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    Joined: Aug 2010
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    Quote
    DD has noted that the ONE constant in her research observations of human subjects in pairs programming is that the male paired subjects tend to be OVER-confident, and the female ones UNDER confident. If anything, their data suggests that the female teams are less error-prone and work faster when they work as a cohesive team.

    This is actually a pretty well-known phenomenon. Men overrate their abilities, intelligence, skill at driving, attractiveness, etc while women underrate themselves, even when, as a group, they test as more competent.

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    Since males and females have different patterns of scores on the sections of the SAT and ACT, and since ability tilts predict college major, according to a recent paper, one should not expect the two sexes to have the same distribution of college majors or attribute differences in college major entirely to discrimination in college or to perceived future discrimination in the workforce.

    http://www.sciencedirect.com/science/article/pii/S016028961400049X
    Ability tilt on the SAT and ACT predicts specific abilities and college majors
    Thomas R. Coyle, Jason M. Purcell, Anissa C. Snyder, and Miranda C. Richmond
    Intelligence
    Volume 46, September–October 2014, Pages 18–24
    Abstract
    This research examined the validity of ability tilt, measured as within-subject differences in math and verbal scores on the SAT and ACT. Tilt scores were correlated with academic abilities (math and verbal) and college majors (STEM and humanities), both drawn from the National Longitudinal Survey of Youth. Math tilt (math > verbal) correlated positively with math ability and negatively with verbal ability, whereas verbal tilt (verbal > math) showed the opposite pattern. In addition, math tilt was associated with STEM majors (e.g., science and math), whereas verbal tilt was associated with humanities majors (e.g., English and history). Both math and verbal tilt were unrelated to non-academic abilities (speed and shop) and g. The results support niche-picking and investment theories, in which investment in one area (math) means less investment in competing areas (verbal).
    Keywords
    Ability tilt; SAT; ACT; General intelligence (g); Investment theories

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    Originally Posted by Bostonian
    Having looked at the introductory CS courses at a few schools (Harvard, Stanford, Princeton, MIT, U of Washington) I see that the introductory CS course is not standardized across institutions in the way that calculus is. AP CS uses Java, which is a common choice in introductory college CS classes, including at Princeton, but there are courses using C (Harvard), JavaScript (Stanford), and Python (MIT).
    Python is now the most popular introductory teaching language at top U.S. universities according to an article by Philip Guo in Communications of the ACM, with 69% share at the top 39 schools in the U.S., followed by Java. Other languages have a much smaller share.

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    Well, just so that everyone knows, I'm not sure that "tilt" there even MATTERS beyond 98th percentile scores.

    DD and I are both prime examples. Oh, sure-- our verbal scores > math, but math is still well above the 90th percentile any way you choose to measure it.

    I have a PhD in a physical science, and had no real intellectual/cognitive struggles getting there. DD finds STEM more challenging than the humanities, but she is often bored with the humanities because it has to move at SUCH a slow pace for most of her peers-- and it's all sort of obvious to her, and doesn't feel "real." Ergo-- STEM.


    Interesting info on teaching code languages, Bostonian-- that echoes what DD was told during advising for her CS major.




    Schrödinger's cat walks into a bar. And doesn't.
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    Looking at intro CS sites led me to an interesting paper on computational thinking which led me to this site:
    http://games.thinkingmyself.com/

    Which takes a direct approach to teaching computational thinking skills; the site sends fairly mixed messages as to their target user/age.

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