptance and trust shown when interacting with a humanoid, and learning favored by the development and programming of robots in an educational context. These themes allow the authors to fully explore, define and delimit this novel field of research for future application in educational and social contexts.
Finally, the book discusses contributions and limitations which have emerged from different methodologies of research, potential educational applications, and concepts of human–robot interaction for the development of the above paradigms.
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Seitenzahl: 358
Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title
Copyright
Foreword
Preface
Introduction
I.1. Origins, positioning and pedagogical exploitations of ER
I.2. A cross-disciplinary heritage
I.3. The educational robot: an ICT like others?
I.4. Three learning paradigms of ER
I.5. Research intentions and scientific questions
1 Learning Robotics: Users’ Representation of Robots
1.1. Introduction: the ontological and pedagogical status of robots
1.2. What do we mean by robot representation?
1.3. Study 1: Robot representation
1.4. Results
1.5. Discussion
1.6. Conclusions, limits and perspectives
2 Learning with Robotics: Functional and Social Acceptance of Robots
2.1. Functional and social acceptance of robots
2.2. Trust as a fundamental indicator of acceptance
2.3. Study 2: robot acceptance
2.4. Results
2.5. Discussion
2.6. Conclusions
3 Learning by Robotics: The Impact of Educational Robots on Learning
3.1. Combining RBI and inquiry-based science
3.2. IBSE and the four dimensions of learning
3.3. Study 3: impacts of ER on learning
3.4. Results
3.5. Discussion
Conclusion and Perspectives
C.1. The necessity of a cross-disciplinary methodology
C.2. Broadening the field of robotics’ educative applications
C.3. New perspectives for human–robot interaction design
Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5
Bibliography
Index
End User License Agreement
List of Illustrations
Introduction
Figure I.1.
Some examples of robots employed in the educational paradigm learning robotics. From left to right: Bot’n Roll®, Bioloid®, Thymio® and Darwin®
Figure I.2.
Some examples of robots employed in the paradigm learning with robotics. From left to right: Qrio®, Rubi®, Roobovie®, Nao®, Aibo®, Paro® and Pleo®
Figure I.3.
Some examples of robots employed in the paradigm learning by robotics. From left to right: Logo Turtle®, LEGO WeDo®, LEGO Mindstorms® and PicoCricket®
1 Learning Robotics: Users’ Representation of Robots
Figure 1.1.
Mean score for each picture on the pre- and post-questionnaire
Figure 1.2.
Mean score for each educational role on the pre-questionnaire
Figure 1.3.
Number of graded scores given for each picture on the pre- and post-questionnaires
Figure 1.4.
Number of graded scores given for each picture on the pre- and post-questionnaire by familiar and unfamiliar participants
Figure 1.5.
Number of graded scores given for each educational role envisaged for robots on the pre- and post-questionnaires
Figure 1.6.
Number of graded scores given for each educational role envisaged for robots in the pre- and post-questionnaires by familiar and unfamiliar participants
2 Learning with Robotics: Functional and Social Acceptance of Robots
Figure 2.1.
Experimental setting. The human and the participant are seated in front of the iCub robot. An operator, hidden behind a wall and not visible by the participant, monitors the experiment and controls the robot to generate appropriate gestures and answers to questions
Figure 2.2.
Functional task and evaluation of images. The robot is gazing at the screen where the image to evaluate is shown
Figure 2.3.
Functional task. The four images used for the evaluation of the dominant color
Figure 2.4.
Functional task: the bottles used in the evaluation of weight: (1) two identical bottles of same weight, (2) two similar bottles of different colors and very different weight, (3) two similar bottles of almost the same weight, and (4) two different bottles of almost the same weight
Figure 2.5.
Functional task: the participant evaluates the weight of the bottles (left); the experimenter gives the bottle to iCub for evaluating the weight (right)
Figure 2.6.
(a–c) Social task: Q1: at school, which is the most important object: (1) the computer or (2) the notebook?; Q2: at the swimming pool, which is the most important object: (1) the bathing-cap or (2) the flip-flops?; Q3: in the rain, which is the most important object: (1) the K-way or (2) the umbrella?
Figure 2.7.
Social task: the experimenter interrogates the participant and the robot
Figure 2.8.
Conformation score in the functional and social tasks for 56 participants
Figure 2.9.
Effect of the imagined HRI scenario on participants’conformation score in the functional task
Figure 2.10.
