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The resources below can provide inspiration as you consider various approaches that will help you achieve your course goals. The collections contain a rich set of materials to draw from in constructing the specific set of learning experiences you want for your students.
How does one teach quantitative reasoning skills? These modules offer pedagogical models and examples of their use. They have been made available through the Pedagogy in Action project:
Compiled by Stuart Boersma, Central Washington University Author Profile
If one is using articles in a quantitative reasoning (QR) course, they should certainly contain numerical information (either in the article itself or, perhaps, in an accompanying graphic). The instructor should also keep in mind the specific students they are targeting. For example, a classroom full of traditional-aged freshmen may not be overly interested in the finances surrounding the purchase of a house or dollar-cost averaging while they may be very invested in the financial truths of obtaining an education. Thus, focusing on articles which stress the importance of quantitative reasoning in a relevant setting is important. Articles which are especially rich are those which allow students to:
It is important to continually reinforce the point that to be critical consumers of quantitative information, one must form a habit of mind to continually check, examine, and analyze quantitative information that is being presented.
After choosing an article or advertisement that has some "quantitative depth" to it, one must decide how best to make use of the article in class. Some of the most common uses are described briefly below:
In general, longer articles should be handed out the day before so they can be read before class begins. Instructors may wish to formalize the reading assignment by attaching some brief accountability to it. For instance, one may require each student to email a short two or three sentence summary to the instructor before class begins. The actual study questions may be used in a variety of formats, the most common being:
A word on technology: Having access to a variety of technology is helpful. A document cam makes it easy for instructors or students to share daily articles. A computer in the classroom makes it possible to pull up internet resources (e.g. online newspaper sites and U.S. census data) and make use of spreadsheets as needed. Access to a computer for every student is not necessary, but having at least one in the class can be helpful (many students read their news online and the online presentation may be more appealing than a printed version).
Since many of the quantitative skills encountered in a news-based course are relatively elementary, students come to class with many pre-formed ideas. Instructors need to be aware of what these preconceptions are and how they may affect one's learning or understanding. A few sources which describe the recent cognitive research on how students construct knowledge as well as pedagogical approaches one may use in the classroom to address this issues are found below:
This material was originally developed as part of the Carleton College Teaching Activity Collection
through its collaboration with the SERC Pedagogic Service.
Whether you are a long time user of quantitative writing assignments or a novice, the decision about how (or whether) to incorporate QW assignments into your course begins with the same question: How will they contribute to the course learning goals?
QW assignments shouldn't be added to a course for their own sake. Rather, they should only be added when they contribute to a course learning goal. Therefore, the first step in thinking about how to use quantitative writing is to explicitly identify your learning goals for the course (typically a short list of 5-8 statements). Learning goals state what a student earning a high grade should be able to do. They typically include an active verb following the phrase "students will be able to . . . ."
Here is an example from an economics course of a learning outcome that could be implemented using a quantitative writing assignment:
"Students will develop effective arguments on economic issues by asking appropriate economic questions, analyzing quantitative data, and using these data evidentially in ways appropriate to the discipline." (Dean Peterson, Associate Professor of Economics, Seattle University)
Once you have identified learning goals that lend themselves to quantitative writing, the next step is to design specific QW assignments.
The key to designing an effective Quantitative Writing (QW) assignment is planning carefully. What exactly do you want the students to accomplish? Spell this out on a handout or a web page. If you don't specify what you want, you can be sure that at least some students will not give it to you. For example, if you expect students to appeal to the data as evidence in their argument, then say so. Sometimes students don't think to do that.
Good design incorporates at least three steps:
Once you've identified your goals, you should consider how a QW assignment can support them. QW assignments shouldn't be "yet another thing" to cram on a syllabus, but rather a means of doing better what you are already trying to do. Is one learning goal more important than the others? Design the assignment so that students will spend their time on the portions that are most important to you.
Good topics for QW assignments immerse students in the analysis of quantitative data relevant to the subject matter of your course. Problems should be open-ended rather than asking for discrete answers. Problems can range from the simple to the very elaborate and can be used either for formal writing assignments or for short exploratory writing. Note that good topics for class discussion often work well as writing assignments.
One way to create a good QW assignment is to base it on a compelling reading. Students can come to understand the real-life stakes underpinning the use of quantitative reasoning when they read persuasive works that interpret quantitative data in different ways or that otherwise use quantitative arguments to support alternative positions. Short books written by experts to lay audiences are often good examples of this type of reading.
