Social Robots in Collaborative Learning: Consequences of the Design on

Students’ Perception

Alix Gonnot

, Christine Michel

, Jean-Charles Marty

and Am

elie Cordier

INSA de Lyon, Univ Lyon, CNRS, LIRIS, UMR 5205, F-69621 Villeurbanne, France

Universit

e de Savoie Mont-Blanc, CNRS, LIRIS, UMR 5205, F-69621 Villeurbanne, France

Hoomano, Lyon, France

Keywords:

Collaborative Learning, Social Robots, Human-robot Interaction.

Abstract:

The interest in using social robots in education is growing as it appears that they could add a social dimension

that enhances learning. However, there is little use of robotics in collaborative learning contexts. This shows

a lack of knowledge about students’ perception of social robots and their use for education purposes. This

paper aims to ﬁll this gap by analyzing, with experimental methods: (1) the inﬂuence of speciﬁc ways of

interaction (facial expressions, voice and text) on the students’ perception of the robot and, (2) students’

acceptability criteria for using robots in a classroom. The target objective is to help the design of future

learning situations. The study shows that the ways used to interact produce signiﬁcant differences in the

perception of the animation, the likeability, the attractiveness, the safety and the usability of the robot. The

study also shows that major improvements must be made on the design of the hedonic characteristics of the

interactions, especially identiﬁcation and stimulation, to favor the student’s acceptance of this kind of learning

support tools.

1 INTRODUCTION

Robots are increasingly used in education where they

can serve many purposes (e.g. tools to learn how

to develop software, telepresence) and more recently,

social robots offer a new opportunity for supporting

people in various tasks. We believe that social robots

can improve collaborative learning since they can

bring back the social dimension, essential for learn-

ing, into the standard tools used in Computer Sup-

ported Collaborative Learning (CSCL).

1.1 Collaborative Learning

Dillenbourg (1999) deﬁnes collaborative learning as

a type of learning where two or more people try to

learn something together. This form of learning is

known for being beneﬁcial on various levels such as

academic, social and psychological (Laal and Ghodsi,

2012).

Jermann et al. (2004) exposed two different ap-

proaches to support collaborative learning. The struc-

turing approach supports collaborative learning by

carefully designing the activity beforehand. The reg-

ulating approach consists in mediating directly the in-

teractions within the group. Mirroring tools, meta-

cognitive tools and guiding systems can be used to

accomplish all or part of this process. However, a lot

of work remains to be done in order to support the

teacher in an effective way. The proposed numeric

tools are sometimes inadequate since they do not pro-

vide the necessary ”human interaction” that is central

for group regulation.

1.2 Social Robots

With the development of robotic technologies, the use

of robots inside classrooms is spreading. They are

currently mainly used as a new teaching tools (Church

et al., 2010) or as telepresence tools (Tanaka et al.,

2014).

Social robots are humanoid, autonomous robots

that provide users with ”social” abilities such as

communicating, cooperating or learning from peo-

ple. They usually communicate with humans through

channels that are typically dedicated to the communi-

cation with other humans (voice, gesture, emotions,

etc.). Most of the time, people anthropomorphize

them and assume they have a form of social intelli-

gence or social skills (Breazeal, 2003).

The social aspects of social robots can be inter-

esting in educational contexts. Social behaviour is

an essential, albeit tacit, component of the student-

teacher interaction (Kennedy et al., 2017). Robots are

Gonnot, A., Michel, C., Marty, J. and Cordier, A.

Social Robots in Collaborative Learning: Consequences of the Design on Students’ Perception.

DOI: 10.5220/0007731205490556

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 549-556

ISBN: 978-989-758-367-4

549

presented as able to add embodiment and a social di-

mension to the learning activity (Mubin et al., 2013).

Saerbeck et al. (2010) also claim that ”employing so-

cial supportive behavior increases learning efﬁciency

of students”. Social characteristics of robots can thus

constitute a progress when compared with standard

tools. This statement leads us to consider robots as

potential regulating tools as those presented in Sec-

tion 1.1.

