Andrew M. Ross
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Are you teaching content courses that future math teachers take?
Take a look at these free classroom materials for Algebra, Geometry, Modeling, and Statistics from an NSF-sponsored project called MODULE(S2):
modules2.com. Our materials are written in the active-learning, discussion-based format that we all want our teacher candidates to use with their future students. We focus on pre-service teachers at the secondary level (grades 6-12), helping them to develop mathematical knowledge for teaching in their upper-level content courses.
Our materials have been used by 60+ faculty at 50+ universities in the US and Canada who have taught 1000+ prospective teachers and university students in university mathematics courses. Our project began through the work of the Mathematics Teacher Education Partnership (MTE-P), which seeks to create a gold standard for the preparation of secondary mathematics teachers across its over 90 member universities.
This webpage used to be at people.emich.edu/aross15. If you come across any broken links related to people.emich.edu/aross15, try replacing that part of the URL with emunix.emich.edu/~aross15 (note the tilde ~ before aross15 in the new version of the URL.)
Classes from Previous Years
In this class, you'll learn Predictive Analytics: how to predict what will happen in a variety of real-world situations like time series data, queueing systems, insurance, etc.
In this class, you'll learn Prescriptive Analytics: how to model real situations as mathematical optimization (minimizing cost, maximizing lives saved, etc.), and the algorithms that computers use to solve those optimization problems. It's incredibly powerful--thousands of variables and constraints in class, and real-world problems can be solved with millions!
It's also related to Predictive Analytics via statistical model fitting and machine learning.
We will probably offer an undergraduate version along with Math 560, numbered Math 479.
In this class, you'll learn Prescriptive Analytics: how to model real situations as mathematical optimization (minimizing cost, maximizing lives saved, etc.), and the algorithms that computers use to solve those optimization problems. It's incredibly powerful--thousands of variables and constraints in class, and real-world problems can be solved with millions!
It's also related to Predictive Analytics via statistical model fitting and machine learning.
We will probably offer an undergraduate version along with Math 560, numbered Math 479.
(see Canvas for all info)
In this class, you'll learn Predictive Analytics: how to predict what will happen in a variety of real-world situations like time series data, queueing systems, insurance, etc.
- Syllabus for 2019, includes textbook information. Will probably run again in Winter 2021.
Other Past classes
Other Stuff
Math Contests
Fun Data Sources
Updated 2023-01-05