Once you've decided on a topic, you'll need to choose an appropriate context for the assignment. Context includes several dimensions: What genre of writing assignment should be used? To whom should the writing be addressed? What should the author's purpose be in the assignment? For example, the purpose could be to persuade readers of a particular point of view. Note that 'purpose' here is a bit different than the instructor's goals of the assignment.
While you are designing the assignment, you should also decide on criteria for assessment. You should communicate those criteria to students on the assignment sheet. QW assignments ask students to explain in writing the answer to an analytical problem. Good assignments, however, go beyond asking a student to convert mathematics to words. Rather, they ask students to interpret the mathematical results.
Most principles of good writing apply to quantitative writing as well. Good writing should be viewed as a process rather than a product. Few writers can produce a finished copy in one draft; most writers require several drafts often making significant global changes between them as a result of discovering new ideas and of trying to meet the needs of readers for forecasting, clear topic sentences, headings, good transitions, and unified and coherent paragraphs. Here are some ways to encourage students to take their writing through multiple drafts:
When you begin incorporating Quantitative Writing into your courses, you may feel like you are 'flying without a net' but as the image to the right shows, there are some safety wires if you just know how to connect them. In this section, we discuss some common challenges when using QW, and how those challenges can be overcome, for example, by using common practices from colleagues in the Composition field.
Miller, Jane E. (2004). The Chicago Guide to Writing About Numbers. Chicago: University of Chicago Press.
Miller, Jane E. (2005). The Chicago Guide to Writing About Multivariate Analysis. Chicago: University of Chicago Press.
Abelson, R. P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum Press.
Bain, Ken and James Zimmerman, "Understanding Great Teaching," Peer Review, Spring 2009, Vol. 11, No. 2.
Bransford, John D., Ann L. Brown, and Rodney R. Cocking, Eds. 1999. How People Learn: Brain, Mind, Experience and School. Washington, DC: National Academy Press.
Bean, John C. and David Carrithers. 2005. "Teaching Audience Adaptation and Critical Thought in Business Case Assignments." Proceedings of the European Association of Teachers of Academic Writing.
Bean, John C., Theresa Earenfight, and David Carrithers. "How University Outcomes Assessment Has Revitalized Writing-Across-the-Curriculum at Seattle University." WAC Journal: Writing Across the Curriculum, 16 (2005): 5-21
Best, J. (2004). More damned lies and statistics. Berkeley: University of California Press.
Burke, Michael and Jean Mach, Salmon Essay - Tools for Thought Learning Community,College of San Mateo.
Dawson, Melanie, Peer Editing Guide (more info) , (Archived Version)
Holston, V. and C. Santa, 1985, A method of writing across the curriculum that works: Journal of Reading, v. 28, p456-457.
King, P. M. and K. S. Kitchener, 1994, Developing Reflective Judgment: Understanding and Promoting Intellectual Growth and Critical Thinking in Adolescents and Adults: Jossey-Bass, San Francisco.
Knoblauch, C.H. & Lil Brannon 1983. "Writing as Learning Through the Curriculum." College English. 45, No. 5 (September) 465-474.
Mathematical Association of America, 1998, Quantitative Reasoning for College Graduates: A Complement to the Standards (more info)
Ramage, J. D., Bean, J. C., & Johnson, J. (2007). Writing arguments: A rhetoric with readings. NY: Pearson.
Rutz, Carol, & Grawe, Nathan D. (2009, December 3). Pairing WAC and quantitative reasoning through portfolio assessment and faculty development . [Special issue on Writing Across the Curriculum and Assessment] Across the Disciplines, 6.
Steen, L. A. (Ed.) (2001). Mathematics and democracy. Princeton, NJ: National Council on Education and the Disciplines.
Stevens, Dannelle D. & Levi, Antonia J. (2004). Introduction to Rubrics: An Assessment Tool to Save Grading Time, Convey Effective Feedback and Promote Student Learning. Alexandria, VA: Stylus. [https://styluspub.presswarehouse.com/clients/sty/Books/BookDetail.aspx?productID=92939]
Quantitative assessment seems to be of two kinds: content and attitudes. An example of each kind is given here with a link to many more examples.
The VALUE rubrics were developed by teams of faculty experts representing colleges and universities across the United States through a process that examined many existing campus rubrics and related documents for each learning outcome and incorporated additional feedback from faculty. The rubrics articulate fundamental criteria for each learning outcome, with performance descriptors demonstrating progressively more sophisticated levels of attainment. The rubrics are intended for institutional-level use in evaluating and discussing student learning, not for grading. The core expectations articulated in all 16 of 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 by shared nationally through a common dialog and understanding of student success. https://www.aacu.org/value/rubrics/quantitative-literacy
At Wellesley College we have used the Mathematics Attitude Survey developed by a team at Dartmouth with NSF support and we have found it to be very helpful in measuring changes in attitudes for our QR students. The 35 item Likert scale survey is quick and easy to administer as a pre- and post- Assessment.