1.3 Social Robotics in Collaborative

Learning

Strohkorb et al. (2016) conducted an experiment to

ﬁnd out if a social robot could inﬂuence the collabora-

tion between two children that were playing the same

game. Mitnik et al. (2008) proposed a framework for

a mediator robot and experimented on it. Short and

Mat

aric proposed a model formalizing a moderation

process (2015) and a moderation algorithm (2017)

that are designed to be used with social robots. The

human-robot interactions used in the various experi-

ments presented above are only one-way interactions,

usually robot to human. There are no communica-

tions between the students and the robot. Program-

ming robots to interact with humans is a demanding

technical challenge and today, a human cannot inter-

act with a social robot at the same level of complexity

they could interact with a human.

Furthermore acceptance problems arise when

social robots are used in class. According to

Sharkey (2016), social robots may generate privacy

issues because robots record and/or use informa-

tion about their environment and users. Moreover,

Sharkey asks if it is moral or safe to let humans think

that a machine is clever and to take the risk that they

become attached to it. It also seems problematic to

put a robot in charge of a classroom without human

supervision. However, it seems ”that the attitude to-

wards social robots in schools is cautious, but poten-

tially accepting” (Kennedy et al., 2016).

We are inclined to think that using speciﬁc means

of communication on the robot (displaying text, us-

ing its voice, etc.) will inﬂuence the students’ per-

ception of the device. The aim of the paper is to

ﬁnd out which means of communication used by the

robot are the most adequate considering several di-

mensions. This is a prerequisite for setting up collab-

orative learning scenarios that use social robots. In

this study we choose to focus only on the students’

perception of the social robot during the interaction

(ethical problems related to the use of this technology

in the classroom are not considered).

In order to address those questions, we designed

an experiment that is described in the Method section

of this paper. We then expose the results and the im-

plications for our project in the Results and Discus-

sion sections.

2 METHOD

The goal of this experiment is to better understand the

students’ perception of a social robot guiding them

through a learning task and to determine whether this

perception is inﬂuenced by the use of certain func-

tionalities of social robots, namely text-to-speech and

the ability to express emotions.

During the experiment, the participants are di-

vided into groups of three and are guided by the robot

through a collaborative learning task. The robot gives

instructions to the groups using different interaction

modalities. The participants are then asked to ﬁll out

a questionnaire about their perception of the robot.

2.1 Task

The collaborative task proposed to the participants is

a lesson on the use of decision matrix, a decision-

making method designed to evaluate several options

by comparing them on a ﬁnite number of criteria.

The robot instructs the participants to read a docu-

ment explaining what a decision matrix is and how to

use it. It then guides them through an exercise were

they are supposed to advise a ﬁctional company on the

selection of new smartphones for its employees. The

robot gives step by step instructions to the participants

to make them ﬁll and use the decision matrix to take

the decision. Once an instruction given by the robot

is completed by the group, the participants touch the

screen of the robot to get the next instruction.

Twice during the process, the robot gives feed-

back to the participants before giving the next in-

struction. The ﬁrst piece of feedback is given when

the participants have chosen the criteria used to eval-

uate the smartphones and the second piece of feed-

back is given after the selection of the smartphone

accordingly to the data presented in the decision ma-

trix. Each time, if the participants provide the right

answer, the robot gives a positive feedback and if the

participants provide the wrong answer, the robot gives

a negative feedback and provides them with an indi-

cation to help pinpoint the error. The participants are

then given a chance to correct their answer and if they

are still wrong, the correct answer is offered to them.

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2.2 Robot

The robot we used for this experiment is a prototype

running on Android. This robot is a non-humanoid

robot equipped with motorized wheels. A tablet is

placed on its head and is usually used to display its

eyes. The robot is able to deliver information in var-

ious ways such as displaying text on its screen or

speaking. It is also able to display several facial ex-

pressions representing various emotions.

