1.
LEIGH-CHANTELLE KOCH^, MICHELLE NEUMANN*, JASON ZAGAMI*, & DAVID NEUMANN^
Online Conference for Undergraduate Research
in Australia (OCURA), 17 September 2020
INVESTIGATINGYOUNGCHILDREN’S
BEHAVIOURSANDEXPERIENCESWHEN
INTERACTINGWITHASOCIALROBOT
^GRIFFITH UNIVERSITY SCHOOL OF APPLIED PSYCHOLOGY
*GRIFFITH UNIVERSITY SCHOOL OF EDUCATION & PROFESSIONAL STUDIES

2.
Greater access to new technologies in home and education
Social robots used in aged care, personal care, and education
Social robots process information and create expected outcomes
e.g., engaging with humans in conversation aka human-robot
interactions (HRI; Moktar, 2019)
Social aspects and physical body enable social robots to play
human roles using gestures, vocalisations, and facial expressions
BACKGROUND

3.
Child-Robot Interaction (cHRI; Lupetti et al., 2019) differences with
children due to language understanding, level of attention,
developmental, and temperamental factors
Robots seen as living beings (anthropomorphic)
Do not notice processing issues (e.g., inappropriate responses)
Using this existing theory in a new direction
BACKGROUND

4.
Advantages of social robots for children is primarily theoretical and
not accessible beyond studies appearing in conferences and
journals dedicated to robotics
Past studies focus on education and learning - not engagement
Majority of studies focus on autistic children and teaching English
as a second language
Previous research shows children like social robots, prefer to learn
from them, and have positive attitudes towards them (e.g., Sidner
et al., 2014)
BACKGROUND

5.
Preschooler’s overall engagement with robot vs
human measured by behavioural engagement,
vocalisations, and emotional engagement
HRI studies show people engage more with robots
than humans (e.g., Sidner, et al., 2004)
H1: Children will be more engaged and attentive
towards the social robot instructor rather than
human instructor
AIMS

6.
Previous research shows a positive relationship between
children’s home use of robots and interactions in an educational
setting (Melenhorst & Bowshusi, 2004)
H2: Positive relationship between children’s previous
experiences with technology and engagement and
communication with social robot
AIMS

7.
Multidisciplinary project focused on exploratory research
Combination of qualitative (descriptive) and quantitative
(statistical) methods both addressing the same aim
Survey data analysed quantitatively (psychology)
Behavioural observations (education) analysed qualitatively
DESIGN

8.
Within-subjects (repeated
measures): all participants receive
both conditions
Independent Variable (IV):
Instructor types
Social robot instructor
Human instructor
DESIGN
Dependent Variables (DV):
Behavioural Engagement
Emotional Engagement
(Positive, Negative)
Vocalisations (Task-Relevant,
Device/Human-Relevant,
Irrelevant)
Number of Words Spoken
Drawing
Name Writing
Smileyometer

9.
Tools to measure DVs
Phase 1: Introduction (4 components)
Phase 2: Simon Says Game (12 components)
Phase 3: Drawing & Writing (8 components)
Phase 4: Conclusion (3 components)
Each sentence of the script is one component
DESIGN:INTERACTIONPHASES

10.
N = 47 typically developing, English speaking
preschoolers
Age: 3-5 years
Data collection at two preschools in South-East
Queensland
PARTICIPANTS

11.
NAO humanoid social robot (version N05)
Positive interactions with children in education
Humanoid body, fixed face, and moveable arms
25 degrees of freedom
Performs range of motor actions
MATERIALS:NAOSOCIALROBOT

12.
Systems interface includes microphones,
speakers, and HD cameras
Pre-programmed movements, gestures, and
speech based on human
NAO-specific, pre-programmed voice
MATERIALS:NAOSOCIALROBOT

13.
Behavioural Engagement:
Directly responding to questions or directions (de Wit et al.,
2018) e.g., “What is your name?”
19 components of four interaction phases
Engagement present i.e., compliance (score of 1) or engagement
absent i.e., non-compliant (score of 0)
MATERIALS:BEHAVIOURALOBSERVATION

