The final publication is available at
http://dl.acm.org/citation.cfm?id=2658881

Can a Social Robot Help Children’s Understanding of
Science in Classrooms?
Tsuyoshi Komatsubara, Masahiro Shiomi, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita
ATR Intelligent Robotics and Communication Laboratory
2-2-2 Hikaridai, Keihanna Science City, Kyoto, Japan{komatsubara.tsuyoshi, m-shiomi, kanda, hagita}@atr.jp, ishiguro@sys.es.osaka-u.ac.jp
ABSTRACTThis study investigates whether a social robot whichinteracts with children via quiz-style conversations
increases their understanding of science classes. We
installed a social robot in an elementary school scienceclassroom where children could freely interact with it
during their breaks. The robot asks children questions
related to their latest science classes to support theirunderstanding of the classes. During interaction, the robot
says children’s name and distribute its gaze among thegroup of children by using a face recognition system and ahuman tracking system. Still, speech recognition is difficultin the noisy elementary school environment; therefore theoperator takes over this function during interactions. In thisstudy our result did not show significant effects of the robot
for helping children’s understanding, but we found several
interesting interaction scenes which shows that the robot
had a certain effect on specific children.
Author Keywords

Robots for children; social robot; field study.
ACM Classification Keywords

I.2.9. Artificial Intelligence: Robotics
INTRODUCTION

Robotics researchers are investigating possibilities of robots
as learning support systems under the context of the
Science Technology Engineering and Math (STEM)
education. For example, small robot-kits like Legomindstorms have been used for teaching materials as a tool
[1, 2]. Visual programming language is particularly usefulfor children [1], and such trials contributed to children’smath scores [2].

the effectiveness of interactive robots for vocabulary andlanguage learning [3, 4]. The presence of a social robot
would contribute to realize an enjoyable learner-centeredclass and encourage children to participate more [5].
We are setting up a social robot into a real elementary
school to increase the science curiosity of children.
Following this context, in this paper, we try to support
children’s understanding towards contents related to scienceclasses through interaction with a social robot at the school.
In previous studies, the fact was found that a design ofgroup interaction is essential for a social robot whichinteracts with children.
For realizing an interaction with a group of children thatwould help their understanding, we employed a quiz-style
conversation which elicits interaction from the children. We
designed quizzes to include important topics related to thelatest science classes therefore the children can review the
topics again through interactions with the robot. We
implemented a face recognition function and a humantracking system for the robot to say children’s name withgaze behaviors to accomplish more natural interactions withthe groups of children.
In this paper, we try to make a robot which helps children's
understanding towards science classes through quiz-style
conversations. For this purpose, we implemented a gazemodel for a robot to distribute the gaze among the group ofchildren. We installed the robot into the science room of anelementary school where science classes are conducted. The
robot interacted with children during the free time
before/after science classes (Figure 1).

Robots are also used as an agent, which interacts withchildren in STEM education. Other studies have reported

Figure 1. Children raised their hands to answer aquestion from the robot.

The final publication is available at
http://dl.acm.org/citation.cfm?id=2658881

RELATED WORK
Gaze model

Gaze behaviors in human-robot interaction have beenbroadly evaluated: turn-taking [6], joint attention [7],influences towards infants' perceptions [8], and eye contacts[8, 9]. For example, Yamazaki et al. constructed a gazemodel that enables a robot to provide information with
precise timing [6]. Nagai et al. investigated how a robot’s
gaze behavior is essential to show the intention of the robot
to others [7].
Several research works focused on gaze behaviors in aninteraction between a robot and a group of people. Mutlu etal have reported that people appropriately distribute theirgaze to interacting people and environments during
conversation with three people and investigated theeffectiveness of such gaze behaviors in the case of humanrobot interaction [10]. Kircher et al designed an imitatedgaze cue for nonverbal robot-group interaction [11]. In theirstudy, a gaze cue is used to show the robot’s intention tointeract with a specific person among group of people.
However, the past works did not focus on distributing
robot's gaze under interactions with more than four people;
our study focused on a model to appropriately distribute therobot’s gaze in such situations.

is difficult to realize robust speech recognition in noisy
environments, such as real classes in an elementary school.
The system integrates the results of face recognition,
estimated positions and speech to decide its actions byusing our gaze model and rules for utterance.