We combined those functionalities to build three

different behaviors. In the ﬁrst behavior, the robot

shows a neutral expression all the time and instruc-

tions are given to participants in text form through a

dialog box displayed on the robot screen on top of

its eyes. The instructions are displayed for 30 sec-

onds on the screen. In the second behavior, the robot

shows a neutral expression all the time and instruc-

tions are pronounced out loud for the participants. In

the third behavior, instructions are also pronounced

out loud and a neutral expression is shown most of the

time. When the robot gives feedback to participants

however, a joyful facial expression is shown when

the feedback is positive and a sad facial expression

is shown when the feedback is negative. The voice

used in the second and third behavior is the default

Android text-to-speech voice.

Those three behaviors constitute our three condi-

tions for the experiment: Instructions given in text

form (C1), Instructions given with a neutral voice

(C2) and Instructions given with a neutral voice and

facial expressions (C3).

(a) Text (b) Neutral (c) Joyful (d) Sad

Figure 1: Elements displayed on the robot’s screen during

the experiment.

We believe that the participants’ perception of the

robot will vary if the robot uses its “social” func-

tionalities such as talking or showing facial expres-

sions. Our hypothesis can be formulated as: Partici-

pants will have a better perception of the robot when

it gives them instructions vocally with facial expres-

sions rather than with voice or text only (H1).

For the robot to deliver instructions and feedback

in a way that adapts to the participants progression

and errors, we chose to use the Wizard of Oz tech-

nique (Kelley, 1984) to control the robot. A human

operator is physically present in the room and makes

the robot react appropriately to the unfolding events.

For example, if the participants completed an instruc-

tion that had not been given yet, the operator would

make the robot skip this instruction. The operator

also decides if the answers provided by the partici-

pants are correct and chooses the error message deliv-

ered by the robot if needed. The participants were not

informed that the robot was entirely controlled by the

operator.

2.3 Questionnaire

At the end of the task, the participants are asked to

ﬁll out a questionnaire meant to understand how the

participants of the study perceive the robot. The ques-

tionnaire is divided into two different parts.

In the ﬁrst part, we chose to use the Godspeed

questionnaire (Bartneck et al., 2009) that is a stan-

dard questionnaire designed to measure user’s percep-

tion of a robot. As the Godspeed questionnaire does

not question the user about the usability of the robot

and the user experience, we decided to also use the

AttrakDiff questionnaire (Hassenzahl et al., 2003).

Since our participants are all students in France or

working in France, we chose to use French transla-

tions of the Godspeed and the AttrakDiff question-

naires to increase participants’ understanding of the

questions. For the Godspeed questionnaire, we chose

to offer our own translation, based on the one already

proposed on Bartneck’s website. For the AttrakDiff

questionnaire, we used the ofﬁcial translation (Lalle-

mand et al., 2015).

The second part of the questionnaire contains

three open-ended questions: ”What could we do to

improve the robot?”(Q1), ”Would you be ready to use

such a robot in class (or in a more general learning

situation)? Why?” (Q2) and ”Do you have other re-

marks?” (Q3).

2.4 Analysis

The ﬁrst part of the questionnaire consists of two

semantic differential scale questionnaires: God-

speed and AttrakDiff. As recommended by the au-

thors (Bartneck et al., 2009) mean scores were com-

puted for each scale of the Godspeed questionnaire.

Five dimension are then analyzed: Anthropomor-

phism, Animation, Likeability, Perceived Intelligence

and Perceived Safety. The same processing was

applied to the AttrakDiff questionnaire (Hassenzahl

et al., 2003) for each of its scale. Four dimensions are

then analyzed: Pragmatic Quality, Hedonic Quality-

Stimulation, Hedonic Quality-Identiﬁcation, Attrac-

tiveness. The ANOVA method was then used to de-

termine the inﬂuence of the conditions C1, C2, C3 on

these nine dimensions.

The second part of the questionnaire contains

Social Robots in Collaborative Learning: Consequences of the Design on Students’ Perception

551

open-ended questions. We carried out a thematic

analysis (Braun and Clarke, 2006) on the participants’

answers for questions Q1 Q2 and Q3. This analy-

sis highlighted two main types of themes: partici-

pants’ expectations about the robot (feedback, emo-

tions, dynamism) and robot’s means of interaction

(voice, movement, content). Another layer of anal-

ysis was added for question Q2 in order to determine

the valence of the answers. We identiﬁed three kinds

of answers: ”Yes”, ”No”, ”Yes, if improvements”.

