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Daniela Di Canio

dblp:319/3673 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 56% Human-robot interaction · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies
embodied learning
0.612022
Embodied Geometric Reasoning with a Robot: The Impact of Robot Gestures on Student Reasoning about Geometrical Conjectures · CHI 2022
Human-robot interaction › nonverbal communication
robot gesture
0.612022
Embodied Geometric Reasoning with a Robot: The Impact of Robot Gestures on Student Reasoning about Geometrical Conjectures · CHI 2022

Methods — techniques the papers use, named apart from their topics

randomized controlled study · 0.6embodied cognition · 0.6
YearPublicationVenuePosition
2022 Embodied Geometric Reasoning with a Robot: The Impact of Robot Gestures on Student Reasoning about Geometrical Conjectures
abstract
In this paper, we explore how the physically embodied nature of robots can influence learning through non-verbal communication, such as gesturing. We take an embodied cognition perspective to examine student interactions with a NAO robot that uses gestures while reasoning about geometry conjectures. College aged students (N = 30) were randomly assigned to either a dynamic condition, where the robot uses dynamic gestures that represent and manipulate geometric shapes in the conjectures, or control condition, where the robot uses beat gestures that match the rhythm of speech. Students in the dynamic condition: (1) use more gestures when they reason about geometry conjectures, (2) look more at the robot as it speaks, (3) feel the robot is a better study partner and uses effective gestures, but (4) were not more successful in correctly reasoning about geometry conjectures. We discuss implications for socially supported and embodied learning with a physically present robot.
Joseph E. Michaelis, Daniela Di Canio
CHI2