Figure 2. System overview

Interactive robots for learning and motivating

Researchers have investigated effectiveness of several
interaction styles of interactive robots in the context ofpromoting children’s learning. For example, Saerbeck et al
have employed game-like interaction for vocabulary andlanguage learning [12]. Han et al also focused on
vocabulary and language learning at home environments [3].
Tanaka et al focused on “learning by teaching” style topromote children’s learning [13]. Kanda et al installed asocial robot which only speaks English into an elementary
school in order to encourage interaction in English forchildren [4].
Researchers are tackling a couple of long-term motivation
issues by using agents and robots. For example, Bickmore
et al. developed a screen agent to motivate people toexercise [14]. Kidd et al. used a robot to motivate users tocontinue their weight-loss activities [15]. In both studies,
agents and robots are designed to engage human-like daily
conversations to explicitly motivate users for exercise
activities. Yet these studies did not focus on groupinteraction with children at a real environment, unlike ourstudy.
SYSTEM OVERVIEW

Figure 2 shows an overview of the system we implemented,
which involves an operator. The system estimates positionsof both children and robot by using depth sensors which areinstalled in the environment. Children’s faces arerecognized by using camera images from the robot’s
camera. Unlike these automatic functions, an operator takes
over speech recognition because with current technology it

Figure 3. Robovie with an external camera
Robot

We used a communication robot, Robovie, which is 120-cm
tall with two arms (4*2 DOF) and a head (3 DOF) (Figure3). Robovie has two cameras as eyes, microphones, and a
speaker. We used speech synthesis software for utterances[16].
We attached external camera for face recognition to thehead of Robovie. For face recognition, we used a OKAO
Vision-based face-recognition software [17]. Before fieldtrial, the robot interacted with each child around 1~2
minutes to introduce itself; during interaction, face images
were collected through the camera to build a database forface recognition.
For this study, an operator takes over the speech recognition
function by using a tele-operation system (Wizard of Oz[18]). This system enables the operator to select the resultsof speech recognition based on pre-determined candidates.
The robot decides its actions by using the speech
recognition results.

The final publication is available at
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The fact has been revealed that robot's best approach tohuman depends on situations of human and robot [19][20].
Therefore it is important to change robot's position
depending on interlocutors. However, in this study, robot
did not move for safety because many children irregularly
move in classroom.

in the area at 30 Hz with accuracy of around 30 cm. Moredetails of the tracking technique are reported in [21].
Figure 5 shows a tracking result (left) of a situation where
Robovie is interacting with five children (right).
We also implemented a function to integrate the estimatedposition information and the results of face recognition.
Face positions from the human tracking system areconverted to the X-Y coordinate on robot’s external camera
images. Then, the system associated face ID to a nearest
person within a certain distance on the X-Y coordinates on
robot’s external camera images.
Environment and setup

Figure 4. Environments and sensors

Robovie was installed in an elementary school scienceroom (Fig. 4). The science room remained open before andafter science lessons. During science classes, Robovie did
not initiate conversation; if children talked to it, it suggested,“I'm sorry. I'm prohibited talking during class.” This feature
was designed to avoid disturbing class activities. During
breaks, children were allowed to freely interact with
Robovie in the free space.
Gaze model

Figure 5. Human tracking system. A pentagon andellipses represent positions of the robot and children.

To implement a gaze model for n-party conversation in thisstudy, we extended Mutlu’s gaze model which considered
three-party conversation [10]. In this model, they followed
Clark’s classification model [22], i.e., they classified
interlocutors into addressees, bystanders and overhearers tobuild the gaze model (the details of definitions of thecategories are reported in [22]). The authors focused on
three situations for modeling gaze behaviors: (1) two-party
conversation with a speaker, an addressee and an
overhearer, (2) two-party-with-bystander conversation witha speaker, an addressee and a bystander, and (3) three-party
conversation with a speaker and two addressees.
We focused on the third situation. They modeled speaker’s
gaze behaviors in three-party conversation by defining theratios of gazing targets, i.e., addressee 1's face, addressee
2's face, addressee 1's body, addressee 2's body, andenvironment. The values were 21%, 35%, 7%, 8%, and29%, respectively. These values were calculated byobserving three-party conversation in laboratory. We added
up probabilities and divided by the number of addressees
toward the ratios for faces and bodies to extend the modelfor n-party conversations (n>3).

