a comparison of children learning new words from robots, tablets, and people jacqueline kory...
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A Comparison of Children Learning New Words From Robots, Tablets, and PeopleJacqueline Kory Westlund, Leah Dickens, Sooyeon Jeong,
Paul Harris, David DeSteno, & Cynthia Breazeal
Word learning
Hart & Risley, 1995; Snow et al., 20072
Word learning
Hart & Risley, 1995; Snow et al., 2007
Early language development
3
Early language development
Literacy
Duranti & Goodwin, 1992; Hart & Risley, 1995; Snow et al., 2007; Vygotsky, 1978
Word learning
4
Early language development
LiteracyAcademic success
Duranti & Goodwin, 1992; Hart & Risley, 1995; Snow et al., 2007; Vygotsky, 1978
Word learning
5
Early language development
LiteracyAcademic success
Life success
Duranti & Goodwin, 1992; Hart & Risley, 1995; Snow et al., 2007; Vygotsky, 1978
Word learning
6
Technology for Language Education
• iPads, tablets• Computers• Robots
Cassell, 2004; Judge et al. 2015; Naigles & Mayeux, 2001; Wartella & Lauricella, 2014; Willoughby et al., 2015; Wolf et al. 2014 7
Children learn from robots
Cassell, 2004
Chang et al., 2010; Tanaka & Matsuzoe, 2012
Breazeal et al., in press
Movellan et al., 2009
Kory Westlund et al., 2015; Gordon et al., in review
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Fast mapping
• Rapid• Approximate• Little or no feedback
Carey, 2010; Carey & Bartlett, 1978; Corriveau et al., 2009;Kucker et al., 2015; Markson & Bloom, 1997
9
Research Questions
1. Will children learn new words via fast mapping from a robot and/or from a tablet as effectively as from a human?
2. Who/what do children prefer as a learning partner?
3. How do children conceptualize the robot?
10
Hypotheses
1. Children will learn equally well from the robot and the human, but less well from the tablet.
2. Children will prefer the human.
3. Children will construe the robot as between human and tablet.
1: Harris, 2007; Kuhl, 2007; Meltzoff et al., 2009; Naigles & Mayeux, 2001; Sage & Baldwin, 2010
3: Kahn et al., 2012; Severson & Carlson, 201011
12
Study design
• Within-subjects (tablet x robot x person)
Two sessions
1. Learning task + recall + questions2. Recall + questions
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Learning task• 10 animal pictures with each interlocutor– 2 animals named– 8 positive but uninformative comment
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Recall test• 5 animals shown at a time– 1 that was named– 4 that were seen, but unnamed
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Construal Questions6 questions: “When a robot ____, is it more like a person or more like an iPad?”
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??
• Answers a question• Teaches you something• Interested in something
• Remembers something• Thinks about something• Tells you something
• Name of animal• What animal eats• Where animal lives
• What gadget is called• What gadget does• Where gadget is found
Preference Questions6 questions: “If you want to find out ____, who would you ask – person, robot, or iPad?”
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Technology
• DragonBot (Setapen, 2012)
– Wizard-of-Oz– Recorded dialogue
• Tablet– Recorded dialogue
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Participants
• 19 children (10 female, 9 male)– Ages 3-5 (M=4.6, SD=.57)– Greater Boston area preschool
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Hypotheses
1. Children will learn equally well from the robot and the human, but less well from the tablet.
2. Children will prefer the human.
3. Children will construe the robot as between human and tablet.
1: Harris, 2007; Kuhl, 2007; Meltzoff et al., 2009; Naigles & Mayeux, 2001; Sage & Baldwin, 2010
3: Kahn et al., 2012; Severson & Carlson, 201020
Hypotheses
1. Children will learn equally well from the robot and the human, but less well from the tablet. Recall tests
2. Children will prefer the human.
3. Children will construe the robot as between human and tablet.
1: Harris, 2007; Kuhl, 2007; Meltzoff et al., 2009; Naigles & Mayeux, 2001; Sage & Baldwin, 2010
3: Kahn et al., 2012; Severson & Carlson, 201021
Hypotheses
1. Children will learn equally well from the robot and the human, but less well from the tablet.
2. Children will prefer the human. Questions
3. Children will construe the robot as between human and tablet.
1: Harris, 2007; Kuhl, 2007; Meltzoff et al., 2009; Naigles & Mayeux, 2001; Sage & Baldwin, 2010
3: Kahn et al., 2012; Severson & Carlson, 2010
Hypotheses
1. Children will learn equally well from the robot and the human, but less well from the tablet.
2. Children will prefer the human.
3. Children will construe the robot as between human and tablet. Questions
1: Harris, 2007; Kuhl, 2007; Meltzoff et al., 2009; Naigles & Mayeux, 2001; Sage & Baldwin, 2010
3: Kahn et al., 2012; Severson & Carlson, 2010
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Results: Learning
robot tablet person0.0
0.5
1.0
1.5
1.11.4 1.4
Animals identified correctly
Session 1 Session 2
Who the animals were viewed with
Num
ber o
f ani
mal
s id
entifi
ed c
orre
ctly
25
Results: Source of new information
Person iPad Robot Other0
2
4
6
8
10
12
9.7
3.75.2
0.5
9.3
4.05.7
0.0
Would children ask a person, robot, or iPad about a novel animal and novel gadget?