14.
Emotional Engagement:
Overt expressions
Positive (Bai et al., 2016) e.g, smiling, clapping, laughing
Negative (Kring & Sloan, 2007) e.g., yawning, frowning, shaking
head
Types and totals for both components
Scored as present (score of 1) or absent (score of 0)
MATERIALS:BEHAVIOURALOBSERVATION

15.
Vocalisations: (Reich et al., 2019)
Task-relevant e.g., “I have a dog!”
Device-relevant e.g., NAO: “He’s talking to me!”; e.g., iPad: “How
do I write?”
Irrelevant e.g., “Time to eat!”
Scored as present (score of 1) or absent (score of 0)
Number of Words Spoken (type total and overall)
MATERIALS:BEHAVIOURALOBSERVATIONS

16.
CODING

17.
Apple iPad Pro 12.9 inch (13.3.1)
Writing/Drawing app (Neumann, 2018)
Draw a picture of a dog
Write your name
Drawings assessed with a 7-point scale (Bloodgood, 1999)
Name writing assessed with a 7-point scale (Neumann, 2018)
MATERIALS:DRAWING+NAMEWRITING
R1 H2

18.
Enjoyment and Feedback
Adapted from 3-point scale (Read and MacFarlane, 2006) to
5-point scale to prevent sensitivity to effects
“What did you think about playing with NAO/Teacher today?”
MATERIALS:SMILEYOMETER
54321
Smileyometer designed by Leigh-Chantelle. Based on Read, MacFarlane, & Casey (2002) Fun Toolkit

19.
Parents completed online questionnaire with questions on:
Demographics (e.g., marital status, education, language and cultural
group, occupation and employment)
Child information (e.g., age, gender, siblings, developmental problems)
Digital technology and robotic toys:
Children’s experience
Parent’s feelings
Ease of use for child
Engagement frequency for parent and child
Previously used by Neumann (2015; 2016; 2018) for digital home use and
literacy with robots and robotic toys questions added
MATERIALS:PARENTQUESTIONNAIRE

20.
Research approval from university ethics and permission from
managers of the two preschools
Parents gave informed consent before the study as part of their
online questionnaire
Both interaction conditions (within subjects/repeated measures)
counter-balanced to control for order effects
Some children participated in both conditions on same day, others
one condition on the first day and the second on the next day
PROCEDURE

21.
Preschoolers on cushion facing instructor in a
quiet room
Verbal consent gained with asking, “Are you
ready to play?”
Each interaction had four phases: introduction,
Simon Says game, drawing (dog) and writing
(name), and conclusion
Immediate feedback with Smileyometer
3 minute interactions filmed on video camera
PROCEDURE

22.
Main effect of instructor group
2 (instructor type) x 3 (vocalisation type) within-subjects ANOVA for
vocalisations
2 (instructor type) x 2 (emotional engagement type) within-subjects
ANOVA for emotional engagement
Repeated measures t-test for
Behavioural engagement
Number of words spoken
Drawing
Name Writing
Smileyometer
Correlations for previous technology experience, age, gender, SES
ANALYSISTOCOME

23.
HRI studies show people engage more with robots than humans
(e.g., Sidner, et al., 2004)
H1: Children will be more engaged and attentive towards the social
robot instructor rather than human instructor
Main effect of instructor group (human vs robot)
NAO interactions expected to show more engagement across all
interaction phases
PREDICTEDRESULTS

24.
Previous research shows a positive relationship between children’s
home use of robots and interactions in an educational setting
(Melenhorst & Bowshusi, 2004)
H2: Positive relationship between children’s previous experiences
with technology and engagement and communication with social
robot
A positive correlation is expected between level of children
engagement with the social robot and previous home experience
PREDICTEDRESULTS

25.
Smiling and Nodding are the most used
positive emotional engagements
Most behavioural engagements from
Phase 2: Simon Says game
Not many vocalisations, but when
someone was vocal they said a lot
PRELIMINARYFINDINGS

26.
Findings will better inform researchers in education if preschoolers
are more engaged with a social robot or human
Helps inform future research to effectively use social robots to
engage young children in educational setting
Positively support early learning
IMPLICATIONS

27.
WATCH video on YouTube
VIEW slides on Slideshare
click to access
@leighchantelle
THANKYOU