Figure 6. Gaze model for 4-party conversation.
Human tracking system

Figure 4 shows a science room at an elementary school thathas eight desks in front of a blackboard, where children
attend science classes. We installed 24 depth sensors(Kinect) on the ceiling of the room for position estimation.
We employed a human tracking system proposed in [21];the system allows us to track the positions of all the persons

Figure 6 shows the proposed gaze model. In this model, thespeaker’s gaze will be distributed into three kinds of areas:addressee's face, addressee's body, and environments. In nparty conversation with a speaker and n-1 addressees, wedefined the probability of gazing at an addressee k's face is56/(n-1)%, the probability of gazing at an addressee k's
body is 15/(n-1)%. The rest of percentages are equallydistributed among candidates in the environment. The
number of candidates is n-2; the areas are defined between
faces of two addressees.

The final publication is available at
http://dl.acm.org/citation.cfm?id=2658881

We also used gamma distributions, defined by parameters θfor shape and k for scale, to calculate gaze duration toward
each target similar to [10]. Figure 6 shows the distribution
parameters values for each target; horizontal axis andvertical axis represent gaze duration and probability which
is used in the calculation process to decide the gaze
duration. For our model, we used the same parametervalues as in [10]. In [10], calculated parameters ofaddressee 1's face, addressee 2's face, addressee 1's body,addressee 2's body, and environment were (θ, k)=(1.25,
1.26), (1.71, 0.93), (1.61, 0.62), (2.23, 0.41), and (1.28,0.70) in three-party conversation. We selected theiraddressee 2's parameters as addressee k's parameters simply
in our model because gazing ratios of addressee 2 weremore than those of addressee 1. We equalized parameters of
all addressees so that robot can gaze equally.
We defined three rules to decide the target person. The firstrule is to express acknowledgement to the new addressee.
The robot looks at a person who approached the robot
within 2.0 m. The second rule is to distribute robot's gaze toperson when it calls his/her name. Gaze duration in these
two rules follows the gamma distribution there the robot
looks at the face. The third rule is to distribute robot’s gazeto interacting people. The robot looks at people who arewithin 3.5 m of the robot and within angle of 160 degrees infront of the robot. If there are people within a range of 2.0
m from the robot, it does not look at people who are within
2.0~3.5 m from the robot. These distance thresholds wereexperimentally decided by considering with the size ofspace where the robot is interacting with children, andknowledge of social distance reported by Hall [23]. Figure
7 shows scenes where the robot looks at children’s faces
based on our gaze model.

(a)

(b)

(c)

Figure 8. Flow of Robovie’s dialogue
Calling children by their names Since the fact was foundthat people appreciated their name being called by robots[4],we considered this strategy and prepared behaviors to greet
the children by names, such as “Hello, Tanaka-san (pseudoname).” If the system did not identify children, the robot
only greets them in a generic way.
Quiz behavior After greeting, the robot starts a quiz related
to science classes. Before the quiz the robot says a topic
related to the latest class: "I think that you already learned
about a spawn of medaka (Japanese rice fish). It is verysmall, but I'm wondering what size the human ovum is". In
this study, almost of all quizzes consist of two or threechoices.
We designed the robot to repeat important parts of quiz inconversations, such as "I'll give you a question about thesize of the human ovum. What is the size of the human
ovum? No.1 1.4 mm, No.2 0.14 mm, or No.3 0.014 mm.
Pick an answer." After explaining choices, the robot tells
the correct answer of the quiz and gives a simple
explanation about it.
Answering science questions When a question was asked,
the operator judged its relevance to science classes. To
answer it, the operator employed two strategies. Whentypical questions were asked, he selected from preimplemented behaviors; otherwise, he directly typed
utterances to implement new behaviors after the session.

Figure 7. Example of gaze motion.
FIELD TRIAL
Details of implemented behaviors

An overview of the dialogue flow is summarized in Fig. 8.
When children lingered around Robovie during breaks, it
started to gaze at them and initiated conversation. Robovie
autonomously interacts with children, except for speech
recognition and behavior selections towards unexpected
conversation flow.

Participants

In this field trial, we targeted four classes of 114 5 th gradestudents at a public elementary school. They usually used
the science room for science classes.
Procedure

Each class had ten lectures during experiment term.
Pendulums and human birth were taught during the lectures.

