skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualization
Award ID(s):
2142977
PAR ID:
10508129
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-2557-7
Page Range / eLocation ID:
26 to 30
Format(s):
Medium: X
Location:
Melbourne, Australia
Sponsoring Org:
National Science Foundation
More Like this
  1. https://futurumcareers.com/can-you-trust-what-you-see-online 
    more » « less
  2. This work proposes Dynamic Linear Epsilon-Greedy, a novel con- textual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning ap- proaches have trade-offs between empirical investigation and max- imal impact on users. Our algorithm seeks to balance these objec- tives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSIST- ments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to bal- ance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. 
    more » « less
  3. This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our algorithm seeks to balance these objectives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSISTments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students’ learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to balance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. 
    more » « less
  4. null (Ed.)
  5. Abstract Empirical findings and theorizations of both imitation and selective trust offer different views on and interpretations of children's social learning mechanisms. The imitation literature provides ample documentation of children's behavioural patterns in the acquisition of socially appropriate norms and practices. The selective trust literature provides insights into children's cognitive processes of choosing credible informants and what information to learn in future interactions. In this paper, we place together findings from both fields and note that they share analogically similar theoretical underpinnings and offer explanations that are complementary to each other. We contend that children's imitative tendency may be due to their selection of in‐group members ascultural experts, who serve as reliable sources of conventional information. Moving forward, we note the importance of evaluating individual differences and cultural factors to provide a more holistic understanding of universality and variation in children's social learning mechanisms. 
    more » « less