andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1056 knowledge-graph by maker-knowledge-mining
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Introduction: Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? Certainly making visualizations is integral to scientific thinking. Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. . . . However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e.g., evaporation). Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many
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1 Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? [sent-1, score-0.573]
2 Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. [sent-3, score-0.068]
3 However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). [sent-7, score-1.842]
4 Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e. [sent-8, score-0.636]
5 Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many representational skills. [sent-11, score-1.042]
6 We suggest five reasons why student drawing should be explicitly recognized alongside writing, reading, and talking as a key element in science education. [sent-12, score-0.853]
7 When I teach, I’m always sketching things on the blackboard. [sent-18, score-0.235]
8 One of my principles of teaching is: Anything you want students to understand , they should do . [sent-19, score-0.177]
9 If the derivation’s in the book or on the board, it should also be in the students’ homeworks and in-class activities. [sent-20, score-0.093]
10 So, yeah, I think students should get practice in drawing (and feedback on their attempts). [sent-21, score-0.741]
11 Just as writing and programming are useful in many different aspects of life, so is drawing. [sent-24, score-0.096]
12 So it’s good to give students instruction in drawing whenever it fits in to the curriculum. [sent-25, score-0.931]
13 Some people are good at algebra, others are good at drawing. [sent-28, score-0.212]
14 It’s good to structure a course so that people with drawing talent have an entry into the field. [sent-29, score-0.763]
15 Similar to how it’s good to give some mathematical subtlety in any course so that the more mathematical students can relate the course to that ability and interest of theirs. [sent-30, score-0.828]
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same-blog 1 0.99999988 1056 andrew gelman stats-2011-12-13-Drawing to Learn in Science
Introduction: Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? Certainly making visualizations is integral to scientific thinking. Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. . . . However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e.g., evaporation). Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many
2 0.13795485 631 andrew gelman stats-2011-03-28-Explaining that plot.
Introduction: With some upgrades from a previous post . And with a hopefully clear 40+ page draft paper (see page 16). Drawing Inference – Literally and by Individual Contribution.pdf Comments are welcome, though my reponses may be delayed. (Working on how to best render the graphs.) K? p.s. Plot was modified so that it might be better interpreted without reading any of the paper – though I would not suggest that – reading at least pages 1 to 17 is recomended.
3 0.12364447 1517 andrew gelman stats-2012-10-01-“On Inspiring Students and Being Human”
Introduction: Rachel Schutt (the author of the Taxonomy of Confusion) has a blog! for the course she’s teaching at Columbia, “Introduction to Data Science.” It sounds like a great course—I wish I could take it! Her latest post is “On Inspiring Students and Being Human”: Of course one hopes as a teacher that one will inspire students . . . But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. This is one of the happy unintended consequences of this course so far for me. She then gives examples of some of the students in her class and some of their interesting ideas: Phillip is a PhD student in the sociology department . . . He’s in the process of developing his thesis topic around some of the themes we’ve been discussing in this class, such as the emerging data science community. Arvi works at the College Board and is a part time student . . . He analyzes user-level data of students who have signed up f
Introduction: One of the most satisfying experiences for an academic is when someone asks a question that you’ve already answered. This happened in the comments today. Daniel Gotthardt wrote : So for applied stat courses like for sociologists, political scientists, psychologists and maybe also for economics, what do we actually want to accomplish with our intro courses? And how would it help to include Bayesian Statistics in them? And I was like, hey! This reminds me of a paper I published a few years ago, “Teaching Bayesian applied statistics to graduate students in political science, sociology, public health, education, economics, . . .” Here it is , and it begins as follows: I was trying to draw Bert and Ernie the other day, and it was really difficult. I had pictures of them right next to me, but my drawings were just incredibly crude, more “linguistic” than “visual” in the sense that I was portraying key aspects of Bert and Ernie but in pictures that didn’t look anything like t
5 0.1172407 1154 andrew gelman stats-2012-02-04-“Turn a Boring Bar Graph into a 3D Masterpiece”
Introduction: Jimmy sends in this . Steps include “Make whimsical sparkles by drawing an ellipse using the Ellipse Tool,” “Rotate the sparkles . . . Give some sparkles less Opacity by using the Transparency Palette,” and “Add a haze around each sparkle by drawing a white ellipse using the Ellipse Tool.” The punchline: Now, the next time you need to include a boring graph in one of your designs you’ll be able to add some extra emphasis and get people to really pay attention to those numbers! P.S. to all the commenters: Yeah, yeah, do your contrarian best and tell me why chartjunk is actually a good thing, how I’m just a snob, etc etc.
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Introduction: Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? Certainly making visualizations is integral to scientific thinking. Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. . . . However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e.g., evaporation). Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many
Introduction: Joe Blitzstein and Xiao-Li Meng write : An e ffectively designed examination process goes far beyond revealing students’ knowledge or skills. It also serves as a great teaching and learning tool, incentivizing the students to think more deeply and to connect the dots at a higher level. This extends throughout the entire process: pre-exam preparation, the exam itself, and the post-exam period (the aftermath or, more appropriately, afterstat of the exam). As in the publication process, the first submission is essential but still just one piece in the dialogue. Viewing the entire exam process as an extended dialogue between students and faculty, we discuss ideas for making this dialogue induce more inspiration than perspiration, and thereby making it a memorable deep-learning triumph rather than a wish-to-forget test-taking trauma. We illustrate such a dialogue through a recently introduced course in the Harvard Statistics Department, Stat 399: Problem Solving in Statistics, and tw
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Introduction: Rachel Schutt (the author of the Taxonomy of Confusion) has a blog! for the course she’s teaching at Columbia, “Introduction to Data Science.” It sounds like a great course—I wish I could take it! Her latest post is “On Inspiring Students and Being Human”: Of course one hopes as a teacher that one will inspire students . . . But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. This is one of the happy unintended consequences of this course so far for me. She then gives examples of some of the students in her class and some of their interesting ideas: Phillip is a PhD student in the sociology department . . . He’s in the process of developing his thesis topic around some of the themes we’ve been discussing in this class, such as the emerging data science community. Arvi works at the College Board and is a part time student . . . He analyzes user-level data of students who have signed up f
Introduction: A couple months ago, the students in our Teaching Statistics class practiced one-on-one tutoring. We paired up the students (most of them are second-year Ph.D. students in our statistics department), with student A playing the role of instructor and student B playing the role of a confused student who was coming in for office hours. Within each pair, A tried to teach B (using pen and paper or the blackboard) for five minutes. Then they both took notes on what worked and what didn’t work, and then they switched roles, so that B got some practice teaching. While this was all happening, Val and I walked around the room and watched what they did. And we took some notes, and wrote down some ideas: In no particular order: Who’s holding the pen? Mort of the pairs did their communication on paper, and in most of these cases, the person holding the pen (and with the paper closest to him/herself) was the teacher. That ain’t right. Let the student hold the pen. The student’s the on
5 0.80520219 516 andrew gelman stats-2011-01-14-A new idea for a science core course based entirely on computer simulation
Introduction: Columbia College has for many years had a Core Curriculum, in which students read classics such as Plato (in translation) etc. A few years ago they created a Science core course. There was always some confusion about this idea: On one hand, how much would college freshmen really learn about science by reading the classic writings of Galileo, Laplace, Darwin, Einstein, etc.? And they certainly wouldn’t get much out by puzzling over the latest issues of Nature, Cell, and Physical Review Letters. On the other hand, what’s the point of having them read Dawkins, Gould, or even Brian Greene? These sorts of popularizations give you a sense of modern science (even to the extent of conveying some of the debates in these fields), but reading them might not give the same intellectual engagement that you’d get from wrestling with the Bible or Shakespeare. I have a different idea. What about structuring the entire course around computer programming and simulation? Start with a few weeks t
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Introduction: Jim Hodges posted a note to the Bugs mailing list that I thought could be of more general interest: Is multi-modality a common experience? I [Hodges] think the answer is “nobody knows in any generality”. Here are some examples of bimodality that certainly do *not* involve the kind of labeling problems that arise in mixture models. The only systematic study of multimodality I know of is Liu J, Hodges JS (2003). Posterior bimodality in the balanced one-way random effects model. J.~Royal Stat.~Soc., Ser.~B, 65:247-255. The surprise of this paper is that in the simplest possible hierarchical model (analyzed using the standard inverse-gamma priors for the two variances), bimodality occurs quite readily, although it is much less common to have two modes that are big enough so that you’d actually get a noticeable fraction of MCMC draws from both of them. Because the restricted likelihood (= the marginal posterior for the two variances, if you’ve put flat priors on them) is
2 0.93094754 1822 andrew gelman stats-2013-04-24-Samurai sword-wielding Mormon bishop pharmaceutical statistician stops mugger
Introduction: Brett Keller points us to this feel-good story of the day: A Samurai sword-wielding Mormon bishop helped a neighbor woman escape a Tuesday morning attack by a man who had been stalking her. Kent Hendrix woke up Tuesday to his teenage son pounding on his bedroom door and telling him somebody was being mugged in front of their house. The 47-year-old father of six rushed out the door and grabbed the weapon closest to him — a 29-inch high carbon steel Samurai sword. . . . Hendrix, a pharmaceutical statistician, was one of several neighbors who came to the woman’s aid after she began yelling for help . . . Too bad the whole “statistician” thing got buried in the middle of the article. Fair enough, though: I don’t know what it takes to become a Mormon bishop, but I assume it’s more effort than what it takes to learn statistics.
3 0.93041706 1128 andrew gelman stats-2012-01-19-Sharon Begley: Worse than Stephen Jay Gould?
Introduction: Commenter Tggp links to a criticism of science journalist Sharon Begley by science journalist Matthew Hutson. I learned of this dispute after reporting that Begley had received the American Statistical Association’s Excellence in Statistical Reporting Award, a completely undeserved honor, if Hutson is to believed. The two journalists have somewhat similar profiles: Begley was science editor at Newsweek (she’s now at Reuters) and author of “Train Your Mind, Change Your Brain: How a New Science Reveals Our Extraordinary Potential to Transform Ourselves,” and Hutson was news editor at Psychology Today and wrote the similarly self-helpy-titled, “The 7 Laws of Magical Thinking: How Irrational Beliefs Keep Us Happy, Healthy, and Sane.” Hutson writes : Psychological Science recently published a fascinating new study on jealousy. I was interested to read Newsweek’s 1300-word article covering the research by their science editor, Sharon Begley. But part-way through the article, I
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Introduction: Joshua Vogelstein points us to an article by Shaaron Ainsworth, Vaughan Prain, and Russell Tytler: Should science learners be challenged to draw more? Certainly making visualizations is integral to scientific thinking. Scientists do not use words only but rely on diagrams, graphs, videos, photographs, and other images to make discoveries, explain findings, and excite public interest. . . . However, in the science classroom, learners mainly focus on interpreting others’ visualizations; when drawing does occur, it is rare that learners are systematically encouraged to create their own visual forms to develop and show understanding (6). Drawing includes constructing a line graph from a table of values, sketching cells observed through a microscope, or inventing a way to show a scientific phenomenon (e.g., evaporation). Although interpretation of visualizations and other information is clearly critical to learning, becoming proficient in science also requires learners to develop many
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Introduction: John Salvatier writes: What do you and your readers think are the trickiest models to fit? If I had an algorithm that I claimed could fit many models with little fuss, what kinds of models would really impress you? I am interested in testing different MCMC sampling methods to evaluate their performance and I want to stretch the bounds of their abilities. I don’t know what’s the trickiest, but just about anything I work on in a serious way gives me some troubles. This reminds me that we should finish our Bayesian Benchmarks paper already.
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