Estimating Children’s Characteristics by Observing
their Classroom Activities
Masahiro Shiomi,* Tsuyoshi Komatsubara*, Thomas Kaczmarek,*† Takayuki Kanda,*† and Hiroshi Ishiguro,* ††*ATR, Kyoto, Japan
Kyoto Univ., Kyoto, Japan††
Osaka Univ., Osaka, Japan
E-mail: m-shiomi@atr.jp, Tel: +81-774-9532†

Abstract— Good teachers recognize how each of their students
is different from the others and adapt how they support them. We
replicate such a capability to understand the individual
specificities of children. Our approach observed the social signalsof fifth graders based on their daily classroom behavior using asensor network. We used depth cameras to track their positions
and identified them with RGB cameras. We observed 84 children(three classes) and used these results to estimate school-relatedchildren’s characteristics: self-efficacy, performance-goal, andexam scores. The estimation yielded 73.0-74.7% accuracy for thetarget variables.
Fig. 1 Differences of children’s school-related personal traits

I.

INTRODUCTION

Previous studies explored the potential use of social robots
in classrooms. For instance, they were used for language
education in a classroom [1, 2]. Social supportive behavior
improves the teaching of foreign languages [3]. Howley et al.
found that it was easier for students to get help from a robot
teacher than a human teacher [4]. These studies show apromising direction to use social robots for supportingchildren’s learning.
Previous research also unveiled the possibility to use social
robots to support children’s social life. For instance, Woods etal. explored how differently children talk to robots about theirbullying and/or victimization experiences [5]. Bethel et al.
concluded that since children’s memory as an eyewitness isless influenced by a robot interviewer than a human, a robot
could be used to investigate sensitive events [6]. Previous
studies indicated that a robot can be a close partner for children.
Tanaka et al. revealed that a robot successfully interacted withchildren for five months and was accepted as a peer [7].
However, a relatively underexplored topic is how to‘understand’ an individual child in a school or other groupsettings. For instance, teachers comprehend the academic skill
and the personality of their students [8]. Such ‘understanding’would be useful for social robots, too. One pioneering finding
was reported by Leyzberg et al. who argued that tutoring
performance is improved when a robot adapts its teachingstrategy to individuals [9]. Nevertheless, little is known about
how a robot grasps children's school-related personal traits.

This research work was supported by JSPS KAKENHI Grant Numbers
JP16K12505, JP18H04121, and JP18H03311, and Tateishi Science andTechnology Foundation.

This study’s goal is to develop a technique to estimate theindividual differences of children by observing their social
signals in a classroom to ameliorate future social robots inschools (Fig. 1).
II.

RELATED WORKA.

Techniques for Observing Individuals’ Behaviors inGroup Settings
Advances in robotics and ubiquitous computing have greatly
improved observation techniques for individual behaviors.
Even though such devices as RFID tags and smartphones cantrack the behaviors of the people carrying them, demandingthat children always have them is problematic and unrealistic.
Without asking them to carry a device, we can still identify
individuals from their faces [10]. Nonetheless, one limitationof face-based identification is that it can only identify people
whose frontal face is observed by a camera. Thus, only with it,
people’s behaviors are not well perceived. Instead, researchers
have explored various techniques to combine tracking
technique with person identification (e.g., [11-13]).
Our study includes an approach that integrates person
identification with tracking. Unlike previous studies, we use
depth camera for tracking, which yielded better tracking
performance than tracking methods with RGB cameras [14].
We expect that our system will provide more robust results
when tracking children’s behavior in a crowded classroom.
B.

Estimating Characteristics in Group Setting
Various techniques have been developed for understanding
individuals from their behavior, typically with wearable
devices. For instance, Choudhury et al. developed a wearable
device called a sociometer that records contact between device

carriers, which provide useful information for analyzing
people’s face-to-face interaction on social networks [15].
Similarly, children’s friendships were estimated withproximity information observed with RFID tags [16]. Olguín
et al. developed a technique to recognize such daily activities
as talking with others and being at particular locations using awearable sensor and found that such personality traits as
extraversion and openness correlate with the perceived
activities [17]. Kalimeri et al. applied support vector machine(SVM) classification to data obtained from wearable sensors
and classified personality with 50-70% accuracy (better than achance ratio of 33%) [18]. Mohammadi et al. analyzed theprosody of people’s speech and developed 2-class classifiers
for personality with 60-72% accuracy (better than a chance
ratio of 50%) [19]. These studies revealed the possibility of
estimating individual’s personal traits from behavior data,
although techniques still need enhancement, especially forbetter accuracy and other personal traits beyond personality.
In contrast to these previous studies, our study introduces
information acquisition from a sensor network without
wearable devices on a population of children in a classroom
context for identifying personal traits: self-efficacy.
C.