Effect of the imagined HRI scenario on participants’ conformation score in the social task
Figure 2.11.
Correlation matrix between the conformation score of the functional and social task and the scores of the S2-NARS
Figure 2.12.
Correlation matrix between the conformation score for functional and social task and those of the DFC
3 Learning by Robotics: The Impact of Educational Robots on Learning
Figure 3.1.
Self-regulation loops in robotic projects according to Denis and Hubert ([DEN 01], adapted from [LEC 95])
Figure 3.2.
Potential of RBI for sustainable learning, according to Catlin and Blamires [CAT 12]
Figure 3.3.
Top: Drawings of bees by children in the modeling phase. In the first drawing (upper-left), students observed a real bee to draw a model; the second drawing (upper-right) was made by students without observing the bee, but simply recalling what they had seen 7 months before; the third drawing (lower-left) was realized by using an iPad app that allows to reproduce a carbon copy on a digital support; the fourth drawing (lower-right) was made after having built the first robotic bee prototype. Bottom: a first prototype of robotic bee made of Lego Mindstorms® components
Figure 3.4.
The researcher and the students in the construction phase of the project
Figure 3.5.
Further prototypes of robotic bees
Figure 3.6.
Students coding the behavior of the robotic bees through TronzCard® interface
Figure 3.7.
As a conclusive phase of the project, students of the construction section and students of the programming section combined their respective constructions and programs in the SHOWbeeZ
Figure 3.8.
The impact of the RObeeZ project on the cognitive, meta-cognitive, affective and social dimension according to pupils’ self-evaluation as retrieved from our questionnaire
Appendix 4
Figure A4.1.
WoZ GUI: the tab dedicated to the quick control of gaze, grasps and hands movements in the Cartesian space. The buttons send predefined commands to the actionsServer module [IVA 14b]. The buttons of the bottom row allow the operator to bring the robot in predefined postures (whole-body joint configurations): they were preprogrammed so as to simplify the control of the iCub during the experiments, in case the operator had to “bring it back” to a predefined configuration that could simplify the interaction for the subjects. They were useful also for prototyping and testing of the experiments
Figure A4.2.
WoZ GUI: the tab related to the robot’s speech. The operator can choose between a list of predefined sentences and expressions, or he/she can type a new sentence on-the-fly: this is done to be able to quickly formulate an answer to an unexpected request of the participant. The operator can switch between French and English speech (at the moment, the only two supported languages), even if in the experiments of this paper of course the robot was always speaking French
Figure A4.3.
WoZ GUI: the tab related to facial expressions. The list of facial expression along with their specific realization on the iCub face (the combination of the activation of the LEDs in eyelids and mouth) is loaded from a configuration file
Guide
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Human-Machine Interaction Set
coordinated byJérôme Dinet
Volume 3
Learning Robotics, with Robotics, by Robotics
Educational Robotics
Ilaria Gaudiello
Elisabetta Zibetti
First published 2016 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2016
The rights of Ilaria Gaudiello and Elisabetta Zibetti to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2016947850
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-099-7
Foreword
There has been a growing interest in the use of educational robots in schools. In the 1960s, after Seymour Paper introduced the LOGO programming language and the floor turtle, robotics became an issue in the educational environment. Robots are slowly being incorporated into our society and currently, the number of service and/or assistance robots has outnumbered industrial robots. So robots are slowly beginning a process of seamless integration into our everyday lives both at home and at school where their applicability is at the core of an increasing number of studies [ALI 13, MUB 13]. However, this does not include “robots for kids”: the impact of educational robotics is even more crucial for children and teenagers, where robots can be used for their cognitive development and intellectual growth. As a consequence, greater attention must be paid to how educational robots can be better integrated into the lives and into the education of young people.