2.5 Participants

21 persons participated to the experiment in total. The

participants were divided into groups of three and

each group was associated to one of the three con-

ditions described in Section 2.2. One group (3 partic-

ipants) was removed from the study because its mem-

bers did not all ﬁll out the ﬁnal questionnaire.

The remaining participants were equally dis-

tributed between the three conditions and the propor-

tions of male and female participants were equiva-

lent. The vast majority of participants are engineering

school students. Two participants are PhD students

and one is a young design engineer. Their ages range

from 20 to 26 years old.

3 RESULTS

3.1 Semantic Differential Scale

Questionnaires

In order to detect variations between the three condi-

tions in the Godspeed and AttrakDiff questionnaires

results, we used the ANOVA method. We performed

the tests on each scale of the two questionnaires to de-

termine whether the mean scores for each condition

were signiﬁcantly different.

3.1.1 Godspeed Questionnaire

As shown on Table 1, signiﬁcant differences exists be-

tween the three conditions for the Animation, Like-

ability and Perceived Safety indicators of the God-

speed questionnaire. No signiﬁcant differences were

found for the Anthropomorphism and Perceived Intel-

ligence scales. It means that the interactions involv-

ing speaking or expressing emotions we used during

the experiment inﬂuenced the perception of the an-

imation, likeability and perceived safety but did not

inﬂuence the way the robot’s is anthropomorphized

by the participants or how intelligent it seems to be.

It then seems that the anthropomorphic characteristics

attributed to our robot are mainly due to its physical

form, that stay the same in all conditions, and it could

explain why we did not detect any variations for the

Anthropomorphism scale. In the same way, we can

guess that the perceived intelligence is directly tied to

the material delivered by the robot in the experiment

and no variations were detected on the Perceived In-

telligence because that material remains identical in

the three conditions.

Table 1: p-value and signiﬁcance of the ANOVA test (p <

0.05) for the Godspeed questionnaire.

Variable p Sig

Anthropomorphism 0.308 No

Animation 0.010 Yes

Likeability 0.024 Yes

Perceived Intelligence 0.075 No

Perceived Safety 0.040 Yes

The mean scores (see Figure 2) for the condition C3

are higher than those for condition C2 on the An-

imation, Likeability and Perceived Safety variables,

meaning that the participants like the robot better and

have the perception of a better animation and safety

when the robot uses facial expressions when it speaks.

Similar results are observed when the robot speaks

(conditions C2 and C3) rather than when it only dis-

plays text on its screen (condition C1). Our hypothe-

sis is thus partially validated for the animation, like-

ability and perceived safety criteria. Participants have

a better perception of these characteristics when the

robot gives them instructions vocally with facial ex-

pressions rather than with voice or text only.

Figure 2: Mean scores of the Godspeed questionnaire in the

three conditions.

3.1.2 AttrakDiff Questionnaire

As shown on Table 2, signiﬁcant differences exists be-

tween the three conditions for the Pragmatic Quality

and Attractiveness scales of the AttrakDiff question-

naire. No signiﬁcant differences were found for the

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552

hedonic variables, meaning that using the robot was

perceived as equally pleasant in all three conditions.

Table 2: p-value and signiﬁcance of the ANOVA test (p <

0.05) for the AttrakDiff questionnaire.

Variable p Sig

Pragmatic Quality 0.020 Yes

Hedonic Quality Stimulation 0.243 No

Hedonic Quality Identiﬁcation 0.137 No

Attractiveness 0.038 Yes

The data presented on Figure 3 show that the mean

scores for the condition C3 are higher than those for

condition C2 for the Pragmatic Quality and Attrac-

tiveness variables. The mean scores are also higher

for condition C2 than for condition C1 for the two

variables.

This means that the participants ﬁnd the robot to

be signiﬁcantly more usable and more attractive when

it gives them instructions vocally with facial expres-

sions rather than with voice or text only.

