Two is Better Than One: Verification of the Effect of Praise from Two
Robots on Pre-school Children’s Learning Time
Masahiro Shiomia,*, Yumiko Tamuraa,b, Mitsuhiko Kimotoa,c, Takamasa
Iioa,b,, Reiko Akahane-Yamadaa, and Katsunori Shimoharaba

ATR-ISL, Kyoto, Japan;b

Doshisha Univ., Kyoto, Japan;cKeio Univ., Kanagawa, Japan;*corresponding author: Masahiro Shiomi, m-shiomi@atr.jp
Masahiro Shiomi received M. Eng. and Ph.D. degrees in engineering from Osaka University in2004 and 2007. During that same time, he was an intern researcher at the Intelligent Robotics
and Communication Laboratories (IRC). He is currently a group leader in the Agent Interaction
Design department at Interaction Science Laboratories (ISL) and the Advanced
Telecommunications Research Institute International (ATR). His research interests include
human-robot interaction, social touch, robotics for childcare, networked robots, and field trials.
Yumiko Tamura received an M. Eng. degree in engineering from Doshisha University, Kyoto,
Japan in 2019. Her research interests include child-robot interaction and storytelling.
Mitsuhiko Kimoto received M. Eng. and Ph.D. degrees from Doshisha University, Kyoto, Japan
in 2016 and 2019. He is currently a JSPS Research Fellow (PD) at Keio University, Kanagawa,Japan. His research interests include human-robot interaction, human-agent interaction, andinteractive AI.
Takamasa Iio received the Ph.D degree from Doshisha University, Kyoto, Japan, in 2012. He is
an associate professor at Doshisha University, Kyoto, Japan. His research interests include
social robotics, group conversation between humans and multiple robots and social behaviors ofrobots..
Reiko Akahane-Yamada, Ph.D. is a Principal Researcher at ATR Intelligent Robotics andCommunication Laboratories, Representative Director & CTO at ATR Learning Technology
Corporation, and an Adjunct Professor at Kobe University, Japan. She got a master degree and
Ph.D. at Faculty of Human Science in Osaka University in Japan. She has been working as a
researcher at Advance Telecommunication Research Laboratories (ATR) since 1986, andconducted series of experiments examining how human-beings acquire language-specificpattern of speech perception and production. Her current main research fields are Second
Language Acquisition, Spoken Language Processing, and Learning Psychology.
Katsunori Shimohara respectively received B.E. and M.E. degrees in Computer Science andCommunication Engineering and a Doctor of Engineering degree from Kyushu University,
Fukuoka, Japan in 1976, 1978, and 2000. From 2001 to 2006, he was the director of the
Network Informatics Laboratories and the Human Information Science Laboratories, Advanced
Telecommunications Research Institute (ATR) International, Kyoto, Japan. He is currently aprofessor in the Department of Information Systems Design, Faculty of Science andEngineering in the Graduate School of Science and Engineering, Doshisha University, Kyoto,
Japan. His research interests include community systems design, relationality design, andrelationality-oriented systems design.

Two is Better Than One: Verification of the Effect of Praise from Two
Robots on Pre-school Children’s Learning Time
This paper reports the effects of praise from two robots on children’s learning time,
their performance, and their acceptance of the robots. Since praise from a single
robot provides positive effects for children as educational support, wehypothesized that praise from two robots will increase such effects because two
robots will enhance the social influences. To verify this hypothesis, we developed
an English learning system that consisted of an e-learning application, a depth
sensor for human-tracking, a tabletop humanoid robot, a doll-type robot, and an
operator. We designed the robots to interactively praise children during theirlearning situations and experimented with a with-in participant design to compare
the effects of praise from two robots and just one. 22 children participated in anexperiment whose results showed that children learned more when two robots
praised them than just one, even with an identical amount of praise. These results
suggest the effectiveness and empirical evidence of using two robots foreducational support.
Keywords: Human-Robot Interaction, Education support, Children, Social reward,
Praise1. Introduction
Building educational-support systems with social robots for children is aburgeoning research topic in human-robot interaction [1]. One main difficulty foreducational support is keeping children to learn. To solve this problem, several studies inhuman science literature have focused on model-based approaches, e.g., the Attention,
Relevance, Confidence, and Satisfaction (ARCS) model of motivation from an
educational technology perspective [2] and the Dick and Carey Model [3] from an
instructional system perspective for designing e-learning contents. Researchers are
already using these models to design educational-support robot systems and have reported
their effectiveness for educational support and designing e-learning contents [4-6].

To keep children motivated to learn, robotics researchers have focused on model-based
approaches and the interaction styles of social robots used in educational support. For
example, several studies developed a social robot to promote rest times and increase thegrowth mindsets for children during self-teaching to maintain their learning motivation[7-9]. Other studies used multiple robots to play roles during storytelling to attract
children’s attention [10-12]. Several studies focused on the actual settings in learning
situations, i.e., installing social robots in elementary schools to support English learningand stimulating interest in Science, Technology, Engineering, and Mathematics (STEM)
contexts [13-15].
In this study, we focused on the effect of praise because it is an essential social
reward to increase children’s learning time. Several studies have investigated the effectof praise from teachers on children [16-18]. For example, a past study conducted anexperiment with more than one hundred children from the classes of Grades IV and VI at
USA and reported that praise from teachers positively affected the learning attitudes ofchildren [19]. In past studies related to robot educational-support systems, someresearchers incorporated praise contents in robot behavior design [9, 20, 21]. Thesestudies described the positive elements of praise, even if they did not exclusively
investigate its effects on educational support for children.
We are interested in how the number of robots affects such praise to increase thepraise effects in the context of encouraging children to learn. More robots generally
increase the social influence on people’s feelings and their behavioral changes [22-24].
A recent study reported that children are more influenced by the number of robots thanadults [25]. Another described the effectiveness of praise from multiple robots in thecontext of offline motor skill improvements [26]. Based on these results, we hypothesizedthat such effects will occur in the context of educational support: praise from two robots

will exert more influence on children’s learning time than praise from a single robot.
Therefore, we investigated the effectiveness of using two robots that praisechildren in the context of educational support by developing a system whose robots praisechildren during learning. We employed an English learning system because learning
English as a second language is a popular activity in Japan. We conducted experiments
where children learned English with a GUI-based learning support system to investigate
whether their learning increased due to the number of robots (Fig. 1) and investigated thefollowing research question:- Does praise from two robots increase children’s learning time more than praise fromjust one robot?