Here's an article that includes info and the Assessment instrument itself on page 29 of 32. https://math.dartmouth.edu/~matc/Evaluation/humeval.pdf Corri Taylor, Wellesley College, Former President of NNN.
The textbooks shown are those authored by NNN members.
Presentation on this site does not mean they are authorized or approved by the NNN. It does mean that they are in alignment with the mission and goals of the NNN.
Books on this page are authored by NNN members.
Providing Open-Access Know How for Directors of Quantitative and Mathematics Support Centers
Michael Schuckers, Mary B. O'Neill, and Grace Coulombe
Envisioning a Quantitative Studies Center: A Liberal Arts Perspective
Authored by Gizem Karaali, Philip I. Choi, Sara Owsley Sood, and Eric B. Grosfils
Handbook for Directors of Quantitative & Mathematics Support Centers This handbook is a resource for people who lead, manage or direct Mathematics and Quantitative Support Centers (QMaSCs). In the chapters below, directors will find information about how to 1) manage a center, 2) interact with other entities on their campus, 3) train and build a staff, 4) assess their center, and 5) start a new center. Each chapter has been written by an experienced center director recognizing the diversity of QMaSCs. Additionally, there are ten case studies authored by directors representing a range of centers, from community colleges to liberal arts colleges to large research institutions.
The Math Learning Center Leaders This group of mathematics center leaders and mathematics education researchers meets biweekly online and annually at the Research in Undergraduate Mathematics Education (RUME) conference. Our aim is to support the leaders of mathematics learning centers and to utilize research to improve mathematics learning centers at a wide variety of universities in the United States of America and around the world.
One of the key aspects of the NNN is the network of professionals eager to help others in promoting education that integrates quantitative skills across all disciplines and at all levels. Here we provide very brief bios of some of our NNN members who are available to serve as speakers or consultants. Please contact the individuals directly if you wish to engage them at your own institution.
Kate Follette, Amherst College
Kate is a Professor of Astronomy at Amherst College. She teaches courses in physics and astronomy, and crusades for an emphasis on quantitative literacy across the undergraduate curriculum. E-mail Kate
Eric Gaze, Bowdoin College
Eric Gaze directs the Quantitative Reasoning (QR) program at Bowdoin College, he is a past chair of SIGMAA-QL (2010-12), a board member of the NNN (2010-13), and past NNN President (2014-2018). E-mail Eric
Nathan Grawe, Carleton College
Nathan Grawe is Professor of Economics at Carleton College where he has helped lead the Quantitative Inquiry, Reasoning, and Knowledge (QuIRK) initiative. E-mail Nathan
John R Jungck, University of Delaware
What does it mean to observe, look, see? How does quantitative reasoning inform our appreciation of the visual world? I argue that we need to do re-visioning to develop insight. E-mail John
Bernie Madison, University of Arkansas
Bernard L. Madison, professor of mathematics at the University of Arkansas and founding president of the National Numeracy Network, has worked in quantitative literacy (QL) for more than a decade.E-mail Bernie
Cat McCune, Smith College
Cat is a former math professor and the founding director of the Spinelli Center for Quantitative Learning. In addition to running a tutoring program and teaching undergraduate math courses, Cat works to create community among quantitative reasoning professionals. E-mail Cat
Victor Piercey, Ferris State University
Victor is a professor of mathematics and director of the honors program at Ferris State University, Big Rapids, MI. He has worked with quantitative reasoning for over a decade, focusing on adapting QR for different professional audiences and integrating ethics and social justice. E-mail Victor
Milo Schield, Augsburg University (Emeritus)
Milo, a Visiting Professor at New College of Florida (Emeritus in Business Administration at Augsburg University), has worked on statistical literacy for almost 30 years. Milo is the author of the textbook: Statistical Literacy: Critical Thinking about Everyday Statistics (2023). His research papers are available at www.StatLit.org/Schield-pubs.htm. E-mail Milo.
Luke Tunstall, Trinity University
Luke is the Director of Trinity’s new Quantitative Reasoning and Skills (QRS) Center. Luke is heavily involved in the quantitative literacy movement in higher education, and is committed to fostering students’ quantitative reasoning practices across the disciplines. He is keenly aware of the complex relationship between quantitative literacy and social justice. E-mail Luke