Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Carmen Gruson

dblp:260/1352 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

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
Human-robot interaction · 61% Learning and educational technologies · 30% Usability and user experience research · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
child-robot interaction
0.412020
Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020
Learning and educational technologies › language learning
second-language vocabulary learning
0.412020
Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020
Human-robot interaction › educational robotics
social robot tutoring
0.412020
Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020
Usability and user experience research
self-determination theory
0.112020
Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020

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

user study · 0.4self-determination theory · 0.4
YearPublicationVenuePosition
2020 Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning
abstract
This study presents a second language word learning experiment using a social robot with motivational strategies. These strategies were implemented in a social robot tutor to stimulate preschool children's intrinsic motivation. Subsequently, we investigated their effect on children's task engagement and word learning performance. The strategies were derived from the Self-Determination Theory, a well-known psychological theory that assumes that intrinsic motivation is strongly related to the fulfilment of three basic human needs, namely the need for autonomy, competence, and relatedness. We found an increase in the strength and duration of task engagement when all three psychological needs were supported by the robot. However, no significant results for learning gains were observed. Our intervention appears a promising method for improving child-robot interactions in educational settings, especially to sustain in long-term interactions.
Peggy van Minkelen, Carmen Gruson, Pleun van Hees, Mirle Willems, Jan de Wit, Rian Aarts, Jaap Denissen, Paul Vogt
HRI2