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Christian Barrientos

dblp:01/5531 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved

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.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › interactive visualization
interactive dashboard
0.712023
Graphical features of interactive dashboards have little influence on engineering students performing a design task · Int. J. Hum. Comput. Stud. 2023
Computing education › STEM education
engineering education
0.212023
Graphical features of interactive dashboards have little influence on engineering students performing a design task · Int. J. Hum. Comput. Stud. 2023

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

between-subjects experiment · 1.3
YearPublicationVenuePosition
2023 Graphical features of interactive dashboards have little influence on engineering students performing a design task
abstract
This study investigates how interactive dashboards influence decision making by exploring how specific dashboard features impact design task performance, efficiency, understanding, and confidence. An experiment was conducted in which undergraduate student participants were given a design activity and randomly assigned to one of five dashboards, each using the same underlying functions but varying in the visualization features employed. These features include different graphical representations of the design decision inputs and performance outputs. Participants were first asked to use their assigned dashboard to design a catapult system that maximizes launch distance while meeting requirements related to height, weight, and cost. Following the design task, they were asked a series of questions about their experiences with the dashboard and their understanding of the catapult model. A between-subjects analysis then evaluated how the dashboard design influenced various outcomes of interest. The results show that students who used the most feature-rich dashboard did not perform objectively better than those with the most feature-sparse dashboard, though their self-reported performance was higher. The performance of female versus male participants was also compared, with no significant differences found. The findings support the notion that dashboards should be designed with minimal features to convey the necessary information, and they also point out the disconnect between objective performance and user-assessed performance with interactive dashboards.
Steven Hoffenson, Cory Philippe, Zuting Chen, Christian Barrientos, Zhongyuan Yu, Brian Chell, Mark R. Blackburn
Int. J. Hum. Comput. Stud.4