Misc
Math Learning Center
Starts Monday (Jan. 22) at 11:30.
Hours are then generally Monday through Thursday mid-day and evening, plus Sunday evening.
See the MLC web site for details.
Grading Guidelines for Random Names
You are lightly graded, 0 to 5 points, as part of “Participation” when I call on you from the random names list. The grade recorded in Canvas for “random names” will be the average of the grades I give this way. Here’s a sense of how I come up with those grades.
- 5 Points: An unexpectedly insightful comment or question.
- 4 Points (what I expect): You say something pertinent to the topic.
- 2 - 3 Points: You say something, but it’s not fully pertinent.
- 1 Point: You’re here, but have nothing to say.
- 0 Points: You aren’t even here to answer (but if I know ahead of time that you have to miss a class I’ll skip over you).
Questions?
Textbook: There is one, and it’s free online.
Syllabus
Summary
What are the key things you should remember from this syllabus and/or get more information about?
Problem sets: Grading will be in face-to-face meetings, during a roughly 3-day window after each problem set’s due date. I’ll demonstrate how to make appointments via Google calendar when you face grading the first problem set.
Problem set vs exam grades: I grade both on a “meeting my expectations earns 80%” scale, but exceeding expectations is easier on exams than problem sets. On problem sets, going beyond expectations in more or less predictable but substantive ways (e.g., extending a problem slightly, having a particularly elegant solution, etc.) earns 90%, but 100% is only for “OMG I didn’t even know this problem set had such a good solution” situations (which do sometimes happen). On exam questions, you can get 100% simply for understanding how to solve a problem and not having typographical/arithmetic errors, egregious mis-statements or digressions along the way, etc.
Real-world conjectures are a chance to get extra credit for doing what mathematicians really do in coming up with a theorem: notice a pattern in some mathematical idea(s) you happen to be thinking about, wonder whether the pattern always holds, find either a proof that it does or a case in which it doesn’t, and not necessarily do any of it elegantly (elegant versions come later). The conjectures don’t have to be deeply insightful.
Some Things I Think Important
Imagine that you get a 70% on an exam. Is that a “good” grade? What letter grade does it correspond to? It typically corresponds to a B. You might or might not consider that “good,” but it’s much better than a 70% would be on a more traditional grading scale.
Are materials for this course available online? Yes, notably through Canvas. In particular...
- The “Modules” tab has everything for this course organized by units and chronologically.
- The “Questions and Comments” discussion can be a way to ask me things or mention things about the course.
There are also two collections of videos related to this class, and materials from last time I taught it, online. See the syllabus for links.
Names: If you want me to call you by a name other than the one I get on class rosters, just let me know.
Learning
What are some things you do when you want to learn something?
- Make mistakes. As long as you learn from them, e.g., by reflecting on why they’re mistakes, getting feedback, etc., they are more educational (even if less fun) than just confirming that you knew how to do something. Also learn to recognize when you have made a mistake.
- Use office hours to ask questions, get help.
- Make intuitve connections to abstract ideas.
- Learn to apply methods to multiple problems.
- Practice using ideas. This is probably the most important, and underlies a lot of the others (e.g., making mistakes, learning to apply methods to problems). It’s one I believe strongly in, which is why lots of this course is organized around you doing things.
- Give help.
The whole question is predicated on the belief that learning is something learners do, not something that can be done to or for them.
Next
Mathematical statements.
Read section 1.1