Traditionally, the majority of studies investigating “educational robotics” has attracted the interest of teachers and researchers as a valuable tool to develop cognitive and social skills for students from pre-school to high school and to support learning in different domains such as science, mathematics, technology, informatics and other school subjects or interdisciplinary learning activities. Even though a review of the scientific literature reveals that educational robotics is a growing field with the potential to significantly impact the nature of science and technology education at all levels, from kindergarten to university, this book is very original for three reasons:
1) In this book, educational robotics is viewed from a psychological point of view, i.e. from a human-centred approach. For some researchers, the main goal of our project is to understand the current and future needs of the robotics industry, the current robotics curriculum, and to analyze the gap that might occur between the two. In my opinion, the main goal is to understand the current and future needs of the users, the users being learners and teachers;
2) If there are many attitudes and opinions about educational robotics produced without scientific arguments, this book provides serious scientific answers to three questions:
– is educational robotics just a servant of other subjects? No. A wider range of possible robotic applications has the potential to engage young people with a wider range of interests [AMI 12]. Pursuing this challenge we need to develop new and innovative ways to increase the attractiveness and learning benefits of robotics projects. And different strategies exist for engaging a broad range of young learners in robotics [RUS 08]: projects focusing on themes, not just challenges; projects combining art and engineering; projects encouraging storytelling; organizing exhibitions, rather than competitions;
– is educational robotics just a fad? Yes and no. Even if robots can have positive educational benefits, they are no panacea [AMI 12]. In the scientific literature, there have been some studies reporting non-significant impact on learners observed in some cases [BEN 11]. It’s the reason why the impact of the Educational Robotics in promoting student learning and in developing sensori-motor and/or cognitive skills needs to be validated through research evidence and scientific proofs. But …
– is educational robotics an excellent tool for teaching? It depends … Empirical and experimental studies are presented.
3) If educational robotics is a broad term that refers to a vast collection of different activities, instructional programs, physical platforms, educational resources and pedagogical philosophy, this books proposes an innovative distinction between the following approaches associated with educational robotics:
– for “learning robotics”, students use a robot as a platform to learn robotics, or, more broadly, engineering (i.e. mechanics, electronics, and programming) in a hands-on and collaborative way;
– for “learning with robotics”, robots are used as human-like (e.g. robots such as Nao, Qrio, Rubi, Roobovie, iCub) or animal-like (e.g. robots such as Aibo, Pleo) assistants for teachers (e.g. displaying multimodal content) or companions for pupils and students (e.g. connecting images and words, memorizing new words of a foreign language).
Finally and as Alimisis [ALI 13] said, “the role of educational robotics should be seen as a tool to foster essential life skills (cognitive and personal development, team working) through which people can develop their potential to use their imagination, to express themselves and make original and valued choices in their lives. Robotics benefits are relevant for all children”.
Jérôme DINETUniversity of Lorraine July 2016
Preface
This book is about how educational robotics (ER) is affecting the representation, acceptance and learning of its users. Through a psychological perspective, the book discusses the three ER learning paradigms that are distinguished by the different hardware, software and correspondent modes of interaction allowed by the robot: (1) learning robotics, (2) learning with robotics and (3) learning by robotics [TEJ 06, GAU 14].
For learning robotics [ALI 09], students use a robot as a platform to learn robotics or, more broadly, engineering – i.e. mechanics, electronics and programming – in a hands-on and collaborative way [PET 04, LIU 10, SOA 11, BEN 12]. In this framework, our objective is to investigate learning robotics under the issue of mental representation [GAU 15]. Here, the underlying research question is which representation users hold about robots when constructing and programming a robot? By robot representation, we mean its ontological and pedagogical status and how such status changes when users learn robotics. In order to answer this question, we will present an experimental study that we carried out based on pre- and postinquiries, involving 79 participants. The results show that building and programming a robot (Bot’n Roll®) fosters a more nuanced judgment about robots’ belonging to the living and non-living categories but, on the other side, a more definite judgment about the pedagogical roles that a robot may serve.
For learning with robotics [DAU 03], robots are used as human-like (e.g. robots such as Nao, Qrio, Rubi, Roobovie and iCub) or animal-like (e.g. robots such as Aibo and Pleo) assistants for teachers – e.g. displaying multimodal content [HYU 08] – or companions for pupils and students – e.g. connecting images and words [TEJ 06], memorizing new words of a foreign language [MOV 09, CHA 10]. In this framework, our objective is to investigate learning with robotics under the issue of users’ functional and social acceptance of robot [KAP 05, AVR 13, LE 13, DIN 14, DIN 15, FRI 14, DE 15]. Here, the underlying research questions are: do students trust in robot’s functional and social savvy? Is trust in functional savvy a prerequisite for trust in social savvy? Which individuals and contextual factors are more likely to influence this trust? In order to answer these questions, we will present an experimental study we have carried out with 56 participants and an iCub robot [IVA 13, IVA 16]. Here, trust in the robot is considered as a main indicator of acceptance in situations of perceptual and sociocognitive uncertainty and is measured by participants’ conformation to answers given by iCub. In particular, we are interested in understanding whether specific user-related features (i.e. desire for control), robot-related features (i.e. attitude toward social influence of robots) and context-related features (i.e. collaborative vs. competitive scenario) impacted trust in iCub. The results show that participants conformed more to iCub answers in functional than in social task. Moreover, the few participants conforming to iCub answers in the social task also conformed less in the functional task: trust in robot’s functional savvy was not a prerequisite for trust in social savvy. Finally, desire for control, attitude toward social influence of robots and type of interaction scenario did not have an impact on trust in iCub.