Session 1Session 2
Children's preferred agent to ask
Num
ber o
f chi
ldre
n
26
Results: Preference
person ipad robot other0
2
4
6
8
10
12
14
16
1 1
12
5
0
2
14
3
Children' preference for learning partner
Session 1Session 2
Preferred partner
Num
ber o
f chi
ldre
n
Results: Perception of robot
• Switch from tablet-like to person-like:– Answers a question– Teaches you something
• More person-like:– Interested in things– Remembers things– Thinks about things
• More tablet-like:– Tells you something
Results: Perception of robot
• Switch from tablet-like to person-like:– Answers a question– Teaches you something
• More person-like:– Interested in things– Remembers things– Thinks about things
• More tablet-like:– Tells you something
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Results: Perception of robot
Session 1 pretest
Session 1 posttest
Session 202468
10121416
10
15
79
4
12
When a robot answers a question, is it more like a person or more like an iPad?
personiPad
Num
ber o
f chi
ldre
n
30
Results: Perception of robot
Session 1 pretest
Session 1 posttest
Session 20
2
4
6
8
10
12
14
9
13 13
10
6 6
When a robot teaches you something, is it more like a person or more like an iPad?
personiPad
Num
ber o
f chi
ldre
n
Results: Perception of robot
• Switch from tablet-like to person-like:– Answers a question– Teaches you something
• More person-like:– Interested in things– Remembers things– Thinks about things
• More tablet-like:– Tells you something
Results: Perception of robot
• Switch from tablet-like to person-like:– Answers a question– Teaches you something
• More person-like:– Interested in things– Remembers things– Thinks about things
• More tablet-like:– Tells you something
33
Results: Perception of robot
Session 1 pretest
Session 1 posttest
Session 20
2
4
6
8
10
12
14
8
6
111112
8
When a robot tells you something you didn't know, is it more like a person or more like an iPad?
person iPad
Num
ber o
f Chi
ldre
n
34
Main findings
• Learned equally from robot, person, tablet– Simple task– Did not require social information
• Preferred person as source of new information
• High enthusiasm for robot• Construed robot as person-like with some
technological qualities
35
Main findings
• Learned equally from robot, person, tablet– Simple task– Did not require social information
• Preferred person as source of new information
• High enthusiasm for robot• Construed robot as person-like with some
technological qualities
36
Main findings
• Learned equally from robot, person, tablet– Simple task– Did not require social information
• Preferred person as source of new information
• High enthusiasm for robot• Construed robot as person-like with some
technological qualities
37
Main findings
• Learned equally from robot, person, tablet– Simple task– Did not require social information
• Preferred person as source of new information
• High enthusiasm for robot• Construed robot as person-like with some
technological qualities
38
Future work
• More complex tasks requiring social information for learning
• What are humans vs. robots best at?• Children’s construal of robots
A Comparison of Children Learning New Words From Robots, Tablets, and PeopleJacqueline Kory Westlund, Leah Dickens, Sooyeon Jeong,
Paul Harris, David DeSteno, & Cynthia Breazeal
So are the robots autonomous?
• Autonomy = hard problem!– child speech recognition, social interaction
• We’re working on it
Don't need autonomy to study how people interact with robots
Robots are not people!• Not a replacement for teachers or caregivers!• Support interactions:– Ask questions, spark conversation– Model beneficial behaviors, conversation
strategies, more advanced language
• Natalie Freed: Sophie study– Robot as facilitator
• David Nuñez: Tinkerbook– Robot as “parent trainer”– Prompts for parents
Early language impact
• Low SES kids heard ~30million words less than high SES kids (Hart & Risley, 1995)
• Unfamiliar words, cognitive challenge -> higher language ability entering kindergarten (Snow et al., 2007)
• Impoverished exposure to novel English words or rich vocab-building curricula -> deficits in language ability (Fish & Pinkerman, 2003; Paez, Tabors, & Lopez, 2007)
Fish, M., & Pinkerman, B. (2003). Language skills in low-SES rural appalachian children: Normative development and individual differences, infancy to preschool. Journal of Applied Developmental Psychology, 23(5), 539-565.
Páez, M. M., Tabors, P. O., & López, L. M. (2007). Dual language and literacy development of spanish-speaking preschool children. Journal of Applied Developmental Psychology, 28(2), 85-102.