About the Numeracy Infusion Course for Higher Education (NICHE)
- QR and Making Numbers Meaningful
- QR Learning Outcomes
- The Brain, Cognition and QR
- QR and Writing
- Discovery Methods
- Representations of Data
- QR Assessment
- QR Stereotypes and Culture
Whether called numeracy, Quantitative Literacy (QL) or Quantitative Reasoning (QR), the importance of infusing quantitative material throughout the curriculum is an imperative of higher education. This website is intended to provide a repository of resources and information for faculty from across the disciplines who are seeking to infuse numeracy/QL/QR in their course instruction.
More specifically, the materials on this website provide guidance for faculty to
- apply QL/QR within a disciplinary context,
- articulate QL/QR learning goals/objectives that reflect best practices for teaching quantitative literacy,
- identify and implement best practices for teaching QL/QR: active learning, collaborative student learning, writing with numerical information, etc.,
- develop and/or adapt exercises and instructional tools that incorporate quantitative reasoning, and
- assess the effectiveness of QL/QR instructional efforts and use the assessment results to further improve teaching.
If you have any questions about NICHE and/or are interested in participating in this course, please contact Esther Isabelle Wilder (firstname.lastname@example.org). We also welcome feedback and suggestions for this website!
The CUNY NICHE Development Team
- Esther Wilder, Principal Investigator, Lehman College (email@example.com)
- Elin Waring, Faculty Liaison and Senior Researcher, Lehman College (firstname.lastname@example.org)
- Frank Wang, co-Principal Investigator, LaGuardia Community College (email@example.com)
- Dene Hurley, co-Principal Investigator, Lehman College (firstname.lastname@example.org)
The CUNY NICHE QR Alliance
- Prabha Betne, Faculty Liaison, LaGuardia Community College (email@example.com)
- Laura Broughton, Faculty Liaison, Bronx Community College (firstname.lastname@example.org)
- Margaret Carroll, Faculty Liaison, Medgar Evers College (email@example.com)
- Judith Duncker, Research Consultant, Lehman College (firstname.lastname@example.org)
- Althea Forde, Lehman College Advisory Committee, Lehman College (email@example.com)
- Sandra Kingan, Faculty Liaison, Brooklyn College (firstname.lastname@example.org)
- Robert Whittaker, Lehman College Advisory Committee, Lehman College (email@example.com)
- Marcie Wolfe, Lehman College Advisory Committee, Lehman College (firstname.lastname@example.org)
Assessment and External Consultants
- Flora McMartin, Broad-based Knowledge, LLC (Assessment Coordinator)
- William Frey, University of Michigan (External Consultant)
- Katherine Rowell, Sinclair Community College (External Consultant)
- Stephen Sweet, Ithaca College (External Consultant)
- Corrine Taylor, Wellesley College (External Consultant)
Special thanks to Stephen Castellano and Alyson Vogel at Lehman College and to William H. Walters at Menlo College for their technical support and enthusiasm for this project. We also acknowledge the extremely helpful feedback provided by the Lehman College 2012-2013 Quantitative Reasoning (QR) fellows including Tzuhao (T) Huang, Hsien-Tseng (Elvin) Wang, Rebecca West, and Ruifang (Grace) Xu. Their assistance has been greatly appreciated!
Esther Wilder, Principal Investigator
250 Bedford Park Boulevard West
Bronx, NY 10468
Unless otherwise indicated, all pages are authored by Esther Isabelle Wilder.
Sample Materials from NICHE
Exercise #1: Graphing your Course along a QR Spectrum. For this brief exercise, we want you to review the definitions of quantitative and reasoning from Merriam-Webster's dictionary (Acrobat (PDF) 53kB May2 13). After studying these definitions, please think about a specific course you teach (it could be anything)! After you have picked your course, please graph where you think it would currently fall based on (a) how quantitative it is, and (b) how much reasoning it involves. Please indicate where your course falls by placing a marking, such as a star (or your initials or some other symbol), on this graph. You can do this very easily by double-clicking on the graph and then inserting a shape (the insert option will automatically appear when you double-click on the graph). Then you will want to move the shape (or your initials) where it belongs (and you will likely want to reduce the size of the shape or the font of your initials). As an example, I have chosen a smiley face and graphed it on this chart. After you graph your course on the chart, please go to the bottom of the graph and identify yourself, indicate the symbol you used, noting what course it represents and why you graphed it where you did.
Exercise #2: Graphing the Three Little Pigs. Please watch the Kurt Vonnegut video which illustrates the use of a line graph to tell a story. After you watch that, please watch the "Three Little Pigs Silly Symphony" (also below). After you finish watching the video of the three little pigs, please graph the story using flockdraw and write about your graph here. You should graph the story in whatever way feels right to you (e.g., choose whatever perspective you want). Please follow the following directions.(1) When you open up "flockdraw," click on "create your own session." This will open a drawing page and you can draw your graph. The drawing tools are pretty self-explanatory, but please label your graph.
(2) When you are finished with your graph, click on the floppy disk to the right of your graph to save it. When you do that, you get a URL that you will want to note.
(3) When you write about your graph, be sure to provide a link to the URL of the graph that you have drawn so that we can all see it.
(4) After you have graphed your story of "The Three Little Pigs," please explain why you graphed the story the way you did.
Kurt Vonnegut on the Shape of Stories
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The Three little Pigs Silly Symphony
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Support for this project has been provided by the National Science Foundation's (NSF) Transforming Undergraduate Education in Science, Technology, Engineering and Mathematics (STEM) (TUES) award #1121844. Any opinions, findings, and conclusions or recommendations expressed in this web site are those of the authors and do not necessarily represent the views of the National Science Foundation.