Fig. 1 Child learns English with two robots

2. RELATED WORKS
Past studies have broadly investigated the effectiveness of educational-support robot
systems [1]. Several investigated the effects of praise from an artificial agent and two or
more robots. In this section, we overview related works about educational support andfocus on two topics that are related to this study’s aims: praise effects and using two
robots. Finally, we summarize the positions of our study.

2.1 Educational-support robot systems
Due to the importance of educational support for children, various robotics
researchers have investigated the effectiveness of social robots [1]. One advantage ofusing them for educational support is that children can physically communicate with them.
For example, gestures are critical in educational support because they elicit speech andimprove performances [14, 15]. The physicality of robots creates advantages forproviding more information than computer-graphics-based agents [27]. Other studies
focused on the physicality of robots in the context of direct manipulation for sucheducational support as handwriting [28], shooting free throws [29], or encouraging
physical exercise during breaks from learning [7, 8]. From another perspective,
implementing the perceptions of such human activities as gestures is also essential for
educational robots [14, 30]. These studies described the effectiveness of social robots foreducational purposes based on laboratory experiments.
Field trials in actual environments, i.e., installing social robots in kindergartensand elementary schools, are also active research approaches in human-robot interaction
research fields. For example, some studies placed social robots in schools to investigate
whether they increase children’s motivation and performance to support the learning ofEnglish [31, 32]. Another study installed a social robot to stimulate children’s interest inscience in STEM education contexts [33]. Robotics researchers are interested in how asocial robot influences the behaviors of children. Another past study placed a social robot
in a kindergarten and described how children treated it on long field trials [34].

However, these studies focused less on the effects of praise from two or morerobots in an educational-support context. Below we discuss related works on the effects
of praise and using two or more robots. Table 1 lists the typically related works byconsidering the educational-support context.

Table 1 Summary of related works that correspond to two or more categories
Ages ofparticipants

Educational
supportPraiseeffects

Two or morerobots10-11✓✓(a part of)-Leite et al.,2014 [21]8-9✓(playing games)✓(a part of)Park et al.,2017 [9]5-9✓✓(a part of)Davison etal., 2020 [51]

6-10

✓

✓
(a part of)Leite et al.,2015 [11]6-8✓(storytelling)✓

Tamura etal., 2017 [12]3-5✓(storytelling)✓

Iio et al.,2017 [23]Visitors to museum(10-50)✓(a part of)✓Khalifa et al.,
University students
2018 [53]✓✓Shiomi et al.,2020 [26]

University students

-

✓

✓

This study

4 to 6✓✓✓

AuthorKomatsubara
et al., 2014

[20]

-

2.2 Praise effects from artificial agents
Praise is a fundamental social reward in human-human interaction [35]. Human
science literature has reported a wide variety of positive effects, including self-efficacy

[36], improving motivation [37, 38], raising academic self-concepts [39], promoting
pleasure [40], improving skill and task performance [38, 39], raising academic
performances [17, 19, 41], and honing motor skills [42, 43]. Such social beings as robots
and computer agents can also praise humans [44-46]. Past studies described the positive
impressions and performance improvements from social beings [47-50].
As shown in Table 1, several studies reported praise effects through experimentswith social robots for educational support [11, 20, 21, 51]. Although they used praisefrom robots as an element of conversational contents, they focused on such different
topics as the robot’s existence and concluded that it was less effective [51]. These studies
varied the effectiveness of the praise from robots/computer agents and provided richknowledge about the possibilities that praise can influence people’s behaviors andincrease the perceived positive impressions from them. However, these studies focusedless on the effects of praise from two or more robots in the context of encouraging
children to learn.

2.3 Effects of multiple social robots in human-robot interaction
People’s behaviors are often influenced by the opinions of others, and the strengthof such effects often increases due to the particular number of others. Robotics researchersinvestigated the effects of the number of robots on behavior changes, i.e., conformity
effects from multiple robots [24, 52] and effective information-providing tasks in realenvironments [22]. A recent study revealed that children are more influenced by the
number of robots than adults [25].
Several studies investigated the positive effects of two or more robots in sucheducation contexts as storytelling for children [11] [12], educational support for science[23], and an educational-support robot system for university students [53]. These studies

gathered essential knowledge about social effects and the power of multiple robots.
Unfortunately, they insufficiently focused on the praise effects from two or more robots.

2.4 Praise effects from multiple robots
To the best of our knowledge, only one past study has focused on the effects ofpraise from two robots [26]. In a context of offline motor skill improvements, that study
conducted a two-day experiment to investigate whether praise from two robots enhanced
the consolidation process of adult participants. It concluded that praise from two robots
has more positive effects than praise from one robot. However, the effect of the numberof robots who praise remains unknown in the context of educational-support contexts forchildren.

2.5 Position of this study
As summarized in Table 1, previous studies on educational-support robot systems
reported their effectiveness in various experiments with children. Such studies on praisefrom agents described how it changed behaviors and impressions. Still, these studies
focused less on praise from two or more agents in the context of educational support. The
unique point of our study is its investigation of the effectiveness of praise from two robots
in the context of an English learning system for children.
Another unique point is to focus on both motivation improvement andperformance improvements for younger children participants, because we thought thatthe main difficulty in younger children's learning compared to adults' learning is
motivation improvement. For example, past studies with adults [26, 53, 54] and relatively
older children [20, 55, 56] mainly focused on task performance improvement within acertain learning time period (e.g., during experiment sessions or school classes).
Therefore, the developed systems in these studies are designed to increase performanceeffectively and implicitly assume that participants can keep learning during the limited
time period.
For young children, on the other hand, it is relatively difficult to maintain theirmotivation to learn. For this reason, past studies with younger children [7-9] have also
focused on motivation improvement for studying because it is directly related to task
performance improvement. Our study focused on praise effects from multiple robots toboth increase motivation and improve performance.

