I've been offering some pointers to this gentleman in Indonesia wishing to learn Python.
What's clear to me is he's chomping at the bit to get to the most impressive use case demonstrations, involving VPython, matplotlib, other 3rd party packages. He's not giving himself much time to just mess around in what we often call "core Python" (prevalent nomenclature, not my invention). This means he's deeply into issues such as re-scaling x-y axes for a plot, before ever writing his own class definition. All those tiny little examples involving Foo and Bar, giving simple step by step explanations, get skipped, in the rush to graph something sexy. I'm thinking a benefit of a curriculum such as Litvins' is you're allowed to stay within the core and not have to master many if any add-ons. That's kind of a cool luxury, to get a lot of face time with pure Python, before needing to encounter Sage, matplotlib, numpy or one of those. One might use other tools entirely for plots sometimes, say Java applets with easy GUI tools. It's a hybrid environment, with many technologies on display, a kind of a smorgasbord. Yes, Python is on the menu, but so is a lot of stuff, varies by teacher. My Saturday Academy courses have been slightly off this core Python path in that I use VPython from the start, because of it's high bang for the buck value. Being able to get some spatial geometry going pretty quickly is a strong enticement, a big win for low overhead. But I provide a lot of initial scaffolding and really spend a lot of time just sticking with core basics, using visual only sparingly in some meetups. Having visual output is one thing, but purely visual input is something else again. [ After a certain age or stage of readiness, I'd rather see learning a coding language as more of a left brain exercise, a lexical activity. Always dragging and dropping icons, working visually, provides lots of work for developers of such environments, yet always feeding those expectations, of "everything graphical" is too often a set up for future disappointment. Coding a video game, and playing one, are two different kinds of activity, we should not lie about this difference by always promising too much eye candy. Core Python includes importing from standard library, is not about denying ourselves access to sys, os, math and many more... including turtle. Programs like Sage on the other hand, take minor liberties with the syntax itself. That's "minor" in one sense, but in another sense you've removed a some boundaries, somewhat fuzzed the picture as to what Python is. If you have the luxury of being able to stay with core for awhile, that might be to your lasting benefit. A pre-college math course that's somewhat low pressure when it comes to whatever scripting language, not pushing too far or too fast, staying safely in a sandbox a lot of the time (not adventuring with add-ons too much), is going to be helping a diverse population. Why? Because a primary objective of this go-slow approach is to explicate broadly applicable mathematical concepts, not narrow concerns about syntax. Because the programming language is only part of the mix, not the only focus, some days the lessons have nothing at all to do with coding. Whole days go by when no whatsoever code is entered or evaluated. We might be making polyhedra with scissors and glue (or in my case, with snap-together identical parts, translucent plastic, no glue required): http://www.flickr.com/photos/17157...@n00/4328444871/ A great example of what a computer language might help demystify is Sigma notation. Math texts are full of capital greek Sigma symbols. However these may come across as highly mysterious on first pass -- the beginning of the end for some students, when they start to glaze over. With the semantics of do-loops (while loops, for loops) side-by-side, one explicating the other, the topic has not become doubly difficult. It's now only half as hard. Two views are better than one. This has been my finding at least, and the implications are far-reaching. Nor am I in any way alone in making this discovery i.e. this train left the station, had gathered steam, before I hopped aboard (I was recruited through APL, is how I tell the story today -- wouldn't have stuck around if it was all Fortran on punch cards... all because of REPL, still important i.e. the fun of immediate feedback, no need to compile and write lots of prompts for inputs to compensate). I'm going with Computational Thinking as a course name for trial balloon purposes. So far it seems to be gathering altitude. There's always the course description for getting wordy. That's where you might say something about discrete math, or a mix of traditional and more contemporary topics. Some curricula will dive into cellular automata, some into polyhedra, some into both, some into neither... My thanks to those who suggested Computational Thinking as a possible moniker. Kirby
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