Children’s Personal Traits in Classroom
Numerous studies on school-related personal traits haveconcentrated on pupils in psychology and education research.
Among them, we focused on self-efficacy and performancegoal, each of which represents critical aspects of children’s
activity in the classroom: learning and social life.
Self-efficacy: self-efficacy is defined as the “[belief] in
one’s capabilities to organize and execute the courses of action
required to manage prospective situations” [20]. In a classroom,
it represents whether a student believes she can manage her
own learning. Children with high self-efficacy tend to learn andhave more success, generally in a self-regulatory manner. For
instance, children with high self-efficacy tend to seek help
when needed, but children with low self-efficacy tend to avoid
that step [21].
We believe that awareness of the self-efficacy of each childis beneficial for a social robot in a classroom. If it could identify
each child’s self-efficacy, it would be able to adjust its
proactivity when offering help, e.g., offering more proactive
assistance to children suffering from low self-efficacy.
Performance goal: performance goal is the motivation of a
student for learning, but it is quite different from self-efficacy.
It refers to whether the student’s learning goal is to get betterscores than the other students and more attention from teachers[22]. For students with a high performance goal, competingwith other students is the primary concern. Students with highperformance goals tend to study hard and achieve better exam
performance [23].
We consider that knowing performance goals in addition toself-efficacy will be beneficial. We assume that if a robot could
recognize them the robot could change its learning support
behavior to stimulate competition for children with highperformance goals.

III.

SYSTEM DESIGN

Figure 2 illustrates the architecture of our developed system,
which was originally developed [24] to estimate social status
for children; in this study we used it to estimate different
characteristics. Our observation system estimates children's
positions by integrating both a people tracking system and aface identification system. When the faces of the children are
identified, their IDs are associated to the tracked entity in the
tracking system. The estimation system predicts the children's
personal traits based on the extracted features from theirpositions by the observation system.
A.

People Tracking
We employed a previously proposed people tracking
algorithm using depth sensors [14], where depth cameras wereattached to the ceiling and their positions and heights were
estimated based on head- and shoulder-shape detection. With
our setting, the tracking system monitors the position of all thepeople in the area at 30 Hz with an accuracy of approx. 30 cm.
This system has robustness toward illumination changes andclothing colors because it uses depth information, which isuseful for a classroom that has sunshine from windows. Since
the depth-camera views from the top down, it is robust forcrowded situations.
We arranged the locations of the depth sensors to efficiently
cover the space. The sensor (Kinect, field of view (FOV) is 57°
horizontally and 43° vertically) covers approx. a 4 m * 3 mspace when attached to the ceiling at 2750 mm. With 24 depth
sensors, 8 by 16 m of the room was covered. Fig. 3 shows adepth image from a sensor and the tracking result in theclassroom, where four to five children are sitting around theirdesks. The system tracked them well in such crowded
situations.

Fig. 2 System overview

Fig. 3 Sensor arrangement

B.

Person Identification
For person identification, we employed a face identification
approach using RGB cameras and a commercial facerecognition software package (Omron, OKAO Vision [10]).
We designed the camera configurations for adequate balance
between the number of cameras and the chance of face
identification. Since OKAO Vision requires that faces beobserved in the frontal direction within 20 degrees (pitch) and35 degrees (yaw), we need many cameras to increase thechance of observing the frontal faces. However, the school
insisted that cameras not obstruct class activities. For instance,
the cameras should not distract the children during class (e.g.,
no cameras on desks facing the students) or obstruct them fromseeing the teacher or the blackboard. They should also be put
higher than an adult’s height to avoid collisions. Here weassume that children will at some point look toward the frontof the classroom, where the teacher usually stands, and so foreach desk, we put only one camera that should capture all of
the children at or near that desk. That is, we set six RGB
cameras (Logicool, C920t, FOV is 70.5° horizontally and 43.6°
vertically). The details of the performances of person
identification were previously reported [24].
C.

