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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
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
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
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
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
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
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
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
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
N = 47 typically developing, English speaking
preschoolers
Age: 3-5 years
Data collection at two preschools in South-East
Queensland
PARTICIPANTS
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
Systems interface includes microphones,
speakers, and HD cameras
Pre-programmed movements, gestures, and
speech based on human
NAO-specific, pre-programmed voice
MATERIALS:NAOSOCIALROBOT
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
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
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
CODING
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
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
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
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
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
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
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
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
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
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
WATCH video on YouTube
VIEW slides on Slideshare
click to access
@leighchantelle
THANKYOU

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Leigh-Chantelle's OCURA2020 Presentation on Preschoolers and their Engagement with Social Robots

  • 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
  • 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