We can conﬁrm that our hypothesis is partially

validated for the Pragmatic Quality and Attractive-

ness variables.

Figure 3: Mean scores of the AttrakDiff questionnaire in the

three conditions.

3.2 Open-ended Questions

3.2.1 Statements About the Experiment

Table 3 presents the results of the thematic analysis

that was performed on the participant’s answers to the

open-ended questions. 76 different items were iden-

tiﬁed and distributed into 11 themes. The different

items are equally distributed between conditions C1,

C2 and C3 (respectively 26, 22, 28). The most rep-

resented themes in those 76 items are the feedback

(20), the voice (20), the movement (11) and the inter-

actions (10). Items are equally distributed in our three

conditions for each of those themes.

Items related to feedback are similar throughout

our three conditions. Participants want the cognitive

Table 3: Themes identiﬁed in the open-ended questions an-

swers.

Theme C1 C2 C3 All

Feedback 7 5 8 20

Voice 9 4 7 20

Movement 4 3 4 11

Interaction 2 3 5 10

User emotion 3 1 1 5

Material/content 0 2 0 2

Class dynamism 0 2 0 2

Robot emotion 0 0 2 2

Naturalness 0 2 0 2

Ease of use 1 0 0 1

Presence 0 0 1 0

All 26 22 28 76

abilities of the robot to be more developed so that it

could be able to guide them, answer questions or pro-

vide custom explanations.

Expectations formulated regarding the voice are

different when they come from participants of condi-

tion C1 or from participants of conditions C2 and C3.

The C1 participants express their regret that the robot

does not speak and that it is not possible to interact vo-

cally with it. The C2 and C3 participants would also

like to interact vocally with the robot but they wish

for a voice more ”realistic, natural, pleasant” and less

”robotic” as well.

When considering the movement theme, we can

note that the items express the same ideas in all con-

ditions. Participants would like for the robot to pro-

duce head movements or moves in order to make the

class more dynamic but also to intervene in the dis-

cussion, to address a student in particular and to be

more ”present”.

Items associated to the interaction theme were

mainly proposed by participants of condition C3.

They think speciﬁc interactions such as the blinking

of the robot’s eyes are interesting and regret that there

is not more of those.

Items related to the user emotion theme are mostly

positive. Participants of all conditions indicated that

they appreciated this type of learning activity that is

fun and new. Participants of conditions C2 and C3

even pointed out that the class was more dynamic

when conducted this way, however they think that

more content should be provided.

Finally, only C2 and C3 participants expressed

some expectations regarding the anthropomorphiza-

tion of the robot, asking for more emotion, presence

and naturalness from the robot.

Social Robots in Collaborative Learning: Consequences of the Design on Students’ Perception

553

3.2.2 Are Students Ready to Welcome Social

Robots in Class?

As stated in Section 2.4, the answers given by the par-

ticipants to the question Q2 were classiﬁed in three

categories: ”Yes”, ”No” and ”Yes, if improvements”.

The answers qualiﬁed as ”Yes” or ”No” were

clearly positive or negative such as ”Yes, absolutely

(saving time rather than calling the teacher)” or ”No,

because it may not be able to answer to my questions

(...)”.

In the third category, ”Yes, if improvements” we

ﬁnd answers that were potentially accepting, under

the condition that improvements were made on the

robot. An example of answers classiﬁed in this cat-

egory is ”No, having to read on a screen makes it as

interesting a chat bot on a computer. However, if it

was more ”interactive”, why not!”.

Table 4: Distribution of the answers into the three categories

for each condition.

Category C1 C2 C3 All

Yes 1 2 0 3

Yes (improv.) 2 2 1 5

No 3 2 5 10

All 6 6 6 18

The Table 4 presents the distribution of the answers

into the three categories. We can count 8 positive an-

swers (”Yes” and ”Yes, if improvements”) out of 18

and 10 negative answers (”No”). With those numbers,

it is difﬁcult to discern a tendency from the partic-

ipants to accept or reject the use of robots in class,

even if the scale tips a little on the negative side.