Contrary to these two preceding learning modes that have been labeled as robotic-assisted instruction [VAN 91] – in so far the robot is a passive assistant of the teacher or a passive platform for the students – learning by robotics is named robotic-based instruction (RBI [KIM 14]), in so far the robot constitutes a medium between the students, the school subjects and the teacher: the robot is a tool – i.e. a constructible and programmable kit – that tangibly embodies the concepts of the lesson, and stimulates creative and collaborative problem solving [DEN 94].
For learning by robotics [RES 96, PAP 80], students learn both about the content of the lesson and about robots (Lego Mindstorms®, Lego WeDo®, PicoCricket®, Robotami®, etc.), by acquiring subject-specific knowledge [BAR 09a, WHI 07, HUS 06] as well as transversal competences [DEN 01, LIN 07, SUL 08], and fostering the four dimensions of learning – cognitive, affective, social and meta-cognitive [CAT 12]. Although by taking the role of facilitator, the teacher is not seen anymore as the only owner of the knowledge or as the evaluator of students’ performance, but he/she catalyzes students’ ideas around a concrete activity and guides their progress [GAT 03, SUL 09]. In this framework, our objective is to investigate learning by robotics under the issue of impact of RBI on students’ knowledge and competence acquisition (when educational robots are used within a specific pedagogical approach, that is inquiry-based science education (IBSE) [QUI 04, BEL 10, RIE XX, GAT XX]. Here, the underlying research questions are as follows: to what extent the combined RBI and IBSE frame [WIL 07, EGU 12, DEM 12, RIB 12] has a positive impact on cognitive, affective, social and meta-cognitive dimensions of learning? Does this combined educational frame improve both domain-specific and non-domain-specific knowledge and competences of students? In order to answer these questions, we will present an experimental study carried during a 1-year RBI and IBSE in the frame of the RObeeZ school project1. The longitudinal experiments that involved 26 pupils and two teachers was based on assessment jointly elaborated by teachers and researchers in order to evaluate the RBI and IBSE effects on four dimensions of learning [FLA 79, SHO 89, VER 96, SAL 98, AND 01] as well as on grades attributed by teachers for evaluating students’ knowledge and competences. Main results show significant improvements in mathematics (measures, geometry and problems) and positive impact of RBI and IBSE on the four dimensions of learning.
The recent field of investigation of effects of ER on learning is extensively spreading in scholar and extra-scholar contexts. At the crossroad of artificial intelligence, psychology and science of education, our book discusses how the processes of these learning paradigms (learning robotics, learning with robotics and learning by robotics) might be improved.
A robot […] is virtually a chimera: all of its components are real, yet it does not exist as an entity. It will affect and transform our lives similarly to the discovery of fire and the inventions of the wheel, the steam engine and the mobile phone. But will it transform us? This fascination with robots is merely an expression of humanity’s seemingly endless ability for discovery: leaving Africa to go and discover what lay beyond. Arriving in Asia and from there Europe and America. Prehistoric men discovered the American continent through its Northern point by crossing the Bering Straight when it was frozen over; they explored it from one end to the other and only then did they begin to dream. The oldest painted caves of the continent are located to the South, where man had reached the end and had nowhere left to explore, no looking glass to go through, other than through thought. The walls of these caves are covered in carvings of men with animal-faces. Robots represent the last frontier for men who have conquered lands, seas and danced with the stars; the only Universe left to explore is themselves.
M.N HIMBERT (2012). Le Robot Pensant, pp. 201.Paris: Editions du Moment.
