Adaptive Assessment of Visualization Literacy

TL;DR

This paper introduces two efficient computerized adaptive tests, A-VLAT and A-CALVI, for measuring visualization literacy, significantly reducing assessment time while maintaining reliability and validity.

Contribution

It presents the development and validation of adaptive visualization literacy assessments using item response theory, improving efficiency over traditional fixed tests.

Findings

High test-retest reliability (ICC: 0.98) for both CATs

Strong convergent validity with original assessments (correlations: 0.81 and 0.66)

Assessment time reduced by half without sacrificing accuracy

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/.