Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ruben Post
dblp:199/1119
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-6903-9208ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 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 |
User interface design and tools · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
User interface design and tools › web site design
website aesthetics |
0.3 | 1 | 2017 | Unity in Variety in website aesthetics: A systematic inquiry · Int. J. Hum. Comput. Stud. 2017 |
User interface design and tools
web site design |
0.1 | 1 | 2017 | Unity in Variety in website aesthetics: A systematic inquiry · Int. J. Hum. Comput. Stud. 2017 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Automated Driving System HMIs: "Clear and Unambiguous"?abstractWhile the introduction of automated driving is intended to make driving safer and more comfortable, it may also make the interaction between user and vehicle more complex and prone to confusion. It is therefore important to be able to assess to what extent ADSs provide their users with clear and unambiguous information concerning system state and concerning allowed or expected actions from the side of the user. In this project, we have developed a questionnaire to measure whether the information provided by an ADS to its user is experienced as clear and unambiguous. The questionnaire consists of nine items, which were shown to discriminate between good and bad HMI designs in terms of user perception and comprehension. The development and evaluation of this questionnaire was done in several steps. First, a set of 15 questionnaire items was developed that could be presented as Likert scale items on a 7 point scale. Next, an online survey was conducted to test the reliability and internal consistency of the questionnaire as well as explore underlying factors. Data analysis showed that responses on some of the items were highly correlated. A subset of 9 items was found to be able to discriminate well between HMIs with different levels of clarity and (un)ambiguity. Exploratory factor analysis revealed two underlying factors, which could be interpreted to relate to perception and comprehension of HMI information. In the final step, the 9-item questionnaire was tested in an experimental setup. 23 Participants were presented with movie clips of transitions from or to automated driving, including a view of the traffic situation (through the windshield) and of the instrument cluster and steering wheel. Again, HMIs were designed to be either clear and unambiguous or unclear/ambiguous. The results showed that the questionnaire discriminated reliably between both versions of the HMI. As in the online survey, high reliability and internal consistency were observed. Additionally, qualitative measurement of user experience by means of Product Reaction Cards also showed clear differences between the two different HMI versions. Jeroen H. Hogema, Ruben Post, Rutger Verstegen, Jan L. Souman |
AutomotiveUI | 2 |
| 2017 | Unity in Variety in website aesthetics: A systematic inquiry
Ruben Post, Tran Nguyen, Paul Hekkert |
Int. J. Hum. Comput. Stud. | 1 |