VALUE Rubrics - Quantitative Literacy 2.0

What are the VALUE rubrics?

The VALUE rubrics articulate fundamental criteria for each learning outcome, with performance descriptors demonstrating progressively more sophisticated levels of attainment. The rubrics are intended for institutional- or programmatic-level use in evaluating and discussing student learning, not for grading. The core expectations articulated in all the VALUE rubrics can and should be translated into the language of individual campuses, disciplines, and even courses. The utility of the VALUE rubrics is to position learning at all undergraduate levels within a basic framework of expectations such that evidence of learning can be shared nationally through a common dialog and understanding of student success.

The rubric you will find here is our REVISED Quantitative Literacy VALUE Rubric—"QL 2.0"—published April 17, 2026.  

The Quantitative Literacy VALUE Rubric 2.0 is available for free download in Word and PDF formats.

Download Now

Definition of Quantitative Literacy

The creation of this VALUE rubric was informed by AAC&U’s collaboration with the National Numeracy Network and the robust scholarly literature that explores the concept of quantitative literacy (QL). Our definition of QL is most influenced by Karaali et al. (2016), who argued that QL consists of four components: (1) an ability or habit of mind (2) grounded in the domain of numerical information (3) where students can analyze or use that information (4) within a particular real-world context. With these components in mind, we define Quantitative Literacy—sometimes referred to as Numeracy or Quantitative Reasoning—as follows:


“The skills needed to represent, analyze, and draw conclusions from quantitative information in meaningful, real-world contexts.”
 

Dimension Overview for QL 2.0 Rubric:

This rubric is divided into the following five dimensions: 

  • Context: A meaningful quantitative question exists within a particular time, place, and circumstance. Students with strong QL skills can identify information that is contained in or surrounds a question or problem that connects it to these real-world circumstances. The best examples of student work will not only acknowledge the relevant contextual information but also clearly indicate how that information influences their subsequent interpretation and conclusions. This can include practical, disciplinary, sociopolitical, ethical, and/or societal considerations, as well as the student’s own position/role in the interpretive process, all of which may influence how student’s analysis can or should be interpreted.  
  • Representation: In many (if not most) quantitative problems, students must convert quantitative information into an alternative form, such as turning a collection of data into a graph or using words to explain what a graph depicts. Students with strong QL skills will not only be able to make these types of conversions but will also be able to move between forms and choose the most appropriate or effective representation for a specific purpose or use. The best examples of such work will represent quantitative information in a way that provides deeper insight and/or facilitates subsequent analyses. 
  • Analysis Using Tools and/or Technologies: In today’s world, it is uncommon for students to perform lengthy calculations by hand. Instead, students will often use some sort of tool or technology to help them perform an analysis. These may include algorithms to solve a particular equation, computer software, research databases, or even artificial intelligence. Students with strong QL skills will choose and utilize tools that are suitable for the analyses they are trying to complete. The best examples of student work will also explain the suitability of the tools and technologies they have chosen. 
  • Conclusions: After performing an analysis, students are typically called upon to draw an appropriate conclusion. Students with strong QL skills draw conclusions that are clearly consistent with the results of the analysis performed. The best examples of student work will also show evidence that students acknowledge the assumptions they may have made in their analysis and describe the ways those assumptions may limit the conclusions drawn. 
  • Insights or Implications: Meaningful quantitative analysis often demands more than just arriving at a numerical answer. Indeed, students with strong QL skills can communicate what those their findings mean and connect their conclusions back to authentic or real-world circumstances. The best examples of student work will include meaningful insights and implications of the work, particularly considering the context in which the analyses were performed.

References Associated With the Rubric Coversheet