DRIVE-T: A Methodology for Discriminative and Representative Items Selection for Design Quality Constructs and Assessments
Angela Locoro, Silvia Golia, Davide Falessi · Applied Sciences · 2026
The lack of measurement constructs for both users’ literacy and artifacts difficulty in HCI hinders the quality of assessment tests. This paper proposes DRIVE-T (Discriminating and Representative Items for Validating Expressive Tests), a methodology designed to construct and evaluate items for measuring progressive levels of users’skills and artifacts usability. Given an artifact (a text, a data visualization, an interface prototype), DRIVE-T supports the identification of items discriminability and representativeness for measuring distinct traits of a quality property to be measured and its levels of progression in users and artifacts. DRIVE-T consists of three steps: (1) tagging task-based items associated with an artifact; (2) rating them by independent raters for their difficulty; (3) analyzing raters’ raw scores through a Many-Facet Rasch Measurement model. The emergence of difficulty levels of the measurement construct can be derived from the discriminability and representativeness of items for each artifact, ordered into Many-Facets construct levels. The DRIVE-T methodology operationalizes an inductive, practice-based measurement construct design. Based on the expertise of the authors in the data visualization domain, visualization literacy is the quality property of users that is exploited as a case scenario for applying DRIVE-T. Results from a pilot test show the validity of the approach.