Arguments in favor of the use of robots in class

were that it could be interesting to use it to help the

teacher. A participant said that using the robot could

save time rather than calling the teacher. It also could

make the class more dynamic, be motivating and ﬁ-

nally, a participant even mentioned that adding the

robot was like adding a nice person to the group.

Overall, it seems that the robot could make the learn-

ing more enjoyable.

When examining the arguments against the use of

the robot in class, it appears that the reason for reject-

ing the use of this technology in class could be that

the robot is not sophisticated enough. Indeed, several

participants point out that the robot is not interactive

enough to be used in class, that its voice is unpleasant

or that the robot is useless if it is only used to display

text. Others express doubts about the robot’s abilities

to answer questions, explain things and illustrate its

explanations with examples taken from personal ex-

perience. The robot was also perceived as difﬁcult to

use or as a potential waste of time.

4 DISCUSSION

4.1 Threats to Validity

Although our study seems to yield interesting results

and partially validates our hypothesis, it is important

to note that our sample was rather small and consti-

tuted mostly of engineering school students. The lack

of participants and diversity among them means that

the results obtained are deeply bound to the context of

the experiment and may not be generalizable as such.

In order to conﬁrm the insights yielded by our exper-

iment, it is essential to conduct future studies with a

greater number and variety of students.

4.2 Acceptability of the Robot in the

Classroom

When counting the negative and positive answers

given by the participants in question Q2, we noted that

we got slightly more ”no” than ”yes” or ”yes, if im-

provement”. In the same way, the results of the ques-

tionnaires showed that anthropomorphism and ani-

mation (with respective average values of 1.86/5 and

2.36/5) were considered insufﬁcient by the partici-

pants. In the Attrakdiff questionnaire, the low val-

ues of Hedonic Qualities of stimulation (HQ-S) show

that the robot should be better used to support stimula-

tion. The negative values of the Hedonic Qualities of

identiﬁcation (HQ-I) indicate that the participants de-

velop no identiﬁcation with the robot or the situation.

Moreover, in the majority of the negative answers, the

participants state that they would not use the robot in

class because the robot is not interactive or intelligent

enough to be useful for learning purpose. The condi-

tions expressed in the ”Yes, if improvement” answers

referred to the same arguments. This could led us to

believe that most participants do not wish to use social

robots in the classroom.

However, we can also note that the values of

the likeability, perceived intelligence and perceived

safety indicators are quite good (with respectively

3.38, 3.17 and 3.46/5). They show that the robot is

well perceived. Similarly, the attractiveness is greater

than 1 (1.43 for C2 and 1.50 for C3) as soon as the

robot speaks. Pragmatic qualities are also greater than

1 (1.29 for C3) as soon as the robot expresses emo-

tions. This means that it is considered pleasant, but

also clear, controllable, effective and practical. The

low values of hedonic qualities show that the design is

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554

not reﬁned enough for learning purpose. Comparative

analysis between conditions C1, C2 and C3 showed

that the more the robot’s own features were devel-

oped, the more positive the experience was.

These results let us think that the behavior of the

robot used for the experiment was too basic: the robot

was only pronouncing or displaying instructions for

a sequential task according to participants’ progress.

Experimentation did not involve robot mobility or

customization of answers. Furthermore, the experi-

ment did not include voice control.

Our belief is that if we were able to make the

robot more intelligent, more interactive or if it was

perceived as such by the students, even in the long

run, most of them will agree to use it in class.

The following section provides the improvements

that appear to be the most critical.

4.3 Improving the Robot

The most critical aspects to improve are features that

promote hedonic qualities of identiﬁcation and stim-

ulation. These are the lowest, and for some of them

negative, values of AttrakDiff. In addition, they will

enable users to achieve the be-goals more satisfacto-

rily, that is to say to ﬁnd reasons why they will con-

tinue to ﬁnd the robot interesting and stimulating for

their own development. This aspect is fundamental

in education. To implement these qualities, it will be

necessary to maintain a high level of animation and

interactivity to meet the expectations of the partici-

pants. Intelligence expectations are also very high.