Features for Estimating Personal Traits
Time spent alone: We measured the ratio of the time thatchildren spent alone. For each child 𝑖 and each time 𝑡 , wecomputed whether other children were within a threshold(𝐷 ) of this child. If someone is present in the threshold, thechild is judged as not being alone at this time:∑
𝑇𝑖𝑚𝑒 𝑠𝑝𝑒𝑛𝑡 𝑎𝑙𝑜𝑛𝑒 𝑖
𝑖𝑠𝐴𝑙𝑜𝑛𝑒 𝑖, 𝑡 ∙ 𝛥𝑡
(1),
𝑖𝑠𝐴𝑙𝑜𝑛𝑒 i, t
1 𝑖𝑓 𝑑𝑖𝑠𝑡 𝑝𝑜𝑠 𝑖, 𝑡 , 𝑝𝑜𝑠 𝑗, 𝑡
0𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒𝐷

𝑓𝑜𝑟 𝑎𝑙𝑙 𝑗 𝑗

𝑖

(2),

where 𝑂𝑏𝑠𝑇𝑖𝑚𝑒 𝑖 is a function that returns the total tracking
time of child 𝑖 , 𝛥𝑡 is the time step (33.3 msec) for thiscalculation, 𝑝𝑜𝑠 𝑖, 𝑡 is the x-y position of child 𝑖, and 𝑑𝑖𝑠𝑡 is
the Euclidean distance between two x-y vectors. We used
multiple thresholds for 𝐷 : 500 mm for the intimate distanceand 1200 mm as the collaboratively working distance.
Moving distance: We measured the average moving
distance of the children per second:

𝑖𝑠𝑂𝑢𝑡𝑠𝑖𝑑𝑒 𝑖, 𝑡

1 𝑖𝑓 𝑑𝑖𝑠𝑡0

𝑝𝑜𝑠 𝑖, 𝑡 , 𝑑𝑒𝑠𝑘 𝑖𝑅𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(5),

where 𝑑𝑒𝑠𝑘 𝑖 is the rectangle area of child 𝑖’s assigned deskand 𝑑𝑖𝑠𝑡 is the shortest Manhattan distance between theposition and the rectangle area on the x-y plane (y is theclassroom’s long side). We set 𝑅 to 300 mm for situations
where children change their own positions around the desk and
600 mm for situations where they go to other desks.
Sitting time: We measured the ratio of the time whenchildren were sitting based on the observed height of theirheads. If a child’s head location is below height thresholds(𝐻 ), he/she is judged to be sitting:∑𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 𝑖
𝑖𝑠𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑖, 𝑡

10

𝑖𝑠𝑆𝑖𝑡𝑡𝑖𝑛𝑔 𝑖, 𝑡 ∙ Δt

(6),

𝑖𝑓 𝐻𝑒𝑎𝑑𝐻𝑒𝑖𝑔ℎ𝑡 𝑖, 𝑡𝐻𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(7),

where 𝐻𝑒𝑎𝑑𝐻𝑒𝑖𝑔ℎ𝑡 𝑖, 𝑡 is the measured height of child 𝑖. We
used two 𝐻 : 1050 mm for sitting and 1200 mm for halfsitting situations (like working at the desk).
Number of surrounding people: We measured the average
number of surrounding people. For each child and each
moment, we computed the number of other people within adistance threshold (𝐷 ) from him/her:𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑢𝑟𝑟𝑜𝑢𝑛𝑑𝑖𝑛𝑔 𝑝𝑒𝑜𝑝𝑙𝑒 𝑖∑∑ ∀ 𝑖𝑓 𝑑𝑖𝑠𝑡 𝑝𝑜𝑠 𝑖, 𝑡 , 𝑝𝑜𝑠 𝑗, 𝑡

𝐷

. (8)

Time spent around the robot: We measured the ratio of thetime that the children spent around the robot. For each childand each moment, we computed whether the child was within
a distance threshold (𝐷 ) from the robot:
𝑆𝑝𝑒𝑛𝑡 𝑡𝑖𝑚𝑒 𝑎𝑟𝑜𝑢𝑛𝑑 𝑡ℎ𝑒 𝑟𝑜𝑏𝑜𝑡 𝑖∑
𝑖𝑓 𝑑𝑖𝑠𝑡 𝑝𝑜𝑠 𝑖, 𝑡 , 𝑝𝑜𝑠 𝑟𝑜𝑏𝑜𝑡, 𝑡

𝐷∙ 𝛥𝑡 .

