Lily Ge

All Publications

• Data Literacy for the 21st Century: Perspectives from Visualization, Cognitive Science, Artificial Intelligence, and Education Ge, L. W., Horn, M. S., Long, D., Fan, J. E., Kay, M., ACM ASSOC COMPUTING MACHINERY. 2026

  View details for DOI 10.1145/3772363.3778701

  View details for Web of Science ID 001792737300015

• Inoculating Against Visualization Misinformation Through Gamification Ge, L. W., Horn, M. S., Kay, M., ACM ASSOC COMPUTING MACHINERY. 2026

  View details for DOI 10.1145/3772363.3798423

  View details for Web of Science ID 001792737300204

• An Autoethnography on Visualization Literacy: A Wicked Measurement Problem IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Ge, L. W., Cabouat, A., Bonilla, K., Cui, Y., Ding, Y., Rakotondravony, N., Creamer, M., Otto, J., Hedayati, M., Kwon, B., Locoro, A., Harrison, L., Isenberg, P., Correll, M., Kay, M. 2026; 32 (1): 1394-1404

  View details for DOI 10.1109/TVCG.2025.3634792

  View details for Web of Science ID 001682680900042

• AVEC: An Assessment of Visual Encoding Ability in Visualization Construction Ge, L. W., Cui, Y., Kay, M., ACM ASSOC COMPUTING MACHINERY. 2025

  View details for DOI 10.1145/3706598.3713364

  View details for Web of Science ID 001496957100281

• Toward a More Comprehensive Understanding of Visualization Literacy Ge, L. W., Hedayati, M., Cui, Y., Ding, Y., Bonilla, K., Joshi, A., Ottley, A., Bach, B., Kwon, B., Rapp, D. N., Peck, E., Padilla, L. M., Correll, M., Borkin, M. A., Harrison, L., Kay, M., ASSOC COMPUTING MACHINERY ASSOC COMPUTING MACHINERY. 2024

  View details for DOI 10.1145/3613905.3636289

  View details for Web of Science ID 001227587700024

• <i>V</i>-<i>FRAMER</i>: Visualization Framework for Mitigating Reasoning Errors in Public Policy Ge, L. W., Easterday, M., Kay, M., Dimara, E., Cheng, P., Franconeri, S. L., ACM ASSOC COMPUTING MACHINERY. 2024

  View details for DOI 10.1145/3613904.3642750

  View details for Web of Science ID 001266059701005

• <i>CALVI</i>: Critical Thinking Assessment for Literacy in Visualizations Ge, L. W., Cui, Y., Kay, M., ACM ASSOC COMPUTING MACHINERY. 2023

  View details for DOI 10.1145/3544548.3581406

  View details for Web of Science ID 001048393804053

• Promises and Pitfalls: Using Large Language Models to Generate Visualization Items IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Cui, Y., Ge, L. W., Ding, Y., Harrison, L., Yang, F., Kay, M. 2025; 31 (1): 1094-1104

  View details for DOI 10.1109/TVCG.2024.3456309

  View details for Web of Science ID 001367808800012

• Adaptive Assessment of Visualization Literacy. IEEE transactions on visualization and computer graphics Cui, Y., Ge, L. W., Ding, Y., Yang, F., Harrison, L., Kay, M. 2023; PP

  Abstract

  Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and repeatable assessments of visualization literacy for individuals. However, current assessments, such as the Visualization Literacy Assessment Test (VLAT), are time-consuming due to their fixed, lengthy format. To address this limitation, we develop two streamlined computerized adaptive tests (CATs) for visualization literacy, A-VLAT and A-CALVI, which measure the same set of skills as their original versions in half the number of questions. Specifically, we (1) employ item response theory (IRT) and non-psychometric constraints to construct adaptive versions of the assessments, (2) finalize the configurations of adaptation through simulation, (3) refine the composition of test items of A-CALVI via a qualitative study, and (4) demonstrate the test-retest reliability (ICC: 0.98 and 0.98) and convergent validity (correlation: 0.81 and 0.66) of both CATs via four online studies. We discuss practical recommendations for using our CATs and opportunities for further customization to leverage the full potential of adaptive assessments. All supplemental materials are available at https://osf.io/a6258/.

  View details for DOI 10.1109/TVCG.2023.3327165

  View details for PubMedID 37878447