The final publication is available at
http://dl.acm.org/citation.cfm?id=2658881

We separated the installation term of Robovie due to thelecture unit; two of four classes learned pendulums with
Robovie, and rest of four classes learned human birth with
Robovie. Science classes have 45 minutes, followed by afive to twenty minute break. During breaks, children could
freely interact with Robovie.
We administered questionnaires to the children. Pre-tests
were conducted before the first science class, and post-tests
were conducted after the final science class of each unit.
Measurements

In both the pre- and post-tests, we measured theirunderstanding about each learning unit through quizzes,
which are developed based on examinations of theelementary school and entrance examinations for juniorhigh schools in Japan. We evaluated and modified the
difficulties of the quizzes with 6th grade students of theelementary school. An example of the quizzes is as follows:- Which of the following changes would speed up a
metronome’s tempo?
A: Increase the amplitude of the pendulum rod
B: Decrease the amplitude of the pendulum rod
C: Lower the position of the weight
D: Heighten the position of the weight
RESULTS

Figure 10. A child raised her hand when robot called her
name.
On the other hand, in the second week, the size of groups
became small. Maybe because some of them lost interest
towards Robovie. Still almost all of the children continued
to greet and to request to hear their name from Robovie
during breaks, only about half continued to join quizzes.
Typically, a group of active children interacted with
Robovie after classes; after they left, children who were
waiting of Robovie took turns interacting with it.
Evaluation

Scores of quizzes under the situation where Robovie wasinstalled and not installed were 7.65 and 7.52 relatively on
average. Each standard deviation were 1.92 and 2.01
relatively. There is no significant difference between themfrom paired t-test results (t(96)=0.50, p=0.62).

General trend

The children’s interaction with Robovie gradually changed
during our two weeks trial which was conducted twice. In
the first week many children seemed interested andgathered around Robovie to answer the quizzes. Figure 9shows a situation where children raised their hands during
answering. Moreover, the robot’s greeting-by-namebehavior attracted their attention (Figure 10). For example,
they often asked, “Do you know my name?”.

Figure 9. Children raised their hands when they regarded
choice robot told as answer.

Observation

In the whole experiment, 68 children listened to Robovie's
quizzes 183 times in total (2.7 times on average). The
maximum number of times for a single child was 11 times.
This child made conversations with Robovie in every break,and spent about 30minutes interacting with it. 46 children
never listened to Robovie's quizzes (40.4% of all children).
Even if the questionnaires results did not show significant
differences, we found several interesting scenes anddescriptions in the questionnaires during the field trial. For
example, at the post-test, three children who interacted with
Robovie and participated to quizzes explicitly indicated thatthey learned specific knowledge about the roles of amniotic
fluid in the answers to the questionnaires. Actually they
only learned that amniotic fluid has a role of protecting
unborn child through their science classes; therefore
interaction with Robovie provided new knowledge to themand they could remember them at the post-test. Figure 11
shows situation where one of these children talked with
Robovie about this knowledge. (Child B in Figure 11)

The final publication is available at
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Robot: What happens to the period of a pendulum if its
weight becomes heavier? If its weight becomes heavier,the period gets shorter, longer, or doesn't change. Pick ananswer.
Child B, C: The period gets longer.
(a)

(b)

Figure 11. A child told the teacher about the knowledge
the robot gave him. (Child A: Left in (a), Child B: Rightin (a), Teacher: Right in (b))
Robot: Do you know what a role amniotic fluid has?
(Figure 11a)Child B: It protects unborn child against danger fromoutside.
Robot: You are familiar with that. But actually, it has twoother roles. Do you know about them?
Child B: What's? I don't know.
Child A: (Child A was considering the question withChild B)
Robot: Actually, the amniotic fluid has a role ofrehydration and ... (At this time, teacher entered theclassroom and the conversation ended.)Child B: Wow! Amazing! (He talked to the teacher) He
is awesome (Figure 11b).
Teacher: What happened?
Child B: He told that unborn child can rehydrate becauseof amniotic fluid.
Teacher: Wow!
He had never listened to Robovie's quiz until this time.
However in the next break, he talked with Robovie forabout ten minutes and listened to four quizzes.

Robot: Who thinks the period gets shorter if the weight
becomes heavier?
Children: ...
Robot: Who thinks the period gets longer?
Child B, C: (They raised their hands.)
Robot: I see. Finally, who thinks the period doesn'tchange if the weight becomes heavier?
Child A: (She raised her hand)
Robot: I’ll tell you answer. If the weight become heavier,the period doesn't change.
Child: Why?
Robot: You may already know, but actually both heavy
matter and light matter fall at the same speed.
Figure 13 shows an example of conversations between
Robovie and the child who listened to Robovie's quizzes themost times in this experiment.
In this case, an interesting observation is that she took someactions other children did not take. On the last day ofexperiment, she came to Robovie immediately after
finishing class (Figure 13a) and talked with it for about
fifteen minutes. In that conversation, she seemed to be
curious to hear Robovie's quiz on her own initiative: shesaid "Please give me another quiz." She also showed her
textbook to ask a question about a topic on it (Figure 13b).
In addition, when Robovie's farewell party was held for thewhole school after experiment, she came to greet it after theparty.