(9)

D.

Classification System
In this study, in addition to the two personal traits (selfefficacy and performance goal), we estimated exam scores andclassified them as high or low. The low class includes children
whose scores are below average. We applied support vector
machine (SVM) for classifications and used different
combinations of the above features to construct each SVM
classifier for all of the personal traits and exam scores. Below
we explain which features we used.

𝑀𝑜𝑣𝑖𝑛𝑔 𝑑𝑖𝑠𝑡 𝑖∑

𝑑𝑖𝑠𝑡 𝑝𝑜𝑠 𝑖, 𝑡 , 𝑝𝑜𝑠 𝑖, 𝑡

Δs

(3),

where we set 𝛥𝑠 as 500 msec to decrease the effects of tracking
noise.
Moving distance outside their own desk areas: In the classactivities, children were split into groups and assigned to desks.
While children often worked within the area of their desks,
sometimes they moved around the classroom. We measured theaverage travel distance during these situations. Here a child isjudged as being outside of his/her own desk area if the distancefrom the desk exceeds a range threshold (𝑅 ). The following
is the computation for feature
𝑀𝑜𝑣𝑖𝑛𝑔𝑑𝑖𝑠𝑡𝑂𝑢𝑡𝑠𝑖𝑑𝑒 𝑖∑
𝑖𝑠𝑂𝑢𝑡𝑠𝑖𝑑𝑒 𝑖, 𝑡 ∙ 𝑑𝑖𝑠𝑡 𝑝𝑜𝑠 𝑖, 𝑡 , 𝑝𝑜𝑠 𝑖, 𝑡

∆𝑠

(4),

IV. DATA COLLECTION
A.

Participants
Our 84 participants were three classes of 5th graders (14females and 14 males in each class) whose average height was
147.2 cm (S.D. was 9.4). The experimental protocol wasapproved by our IRB and school administrators. All thechildren and parents signed consent forms and agreed to bevideo-recorded.
B.

Environment
We conducted data collection in an elementary school’s
science room about twice a week per class. Four to fivechildren sat around each desk (six desks from the front areused) during 45-minute lessons that are followed by a five totwenty minute break.
C.

Robot’s Task
In this data collection, we installed a robot in the room toincrease the children’s understanding of their science lessons.
The robot interacted with children by quiz-style conversations
whose contents were prepared from lesson materials. With thequizzes, children can review their recent lessons. For this study,
the robot was semi-autonomously controlled and was only
available during breaks before/after classes. Further details
about the robot are available [25, 26].
D.

Procedure
Each class had four lectures during the study. The roomremained open before and after the science lessons. Among thefour lectures, breaks during the two lectures were with therobot. The class was usually divided into two parts: lecture andgroup-work. During the lecture, the teacher usually spoke at thefront, and the students were sitting and listening. During groupwork, the students formed groups based on their seats andconducted an experiment or used various science instruments,
for instance, changing a pendulum’s weight and initial angle tostudy its characteristics.
E.

Questionnaires and Exam
We gathered questionnaires before the study. Below are thescales that were used as the estimation targets.
Self-efficacy: Among many scales for self-efficacy, we
adopted one specifically prepared for children of similar ages[27] that consists of three items, such as, “No matter how much
effort I make, I cannot learn science (reversed item),” rated on
a 1-to-5 point scale. Cronbach’s α was 0.71, indicating
acceptable internal consistency.
Performance-goal: We adopted a scale specifically
prepared for children of similar ages [27] that consists of two
items, such as, “I participate in science class to outperform
other students,” rated on a 1-to-5 point scale. Cronbach’s alphawas 0.67, indicating that the internal consistency remained
acceptable.
Exam score: We gave exams during the study period thatcovered all three topics they studied. We averaged the scores,
which ranged between 0 and 10.
F.