3. Proposed system
Figure 2 shows the architecture of our developed system, which consists of an
English e-learning application, a human-tracking system with a depth sensor, a tabletopsized humanoid robot, a doll-type robot, and an operator as a speech recognizer. The
application sends the correct/wrong information about the children’s answers to the
behavior controller to decide the praise contents from the robots. The tracking system andthe speech recognizer send the children’s position information and their recognizedspeech to the behavior controller to control the behaviors of the humanoid robot. The
following are the details of each system.

GUI

Englishlearning
application

Depth
sensorHuman
tracking

Micro
phone

Speechrecognition

Humanoid
robot
Behavior
controller
Doll-typerobot

Praise contents

Fig. 2 System configuration

Operator
Autonomous

Fig. 3 Touch-based GUI for our e-learning system. Children can hear English questions
by repeatedly pressing the play button. They answer by touching the image. Blue button
in upper-right corner ends the system.

3.1 English e-learning application
We implemented a simple touch-based GUI to select English words by listening
and reading English text (Fig. 3) using JavaScript. Children answer questions by touching
the image shown on a screen. We prepared 60 questions, each of which consisted ofspeech content, a text, and two images. The speech contents and the images are part of an
English e-learning system named ATR CALL, which was developed to examine thelearning process of second language speech and has been used in several studies [57-60]
and supports listening, talking, reading, and writing. The system records the touch logs
of children and their working times for analysis.

3.2 Human-tracking system
We used a Kinect V2 sensor to track the head positions of the children in theexperiment. The tracking system sends their position information to the humanoid robot
to control its face and body orientation, because such non-verbal information is importantfor smooth and natural interactions between robots and people [61].

3.3 Tabletop-sized humanoid robot
The humanoid robot explains the experiment to the children and praises them. We
used such a humanoid robot as host because it has minimum capabilities for such gestures
as pointing and eye-contact, which are essential to maintain joint attention in humanrobot interaction [62].
We used Sota (Fig. 4), which has three DOFs in its head, two DOFs in its
shoulders, two in its elbows, and one in its base. It is 28 cm tall with both network
connection and voice synthesis functions. It has a function that links its mouth’s LED
based on the sound level to indicate when it is talking. The robot always faces the children
using position information from the human-tracking system.

3.4 Doll-type robot
In this study, we used a different-type robot to investigate praise effects frommultiple robots due to below reasons: 1) the children can easily understand that different
robots are present and which is speaking, and 2) if using an additional robot that praises
children for education support robot systems is a promising approach for existingeducation support systems, a cheap and simple robot, such as a simper doll-type robot
and an additional wireless speaker, would be reasonable. For this reason, we used a teddybear robot with one DOF in its head and another in its mouth (Fig. 5). It has a microphone
and a speaker. Its head and mouth automatically move due to the sound level to indicate

when it is talking. In our system, the server system sends speech contents to its speakerby a network. We used VOICEROID + TohokuZunko 1 (AHS Co. Ltd.) as a speechsynthesis function.

3.5 Operator
We used a human operator who assumed control of the timing of thestarting/ending system and interrupting by following pre-determined rules (Wizard of Oz[63]) because accurate speech recognition for children’s speech remains difficult [64].
The operator controlled the timing of the following conditions: 1) the robot’s introduction
at the beginning of the experiment; 2) the goodbye information at its conclusion; and 3)
suggestions that the children touch an image to answer questions if they are inactive or
reluctant during the experiment.

Fig. 4 Tabletop-sized humanoid robot

1

https://www.ah-soft.com/vocaloid/zunko/top.html

Fig. 5 Doll-type robot

Press “start” button
To the next quiz

Show quiz / play audio
YES

Press “end” buttonNO

YES
Is first selection
correct?

Praise about correct answer

NO

Praise about wrong answer

Display results
Praise about learning
Fig. 6 Praise timing and the basic flow of the system during experiment

3.5 Praise contents
The robots praised the children when they 1) answered correctly, 2) answeredincorrectly, and 3) completed the application as shown in Fig. 6. All the praise consistedof two sentences: “You're getting used to English, aren't you? You're working very hard”(when children answered correctly); “Don’t worry about mistakes. Keep trying” (whenthey answered incorrectly); and “I think your answers are better than they were at the
beginning. Your hard work is paying off” (when they completed the application). We
prepared 40 praise sentences (20 for answering correctly, 10 for answering incorrectly,
and 10 for finishing) and integrated two praise sentences into one praise comment. To
avoid repeating the same sentence set, we changed the combinations of the sentences andtheir order when the robots praised the children. Thus, during the experiments, the robots
and the English e-learning system are basically autonomous while the children are
answering the questions.

4. Experiment
4.1 Hypotheses and predictions
Past studies reported that the effectiveness of social rewards produces such
beneficial effects as motor skill improvements [26, 47-50]. People’s behaviors are
influenced by the number of interacting targets, i.e., since many people and agents havestronger social influence [22, 23]. Another study reported that children are more
influenced by the number of robots than adults [25]. However, such effects in the context
of educational-support robot systems haven’t been scrutinized yet.
Based on these considerations, we hypothesized that praise from two robots willencourage more learning in children than praise from a single robot. As a result, children
who are praised by two robots will use the English e-learning applications longer thanchildren who are just praised by a single robot. In addition, we expected that morelearning time will increase the number of correct answers. Therefore, we made thefollowing predictions:
Prediction 1: Praise from two robots will increase the learning time of children
more than praise from just a single robot.
Prediction 2: Praise from two robots will increase the scores of children morethan praise from just a single robot.

4.2 Environment
Figure 7 shows our experimental environment. The room is about 40 m2. We set
the touch-panel display, placed the robots around it, and set a Kinect V2 behind it. The
children sat on the floor to look at the display.

