Quantitative reasoning and quantitative literacy (NNN 2014)

Earlier this month, Rich and I traveled to Carleton College to join people from 23 institutions of higher education for the annual conference of the National Numeracy Network (NNN). The NNN focuses on building quantitative literacy for all citizens, with education being central to that work. The majority of attendees were from smaller liberal arts colleges – predominantly staff, with some faculty joining.

The conference was a mix of presentations, breakout sessions, and group discussions centering on how to best build quantitative competencies at our respective campuses. (You can find more information about the conference, and slides from all talks, here.)

Some highlights from my notes:

  • Programming for connecting with faculty to build more quantitative material into curriculum, such as the two-day Pedagogy In Action summit at Vassar
  • Centralized support for quantitative/data work The newly-rebranded Empirical Reasoning Lab at Barnard has some similar offerings to our Data at Reed team
  • Quantitative concepts cross disciplinary boundaries; one attendee mentioned that GIS can serve as a “gateway” to analytical skills and/or concepts (e.g. data precision – raster scale in GIS, significant figures in chemistry)
  • Attendees shared a variety of entirely math-free curricula for quantitative concepts, including Fermi problems and other quantitative brain teasers, and teaching quantitative literacy through role playing

Assessing quantitative reasoning/literacy was a frequent topic of discussion. How do educators show that students are (or are not) developing quantitative reasoning? Testing basic math concepts provides information on math, but not quantitative reasoning, and what material is tested can communicate priorities to students; a strong sentiment from conference attendees was that we should be promoting reasoning and logic as much as math itself.

Another common thread was identifying faculty priorities for student knowledge. Some colleges do not have a cross-disciplinary quantitative requirement, so a graduate’s quantitative skills may vary depending on department of study. One attendee presented a trio of questions for faculty; information from these questions helped build the quantitative support for that course.

  1. What do you want students to know at the beginning of your course?
  2. What skills and competencies do you reinforce in class?
  3. What do you expect students to know when they leave your course?

One treat from the conference was being able to attend a convocation speech by Jake Porway of DataKind (“using big data for the greater good”). You can find his engaging talk, introducing students to the possibilities and applications of “big data” here.

Carleton did a wonderful job hosting the conference and has had great success integrating quantitative reasoning across the curriculum. Some compelling notes from their past programming:

  • a quantitative speaker series, kicked off by an art historian, with presentations from all corners of scholarship detailing how quantitative methods are used in their discipline
  • a faculty “Quant Squad” who review students’ skills in writing with numbers in sophomore-level papers
  • the development of accessible materials for faculty and students looking to further develop their quantitative skills, such as these 10 foundational quantitative reasoning questions

See Carleton’s QuIRK initiative for more details, materials, and inspiration.

If you would like to look at ways to introduce more quantitative reasoning into your courses, increase your own quantitative or data-related skills, please get in touch; ITS staff and the Data @ Reed team are here to help.




This entry was posted in Quantitative analysis and tagged . Bookmark the permalink.