We have recently started to implement smartboards here at my university. They have many advantages (which others have listed here), but their usefulness depends on the class you are teaching.
The primary problem I have found is not their resolution, but that they are small. You can line a wall with whiteboards, but the footprint of smart boards forces them in a corner (and this should be true for the forseeable future).
It certainly is not economical to line the wall with them.
With paging, you would think this is not a problem. And this is true if your class is taught lecture style. However, I am currently teaching a Moore method mathematics class in a room with a smart board. I don't present; the students go up to the board and work things out on their own - with comments from me and the class. Because students build on each other's results, it is important that I leave everything up that has been done for the day and not erase it. Paging is not sufficient, as the typical student will keep flipping throught the pages (ever see what a student does to their notes when you ask them a question?) and not think. So I have abandoned the smart board and moved back to white boards.
The primary problem with Smart boards is that they are small.
I am a college professor, and while my area is mathematics and computer science, I have seen my share of cheating. Recently a student managed to steal a programming project from a student who was too liberal with write permissions on his account, and pass it off as his own.
Because of my experience at various universities, seeing what works and what does not, I have a draconian stance on honor policies. Suspend them on the first offense, expel them on the second (and even expel on the first if it is extreme enough). I say this, because this seems to be far more effective at reducing cheating than any tools you might have.
99% of all cheaters cheat poorly. The student above went through and modified all the comments and output statements, but forgot to remove the original student's name from the headers. These people are easy to catch and you do not need a service for them. Yes, it is a little harder with English and Philosophy papers, but by adding some unique flavor to your assignments (which you should do anyway), my colleagues can cut down on the material that they can copy.
The problem is prosecuting them. If you have a university with a weak honor code, students will cheat because they feel like they have
nothing to lose. It is not enough to fail a cheater on the assignment -- he was going to fail anyway. Similarly, it is not enough to fail them in the course. You have to make the expected value of cheating horrendous.
And if the expected value is horrendous, all you have to do is catch those easy 99%. If students see others being caught and the sentences imposed, my experience has shown that the "casual cheaters" will think twice about cheating.
When did this topic get posted, and why did I only see it now?
I am in my second year in a tenure track job in mathematics, having completed an undergraduate degree at Dartmouth and my PhD at Cornell. Not only have I gone through the hiring process, I am now on the other end of it as we complete faculty searches for the next year.
First of all, you should not become a professional mathematician unless you love it. Research in this area is infamous for breeding manic depressives because you can work for a year on a result., have it fall apart, and have nothing to show for it when you are done (I speak from experience here). Teaching jobs are much more stable, but unless you keep up even minimal research, you tend to calcify and it adversely affects your teaching. If you are willing to starve for your art, just like a struggling artist does, then proceed.
This is not to say you have to starve - just that if you are going to be any good, you have to have this attitude. Depending on the area you go into, there are tons of research jobs in and out of academia.
The NSA (that's got to be popular on Slashdot) is the largest employer of mathematicians in the world.
Think tanks like RAND hire a lot of operation research types.
Some high end game and streaming media companies need topologists to "prove" that their compression algorithms do not cause too much distortion from the original manifold
When the stock market was soaring, a lot of functional analysts went to work for stock brokers on derivatives (bad idea if you ask me).
The list goes on.
Academia is usually the standard place math PhDs go. Even here there are a lot of choices.
You have
Major research institutions. Teach one class a semester. Do some really good research, but spend most of time writing grants for yourself or graduate students.
Teaching institutions with research commitment (I am here). Teach 2-3 classes a semester. Do some good/acceptable research. Write grants for summer support (three words: 9 month contracts).
Teaching institutions with little or no research commitment (I am here). Teach 4-5 classes a semester. Research? Write grants for math education and teaching initiatives.
Now it just comes down to marketing yourself to the job (Thought you could escape that in academia? Wrong!). If you apply to the first, it is a matter of how many papers you have and how many more you can get. The second wants a mix of papers and teaching. The third really just wants to see how you can teach, and expects you to show that you can come up with innovative and new ways to motivate students (Buzzwords like seminar style, collaborative learning, and Moore method get thrown around a lot).
You also need to find ways to distinguish yourself for the jobs you are applying. If you apply to a school where math and CS are one department, the CS masters is a plus (helped me). But if the CS program is in the engineering school while the math program is Arts and Sciences, it is useless. Masters in similar, but related, fields are nice in this regard (Statistics, OR etc..), but departments are starting to get wary of them and their quality. As a case in point, a lot of CS masters for math PhDs are all theory (and so just more math), whereas Cornell would not allow me to count any theory classes to my masters (engineering/systems only).