R
D

C

8120
mm

5200 mm
C Child

R Humanoid robot
D Doll-type robot

Fig. 7 Experiment environment
4.3 Conditions
This study had a within-participant design. All the participants experienced two
trials: single-robot and two-robot conditions. We separated 60 questions into two sets of
30 and used a different question set for each condition. The experiment had two sessions;the order of the conditions and the question sets for each session was counterbalanced.
1) Single-robot condition: we only used the humanoid robot, i.e., the robot praised thechildren with two sentences. It praised them based on pre-defined rules as described in
section 3.5.
2) Two-robot condition: we used both the humanoid and doll-type robots. All comments
including two sentences were separately made by both robots. The speaking sequence
was the same. The humanoid robot spoke first, followed by the doll-type robot.

4.4 Participants
Twenty-two children (11 girls and 11 boys from four to six years old, average ageof 5.23, and S.D is 0.79) participated in our experiment. Their mothers observed in a
separate room.

4.5 Procedure
Before the experiment session, the mothers received a brief description of ourexperiment’s purpose and procedure. Its protocol was approved by our institutional
review board, and all the mothers signed consent forms. First, the children practiced with
the English e-learning system by answering three questions. After the practice session,
the experimenter and the mothers left the experiment room, and the robot requested thechildren to click on the “start” button. They were free to finish the experiment at any timeby clicking on the “end” button. At the end of the first session, the mothers andexperimenter returned to the room, the children and their mothers were given a fiveminute break. Then the experimenter and mothers again left the experiment room, andthe second session began. They observed their children in the experiment room bycameras and microphones.

4.6 Measurements
In this experiment, we objectively measured the amount of learning time, thenumber of answered questions, the number of correct answers, and the rate of correct
answers to measure their performances. The definition of the learning time is from when
the children clicked “start” button and “end” button. We note that the time of utterancesfrom the robots was the same between conditions. The children can answer the questions
or finish the session by clicking buttons regardless of the robot’s utterances; therefore,

the time of utterance did not influence the learning time in a comparison between theconditions.

5 Results
5.1 Verification of prediction 1
Figure 8 shows the average learning time for the children in each condition, andTable 2 shows the average and median values. We performed the Kolmogorov-Smirnov
test to check the normality of the results. Since the results were not normally distributed,
we conducted a Wilcoxon signed-rank test instead of a paired t-test and found asignificant difference among the conditions (z=2.191, p=0.028, r=0.47). Thus, prediction
1 was supported.
** :p<0.05

Total learning time (seconds)

800
700
600
500
400
300
200
100
0
Single robot

Two robots

Fig. 8 Average learning time of children

Table 2 Average and median values of learning time
Learning time (seconds)
Average

MedianSingle robot

497.7

356.5

Two robots

678.3

479.5

5.2 Verification of prediction 2
Figures 9 and 10 show the number of answered questions and correct answers.
We performed a Kolmogorov-Smirnov test to check the normality of the results. Since
the results were not normally distributed, we conducted a Wilcoxon signed-rank test
instead of a paired t-test and identified significant trends among the conditions for boththe number of answered questions (z=1.947, p=0.051, r=0.42), the number of correct
answers (z=1.843, p=0.065, r=0.39), and the rate of correct answers (z=1.025, p=0.305,
r=0.22), but the differences were not significant. Thus, prediction 2 was not supported.
+ :p<0.1

60

60

50

50

+
Number of correct answers

Number of answered questions

+

40

30
20
10

+ :p<0.1

40
30
20
10

0

0
Single robot

Single robot

Two robots

Fig. 9 Answered questions

Two robots

Fig. 10 Correct answers

Table 3 Average and median values of answered questions, correct answers andrate of correct answers
Answered questions

Correct answers

Rate of correct answers (%)

Average

MedianAverage Median

Average

MedianSingle robot

34.9

22.5

25.4

12.5

69.3

64.2

Two robots

48.0

33.5

38.0

26

74.7

81.2

5.3 Observations of children’s behaviors
Based on increasing learning times and preferences, using an additional robot thatpraises children for education support robot systems is a promising approach to increase
learning effects. In this sub-section, we described the results of observations of children
during the experiment and the interview results with them and their parents.

In both conditions, the children’s behaviors were basically similar. They quietlylistened to the humanoid robot’s explanations and answered the questions (Fig. 11). They
often paid attention to the robots when they were talking and smiled when they werepraised. A few children touched the heads of the humanoid robot and/or the doll-type
robot when they praised the children (Fig. 12, left). Some hugged the doll-type robot
during the experiment (Fig. 12, right).
In addition, we asked children and parents about their impressions of the robots.
Most children preferred the doll-type robot (e.g., they said “The bear is cute,” and “I felt
lonely without the bear.”), as well as their parents, For example, parental responses
include “my child seemed to feel nervous in the front of the robot, but the bear robot is adoll, making it more acceptable;” “a trialogue style seems better for children than a oneto-one style.” One child said, “I felt more praised when the bear was there,” and a mothersaid, “my child was more motivated by praise from the two robots than from just a single
robot.” They felt this way even if the amount of praise was identical between theconditions.

Fig. 11 Single-robot condition and two-robot condition

Fig. 12 Child pats humanoid robot and child hugs doll-type robot

6 Discussion
6.1 Application for schools
Our system enables children to learn more by praise from two robots. With thisapproach in school settings, e.g., if both a teacher and a robot praise children, they willlearn more. A past study reported that social comparison is deleterious for children [17],and they prefer individual praise compared to praise in front of their peers [65].
Therefore, praise content should be carefully designed in such settings. Since related
past studies mainly focused on praise situations for school-aged children at school, it isunknown whether toddlers also have similar feelings.
Our experiment has a self-study setting, and it didn’t focus on praise contents ina social-comparison fashion. Our setting avoided such problems because the children
were alone in the room with the robots. Although we believe that praise from two robots
is effective for self-study settings with school-age children, its use should be scrutinizedin different settings, such as classrooms.

