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It would seem unfair to criticizes academics for your experience, at least based on the description you gave.


You might be interested in StoneSoup, a project a friend of mine developed for teaching his week long Intro to Video Game Programming for middle school and high school students. Most of the students have little to no prior programming experience, and by the end of the week they have several games in Processing which they can call their own.

https://github.com/JohnEarnest/StoneSoup


... or through the lens of "Stephen Wolfram has a huge ego".


Stephen Wolfram is ego.


Many universities are utilizing MOOCs and/or online material in order to implement a reverse classroom. In a reverse classroom, students watch lectures and do reading at home and class time is used to extend and reinforce that material usually by answering questions and working through more examples.

Students take to it pretty well when video lectures are available (my experience is that book only reverse classrooms don't work as well). It also generally leads to better retention, since more time is spent on hands-on experience.

Instructors also take it pretty well. Using an existing set of lecture materials reduces the preparation workload, especially in the first year teaching a course.


From what I read, I don't believe it is. In my opinion it should also never be used for this purpose.

While you could probably catch a lot of cheaters this way, there is a possibility for a large false positive rate. If this is true then I would especially advise against deploying this type of software in a traditional university since the academic dishonesty policies can often cause significant and undue harm on an innocent student.


Good comment, but I wouldn't say never.

As an instructor of programming on a university level, I like to think that I have enough sense to know that particularly for "trivial" assignments, some similarity is expected. However, as I've encountered, a great deal of similarity over multiple assignments (and exams) between two students of the same nationality who sit together in class provides additional evidence of plagiarism.

So, yes, I agree a single data point of similarity is insufficient, but a history of similarity, particularly in complex projects, becomes more damning.


I got flagged as a freshman for "55% similarity" (whatever that meant) to another students submission in a "learn how to write shit in C++" type assignment. As far as I could tell, the only thing that triggered the software was the fact that both I and the other kid used do-while loops, while nobody else in the course did. The rest of the programs were semi-similar, just a few lines of cout/cin/<</>>/... to ask your name and echo it back.

So basically what I'm saying here is that I think "for "trivial" assignments, some similarity is expected" isn't always widely understood, to the detriment of students.

I think these sort of systems become most valuable when used to check work against work submitted from previous years to bust frat-house collections of answers, but varying questions year from year probably helps even more in that regard. Similarity between complex projects in the class sizes that were typical at my university (in classes advanced enough to have complex answers) was pretty easy to spot manually. Maybe edit-distance software is useful there to put some weight behind accusations?


Seems like just another case of Betteridge's Law of Headlines.


I wouldn't call it the "Java" philosophy. Rather, I would call it the "wet behind the ears" philosophy. It just so happens that they correlate because of the way people are trained.


We actually lost a nuclear weapon just off the coast of Spain. Even scarier is that these are not the only two instances in which nuclear weapons have gone (and still remain) missing due to negligence or accident.

http://en.wikipedia.org/wiki/1966_Palomares_B-52_crash


It often is. Whether or not students learn anything is the issue.

Perhaps you could say that basic statistical competency should be required for a college degree.


There was a paper not too far back that used SVMs in deep learning and leaped over the then state-of-the-art performance.

http://arxiv.org/abs/1306.0239


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