“Teach me to imagine a result without mourning if it emerges differently”
P. ARTISAN’S
The Robolution isn’t a rhetorical term or a marketing strategy. It is an entirely new approach to Science and Technology. This Robolution causes so many upsets to our way of life that it is essential to think about it not only in economic terms but in pedagogical terms as well (…) Most robots are so recent that their perceived value is often higher than their real value. (…)
B. BONNEL (2010) Viva la Robolution, pp. 279–284.Paris: Editions JCLattès.
Ilaria GAUDIELLOElisabetta ZIBETTIJuly 2016
1
The research has been made through the FP7 EU project Pri-Sci-Net:
http://www.prisci.net/
.
Introduction: Educational Robotics
The process of democratization of technology that has taken place since 1980 in the professional, tuition and entertainment spheres has paved the way for a renewal of education. Soon after the computer entered our society, Papert and Solomon [PAP 72] published “Twenty things to do with a computer”. At that time, these authors observed that, when asked what they thought about computers in education, people had different ideas. Some imagined future students as computer programmers: these people thought that the next generation would have learnt and mastered programming as a normal process of alphabetization; others, by contrast, apprehended the possibility that the computers would have “programmed” the students, i.e. a massive use of technology in education could have irreversibly transformed students ways of thinking and communicating in a machine-like manner.
Today, a new technological revolution has started, namely the robolution [BON 10]. This revolution seems to be so powerful and pervasive that our times have been defined as “the era of the robot”. Daily use of robotics is encouraged in an extensive range of domains, among which is the educational domain. However, caution should be used with regard to a revolution that could be dictated by industrial development and technological progress more than by authentic educational needs.
It thus becomes urgent to understand the usefulness of integrating robots in the educational system. Such urgency results in the emergence of a new specific field of study: educational robotics (ER) [EGU 10]. ER aims to introduce to the classroom a variety of embodied artificial intelligence technologies (human-like as well as animal-like robots and robotic kits). According to Bussi and Mariotti [BUS 09, p. 2], who borrow from Vygotsky’s notion of semiotic mediation [VYG 78], educational robots are intended as “semiotic tools”:
“(…) semiotic potential resides in any artifact consisting of the double semiotic link that the artifact has with both the personal meanings that emerge from its use and the knowledge evoked by that use (…) in educational settings”.
By means of such tools, the general objective of ER is to scaffold and renew teaching on the one side and learning on the other side [DEN 94].
After 30 years since the arrival of Logo Turtle1 [PAP 80, PEA 83, KLA 88, CLE 93], the first educational robot, we believe it is time to clarify the nature of ER and to start thinking about “Twenty things to do with a robot”, in particular with an educational robot – Appendix 1 [RES 96].
In order to do this, we will first outline the historical origins of ER and describe its position with respect to other current information and communication technologies (ICTs). Then, we will illustrate the three learning paradigms presently supported by the types of robots available on the market: learning robotics, learning with robotics and learning by robotics. These three learning paradigms are the focus of our research and motivate the tripartite structure of this book. Their definition is of pivotal importance for introducing our three experimental investigations and will therefore be deepened all along the present work. Finally, we will present the research questions from which we have moved to develop this work.
I.1. Origins, positioning and pedagogical exploitations of ER
ER finds its origins in a historical moment where the gap dividing the generation of “digital natives” and the previous one of “digital immigrants” becomes manifest in terms of technology fluency and ways of thinking [PRE 01]. Surrounded by digital technologies from their birth, young people today might treat information differently from their predecessors, who nowadays experience difficulties in adapting to such an omnipresence of technology.
If so, this technogenerational gap is particularly relevant in educational contexts, where these two generations, represented by teachers and students, interact to develop new knowledge and competences by using educational tools, which are capable of shaping students’ intellectual growth. For this reason, a debate has been raised about limiting new technologies to extra-school contexts (e.g. summer campus and competitions) versus employing them at school [ARR 03]. Although education is already familiar with questions about the suitability of technologies in the class, it is indeed new to questions about the suitability of this specific technology, i.e. robotics. It is thus crucial to systematize theoretical and experimental knowledge about ER to understand its possible applications and consequent impacts on education. In fact, though being still a “babbling” discipline [MAT 04], ER already presents three fundamental characteristics: (1) a multidiscipline heritage, (2) a specific positioning with respect to other current ICT, and (3) different hardware–software combinations, which serve different pedagogical exploitations. In the following sections, we will examine these three characteristics to delineate the identity of ER.
I.2. A cross-disciplinary heritage
ER is at the crossroads of three disciplines belonging to the broader area of research of cognitive sciences: psychology, educational science and artificial intelligence.
