Demo Data from SIGIR 2026 / reviewers in the wild / expert
Pleun van Hees
dblp:260/1371
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
child-robot interaction |
0.4 | 1 | 2020 | 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 |
0.4 | 1 | 2020 | Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020 |
Human-robot interaction › educational robotics
social robot tutoring |
0.4 | 1 | 2020 | Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word Learning · HRI 2020 |
Usability and user experience research
self-determination theory |
0.1 | 1 | 2020 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Using Self-Determination Theory in Social Robots to Increase Motivation in L2 Word LearningabstractThis 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 |
HRI | 3 |