Obtained Dataset
Behavioral data: The observed children’s behaviors arequite different across the three class phases: lecture, groupwork, and free-time. As previously discussed, we did not use
any features of the lecture phase.
During group-work, children generally stayed at their desks,
where they engaged in activities related to science experiments.
Some worked alone, and others worked together. Somechildren visited other desks to check the progress of the othergroups or to ask about their problems.
During free-time, children were often with their friends.
Before the science class, many were talking with friends, andsome were sitting at their own desks and waiting for class tostart. After the science classes, some children gathered aroundthe robot and interacted with it, and others briefly chatted withfriends or returned to their homerooms.
We separated the data based on the above phases and usedboth phases for estimation purposes. We collected 235 minutesof group-work data and 112 minutes of free-time data.
Questionnaire data: Overall, we got 74 valid data samples.
Some children were absent when the questionnaires wereadministrated, and others failed to completely fill them out
even after being reminded a few times. As predicted in theliterature, their exam scores are significantly correlated with
self-efficacy (p=.007) and almost significantly correlated withperformance goal (p=.089). We identified no significant
correlations among the three personal traits.
V.

EVALUATION

A.

Performance
We trained the classifiers using the obtained data andsearched for the best features from all the combinations offeatures and parameters for SVM through a grid search. We
used 5-fold cross-validations to evaluate their performance.
Table 1 shows the obtained features and performances.
Overall, our system achieved 73.0-74.7% accuracy. The bold
fonts represent the highly contributory features. That is,removing them decreases the performance by more than 15%
from this final result.
The SVM classifier achieved 73.0% self-efficacy accuracy.
As we expected, the key features are the number of surrounding
people in the group work and the spent time around the robot.
Associated with spent time around the robot and the number ofsurrounding people, children were not generally alone because
they often interacted with the robot, and hence these features
also contributed. The sitting time in group-work also
contributed (correlation is 0.11).
The SVM classifier achieved 73.3% performance goal
accuracy. Children with a high performance goal have moretime spent alone, as we expected. Unexpectedly, free-timefeatures also contributed. For instance, these children havelower number of surrounding people values in free-time(correlation is -0.16), and more sitting time (0.13), possibly
because they tended to stay in the room after class and workwith their notebooks for a while.
The SVM classifier achieved 74.7% exam score accuracy.
Children with high exam scores seemed to seriously study
during the classes. Sitting time highly contributed (correlation
is 0.20), and the moving distance outside one’s own desk also
contributed (-0.10). Thus, they did not move around much andsat for a longer time.
B.

Case Studies
The estimation result provides insight about the relationshipbetween children’s behavior and their personal traits. Here we
further scrutinized some children’s behavior and retrieved
scenes based on the contributing features for the classifiers. For
self-efficacy, the number of surrounding people in both freetime and group-work and the sitting time in group-work weremajor contributory features. Fig. 4 shows scenes where a childwith high self-efficacy stays with friends around the robot. Shefrequently interacted with it, often invited her friends to joinher, and engaged with them by answering the robot’s classrelated quiz-style conversations.
For the performance-goal, the number of surrounding people
and the sitting time during free-time were major contributing
features. Fig. 5 shows scenes where a high performance-goalchild was still studying after class. Even when many children
were talking near her and returning to their rooms, she
continued to write down notes.
For the exam scores, the sitting time during group-work was
one major contributing feature. Fig. 6 shows scenes where twochildren with high (solid circles) and low (dotted circles) exam
scores are studying during group-work. The high exam scorechild seriously worked on his experiment at his desk, but thelow exam score child did not join the experiment and playedwith his friend.
VI. DISCUSSIONSA.