Fundamental studies from psychology on the role of experiential learning [PIA 52], intrinsic versus extrinsic motivation [LEP 00], social dynamics of learning [VYG 78] and meta-cognition [GAG 09] are crucial for investigating the mental processes implied by the use of a new technology for educational objectives [AND 08].
Educational sciences, which seek to implement research on cognitive and emotional mechanisms at play during learning [MEL 09], provide a number of case studies that are representative of current pedagogical approaches [BRU 02], monitor trends in learning results – see, for example, OECD-PISA (The Organisation for Economic Cooperation and Development-Programme for International Student Assessment)2 – and also support the design of guidelines for the adaptation of the educational system to contemporary society [VOS 01].
Artificial intelligence [HEU 94], more recently labeled as “cognitive informatics” [WAN 10], continuously raises new challenges in terms of robot prototypes with physical and functional features engendering a variety of interaction possibilities. In this sense, ER confronts young students with a technology at the boundary of living and non-living entities, which can be built and programmed for obtaining specific functions and behaviors [MAR 00].
We argue that it is the combination of these three disciplines that contributes to defining the technological status and pedagogical exploitations of educational robots, as distinct from previous educational technologies.
I.3. The educational robot: an ICT like others?
In the last 20 years, different types of technologies, suited for different educational exploitations, have appeared. A variety of educational softwares have been conceived for interactive learning on traditional hardware supports (computers, tablets, etc.) [DE 01]. Other tools – such as the e-learning platforms [ROS 01] and the digital schoolbag [TIJ 06] – allow customization of the educational interface according to students’ needs.
Critical reflections about the integration of ICT at school have been at the heart of committed debates among educators, researchers and decisions makers, engendering questions such as “What is the role of media in education?” and, among the media, “What is the role of the computer?”. With the birth of ER, further questions have been raised: what similarities do robots share with their technological precursors? What distinguishes the former from the latter?
As a first answer, two features of the robot and of its precursor, the computer, can be examined: their “technological status” and their “pedagogical exploitations”.
With respect to technological status, the computer presents a double specificity: this technology can be either an end in itself – i.e. an engineering object that it is employed as a platform to understand how computers are assembled and programmed – or an ICT [AND 08] that can be defined as a medium, a processor and a tool [BAR 96]. As a “medium”, the computer supports software that students use to interactively acquire new knowledge. As a “processor”, the computer facilitates treatment and storage of information in a way that is specific to the type of content. As a “tool”, the computer can be employed to elaborate documents, visualize numerical data, etc.
If we apply this distinction to robots, we find that, as an ICT, the robot can be defined in terms of object – i.e. a constructible and programmable device that can be used to learn mechanics, electronics and informatics (e.g. [MIK 06]) – or of tool – i.e. a device employed to acquire new knowledge and competences [ION 07].
With respect to pedagogical exploitations, when using a computer, students can learn either “from” or “with”. In the first case, the computer is used to augment pupils’ knowledge with software, which facilitates the understanding of subject-related knowledge [BOT 02]. In the second case, technology can be applied to enhance higher-order thinking skills [RIN 02]. This is the case of those software that aim at developing meta-cognitive competences [ZIB 11], as well as motivation and engagement [PRE 05].
Although the pedagogical exploitations of educational robots are related not only to the type of software but also to the type of hardware, as embodied artificial intelligence entities, robots are endowed with shapes and behaviors that add something to computers and consequently raise new learning paradigms.
I.4. Three learning paradigms of ER
In the past 30 years, the extensive spread of educational robots in scholarly and extra-scholarly contexts has led to emergence of three ER learning paradigms, distinguished by the different hardware, software and corresponding modes of interaction allowed by the robot: learning robotics, with robotics and by robotics ([TEJ 06, GAU14], see Table 1.1).