6.2 Gender effects
This study did not investigate the gender effects because this paper investigates
the praise effects and a past study that investigated the gender effects in praise effects,

which reported no significant effects for gender [66]. In fact, past similar studies also did
not evaluate gender effects, although the gender ratios of the participant children are notbiased [9, 11, 21, 51]. As a reference, we conducted an ANOVA to investigate the gendereffects for all measurements (learning time, answered the question, correct answers, andthe ratio of correct answers), but all comparisons did not show any significant effects ingender effects.
However, several studies reported the reported gender differences in the context
of second language learning, in particular the advantages of females [56] [54]. We notethat these studies mainly focused on longer learning effects (i.e., not learning time andthe number of answered questions) and relatively older people (i.e., children with morethan nine years old or adults). Therefore, investigating the gender effects toward praisesin the context of second language learning for younger children is not well investigatedyet; it should be one interesting future work.

6.3 Robot group structure’s effects
In this study, we only investigated the effects of the hetero robot group. Past
studies that investigated the effects of multiple robots adopted both hetero and homo types(i.e., using the same robots or different robots), and they less focused on the effects ofrobot group structures [12][23][26]. We thought that both structures have different merits
depending on target users and development perspectives. For example, using a heterostyle robot group would be effective in assigning different roles and behaviors for eachrobot because their appearances are different. Such merit would be useful for applications
toward younger children by decreasing cognitive load. In addition, a variety of robot’s
appearance might have positive effects on such children to keep their motivation andinterests.

On the other hand, using the homo-style of the robot group would be effective in
expressing a sense of unity because their appearances are the same. For example, theirsynchronized behaviors would provide different effects in the praise context. Moreover,
we thought that the praise from the homo-style of the robot group would provide similar
positive effects because our past study showed the positive effects of the praise from twosame robots.
On the other hand, using the homo-style of the robot group would provide
different effects by expressing a sense of unity because their appearances are the same.
For example, a past study showed that synchronized behaviors of the homo-style of therobot group provide different impressions and behavior changes compared to nonsynchronized behaviors in the context of peer pressure [67]. Moreover, our past study
showed the positive effects of the praise from two same robots in the context of offline
motor improvement [26]. Therefore, the praise from the homo-style of the robot groupalso might provide different but positive effects in the context of education support, we
think. Such effects are out of focus in this paper, but investigating the robot groupstructure effects would be one interesting future work.

6.4 Can the system be autonomous?
Our system involved a human operator controlling the timing of the robot’s
behaviors because we assumed that children behave unexpectedly. However, suchsituations really occurred in the experiment, only when a child cried up due to the absenceof his mother. Moreover, the developed three rules are easily automated by connectingwith the developed GUI. For example, the system autonomy can be enhanced by detecting
when the "start" and "end" buttons of the GUI are pressed and long idle times. Therefore,
the system can easily be autonomous.

We note that a minimum function to collaborate with a human operator will be
important when the system detects the wrong situations like breaking the PC or detecting
crying behaviors of children. In such situations, humans’ supports would be needed tosolve the problems. In this context, if the system is mostly autonomous, the operator does
not need to operate the system thoroughly. Moreover, past studies reported that one
operator control more than four robots with more complex settings (conversational
mobile robots in a real shopping mall) [68]. One possible application is for a professional
teacher or a parent to operate several robots simultaneously to support children's learningin various locations such as a home, a kindergarten, and an elementary school.

6.5 Other possible factors that influence praise effects
In this study, the experiment results did not show significant differences in
performance improvement. However, past studies reported the performance improvement
effects of praises, which focused on a relatively long time period, such as a performancethe next day [26, 42, 43]. If we conduct this experiment with a longer time period, such
effects might appear in our settings. Moreover, our robots always praise children eventhough the children answer wrongly. If the robots provide praise and additional followup comments about the mistake, it might be more effective in improving the children's
performance. Such long-time effects and different praise strategies are not investigated
yet in this study.
Another possible factor is basic learning performance or activities for children's
English learning. We did not measure children’s basic performances before theexperiment, but we asked their daily activities about English learning of children, such as
lessons. Therefore, we conducted an additional analysis by separating children into twoclasses: with (12 children) or without (10 children) daily activity. We used a repeated

ANOVA by using the daily-activity factor and number factor for learning time, thenumber of answered questions, the number of correct answers, and the rate of correct
answers. But, all factors and metrics did not show significant differences.

6.6 Limitations
This study has several limitations. It was conducted with specific settings, such as
one kind of humanoid robot (Sota) and a doll-type robot for investigating the effects ofpraise on pre-school children. As shown above, the effects of praise from different robots
and roles have not been investigated yet. Since we only investigated praise from two
robots, its effects from additional robots on children remain unknown. Of course,
increasing the number of robots would eventually saturate such benefits, and such future
work might be illuminating.
Note that since we fixed the roles of the robots, we don’t know whether we canobserve the same effects if their roles were switched. However, our approach seems
reasonable because an MC robot needs sufficient capability to explain a system with
understandable gestures. In fact, e-learning robot systems are already equipped with suchabilities [1, 69-71]. On the other hand, additional robots that praise children only needspeaking and connection capability.
We believe that our study provides valuable insight and knowledge for readers
who are interested in child-robot interaction and praise effects from two robots forchildren.

7 Conclusion
We investigated whether the length of time children spend with an English elearning application increases and whether their performances improve through praise

from two robots more than praise from a single robot. We developed an English elearning-support robot system using a tabletop humanoid robot and a doll-type robot andexperimented with 22 children. The children’s learning times were significantly longer
when two robots praised the children than when just a single robot offered praise, even if
the amount was identical. These results suggest that praise from two robots has several
advantages for educational support purposes.

Acknowledgements
This research work was supported in part by JST CREST Grant Number
JPMJCR18A1, Japan JSPS KAKENHI Grant Numbers JP18H03311 and JP19J01290,
and JST, PRESTO Grant Number JPMJPR1851, Japan.

