Data visualisation literacy (DVL) is increasingly recognised as a key component of data and information literacy in data-intensive learning environments. Existing assessment instruments such as the Visualisation Literacy Assessment Test (VLAT) and Mini-VLAT provide reliable measures of interpretative performance but offer limited insight into the reasoning processes underlying learners’ interpretations of visual data. This study aims to propose a formative assessment framework designed to capture how students interpret, evaluate and justify inferences from visual representations of data.
The study develops a reasoning-centred assessment framework that integrates diagnostic testing, structured critique of visualisations and reproducible redesign tasks, organised as a progressive two-phase protocol (Core I and Core II). The diagnostic phase and Core I were implemented in a mixed-methods pilot study in an undergraduate data analytics course (n = 48), while Core II is proposed as a subsequent assessment phase. Students completed a Spanish-adapted Mini-VLAT in pre- and post-test anonymous cohort cross-sections and participated in guided critical-analysis activities based on materials from the critical thinking assessment for literacy in visualisations (CALVI).
Mini-VLAT scores remained largely stable across administrations. However, qualitative evidence revealed increasingly explicit criterion-based reasoning, more critical assessment of inferential validity and better justified redesign proposals, suggesting that the piloted component of the framework captures reasoning processes not visible through conventional outcome-based measures.
The pilot study was conducted in a single course context with a limited sample, and the anonymous cohort design precluded individual-level trajectory analysis. Core II constitutes a designed extension of the framework and awaits empirical validation. Future research should implement Core II and examine the framework across different disciplines and learning environments.
The framework provides educators with a structured approach to assessing students’ reasoning with visual data representations.
The study introduces a preliminary, empirically informed assessment framework that conceptualises DVL as a reasoning-centred form of data and information literacy.