4.3.1 Working on Hedonic Qualities of

Stimulation

The HQ-S can be strengthened by features that make

the robot original, creative and captivating. The an-

thropomorphic characteristics can be used to design

interactions using the voice, the movements or the

emotions that serve this objective.

Participants suggested improvements such as

making the robot able to nod, move or address a spe-

ciﬁc person in the group. They suggested also to make

the robot more natural, expressive, dynamic and en-

thusiastic. Very concrete suggestions on the matter

were also provided, such as slow down the blinking

animation to make it look more natural and to make

the robot follow the users with its eyes.

4.3.2 Working on Hedonic Qualities of

Identiﬁcation

The HQ-I are stimulated by personalized interactions

that reinforce the professional/realistic aspects of the

situation or the link with others.

Individual feedback functionalities can serve this

purpose by stimulating access to appropriate informa-

tion or content, assessment of activities or advice for

task completion. Some participants proposed to make

the robot more intelligent, for example by providing

more instructions, talking more, making the students

able to understand the mistakes they made and help

them. It was also suggested that the robot ask them

questions about their progress in the task. The robot

could also be able to take initiatives and intervene in

the users’ discussion, directly by arguing on the con-

tent or by calling a speciﬁc student by his ﬁrst name.

In a more general way, it could also be interesting

to use the robot as a mediator in collaborative learn-

ing activities. For example, the robot could analyze

students’ work time or participation in order to regu-

late the collective work. It could play a speciﬁc role

in a project or game learning situation and follow an

adaptive scenario to improve learners’ immersion and

motivation.

This experiment is a ﬁrst step towards the intro-

duction of new tools, the social robots, in learning sit-

uations and the emergence of new ways of teaching

with technology.

5 CONCLUSION

This paper analyses the students’ point of view on in-

troducing social robots in collaborative learning envi-

ronments.

The use of social robots in education is growing

as it appears that they could add a social dimension

that enhances learning. Some experiments have been

engaged but the interactions used, especially the com-

munication between the human and the robot, are not

considered as natural and interactive enough. More-

over, the use of social robots may cause privacy and

acceptance problems.

The aim of this paper is, on one hand, to test the

inﬂuence of speciﬁc ways of interaction on the stu-

dents’ perception of a social robot. Our hypothesis

is that the participants do have a better perception of

the robot when it gives them instructions vocally with

facial expressions rather than with voice or text only.

On the other hand, we aim to identify student’s ac-

ceptability criteria of a social robot in class in order

to help the design of future learning situations.

We presented the results of a comparative study

conducted with potential users interacting with the

social robot Ijini in a collaborative problem solving

learning situation, using different ways of communi-

cation for the robot.

Social Robots in Collaborative Learning: Consequences of the Design on Students’ Perception

555

The experimentation shows that signiﬁcant differ-

ences where found on ﬁve variables: animation, like-

ability, attractiveness, safety and usability. No signif-

icant differences were found on anthropomorphism,

perceived intelligence and hedonic quality.

The analysis of the participants’ recommendations

shows that they can potentially accept social robots in

the classroom if we come up with a better design. The

major improvements to be made are to support he-

donic qualities of identiﬁcation and stimulation. The

stimulation goal could be achieved by using anthropo-

morphic characteristics such as voice, movement and

expression of emotion in order to make the robot more

interactive. The identiﬁcation goal could be achieved

with the intelligence and animation characteristics of

the robot. They can be used to provide the students

with personalized feedback or to play an adaptive role

in collaborative situations.

In the next steps of our work we will implement

the discovered critical improvements and conduct a

new and larger study to conﬁrm the insights that were

exposed in this work. This next study will also pro-

vide us with the opportunity to explore the idea to use

the social robot as a regulating tool for collaborative

learning activities.

REFERENCES

Bartneck, C., Croft, E., and Kulic, D. (2009). Measurement

instruments for the anthropomorphism, animacy, like-

ability, perceived intelligence, and perceived safety

of robots. International Journal of Social Robotics,

1(1):71–81.

