Course Syllabus for ENVS 298:

Fall Term 2014:

  • Class Meets in Library 101 MWF 1000-1050 day
  • There is no required textbook


Instructor: G. Bothun, Dept of Physics

  • Office/Lab: 417 Willamette Hall
  • email: dkmatter@uoregon.edu
  • office hours: 10:30-12 Tuesday, Thursday




Grading and Course Requirements:



  • Homework Assignments -- assignments will be worth a variable number of points as some are more involved than others. They will count for about 40% of your grade. You may collaborate with up to 2 other people (e.g. group size = 3) in doing the homework.

  • Two Midterms: worth 25% of your grade

  • Comprehensive final -- worth 30% of your grade

      Final Exam Tuesday 12/9 at 10:15 am in Library 101


  • In class participation -- show up with an informed opinion.


Course Grades:

  • Each assignment and test is worth some number of points. No letter grades are assigned to these. At the end of the term there will be some distribution of total points. From that rank order of total points, letter grades will be determined.

    In general:

    • those in the upper 20% of the class will get "A" grades.
    • Those in the lower 30% of the class will get "C" grades.
    • Those in the middle 50% will get "B" grades.


This class is new so the course material will be fluid. The content table I gave in the course proposal is shown below and we will try to stick to this, somewhat.





Learning Outcomes:

The principle objective of the proposed course is to improve the QL/QR skills of the students by using graphical means, in combination with many rich, real life data sets, as the vehicle for producing this literacy. Just as in the case of "critical thinking", student learning outcomes and/or course objectives are highly dependent upon the definition of quantitative reasoning.

While there is no one, universal definition, the following table outlines 6 observable skills that can be reasonably associated with QR and that will be designed into the proposed class:





Another major outcome will be the development of data literacy and understanding the vital role that data plays in shaping scientific hypothesis, evaluation, prediction, and future research. Data literacy also entails training students on issues related to data bias (an extant problem in much climate data), data incompleteness and data ambiguity. Indeed, a major piece of core data literacy involves dealing with noisy data that can support several different viewpoints. The overall ability to resolve ambiguity is also a necessary component of critical thinking.