Table I.1.Types and functions of robots along with their technological status, learning paradigms and educational objectives in robotics-assisted instruction (RAI) and robotics-based instruction (RBI)
Type of robot
Function/role of the robot
Status
Learning paradigm
Educational objectives
Robotics-assisted instruction (RAI)
Modular robots or robotics kits (e.g. LEGO WeDo
®
and Mindstorms
®
, Bioloid
®
, Bot’n Roll
®
, Darwin
®
and Thymio
®
)
Platform of construction and programming
Object, end in itself (transparent technology)
Learning robotics
Acquiring knowledge and competences in mechanics, electronics and informaticsDeveloping problem-solving and collaborative attitude
Human-like robots (e.g. Nao
®
, Qrio
®
, Rubi
®
and Roobovie
®
)
Support the teacher and illustrate the lesson; colearn with students
Assistant; peer (black box or semitransparent technology)
Learning with robotics
Acquiring domain-specific knowledge (e.g. foreign language) in an interactivemultimodal way
Animal-like robots (e.g. Aibo, Furby, Paro and Pleo)
Accompany students in a playful discovery of technology, colearn with them; supporting students with cognitive and emotional impairments
Companion (black box technology)
Learning with robotics
Discovering technology in a playful way and acquiring domain-specific knowledge through interaction
Robotics-based instruction (RBI)
Robotic kits (e.g. LEGO WeDo
®
and Mindstorms
®
, PicoCricket
®
, Roamer
®
and Robotami
®
)
Tool supporting short- and long-term collaborative projects or inquiries on scientific as well as humanistic issues
Tool (transparent technology)
Learning by robotics
Acquiring subject-related as well as transversal knowledge and competences, by fostering the cognitive, affective, social and meta-cognitive aspects of learning
In the first learning paradigm (Figure 1.1), the robot is an end in itself: students use it as a platform to learn robotics or, more broadly, engineering – i.e. mechanics, electronics and informatics – in a hands-on and collaborative way [MIO 96, AHL 03, PET 04, ROG 04, MIT 08, ALI 09, NUG 10, SUL 15].
Figure I.1.Some examples of robots employed in the educational paradigm learning robotics. From left to right: Bot’n Roll®, Bioloid®, Thymio® and Darwin®
In the second learning paradigm (Figure 1.2), robots are human-like (e.g. Nao, Qrio, Rubi and Roobovie) or animal-like (e.g. Aibo, Furby and Pleo) assistants, peers or companions that are supposed either to help teachers – for example displaying multimodal content [HYU 08] – or to learn at the same time as the pupil – e.g. connecting words and images [TEJ 06], memorizing new words of a foreign language [KAN 04a, HAN 09, MOV 09, CHA 10], or doing sport [TAN 05]. These two typologies of robots have been defined as “non-transparent” or “black box” [KYN 08] since in most cases their functions or behaviors, which are in-built, cannot be modified: users do not have access to the internal system and cannot reprogram the robot.
In the third learning paradigm (Figure 1.3), the robot is a “transparent” device – i.e. a constructible and programmable kit – that embodies the philosophy of “playing pianos, not stereos” [RES 96]: these robots stimulate students to become authors of educational technology, rather than simply users. By constructing and programming robots, students encounter problems [DEN 94], create projects [ALI 09] and carry out inquiries [DEM 12, EGU 12].
Figure I.2.Some examples of robots employed in the paradigm learning with robotics. From left to right: Qrio®, Rubi®, Roobovie®, Nao®, Aibo®, Paro® and Pleo®
Figure I.3.Some examples of robots employed in the paradigm learning by robotics. From left to right: Logo Turtle®, LEGO WeDo®, LEGO Mindstorms® and PicoCricket®
While the first and the second learning modes have been labeled as robotic-assisted instruction [VAN 91] – in so far as the robot is a passive assistant of the teacher or a passive platform for the students – the third is called robotic-based instruction (RBI [KIM 14]), in which the robot constitutes a medium among the students, the school subjects and the teacher: students learn both about the content of the lesson and about robots by proposing ideas and solutions, collaborating, relying on the immediate feedback of the robot to evaluate what they do and developing learning strategies, whereas by taking the role of facilitator, the teacher is not seen anymore as the only bearer of the knowledge or as the evaluator of students performances, but he/she catalyzes students ideas around a concrete activity and guides their progress.
I.5. Research intentions and scientific questions
In the present work, we investigate the three ER learning paradigms from a cognitive psychological perspective.
Learning robotics is investigated under the issue of mental representation (Chapter 1). This first learning paradigm presents two specificities: not only are robots at the boundary of living and non-living entities, but students are at the same time designers and users, since they construct and program robots in order to interact with them afterward. Given such a peculiar status of robots and of students in this learning paradigm, and to purposefully prospect and implement applications of robots in our educational system, we argue that it is crucial to approach fundamental issues such as (1) what is the position of robots in students’ common-sense ontology? and (2) What pedagogical role(s) should students attribute to robots?
Learning with robotics is investigated under the issue of users’ acceptance of robots in functional and social tasks (Chapter 2
