Below are the questions exactly as you will see them in the Flag Proposal System when you propose your course for a Quantitative Reasoning flag. Your responses to these questions should allow the faculty flag committee to make an informed decision regarding your proposal.
Question 1
Please give a brief description of the course. Include a description of the specific Quantitative Reasoning skills that students will learn and apply within this course.
Clearly list the logical, numerical, and/or statistical skills that students learn how to use in your course. Technical jargon should be avoided or explained such that it can be understood by UT faculty from any discipline.
From a faculty member in geography: Students learn and apply quantitative skills specific to the type of research questions being asked and data analysis plan. Students learn to quantify and interpret common measures of morbidity (e.g., cumulative incidence, incidence density, period prevalence, and point prevalence) and mortality (e.g., maternal mortality ratios, infant mortality ratio, case fatality rates, cause-specific mortality), measures associated with the validity and reliability of screening tests (e.g., sensitivity, specificity, positive predictive value, negative predictive value), measures of prognosis (e.g., 5-year survival probabilities, and life table analysis), measures of association (e.g., risk ratios, rate ratios, and odds ratios), measures of public health impact (e.g., attributable risks, population attributable risk, attributable risk percents, and population attributable risk percents), measures of adjustment (e.g., direct adjustment using standard populations, and indirect adjustment using standard mortality ratios), and interpreting statistical models (e.g., linear and logistic regression).
Question 2
Courses that carry the Quantitative Reasoning Flag must emphasize how QR skills can be applied in students’ everyday or professional lives. Please describe the kinds of applications the course uses to teach Quantitative Reasoning. Specific examples from assignments or exams are strongly encouraged.
All courses carrying the QR Flag must connect the QR skills taught to authentic, real-world applications. Examples of applications or real-world examples may have to be explained if they contain technical jargon, such that they would be clear to UT faculty from any discipline.
Sample response from a faculty member in African and African Diaspora Studies: Issues related to critical race and feminist methodologies in quantitative research. Students will also be required to bring in two news articles. Each article can be from a magazine, newspaper, or credible online news source but must include at least two charts, graphs and/or tables. Along with the article, students must include a two- page report summarizing the article, describing the data represented in the tables, and offering a critique of the authors findings.
Question 3
Courses that carry the Quantitative Reasoning Flag go beyond a superficial application of equations and strive for understanding of the underlying concepts. Please describe how you teach and assess conceptual understanding of Quantitative Reasoning. Specific examples from class, assignments, or exams are strongly encouraged.
Briefly explain the pedagogical choices that are behind the scenes of this course. How do you teach students to understand what they’re doing, and to develop skills they can apply beyond your course? And then how do you know whether it worked? Describe how you conduct class, what kinds of questions you ask, what tasks you assign, etc., to allow students to demonstrate their understanding of the concepts underlying the quantitative skills they use in the course.
From a faculty member in educational psychology: Graded assignments consist of homework assignments and in-class exams. Homework assignments will involve one or more of the following components: computation of statistical procedures, interpretation of analysis results, and a critical evaluation of findings reported in different contexts. Excel will be used to support hand calculations for the homework assignments that involve computation. Exams will consist of true-false, multiple-choice, and short-answer questions including both conceptual and computational problems. Students will also have ungraded practice assignments and quizzes to help them master the topics presented in class.
Question 4
To satisfy the Quantitative Reasoning Flag, at least half of the course grade must be based on the use of quantitative skills. Please describe the course grading scheme in such a way that clearly demonstrates at least half of the grade requires Quantitative Reasoning. Denote which components require Quantitative Reasoning and the total grade percentage these comprise.
Please list the graded components of the course that require quantitative reasoning skills. Estimate what percentage of each component requires quantitative reasoning skills, to total at least 50% of the final grade. It is not necessary to go to the level of individual problems on exams or parts of problems; but do give your best estimate, in a way that is easily comparable to your syllabus.
From a faculty member in biology: The inquiry grading rubrics contain specific sections requiring proper use of statistics and mathematical modeling. No inquiry receives a high grade unless appropriate techniques are employed correctly. Inquiry papers where statistics or modeling is mandatory comprise 40% of the course grade. In addition, 6 of 11 homework assignments, comprising an additional 12% of the grade involve mathematical modeling or statistics. Approximately half the class sessions specifically involve quantitative reasoning, and attendance is 10% of the grade. So 57% of the grade is specifically tied to Quantitative Reasoning.
Supporting Documents
You may attach up to three supporting documents, such as a syllabus or sample assignment. Submission of supporting documents is not required, but is strongly encouraged to expedite the approval process.
(Allowed file formats: doc, docx, ppt, pptx, txt, pdf, xls, xlsx)