| Title | Applied Environmental Studies: Preparation for Problem Solving |
| Lecture | MW 0900-1005 in ???? |
| Lab | see link |
| Instructor | John Rueter, Professor Environmental Sciences and Management Program |
| Office | SB2 218 |
| Office hours | to be announced |
| phone | 503 725 3194 |
| rueterj@pdx.edu |
All students should have a good background in math (such as Math111) and general science.
ESR 220 is required.
This course will examine and try to understand environmental problems that we have seen before (in ESR220). The definition I use of "understanding" is that you will know what action to take. Please see understanding.html for a discussion of this.
As professional environmental scientists, managers or policy makers you will be expected to deal with multiple representations of these problems, such as descriptions, graphs, mathematical equations, and models. In fact, a expert in our field will be expected to move back and forth between these different representations.
A key aspect of whether you know what to do next is to create a model of the problem. We will learn how to create systems models of the problems presented in the class. These models will be used to:
You will be creating models in a systems dynamics language called STELLA. This modeling environment will force you to clearly represent each problem and support your efforts. Modeling is a creative activity that can be challenging and even frustrating at times. We will discuss personal styles and approaches to modeling.
The course will be presented in four units (which might be modified this term):
1) representations of information and static models,
2) dynamic models,
3) complex models and simultaneous processes, and
4) models for decisions.
The course has a required lab that will be essential for learning the material and demonstrating your understanding.
The course work will include discussions in class, problem sets, quizzes, lab assignments, a lab project and a presentation of your group lab project.
This course has a list of specific learning objectives in the area of using different types of models and approaches to collecting and analyzing data.
A list of learning objectives that are addressed in this course.
Grading for this course will be based on:
Lecture portion:
4 problem sets (10 points each)
2 multiple representation exercise (10 points each)
2 quizzes (20 points each)
Lab portion:
3 projects
simulations exercises
group model and presentation
The lecture portion counts for 75% and the lab 25% of the grade.
As the instructor, I reserve the right to provide alternative assignments.
I expect collaboration between students. All students are charged with helping any other student in the class. Once you have experience in our field you will see that it is easy to acknowledge sources that you received help from (academic journals or other students). The key is to take what you got from other students or resources and rework it so that it represents your personal work.
I expect strict adherence to the student conduct code as described in the PSU Bulletin. Any infraction of this code will result in a score of zero on all related assignments AND referral to the Office of Student Affairs.
Please refer to my statement on Academic Honesty.
We will discuss the difference between collaboration and cheating in class.
John Rueter
ESR221
December 17, 2008