Demo Data from EDBT 2026 / reviewers in the wild / expert
Mackenzie Michael Creamer
dblp:397/6036
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0002-7737-269XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Design research and methods · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visualization evaluation
empirical visualization research |
1.0 | 1 | 2026 | An Autoethnography on Visualization Literacy: A Wicked Measurement Problem · IEEE Trans. Vis. Comput. Graph. 2026 |
Visualization and visual analytics
visualization literacy |
1.0 | 1 | 2026 | An Autoethnography on Visualization Literacy: A Wicked Measurement Problem · IEEE Trans. Vis. Comput. Graph. 2026 |
Design research and methods › first-person perspectives
autoethnography |
0.3 | 1 | 2026 | An Autoethnography on Visualization Literacy: A Wicked Measurement Problem · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
interviews · 2.0autoethnography · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | An Autoethnography on Visualization Literacy: A Wicked Measurement ProblemabstractWe contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a "wicked" problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity. Lily W. Ge, Anne-Flore Cabouat, Karen Bonilla, Yiren Ding, Noëlle Rakotondravony, Mackenzie Michael Creamer, Jasmine Otto, Maryam Hedayati, Bum Chul Kwon, Angela Locoro, Lane Harrison, Petra Isenberg, Michael Correll, Matthew Kay 0001 |
IEEE Trans. Vis. Comput. Graph. | 7 |
| 2025 | CSEdD: CS Education Dashboard for Student Success, Retention, and Performance InvestigationsabstractFigure 1: Breakdown of the dashboard used to perform exploratory analysis on student information.This figure shows students below a incoming math course cutoff and students at or above that same math course cutoff, with those below the cutoff seeing fewer favorable outcomes. Mackenzie Michael Creamer, Penny Rheingans |
SIGCSE (2) | 1 |