Quantitative Reasoning and Data Literacy in the Core Curriculum

Modern life and social interactions in the 21st century are heavily influenced by data. It permeates every aspect: from our personal lives to our roles as citizens in a democratic society. But data – in and of itself – is meaningless. What transforms data into a powerful tool is the ability to know how to gather, assess and extract meaning from it, and to understand both the utility and the limitations of data. To this end, the importance of data interpretation in contemporary society is difficult to overstate. We believe that a well-rounded liberal science and arts education must provide students with the power to use data in order to communicate cogent arguments about complex phenomena.

The Quantitative Reasoning and Data Literacy (QRDL) requirement in the Core Curriculum will help students learn to understand how real-world interpretation of data progresses from a set of numbers, frequently incomplete and with measurement errors, to a visual distillation that clearly conveys meaning. While mathematics and statistics provide a necessary foundation for quantitative reasoning and data literacy, the power of pure mathematics lies in its abstraction. In contrast, QRDL emphasizes a distinctive variety of concrete contexts and applications. Specifically, the QRDL requirement will differ from a mathematics requirement in two important ways. Emphasis will be first on the applications of quantitative methods to academic real-world subjects and second on understanding the inherent messiness of data. This “messiness” might require the use of estimations and approximations, often known as back-of-the-envelope calculations, to determine an appropriate launch point for more precise analysis.

As instructors design their courses, they need to consider three categories of learning goals, and the specific goals within each category, that collectively serve to anchor the QRDL curriculum:

Build a Quantitative Data Toolbox

• To gather and organize data to highlight patterns and make it accessible for analysis.
• To recognize the roles of estimation, approximation and precision as tools important in quantitative analyses.
• To become comfortable performing and/or evaluating quantitative analyses.
  • Select and apply pertinent statistical methods to explore and analyze data on one’s own.
    • and/or
  • To rigorously evaluate quantitative data and interpretations of these data when presented by others.

Translate and Make Meaning

• To translate real-world and disciplinary questions or intellectual inquiries into quantitative frameworks using graphical, symbolic, and numerical methods.
• To be aware of the strengths and weaknesses, uncertainties and biases inherent in quantitative data and quantitative analyses and to incorporate this understanding into the interpretation and representation of data.
• To think critically about numerical representations and to employ them in one’s critical thinking and when making complex claims.

Communicate and Consider Impact

• To effectively communicate statistical methods and results both visually and textually to a diversity of audiences with considerations to contextual, social, or ethical issues.