These are all important issues you need to think about when you start graduate school. Any respectable graduate program realizes this, and will run seminars on how to develop teaching portfolios or research CVs starting your first year. Its in their best interest as a graduate program that you get a job, as you can refer future undergraduates back to them.
After all that, you still have to accept the fact that it is all cyclical, and you can never predict what will happen. A lot of professors were hired in the 60s for the GI bill, and tthey were supposed to retire ten years ago, but never did (you can hang around longer in academia). Then in the early 90s the collapse of the Soviet Union brought a lot of world class mathematicians into junior colleges (will prof for food).
Because of these two factors, when I entered grad school in 94, there was nothing but doom and gloom about how bad the job market was. So no one went to grad school. And many others left grad school to make money in tech. The result? Six years later I had my choice of professorship jobs, and now have my dream job while those that want to tech are moving about.
Enough of my ramblings... It all comes down to one thing: do you love math. If so, then go for it. If not, maybe you should look at something else.
The P=NP problem has been the death of many a
young researcher because it is such a hard problem
and there are many subtleties involved in such
a proof. Every year there are genuinely smart
people who propose a proof one way or another and
it requires a significant amount of peer review and analysis to spot the mistake in their proof.
After this, of course, they get shunned for wasting everyone's time for submitting a bad
proof, even though it took a large number of
man-hours to spot the flaw.
There are many interesting "proofs" out there
right now for which I am interested in learning
the results. A very respected proof theorist
this summer submitted a proof in the major logic
conference in Paris this summer. He claims that the P=NP problem is independent of the Peano axioms (Something that I have long since believed, but never tried to prove). This means that any
proof either way about this question would require
very high level techniques from set theory, and
that standard CS combinatorial arguments are not
enough.
So, I suggest that we just sit back and wait for
each of these to be peer reviewed. Either way,
the answer should be very interesting.
If my knowledge of copyright law is correct (Not necessarily so), the user posts may fall under fair use -- provided that not too much was used. Fair use is not just limited to educational use; critique and public commentary are included.
This is even more true because the posts are noncommercial. Otherwise AC has some serious royalties due him/her.
Late me start out saying that this is great news. I use a Macintosh with LinuxPPC on one drive and MacOS on another. It is the very fact that I have to be in MacOS to use Canvas that keeps me from using LinuxPPC as my default system.
I heavily rely on Canvas for techinical illustrations in my research documents. I draw them in Canvas (6 -- haven't bothered to upgrade to 7 yet), convert them to EPS, and include them in my LaTeX documents. I can draw the graph of a complex 20 state finite automata in 5 minutes with this program. For what I do (your results may vary), there is simply nothing like this program currently available for Linux.
GIMP is a wonderful program, but you must understand what its purpose is. GIMP is an image manipulation program. It is meant to be a freeware replacement to Photoshop. It is for manipulating bitmapped images (Of high resolution) and performing cool effects on them.
Canvas, on the other hand, got its start as a technical illustration tool. Sort of like a poor man's CAD. In many ways it is closer to xfig, though its interface is far superior. All objects are represented as vectors unless they are draw within a image box (A bitmap object). This gives Canvas several features that GIMP is sorely (At least in the stuff that I do) lacking.
Geometric objects (Circles, rectangles, beziers) in Canvas are not bitmapped and hence can be rescaled on the fly with no jaggies or need for antialiasing.
While I am sure Slashdotters will correct me, I believe that object placement in GIMP is visual. You cannot select an object and type in a coordinate for it to move to. You can do this in Canvas relative to your current measurement unit (Points, centimeter, inches, whatever). This allows you to align objects so they will print correctly, as screen resolution is simply not good enough for press.
Text support in GIMP is just too primitive; this is my primary complaint with this program. In Canvas, text remains text. You can always reedit the text after you have bent it around a line, filled it with a gradient, rotated it 234.5 degrees, or whatever. And when you export the image to PDF, the text is still editable there. This is particularly important to me as various publications have different font requirements and I do not want to have to redraw my pictures.
With Canvas 5, Deneba extended their product from a technical illustration to tool to an All-In-One tool. It now can do a lot of what Photoshop does, and hence does compete directly with GIMP in that regard.