References:[1] T. Belpaeme, J. Kennedy, A. Ramachandran, B. Scassellati, and F. Tanaka, “Social
robots for education: A review,” Science robotics, vol. 3, no. 21, pp. eaat5954,
2018.
[2] J. M. Keller, “Development and use of the ARCS model of instructional design,”
Journal of instructional development, vol. 10, no. 3, pp. 2, 1987.
[3] W. Dick, “The Dick and Carey model: Will it survive the decade?,” Educational
technology research and development, vol. 44, no. 3, pp. 55-63, 1996.
[4] I.-C. Hung, K.-J. Chao, L. Lee, and N.-S. Chen, “Designing a robot teachingassistant for enhancing and sustaining learning motivation,” Interactive learning
environments, vol. 21, no. 2, pp. 156-171, 2013.
[5] K.-Y. Chin, Z.-W. Hong, and Y.-L. Chen, “Impact of using an educational robotbased learning system on students’ motivation in elementary education,” IEEE
Transactions on learning technologies, vol. 7, no. 4, pp. 333-345, 2014.
[6] E. Castro, F. Cecchi, P. Salvini, M. Valente, E. Buselli, L. Menichetti, A. Calvani,and P. Dario, “Design and impact of a teacher training course, and attitudechange concerning educational robotics,” International Journal of Social
Robotics, vol. 10, no. 5, pp. 669-685, 2018.
[7] R. Maeda, J. Even, and T. Kanda, “Can a Social Robot Encourage Children’s SelfStudy?,” in 2019 IEEE/RSJ International Conference on Intelligent Robots andSystems (IROS), pp. 1236-1242, 2019.
[8] A. Ramachandran, C.-M. Huang, and B. Scassellati, “Give me a break!
Personalized timing strategies to promote learning in robot-child tutoring,” inProceedings of the 2017 ACM/IEEE International Conference on Human-Robot
Interaction, pp. 146-155, 2017.
[9] H. W. Park, R. Rosenberg-Kima, M. Rosenberg, G. Gordon, and C. Breazeal,“Growing growth mindset with a social robot peer,” in Proceedings of the 2017

ACM/IEEE International Conference on Human-Robot Interaction, pp. 137-145,
2017.
[10] M. Fridin, “Storytelling by a kindergarten social assistive robot: A tool for
constructive learning in preschool education,” Computers & education, vol. 70,
pp. 53-64, 2014.
[11] I. Leite, M. McCoy, M. Lohani, D. Ullman, N. Salomons, C. Stokes, S. Rivers,
and B. Scassellati, “Emotional Storytelling in the Classroom: Individual versus
Group Interaction between Children and Robots,” in Proceedings of the Tenth
Annual ACM/IEEE International Conference on Human-Robot Interaction,
Portland, Oregon, USA, pp. 75-82, 2015.
[12] Y. Tamura, M. Kimoto, M. Shiomi, T. Iio, K. Shimohara, and N. Hagita, “Effects
of a Listener Robot with Children in Storytelling,” in Proceedings of the 5thInternational Conference on Human Agent Interaction, Bielefeld, Germany, pp.
35-43, 2017.
[13] T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro, “A two-month field trial in anelementary school for long-term human–robot interaction,” IEEE Transactions
on Robotics, vol. 23, no. 5, pp. 962-971, 2007.
[14] T. Komatsubara, M. Shiomi, T. Kanda, and H. Ishiguro, “Can Using Pointing
Gestures Encourage Children to Ask Questions?,” International Journal ofSocial Robotics, vol. 10, no. 4, pp. 387-399, 2017.
[15] L. P. E. Toh, A. Causo, P. W. Tzuo, I.-M. Chen, and S. H. Yeo, “A Review on the
Use of Robots in Education and Young Children,” Educational Technology &Society, vol. 19, no. 2, pp. 148-163, 2016.
[16] A. Miller, E. Ferguson, and R. Simpson, “The Perceived Effectiveness of Rewards
and Sanctions in Primary Schools: adding in the parental perspective,”
Educational Psychology, vol. 18, no. 1, pp. 55-64, 1998.
[17] J. Henderlong, and M. R. Lepper, “The effects of praise on children's intrinsic
motivation: a review and synthesis,” Psychological bulletin, vol. 128, no. 5, pp.
774, 2002.
[18] M. T. Floress, and L. N. Jenkins, “A preliminary investigation of kindergarten
teachers’ use of praise in general education classrooms,” Preventing School
Failure: Alternative Education for Children and Youth, vol. 59, no. 4, pp. 253262, 2015.
[19] E. B. Hurlock, “An evaluation of certain incentives used in school work,” Journalof Educational Psychology, vol. 16, no. 3, pp. 145, 1925.
[20] T. Komatsubara, M. Shiomi, T. Kanda, H. Ishiguro, and N. Hagita, “Can a social
robot help children's understanding of science in classrooms?,” in Proceedingsof the second international conference on Human-agent interaction, Tsukuba,
Japan, pp. 83-90, 2014.
[21] I. Leite, G. Castellano, A. Pereira, C. Martinho, and A. Paiva, “Empathic Robots
for Long-term Interaction,” International Journal of Social Robotics, vol. 6, no.
3, pp. 329-341, 2014.
[22] D. Sakamoto, K. Hayashi, T. Kanda, M. Shiomi, S. Koizumi, H. Ishiguro, T.
Ogasawara, and N. Hagita, “Humanoid Robots as a Broadcasting
Communication Medium in Open Public Spaces,” International Journal ofSocial Robotics, vol. 1, no. 2, pp. 157-169, 2009.
[23] T. Iio, Y. Yoshikawa, and H. Ishiguro, “Retaining Human-Robots Conversation:
Comparing Single Robot to Multiple Robots in a Real Event,” Journal ofAdvanced Computational Intelligence Intelligent Informatics, vol. 21, no. 4, pp.
675-685, 2017.