Implications
This study shows promise for estimating children’s learningrelated personal traits and suggests that we can develop a robotthat supports a child’s learning by adapting to individuals. For
example, if a robot recognizes a child who has low self-efficacy,
it should provide proactive help when the child seems to be
rudderless. Otherwise, such a child is probably reluctant to get
help. If a robot found a child with a high performance goal, it
might elicit more competition, which would encourage thatchild to study even more.
Further, in a classroom, a robot might be expected to findchildren who need learning support, e.g., children with low
performance goal or low self-efficacy. In the future, a robot
might be used to increase such children’s curiosity or interestin learning [25]. At the moment, it remains unknown how toeffect such encouragement, and finding a way to make robot
behaviors for such purposes is one future work enabled by thisstudy.
Privacy Concern
Our system achieved identification, position tracking, andthe estimating of personal traits of children in school, which isuseful information if used appropriately. However, muchdiscussion has focused on privacy concerns with positioning
systems and our method admittedly increases privacy risks. It
is important to analyze what we can know from such sensor
data. Informed consent is essential from children, their parents,
and teachers. They should clearly understand the potential
ramifications of such studies. Our study certainly provides
information for this.
In future use, the system’s benefits to the detriment ofprivacy are important. For example, for future applications,social robots may be able to better serve children’s learning if
they can identify children’s individual differences. We believethat social robots will offer such benefits.

style, and the robot’s role/behavior. For instance, although it
was conducted solely in a science room, children might behave
quite differently in their homerooms. If we use the systemelsewhere or in other rooms, we need to adjust such aspects ascamera arrangements, since the desk arrangements will bedifferent. Nevertheless, we believe that our study framework isvalid for many different settings, if we train SVM again fordifferent settings.
VII. CONCLUSIONS
We developed a personal trait estimation system thatconsists of a people tracking system using depth sensors and aperson identification system using RGB cameras. The system
extracts features from children’s behaviors including social
signals during classes and free-time and estimates their schoolrelated personal traits using SVM classifiers. We gatheredchildren’s behaviors at a science room in an elementary school.
Our system achieved 73.0-74.7% accuracy for estimations of
two personal traits (self-efficacy and performance goal) andexam score.
Table 1 Performance of SVM classifiers
Features

Personal
traitsPerformance

Self-efficacy

73.0%- Sitting time
- Number ofsurrounding people- Time spent alone

Performance
goal

73.3%

- Time spent alone

Exam score

74.7%- Sitting time
- Moving distance- Moving distanceoutside own desk

Group-workFree-time- Time spent alone- Sitting time
- Number ofsurrounding people- Spent time aroundthe robot- Sitting time
- Number ofsurrounding people- Moving distanceoutside own desk
- Moving distanceoutside own desk

B.

Generalizability and Limitations
The study was conducted at a specific environment withmany specific personal traits, such as culture, language, lecture

Fig. 4 Child with high self-efficacy

Fig. 5 Child with high performance goal

C.