As far as I know, Canvas can now do everything that GIMP does. As to how well it does all this (compared to GIMP, Photoshop, or whatever), I cannot tell you that. I just do technical illustrations, not image manipulation.
I am completely in agreement with you about the exploitation of Third Graders for political causes. It is completely shameless.
With that said, however, there is a real issue here. Tesla is forgotten because Edison was a ruthless SOB who was very good at marketing and smear campaigns. Because it competed with DC, Edison denounced AC as a dangerous health hazard and faked several demonstrations to "show" it. Tesla was not charismatic enough to overcome all this press.
This is one of many unethical business practices of Edison. He was the Bill Gates of his generation, and we shouldn't be surprised about the cult of Bill in this country when we revere Edison.
And I won't be surprise if the Smithsonian credits Bill for personal computers in the future.
For all intents and pruposes, Microsoft bought out the computer science department here at Cornell back in 1996. Some of the faculty were so angry, they replaced the department's web page with a "Weclome to Microsoft" web page for a few days as a joke.
It has had some adverse effects. In particular, the undergraduate computer lab is all NT machines, with default networking capability. So you cannot do graphically-oriented programming assignmmets remotely from them like you could in the old days when you just set your DISPLAY, or (preferably) used ssh.
That's pretty key on a big compus like this when students have accounts in multiple departments (As a logician, I have four) and want to centralize. And graphical assignements are (enter value judgment here) becoming the norm these days.
It also cuts the undergraduates off from some of the cool research (like NuPRL and its applications to code optimization) being done at Cornell, because they were geared towards Solaris and the faculty refuse to bring them over.
The graduate students are largely unaffected. The computer science graduates have access to every machine imaginable; they are just behind the firewall where the undergraduates cannot touch them. The hybrids like myself all switched to the Ultra Sparcs in the Center for Applied Math.
So the end result? The undergraduates get screwed. They pay (a lot) to come to one of the top computer science departments in the country and get access to the same resources they could get anywhere else.
Apple's choice of BSD for Mac OS X is entirely a a matter of legacy code. Mac OS X is meant to integrate the technology they acquired from the NeXT buyout into the Mac OS.
This is not an easy task, and they are trying to do this is fast as they can. BSD is what NeXT used, and since there is really no need to change that part of the OS (As opposed to integrating backwards compatibilty, and the dubious shift from an Object C API to a Java one), they stuck with it.
NeXT's choice of BSD is again a matter of legacy. When the Mach micorkernel was developed at CMU, they put BSD on top of it to show it off. It worked so well, no one felt the need to do anything else with Mach. NeXT used it as is, because it was the fastest way to get a shipping product.
Slashdot Mirror
The primary problem I have found is not their resolution, but that they are small. You can line a wall with whiteboards, but the footprint of smart boards forces them in a corner (and this should be true for the forseeable future). It certainly is not economical to line the wall with them.
With paging, you would think this is not a problem. And this is true if your class is taught lecture style. However, I am currently teaching a Moore method mathematics class in a room with a smart board. I don't present; the students go up to the board and work things out on their own - with comments from me and the class. Because students build on each other's results, it is important that I leave everything up that has been done for the day and not erase it. Paging is not sufficient, as the typical student will keep flipping throught the pages (ever see what a student does to their notes when you ask them a question?) and not think. So I have abandoned the smart board and moved back to white boards.
The primary problem with Smart boards is that they are small.I am a college professor, and while my area is mathematics and computer science, I have seen my share of cheating. Recently a student managed to steal a programming project from a student who was too liberal with write permissions on his account, and pass it off as his own.
Because of my experience at various universities, seeing what works and what does not, I have a draconian stance on honor policies. Suspend them on the first offense, expel them on the second (and even expel on the first if it is extreme enough). I say this, because this seems to be far more effective at reducing cheating than any tools you might have.
99% of all cheaters cheat poorly. The student above went through and modified all the comments and output statements, but forgot to remove the original student's name from the headers. These people are easy to catch and you do not need a service for them. Yes, it is a little harder with English and Philosophy papers, but by adding some unique flavor to your assignments (which you should do anyway), my colleagues can cut down on the material that they can copy.
The problem is prosecuting them. If you have a university with a weak honor code, students will cheat because they feel like they have nothing to lose. It is not enough to fail a cheater on the assignment -- he was going to fail anyway. Similarly, it is not enough to fail them in the course. You have to make the expected value of cheating horrendous.