[24] M. Shiomi, and N. Hagita, “Do the number of robots and the participant’s genderinfluence conformity effect from multiple robots?,” Advanced Robotics, vol. 33,
no. 15-16, pp. 756-763, 2019.
[25] A.-L. Vollmer, R. Read, D. Trippas, and T. Belpaeme, “Children conform, adults
resist: A robot group induced peer pressure on normative social conformity,”
Science Robotics, vol. 3, no. 21, 2018.
[26] M. Shiomi, S. Okumura, M. Kimoto, T. Iio, and K. Shimohara, “Two is better thanone: Social rewards from two agents enhance offline improvements in motor
skills more than single agent,” PloS one, vol. 15, no. 11, pp. e0240622, 2020.
[27] D. Leyzberg, S. Spaulding, M. Toneva, and B. Scassellati, “The physical presenceof a robot tutor increases cognitive learning gains,” in Proceedings of the annual
meeting of the cognitive science society, pp. 1882-1887, 2012.
[28] D. Hood, S. Lemaignan, and P. Dillenbourg, “When Children Teach a Robot toWrite: An Autonomous Teachable Humanoid Which Uses Simulated
Handwriting,” in Proceedings of the Tenth Annual ACM/IEEE International
Conference on Human-Robot Interaction, Portland, Oregon, USA, pp. 83–90,
2015.
[29] A. Litoiu, and B. Scassellati, “Robotic Coaching of Complex Physical Skills,” inProceedings of the Tenth Annual ACM/IEEE International Conference on
Human-Robot Interaction Extended Abstracts, Portland, Oregon, USA, pp. 211–
212, 2015.
[30] K. Dautenhahn, “Methodology & themes of human-robot interaction: A growing
research field,” International Journal of Advanced Robotic Systems, vol. 4, no.
1, pp. 15, 2007.
[31] T. Kanda, T. Hirano, D. Eaton, and H. Ishiguro, “Interactive robots as social
partners and peer tutors for children: A field trial,” Human–Computer
Interaction, vol. 19, no. 1-2, pp. 61-84, 2004.
[32] M. Alemi, A. Meghdari, and M. Ghazisaedy, “Employing humanoid robots forteaching English language in Iranian junior high-schools,” International Journalof Humanoid Robotics, vol. 11, no. 03, pp. 1450022, 2014.
[33] M. Shiomi, T. Kanda, I. Howley, K. Hayashi, and N. Hagita, “Can a Social Robot
Stimulate Science Curiosity in Classrooms?,” International Journal of Social
Robotics, vol. 7, no. 5, pp. 641-652, 2015.
[34] F. Tanaka, A. Cicourel, and J. R. Movellan, “Socialization between toddlers androbots at an early childhood education center,” Proceedings of the National
Academy of Sciences, vol. 104, no. 46, pp. 17954-17958, 2007.
[35] A. H. Buss, Social Behavior and Personality (Psychology Revivals): Routledge,
2014.
[36] A. Bandura, “Self-efficacy: toward a unifying theory of behavioral change,”
Psychological review, vol. 84, no. 2, pp. 191, 1977.
[37] C. Ames, “Motivation: What teachers need to know,” Teachers college record,
vol. 91, no. 3, pp. 409-421, 1990.
[38] G. Kohls, J. Peltzer, B. Herpertz‐Dahlmann, and K. Konrad, “Differential
effects of social and non‐social reward on response inhibition in children andadolescents,” Developmental science, vol. 12, no. 4, pp. 614-625, 2009.
[39] K. Chalk, and L. A. Bizo, “Specific praise improves on‐task behaviour andnumeracy enjoyment: A study of year four pupils engaged in the numeracy
hour,” Educational Psychology in Practice, vol. 20, no. 4, pp. 335-351, 2004.
[40] R. Aliyev, M. Karakus, and H. Ulus, “Teachers’ Verbal Cues That Cause Students
to Feel Various Emotions,” Anthropologist, vol. 16, no. 1-2, pp. 263-272, 2013.

[41] S. Kulkarni, and S. Mitra, “Management of Remote Mediation for Children’s
Education over the Internet,” in Global Learn, pp. 2044-2049, 2010.
[42] V. M. Catano, “Relation of improved performance through verbal praise to sourceof praise,” Perceptual and Motor Skills, vol. 41, no. 1, pp. 71-74, 1975.
[43] S. K. Sugawara, S. Tanaka, S. Okazaki, K. Watanabe, and N. Sadato, “Social
rewards enhance offline improvements in motor skill,” PLoS ONE, vol. 7, no.
11, pp. e48174, 2012.
[44] B. Reeves, and C. Nass, “How people treat computers, television, and new media
like real people and places,” CSLI Publications and Cambridge, 1996.
[45] P. H. Kahn Jr, T. Kanda, H. Ishiguro, N. G. Freier, R. L. Severson, B. T. Gill, J. H.
Ruckert, and S. Shen, ““Robovie, you'll have to go into the closet now”:
Children's social and moral relationships with a humanoid robot,”
Developmental psychology, vol. 48, no. 2, pp. 303, 2012.
[46] K. Hayashi, M. Shiomi, T. Kanda, and N. Hagita, “Are Robots Appropriate forTroublesome and Communicative Tasks in a City Environment?,” IEEE
Transactions on Autonomous Mental Development, vol. 4, no. 2, pp. 150-160,
2012.
[47] C. Nass, J. Steuer, and E. R. Tauber, “Computers are social actors,” in Proceedingsof the SIGCHI conference on Human factors in computing systems, pp. 72-78,
1994.
[48] D. Johnson, J. Gardner, and J. Wiles, “Experience as a moderator of the media
equation: the impact of flattery and praise,” International Journal of HumanComputer Studies, vol. 61, no. 3, pp. 237-258, 2004.
[49] D. Leyzberg, S. Spaulding, and B. Scassellati, “Personalizing robot tutors toindividuals' learning differences,” in Proceedings of the 2014 ACM/IEEE
international conference on Human-robot interaction, pp. 423-430, 2014.
[50] Z. E. Warren, Z. Zheng, A. R. Swanson, E. Bekele, L. Zhang, J. A. Crittendon, A.
F. Weitlauf, and N. Sarkar, “Can robotic interaction improve joint attention
skills?,” Journal of autism and developmental disorders, vol. 45, no. 11, pp.
3726-3734, 2015.
[51] D. P. Davison, F. M. Wijnen, V. Charisi, J. v. d. Meij, V. Evers, and D. Reidsma,“Working with a Social Robot in School: A Long-Term Real-World
Unsupervised Deployment,” in Proceedings of the 2020 ACM/IEEEInternational Conference on Human-Robot Interaction, Cambridge, United
Kingdom, pp. 63–72, 2020.
[52] N. Salomons, M. v. d. Linden, S. S. Sebo, and B. Scassellati, “Humans Conformto Robots: Disambiguating Trust, Truth, and Conformity,” in Proceedings of the2018 ACM/IEEE International Conference on Human-Robot Interaction,
Chicago, IL, USA, pp. 187–195, 2018.
[53] A. Khalifa, T. Kato, and S. Yamamoto, “The Retention Effect of Learning
Grammatical Patterns Implicitly Using Joining-in-Type Robot-Assisted
Language-Learning System,” in International Conference on Text, Speech, andDialogue, pp. 492-499, 2018.
[54] A. Bryła-Cruz, “The gender factor in the perception of English segments by nonnative speakers,” Studies in Second Language Learning and Teaching, vol. 11,
no. 1, pp. 103-131, 2021.
[55] W. C. Resing, M. Bakker, J. G. Elliott, and B. Vogelaar, “Dynamic testing: Can arobot as tutor be of help in assessing children's potential for learning?,” Journalof Computer Assisted Learning, 2019.