Fig. 6 Children with high/low exam scores

ACKNOWLEDGMENT
We thank the staff of the elementary school for their support.
REFERENCES[1] 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.
[2] J. Han, M. Jo, S. Park, and S. Kim, “The Educational Use of Home
Robots for Children,” in IEEE Int. Workshop on Robot and Human
Interactive Communication (RO-MAN2005), pp. 378-383, 2005.
[3] M. Saerbeck, T. Schut, C. Bartneck, and M. D. Janse, “Expressive
Robots in Education: Varying the Degree of Social Supportive
Behavior of a Robotic Tutor,” in ACM Conference on Human Factors
in Computing Systems (CHI2010), pp. 1613-1622, 2010.
[4] I. Howley, T. Kanda, K. Hayashi, and C. Rosé, “Effects of Social
Presence and Social Role on Help-Seeking and Learning,” in
ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI2014). .
[5] S. Woods, M. Davis, K. Dautenhahn, and J. Schulz, “Can robots
be used as a vehicle for the projection of socially sensitive issues?
Exploring children's attitudes towards robots through stories ” in IEEE
Int. Workshop on Robot and Human Interactive Communication (ROMAN2005), pp. 384-389, 2005.
[6] C. L. Bethel, D. Eakin, S. Anreddy, J. K. Stuart, and D. Carruth,“Eyewitnesses are misled by human but not robot interviewers,” inHuman-Robot Interaction (HRI), 2013 8th ACM/IEEE International
Conference on, pp. 25-32, 2013.
[7] F. Tanaka, A. Cicourel, and J. R. Movellan, “Socialization
between Toddlers and Robots at an Early Childhood Education
Center,” in Proceedings of the National Academy of Sciences of the
USA (PNAS), pp. 17954-17958, 2007.
[8] D. Blease, “Teachers’ judgements of their pupils: broad categories
and multiple criteria,” Educational Studies, vol. 21, no. 2, pp. 203-215,
1995.
[9] D. Leyzberg, S. Spaulding, and B. Scassellati, “Personalizing
robot tutors to individuals' learning differences,” in Proceedings of the2014 ACM/IEEE international conference on Human-robot
interaction, pp. 423-430, 2014.
[10] S. Lao, and M. Kawade, “Vision-based face understanding
technologies and their applications,” in Advances in Biometric Person
Authentication, pp. 339-348, 2005.
[11] K. S. Huang, and M. M. Trivedi, “Distributed video arrays for
tracking, human identification, and activity analysis,” in 2012 IEEE
International Conference on Multimedia and Expo, pp. 9-12, 2003.
[12] M. Shiomi, K. Kurumizawa, T. Kanda, H. Ishiguro, and N.
Hagita, “Finding a person with a Wi-Fi device in a crowd ofpedestrians,” Advanced Robotics, vol. 28, no. 7, pp. 441-448, 2014.
[13] M. Shiomi, and N. Hagita, “Finding a person with a wearable
acceleration sensor using a 3D position tracking system in daily
environments,” Advanced Robotics, vol. 29, no. 23, pp. 1563-1574,
2015.
[14] D. Brscic, T. Kanda, T. Ikeda, and T. Miyashita, “Person tracking
in large public spaces using 3D range sensors,” IEEE Transaction on
Human-Machine Systems, vol. 43, no. 6, pp. 522 - 534, 2013.
[15] T. Choudhury, and A. Pentland, “Sensing and Modeling Human
Networks using the Sociometer,” in IEEE International Symposium
on Wearable Computers (ISWC2003), pp. 216-222, 2003.
[16] T. Kanda, and H. Ishiguro, “An approach for a social robot tounderstand human relationships: friendship estimation throughinteraction with robots,” Interaction Studies, vol. 7, no. 3, pp. 369-403,
2006.
[17] D. O. Olguín, P. A. Gloor, and A. Pentland, “Wearable sensors
for pervasive healthcare management,” in Pervasive Computing

Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd
International Conference on, pp. 1-4, 2009.
[18] K. Kalimeri, “Towards a dynamic view of personality:
multimodal classification of personality states in everyday situations,”in Proceedings of the 15th ACM on International conference on
multimodal interaction, pp. 325-328, 2013.
[19] G. Mohammadi, and A. Vinciarelli, “Automatic personality
perception: Prediction of trait attribution based on prosodic features,”
Affective Computing, IEEE Transactions on, vol. 3, no. 3, pp. 273-284,
2012.
[20] F. Pajares, “Self-efficacy beliefs in academic settings,” Review
of educational research, vol. 66, no. 4, pp. 543-578, 1996.
[21] A. M. Ryan, and H. Shin, “Help-seeking tendencies during earlyadolescence: An examination of motivational correlates andconsequences for achievement,” Learning and Instruction, vol. 21, no.
2, pp. 247-256, 2011.
[22] A. J. Elliot, and H. A. McGregor, “A 2× 2 achievement goal
framework,” Journal of personality and social psychology, vol. 80, no.
3, pp. 501, 2001.
[23] A. J. Elliot, H. A. McGregor, and S. Gable, “Achievement goals,
study strategies, and exam performance: A mediational analysis,”
Journal of educational psychology, vol. 91, no. 3, pp. 549, 1999.
[24] T. Komatsubara, M. Shiomi, T. Kaczmarek, T. Kanda, and H.
Ishiguro, “Estimating Children’s Social Status Through Their
Interaction Activities in Classrooms with a Social Robot,”
International Journal of Social Robotics, pp. 1-14, 2018.
[25] 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.
[26] T. Komatsubara, M. Shiomi, T. Kanda, H. Ishiguro, and N.
Hagita, “Can a social robot help children's understanding of science inclassrooms?,” in Proceedings of the second international conferenceon Human-agent interaction, Tsukuba, Japan, pp. 83-90, 2014.
[27] H. L. Tuan, C. C. Chin, and S. H. Shieh, “The development of aquestionnaire to measure students' motivation towards sciencelearning,” International Journal of Science Education, vol. 27, no. 6,
pp. 639-654, 2005.