And if the expected value is horrendous, all you have to do is catch those easy 99%. If students see others being caught and the sentences imposed, my experience has shown that the "casual cheaters" will think twice about cheating.
When did this topic get posted, and why did I only see it now?
I am in my second year in a tenure track job in mathematics, having completed an undergraduate degree at Dartmouth and my PhD at Cornell. Not only have I gone through the hiring process, I am now on the other end of it as we complete faculty searches for the next year.
First of all, you should not become a professional mathematician unless you love it. Research in this area is infamous for breeding manic depressives because you can work for a year on a result., have it fall apart, and have nothing to show for it when you are done (I speak from experience here). Teaching jobs are much more stable, but unless you keep up even minimal research, you tend to calcify and it adversely affects your teaching. If you are willing to starve for your art, just like a struggling artist does, then proceed.
This is not to say you have to starve - just that if you are going to be any good, you have to have this attitude. Depending on the area you go into, there are tons of research jobs in and out of academia.
The list goes on.
Academia is usually the standard place math PhDs go. Even here there are a lot of choices. You have
Now it just comes down to marketing yourself to the job (Thought you could escape that in academia? Wrong!). If you apply to the first, it is a matter of how many papers you have and how many more you can get. The second wants a mix of papers and teaching. The third really just wants to see how you can teach, and expects you to show that you can come up with innovative and new ways to motivate students (Buzzwords like seminar style, collaborative learning, and Moore method get thrown around a lot).
You also need to find ways to distinguish yourself for the jobs you are applying. If you apply to a school where math and CS are one department, the CS masters is a plus (helped me). But if the CS program is in the engineering school while the math program is Arts and Sciences, it is useless. Masters in similar, but related, fields are nice in this regard (Statistics, OR etc..), but departments are starting to get wary of them and their quality. As a case in point, a lot of CS masters for math PhDs are all theory (and so just more math), whereas Cornell would not allow me to count any theory classes to my masters (engineering/systems only).
These are all important issues you need to think about when you start graduate school. Any respectable graduate program realizes this, and will run seminars on how to develop teaching portfolios or research CVs starting your first year. Its in their best interest as a graduate program that you get a job, as you can refer future undergraduates back to them.
After all that, you still have to accept the fact that it is all cyclical, and you can never predict what will happen. A lot of professors were hired in the 60s for the GI bill, and tthey were supposed to retire ten years ago, but never did (you can hang around longer in academia). Then in the early 90s the collapse of the Soviet Union brought a lot of world class mathematicians into junior colleges (will prof for food).
Because of these two factors, when I entered grad school in 94, there was nothing but doom and gloom about how bad the job market was. So no one went to grad school. And many others left grad school to make money in tech. The result? Six years later I had my choice of professorship jobs, and now have my dream job while those that want to tech are moving about.
Enough of my ramblings ... It all comes down to one thing: do you love math. If so, then go for it. If not, maybe you should look at something else.
The P=NP problem has been the death of many a young researcher because it is such a hard problem and there are many subtleties involved in such a proof. Every year there are genuinely smart people who propose a proof one way or another and it requires a significant amount of peer review and analysis to spot the mistake in their proof. After this, of course, they get shunned for wasting everyone's time for submitting a bad proof, even though it took a large number of man-hours to spot the flaw. There are many interesting "proofs" out there right now for which I am interested in learning the results. A very respected proof theorist this summer submitted a proof in the major logic conference in Paris this summer. He claims that the P=NP problem is independent of the Peano axioms (Something that I have long since believed, but never tried to prove). This means that any proof either way about this question would require very high level techniques from set theory, and that standard CS combinatorial arguments are not enough. So, I suggest that we just sit back and wait for each of these to be peer reviewed. Either way, the answer should be very interesting.
If my knowledge of copyright law is correct (Not necessarily so), the user posts may fall under fair use -- provided that not too much was used. Fair use is not just limited to educational use; critique and public commentary are included.
This is even more true because the posts are noncommercial. Otherwise AC has some serious royalties due him/her.
-Walker
Late me start out saying that this is great news. I use a Macintosh with LinuxPPC on one drive and MacOS on another. It is the very fact that I have to be in MacOS to use Canvas that keeps me from using LinuxPPC as my default system.