Braun, V. and Clarke, V. (2006). Using thematic analysis in

psychology. Qualitative research in psychology, 3:77–

101.

Breazeal, C. (2003). Toward sociable robots. Robotics and

Autonomous Systems, 42(3):167–175.

Church, W. J., Ford, T., Perova, N., and Rogers, C. (2010).

Physics with robotics - using lego mindstorms in high

school education. In AAAI Spring Symposium: Edu-

cational Robotics and Beyond.

Dillenbourg, P. (1999). What do you mean by collaborative

learning? Oxford: Elsevier.

Hassenzahl, M., Burmester, M., and Koller, F. (2003).

AttrakDiff: Ein Fragebogen zur Messung

wahrgenommener hedonischer und pragmatis-

cher Qualit

at, pages 187–196. Vieweg+Teubner

Verlag, Wiesbaden.

Jermann, P., Soller, A., and Lesgold, A. (2004). Computer

software support for collaborative learning.

Kelley, J. F. (1984). An iterative design methodology for

user-friendly natural language ofﬁce information ap-

plications. ACM Trans. Inf. Syst., 2(1):26–41.

Kennedy, J., Baxter, P., and Belpaeme, T. (2017). Nonverbal

immediacy as a characterisation of social behaviour

for human–robot interaction. International Journal of

Social Robotics, 9(1):109–128.

Kennedy, J., Lemaignan, S., and Belpaeme, T. (2016). The

cautious attitude of teachers towards social robots in

schools. In Proceedings of the 21th IEEE Interna-

tional Symposium in Robot and Human Interactive

Communication, Workshop on Robots for Learning.

Laal, M. and Ghodsi, S. M. (2012). Beneﬁts of collaborative

learning. Procedia - Social and Behavioral Sciences,

31:486–490.

Lallemand, C., Koenig, V., Gronier, G., and Martin, R.

(2015). Cr

eation et validation d’une version franc¸aise

du questionnaire attrakdiff pour l’

evaluation de

l’exp

erience utilisateur des syst

emes interactifs. Re-

vue Europ

eenne de Psychologie Appliqu

ee/European

Review of Applied Psychology, 65(5):239 – 252.

Mitnik, R., Nussbaum, M., and Soto, A. (2008). An au-

tonomous educational mobile robot mediator. Au-

tonomous Robots, 25(4):367–382.

Mubin, O., Stevens, C. J., Shahid, S., Al Mahmud, A., and

Dong, J.-J. (2013). A review of the applicability of

robots in education. Journal of Technology in Educa-

tion and Learning, 1:209–0015.

Saerbeck, M., Schut, T., Bartneck, C., and Janse, M. D.

(2010). Expressive robots in education: varying the

degree of social supportive behavior of a robotic tu-

tor. In Proceedings of the SIGCHI Conference on

Human Factors in Computing Systems, pages 1613–

1622. ACM.

Sharkey, A. J. C. (2016). Should we welcome robot teach-

ers? Ethics and Information Technology, 18(4):283–

297.

Short, E. and Mataric, M. (2015). Multi-party socially as-

sistive robotics: Deﬁning the moderator role.

Short, E. and Mataric, M. J. (2017). Robot moderation

of a collaborative game: Towards socially assistive

robotics in group interactions. In 2017 26th IEEE In-

ternational Symposium on Robot and Human Interac-

tive Communication (RO-MAN).

Strohkorb, S., Fukuto, E., Warren, N., Taylor, C., Berry, B.,

and Scassellati, B. (2016). Improving human-human

collaboration between children with a social robot. In

Robot and Human Interactive Communication (RO-

MAN), 2016 25th IEEE International Symposium on,

pages 551–556. IEEE.

Tanaka, F., Takahashi, T., Matsuzoe, S., Tazawa, N., and

Morita, M. (2014). Telepresence robot helps children

in communicating with teachers who speak a different

language. In Proceedings of the 2014 ACM/IEEE In-

ternational Conference on Human-robot Interaction,

pages 399–406. ACM Press.

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