Figure 12 shows a situation where the child made a mistakein the quiz and then he asked the robot to explain thecorrect answer. The children had learned the principle thatthe period of the pendulum has nothing to do with its
weight in the previous class.

(a)

(b)

Figure 13. A child seemed to build a close relationshipwith the robot.
Figure 12. A child directed a question at the robot when
the robot gave the quiz answer. (Child A: Center, Child
B: Second from the right, Child C: Right)

Child: What is this? (Pointing at a topic on the textbook
which has no relevance to the class)

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Robot: Well, I don't know about it because that topic is
not related to today's class.

intention. In terms of this suggestion, at least, Robovie
could have a role of a communication partner.

Child: (Child nodded.) Hey Robovie, what principle does
this toy apply? (Pointing at a pendulum-based toy on the
textbook)

We conjecture that there are two reasons why children
could not be absorbed in listening to Robovie: nosignificant flexibility and answering design. Firstly,
Robovie could not deal flexibly with children's behaviors.
For example, Robovie continued explanation for a quizthough children answered it immediately. In addition, since
we designed the Robovie to repeat important parts of quiz,children sometimes seemed to find it boring. As a result,situations were often observed where children had theirattention caught by something other than conversation(Figure 15).

Robot: Well, that toy applies the principle that the periodof a pendulum changes depending on its length. We canplay with the toy to change the length to swing the clawsof the crab.
No other children got into similar actions. It seems that she
had an interest in science or Robovie from the fact that she
listened to Robovie alone in the beginning of theexperiment. We conjecture that her interest in science or
Robovie graded into relationship such as friendshipbetween them through having conversation several times.
These facts show that robots have a potential to make astrong impact on specific children.
DISCUSSION
Why robot could not increase children’s score?
In our study, in the cases of figures shown in Observation,

Secondly, it seemed that Robovie bored children because ofits design that it answers only questions related to thescience class. For its design, Robovie could not sometimes
answer a very easy question which is not related to scienceclass. Once, a child said "You can answer that question,
can't you?" after it could not answer his question: “Howmuch is 1 + 1 ?" Avoiding such simple questions would
hinder the children‘s interest in conversation with Robovie.

children replied reaction to robot well. There were only one
to six children around Robovie. And there was no child
who was not interested in Robovie's quiz or interfered with
its utterance. In addition, the classroom was relatively quiet
because there were few children in classroom. It was thus asuitable situation for children to concentrate on listening to
Robovie's utterance. In almost of all such situations in thisexperiment, conversations between children and Robovie
through the quizzes were successful. We conjecture thatchildren replied reaction well due to this suitable situation.
Figure 15. Children directed their attentions on Robovie's
microphone.
CONCLUSION

Figure 14. Children covered the robot's eyes with theirhands.
However, in our study, Robovie could not increase theunderstanding of science classes for children significantly.
We conjecture that the effect on understanding was not
significant because situations where children cannot be
absorbed in listening to Robovie happened most of the time.
Figure 14 is an example of such situations. Figure 14 showsthat children had an interest not in conversation with
Robovie but covering its eyes with their hands. A similar
observation was reported in Hato et al. [24]. Theysuggested that this behavior is due to human's
understanding of robot’s communicative and informative

Our field study investigated whether a social robot whichinteracts with children via quiz-style conversations canincrease the understanding of science classes for them.
Robovie was installed in a science room for five weeks,
where children were allowed to freely interact with it
during breaks. Although Robovie could not increase thechildren's score, some interesting scenes were observed,
which shows that Robovie has a potential to make a strong
impact on children in specific situations: it gives themknowledge they do not know, or it builds a close
relationship with them so that they could ask a question
freely. We conclude that Robovie can have an effect onchildren in suitable situations, in which children can listen
to Robovie sufficiently. However, in situations wherechildren tend to take actions besides conversation, it isdifficult for Robovie to have an effect on them.
ACKNOWLEDGMENTS

We thank the staff at the Higashi-Hikari elementary school,
its students, and their parents for their cooperation. We also

The final publication is available at
http://dl.acm.org/citation.cfm?id=2658881

thank Prof. Naomi Miyake (Univ. of Tokyo) for her helpfuladvice, Kanako Tomita who helped conduct the experiment,
and Thomas Kaczmarek who helped system development.
This work was supported by JSPS KAKENHI Grant
Numbers 21118001, 21118003, 21110008, and 25240042.
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