I heavily rely on Canvas for techinical illustrations in my research documents. I draw them in Canvas (6 -- haven't bothered to upgrade to 7 yet), convert them to EPS, and include them in my LaTeX documents. I can draw the graph of a complex 20 state finite automata in 5 minutes with this program. For what I do (your results may vary), there is simply nothing like this program currently available for Linux.
GIMP is a wonderful program, but you must understand what its purpose is. GIMP is an image manipulation program. It is meant to be a freeware replacement to Photoshop. It is for manipulating bitmapped images (Of high resolution) and performing cool effects on them.
Canvas, on the other hand, got its start as a technical illustration tool. Sort of like a poor man's CAD. In many ways it is closer to xfig, though its interface is far superior. All objects are represented as vectors unless they are draw within a image box (A bitmap object). This gives Canvas several features that GIMP is sorely (At least in the stuff that I do) lacking.
Geometric objects (Circles, rectangles, beziers) in Canvas are not bitmapped and hence can be rescaled on the fly with no jaggies or need for antialiasing.
While I am sure Slashdotters will correct me, I believe that object placement in GIMP is visual. You cannot select an object and type in a coordinate for it to move to. You can do this in Canvas relative to your current measurement unit (Points, centimeter, inches, whatever). This allows you to align objects so they will print correctly, as screen resolution is simply not good enough for press.
Text support in GIMP is just too primitive; this is my primary complaint with this program. In Canvas, text remains text. You can always reedit the text after you have bent it around a line, filled it with a gradient, rotated it 234.5 degrees, or whatever. And when you export the image to PDF, the text is still editable there. This is particularly important to me as various publications have different font requirements and I do not want to have to redraw my pictures.
With Canvas 5, Deneba extended their product from a technical illustration to tool to an All-In-One tool. It now can do a lot of what Photoshop does, and hence does compete directly with GIMP in that regard.
As far as I know, Canvas can now do everything that GIMP does. As to how well it does all this (compared to GIMP, Photoshop, or whatever), I cannot tell you that. I just do technical illustrations, not image manipulation.
-Walker
I am completely in agreement with you about the exploitation of Third Graders for political causes. It is completely shameless.
With that said, however, there is a real issue here. Tesla is forgotten because Edison was a ruthless SOB who was very good at marketing and smear campaigns. Because it competed with DC, Edison denounced AC as a dangerous health hazard and faked several demonstrations to "show" it. Tesla was not charismatic enough to overcome all this press.
This is one of many unethical business practices of Edison. He was the Bill Gates of his generation, and we shouldn't be surprised about the cult of Bill in this country when we revere Edison.
And I won't be surprise if the Smithsonian credits Bill for personal computers in the future.
For all intents and pruposes, Microsoft bought out the computer science department here at Cornell back in 1996. Some of the faculty were so angry, they replaced the department's web page with a "Weclome to Microsoft" web page for a few days as a joke.
It has had some adverse effects. In particular, the undergraduate computer lab is all NT machines, with default networking capability. So you cannot do graphically-oriented programming assignmmets remotely from them like you could in the old days when you just set your DISPLAY, or (preferably) used ssh.
That's pretty key on a big compus like this when students have accounts in multiple departments (As a logician, I have four) and want to centralize. And graphical assignements are (enter value judgment here) becoming the norm these days.
It also cuts the undergraduates off from some of the cool research (like NuPRL and its applications to code optimization) being done at Cornell, because they were geared towards Solaris and the faculty refuse to bring them over.
The graduate students are largely unaffected. The computer science graduates have access to every machine imaginable; they are just behind the firewall where the undergraduates cannot touch them. The hybrids like myself all switched to the Ultra Sparcs in the Center for Applied Math.
So the end result? The undergraduates get screwed. They pay (a lot) to come to one of the top computer science departments in the country and get access to the same resources they could get anywhere else.
Apple's choice of BSD for Mac OS X is entirely a a matter of legacy code. Mac OS X is meant to integrate the technology they acquired from the NeXT buyout into the Mac OS.
This is not an easy task, and they are trying to do this is fast as they can. BSD is what NeXT used, and since there is really no need to change that part of the OS (As opposed to integrating backwards compatibilty, and the dubious shift from an Object C API to a Java one), they stuck with it.
NeXT's choice of BSD is again a matter of legacy. When the Mach micorkernel was developed at CMU, they put BSD on top of it to show it off. It worked so well, no one felt the need to do anything else with Mach. NeXT used it as is, because it was the fastest way to get a shipping product.