This multi-disciplinary course focuses on the concept of models in science. What is a model? What is it useful for? How does it help our understanding of science? When is one model better than another? These are the questions we will be addressing by developing our own models and studying others. We will discover features common to all scientific models, regardless of discipline, and learn to construct and use our own models.
The course is built around two meta-goals. Years from now every student should still…
Investigate new ideas with curiosity and skepticism.
Understand the role of science, particularly its advantages and limitations.
To achieve these meta-goals the course is constructed with four concrete goals. On completion of the course each student should be able to…
Construct an original scientific model.
Assess a given scientific model.
Explain what science is.
Explain what scientific models are.
The following schedule is subject to change but should give you a sense of what we'll cover in the class. Visit the course Connect page for current information and updates.
|Tutorial 1||2017-09-06||Introduction & first model|
|Lecture 1||2017-09-11||NetLogo 1 - play||Rauch 2002, pp. 1-6 (6 pages)|
|Tutorial 2||2017-09-13||NetLogo 2 - implement first model|
|Lecture 2||2017-09-18||Ways of knowing||Wilensky & Rand 2015, pp. 128-141 (14 pages)||Assignment 1|
|Tutorial 3||2017-09-20||NetLogo 3 - exploration|
|Lecture 3||2017-09-25||Science vs. pseudoscience||Behe et al. 2002 (8 pages)||Project literature search|
|Tutorial 4||2017-09-27||Pitch your project|
|Lecture 4||2017-10-02||Define science and scientific model||Popper 2002, pp. 9-10, 16-20 (7 pages)||Peer-grade literature searches|
|Tutorial 5||2017-10-04||Speed dating to find project partner|
|Thanksgiving day (holiday)||2017-10-09|
|Tutorial 6||2017-10-11||Building a project outline||Assignment 2|
|Lecture 5||2017-10-16||Kinds of models||Project outline|
|Tutorial 7||2017-10-18||Work session|
|Lecture 6||2017-10-23||Parsimony vs. accuracy|
|Tutorial 8||2017-10-25||Model appraisal|
|Lecture 7||2017-10-30||Other modeling trade-offs|
|Tutorial 9||2017-11-01||Work session||Project testing & calibration|
|Lecture 8||2017-11-06||To be announced|
|Tutorial 10||2017-11-08||To be announced|
|Holiday in lieu of Remembrance Day||2017-11-13|
|Tutorial 11||2017-11-15||Work session|
|Lecture 9||2017-11-20||Poster design|
|Tutorial 12||2017-11-22||Your project in the big picture|
|Lecture 10||2017-11-27||Poster session||Project poster|
|Tutorial 13||2017-11-29||Wrap up||Project report|
Grades will be based on assignments, a term project, and a final exam. The mark distribution is:
There will be two assignments, each worth 10 marks, and a term project. The project will give students hands-on experience in developing a model to answer an original research question. It will be broken up into five stages to give lots of opportunity for feedback and direction:
|Literature search (peer-graded)||10|
|Model testing & calibration||10|
Students may accumulate up to 20 flex points over the term; at the end of the course they will be used to automatically modify the weights of the components of the course. The weight of each student's worst component will be automatically reduced by their accumulated flex points and the weight of the best component increased accordingly. By earning flex points you can tailor the grading scheme to favour your learning style!
Rikky does well on assignments but lousy on tests. He scores 90% on the assignments, 75% on the project, and 60% on the exam. With the default grading scheme Rikky would earn an overall grade of 75%.
But throughout the course Rikky earned 10 flex points, so the weights of his best and worst component are adjusted as shown:
So, Rikky is able to increase his overall grade from 75% to 78% by earning flex points throughout the term.
Late submission of homework assignments and project work may be possible, but only upon written application in advance of the deadline with supporting arguments. The standard penalty of 10% per day may be reduced for convincing arguments. While it's easier to ask forgiveness than it is to get permission, the reverse is more likely to be successful.
The following materials are required:
- Laptop computer running Mac OS X, Windows, or Linux. (Sorry, iOS, Windows Phone, or Android won't suffice for our purposes.)
Additional course content is available to registered students via Connect:
- Lecture Notes
To log in, please click on the CWL Login button below: