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The Average Student Does Not Exist

89 点作者 ibrahima将近 8 年前

14 条评论

forgotpwtomain将近 8 年前
Somehow despite all the conversations around education in the US the education system still sucks. I went to one of the highest funded (amount spent per child) public schools in my state, and as far as I am aware it was far behind in terms of curriculum strength compared to what my parents were taught in the Soviet Union at the same age.<p>I mean we didn&#x27;t read a classic American author till 6th or 7th grade! And if I recall correctly there were still M&amp;M&#x27;s in math class in grade 4!<p>The US may have an education problem but somehow the Soviet Union and China did fine years ago with out all the ed-tech snake oil.
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closed将近 8 年前
To be honest, I like that this article tries to perform simple analyses, but find their rationale pretty confusing.<p>This kind of data is commonly modeled using item response theory (IRT). I suspect that even in data generated by a unidimensional IRT model (which they are arguing against), you might get the results they report, depending on the level of measurement error in the model.<p>Measurement error is the key here, but is not considered in the article. That + setting an unjustified margin of 20% around the average is very strange. An analogous situation would be criticizing a simple regression, by looking at how many points fall X units above&#x2F;below the fitted line, without explaining your choice of X.
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jjaredsimpson将近 8 年前
Does this article say anything more profound than, &quot;If you roll 10 dice, you&#x27;ll expect a score of 35, however any pair of rolls which sum to 35 are unlikely to be similar.&quot;<p>All the worst students will be very similar and all the best students will be very similar because the number of available states is low. Average students are all unique in their average-ness.<p>Am I missing some subtle statistical understanding that the toy example doesn&#x27;t capture?
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tpeo将近 8 年前
&gt;Out of 4,063 pilots, not a single one fell within the average 30 percent on all 10 dimensions.<p>I wondered about a very similar problem some weeks ago. I was bothered about the terms &quot;ectomorph&quot; and &quot;mesomorph&quot; because they seemed useless once you considered height: the vast majority of &quot;ectomorphs&quot; seemed to be taller than the average while the vast majority of &quot;mesomophs&quot; seemed to be of average height, so there&#x27;s no point to these words. And so I wondered how would shoulder width would change given height (which seems to have some kind &quot;decreasing returns&quot;), and how the average measures would relate to actual average build. I mean, is the &quot;average guy&quot; really the guy with the average height and average shoulders? Because it&#x27;s not as if the scale had just changed, like doubling the size of a cube, but there seems to be some deformation going on as well.<p>Anyway, didn&#x27;t get past the wondering phase at the time. But I think it&#x27;s too much of an important problem to be casually thrown as part of a pitch. I don&#x27;t see an immediate reason why the average tuple should be the tuple of all averages, because some of the variables might be &quot;dislocated&quot; and thus not coincide with the averages of other variables. Some guy might be very close to average height yet still somewhere in the left-tail when it comes to body mass, shoulder width or any other measure. So there might be a typical student, but I don&#x27;t think this is the way to find him.
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connoredel将近 8 年前
There is an analogy to clustering (an unsupervised learning technique) here.<p>Take the simple case of 2 dimensions (each observation is plotted in 2D space) with possible values of 0-10. Let&#x27;s say the extreme (far from average) space is within 5% of the border. The total extreme area is (10x10)-(9x9) = 19 (i.e. 19%). Now add a 3rd dimension. The extreme &quot;volume&quot; in 3d space is now (10x10x10)-(9x9x9) = 271 (i.e. 27%). You can see where this is trending. Add enough dimensions, and every observation is now &quot;extreme.&quot; They become so far apart that each observation almost deserves its own cluster, and you lose any idea of similarity.<p>Back to this particular article: when you _add_ (or average) all of the dimensions -- like you do on an exam -- suddenly they are close again.
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fnovd将近 8 年前
A silly headline.<p>According to the article, the average <i>person</i> doesn&#x27;t exist, either. I don&#x27;t know many people that are 13% fluent in Mandarin, 13% fluent in English, 9% fluent in Hindi... At the same time, having ~2 hands and ~10 fingers seems about right. Some metrics work with averages, some don&#x27;t.
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PotatoEngineer将近 8 年前
This question of &quot;what skills are students missing?&quot; reminds me of the new teaching methods they were trying out as I started high school. The new teaching program centered around objectives. The idea was that each objective was a skill that the student needed to learn, but the upshot was that you had to score more than 70% on every single quiz to pass the class, and that you could retake every quiz you failed, repeatedly.<p>The implementation varied between classes - in my World History class, there were a large number of objectives, and each objective was met by a small quiz that tested ~one skill. (There were a <i>lot</i> of retaken quizzes in that class.) In Biology, there were about 10 objectives for the entire semester, so you could still pass while missing a few small skills, as long as those missing skills were spread out among different units.<p>My high school used that &quot;objectives&quot; system less and less as I moved up the grades -I assume that most teachers got tired of it pretty quickly and just decided to make their usual teaching material &quot;look like objectives&quot; rather than rebuild their curriculum in later years.
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opportune将近 8 年前
I don&#x27;t like the way this headline is written to match the article. All they showed is that students with similar average scores over multiple questions differed in their scores on individual questions. That is kind of obvious.
timemachiner将近 8 年前
This makes me wonder. What is the &quot;best&quot; way to teach computer science to students? Universities are not trade schools (nor should they be), but it seems apparent that CS graduates in general are unprepared entering the workforce. The other extreme (bootcamps) seem to produce graduates that are more &quot;industry ready&quot; but only at a superficial level. These graduates seem to lack rigor&#x2F;theory. Makes me wonder if there is a more optimal training path for training students.
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pacaro将近 8 年前
If my memory and understanding are correct, the way that Mathematics is graded at Cambridge is interesting here.<p>Questions are scored <i>alpha</i> for a completely correct solution, <i>beta</i> if the examinee demonstrated that they knew what they were doing by maybe made some small mistake, and <i>gamma</i> for a reasonable effort.<p>The bare minimum pass mark is one alpha.
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QML将近 8 年前
When I read that the data was collected from 1500 CS finals, my immediate guess was that the class was CS61A.<p>---<p>I suspect that the distribution of the curve has to depend on: subjectiviness of the test and on the grading. Tests with questions where you know it or you don&#x27;t. And how much partial credit graders are willing to give.
bryanrasmussen将近 8 年前
what about the 10X student?
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crimsonalucard将近 8 年前
Given a large enough sample size, I&#x27;m sure you&#x27;ll find such a student. Additionally, you will have plenty of students who beat the average and are below average. Performance below or above average matters because student performance is ranked while cockpit dimensions are not.
suyash将近 8 年前
Average is just a statistical concept - in reality there is no average.