[56] D. D. Burman, T. Bitan, and J. R. Booth, “Sex differences in neural processing of
language among children,” Neuropsychologia, vol. 46, no. 5, pp. 1349-1362,
2008.
[57] R. Akahane-Yamada, “Learning non‐native speech contrasts: What laboratory
training studies tells us,” Acoustical Society of America, 1996.
[58] Y. Ishikawa, R. Akahane-Yamada, M. Kondo, H. Hatakeda, and N. Wada,“BLENDED LEARNING PRACTICES AND THE DEVELOPMENT OF
SELF-REGULATED LEARNING: USING ATR CALL TO HELPSTUDENTS PREPARE FOR TOEIC TESTS,” in INTED2012 Proceedings, pp.
6732-6739, 2012.
[59] S. Yang, and R. Akahane-Yamada, “Effects of perception and vocabulary trainingof Mandarin tones for native speakers of Japanese: Pre-, post-, and retention testcomparison,” The Journal of the Acoustical Society of America, vol. 140, no. 4,
pp. 3392-3392, 2016.
[60] C. Fujiyuki, M. Kitamura, S. Eguchi, S. Yang, and R. Akahane-Yamada,“Training native speakers of Japanese to distinguish English/ɹ/and/l: Order effectof the perception training and production training,” Acoustical Science andTechnology, vol. 38, no. 4, pp. 229-231, 2017.
[61] C. Shi, M. Shiomi, T. Kanda, H. Ishiguro, and N. Hagita, “Measuring
Communication Participation to Initiate Conversation in Human–Robot
Interaction,” International Journal of Social Robotics, vol. 7, no. 5, pp. 889-910,
2015.
[62] M. Imai, T. Ono, and H. Ishiguro, “Physical relation and expression: joint
attention for human-robot interaction,” IEEE Transactions on Industrial
Electronics, vol. 50, no. 4, pp. 636-643, 2003.
[63] N. Dahlbäck, A. Jönsson, and L. Ahrenberg, “Wizard of Oz studies: why andhow,” in Proceedings of the 1st international conference on Intelligent userinterfaces, Orlando, Florida, USA, pp. 193-200, 1993.
[64] J. Kennedy, v. Lemaignan, C. Montassier, P. Lavalade, B. Irfan, F. Papadopoulos,
E. Senft, and T. Belpaeme, “Child Speech Recognition in Human-Robot
Interaction: Evaluations and Recommendations,” in Proceedings of the 2017
ACM/IEEE International Conference on Human-Robot Interaction, Vienna,
Austria, pp. 82-90, 2017.
[65] J. Infantino, and E. Little, “Students’ perceptions of classroom behaviour problems
and the effectiveness of different disciplinary methods,” Educational
Psychology, vol. 25, no. 5, pp. 491-508, 2005.
[66] L. Lessard, A. Grossman, and M. L. Syme, “Effects of gender and type of praiseon task performance among undergraduates,” Psi Chi Journal of PsychologicalResearch, vol. 20, no. 1, pp. 11-17, 2015.
[67] M. Shiomi, and N. Hagita, “Do Synchronized Multiple Robots Exert Peer
Pressure?,” in Proceedings of the Fourth International Conference on Human
Agent Interaction, Biopolis, Singapore, pp. 27-33, 2016.
[68] M. Shiomi, T. Kanda, D. F. Glas, S. Satake, H. Ishiguro, and N. Hagita, “Field
trial of networked social robots in a shopping mall,” in Intelligent Robots andSystems, 2009. IROS 2009. IEEE/RSJ International Conference on, pp. 28462853, 2009.
[69] P. Vogt, M. De Haas, C. De Jong, P. Baxter, and E. Krahmer, “Child-robot
interactions for second language tutoring to preschool children,” Frontiers inhuman neuroscience, vol. 11, pp. 73, 2017.

[70] J. Han, “Emerging technologies: Robot assisted language learning,” Language
Learning & Technology, vol. 16, no. 3, pp. 1-9, 2012.
[71] T. Iio, R. Maeda, K. Ogawa, Y. Yoshikawa, H. Ishiguro, K. Suzuki, T. Aoki, M.
Maesaki, and M. Hama, “Improvement of Japanese adults' English speaking
skills via experiences speaking to a robot,” Journal of Computer Assisted
Learning, vol. 35, no. 2, pp. 228-245, 2019.