(This post isn't intended to be personal so I hope I don't cause any offence).
I often see people talking about how great they personally found a MOOC course, but that self-assessment is largely useless. To put it bluntly: you don't know what aspects of a topic you don't understand, were not told about, or whether the information you received was at all accurate, because you lack the expertise to determine that.
For example, people thought they had learned a lot from the Udacity Statistics 101 course and it received glowing reviews from participants. But unfortunately they did not learn about some of the most important basic ideas in statistics, and some of the things they did learn were just wrong:
the course is amazingly, shockingly awful. It is poorly structured; it evidences an almost complete lack of planning for the lectures; it routinely fails to properly define or use standard terms or notation; it necessitates occasional massive gaps where “magic” happens; and it results in nonstandard computations that would not be accepted in normal statistical work.
Astoundingly, the Udacity Introduction to Statistics course manages to go almost its entire length without ever mentioning or making any distinction between the population and sample in a study. I say I'm “astounded” because in my classes (and any one I've surveyed or looked at), this is the key idea in introductory inferential statistics. It's the very first thing that is mentioned in my class (or the book), and it's the very last thing on the last day, too. It's the entire reason why inferential statistics is necessary in the first place. In fact, the very word “statistics” means measures for one (sample) and not the other (population) – but you'll never learn that from this class.
"I often see people talking about how great they personally found a MOOC course, but that self-assessment is largely useless. To put it bluntly: you don't know what aspects of a topic you don't understand, were not told about, or whether the information you received was at all accurate, because you lack the expertise to determine that."
And? This also applies to to normal educational environments, not just MOOCS. Why do people assume that courses delivered on premises by an average teacher is going to be better than a MOOC delivered by world class guy. I'd wager that on average on premises course has more gaps/problems/mistakes than a MOOC. These on-premise courses will never be exposed to criticism, mistakes never seen. Moocs are exposed to every educational 'expert' there is, so lots of criticism pops up. That's a good thing. Normal courses don't have so many 'experts' looking at them.
You're dead on. Whether or not MOOCs reach the standards we want today, they allow far more scrutiny than the closed courses they'll end up replacing. Over time, this will help them improve, a great boon to everyone.
I like to think of Khan Academy, Coursera and Udacity as raising the lower bar for everyone. If your course / institution / whatever is worse, you can easily draw on them materials as a supplement. It you can find better, well, no harm done.
>Why do people assume that courses delivered on premises by an average teacher is going to be better than a MOOC delivered by world class guy.
Assume? I just gave you an example of a shit course delivered by a 'world class guy'. I've done plenty of real life classes in statistics and none of the teachers/professors were startlingly brilliant communicators but at least they were able get over the basic principles of the subject. They also didn't waste my time telling me how great they were.
You guys may be delivering good stuff, but there are a lot who are not. Remember how many community colleges/for-profits/universities/high-schools there are in the world. The majority of the teachers recruited were probably averagely talented. These courses will never be exposed to criticism, but moocs are. That's a good thing.
If you understand statistics you should understand the difference between anecdotal evidence and statistical data. You have one online course you're comparing to a handful of in-person courses you, personally, have taken.
There are hundreds of online courses and hundreds of thousands of in-person courses. You should know it's not a good idea to make inference about ALL MOOC's based on one example or ALL in-person courses based on a handful of classes.
If your statistics classes didn't teach you this concept, I'd say it was those classes that did a poor job.
It's interesting that in a thread about inferential statistics you are asserting inferences based on anecdotes with very small sample size, rather than statistics. It's possible that there haven't been enough MOOC statistics classes to really make a significant inference, but that seems to suggest "we'll have to wait and see" rather than "they're all shit".
I don't take it personally at all. I see your point and fully understand it. I consider someone to be an expert at something when they 'know what they don't know'.
But, what you are saying isn't limited to MOOCs.
When you are learning on your own to fully understand something, you don't let magic fly. When I learned my first programming language I didn't allow myself to use any libraries at all. I made myself make everything from scratch so I knew what every moving piece was doing.
In my opinion, the onus is on the student. I work with plenty of devs who have CS degrees from top schools who will look for a library to do something that can be implemented with a line or two or code if they would take a few minutes to learn about it. Sometimes it's laziness, sometimes they don't seem to have the ability to learn something without it being force fed to them. I've also had problems with some of them during code reviews because they don't know what they don't know, and in their case they have a piece of paper that says they are experts so they refuse to listen and learn.
These courses are far from perfect, but they are a stepping stone for people who have a burning desire to learn. It's 3:30am where I am. I've been working on an algorithms class since I got home from work at 6pm. (with occasional HN breaks). I'll get a few hours of sleep and go to work. This has been my life for almost a year. To say I lack any expertise at this point is a little silly. I have nothing but a HS diploma and I'm keeping up with MIT open courseware. I would say they are doing something right.
And I think that going from tech support to developer is a little more than a self-assessment.
It's a bit weird for a community college lecturer to be attacking the lectures from a Professor at Stanford, and then concluding that "MOOC's" are the problem.
What part of his critique doesn't apply to the real life Stanford courses taught by the same person?
Note that I'm quite happy to believe that famous people teaching at famous institutions are bad at teaching, for reasons he gets into in his final paragraphs.
I just don't see how that's a problem with MOOCs, except to the degree that people are choosing them based on prestige, and even then that's no different from the status quo.
Vague criticism which can be applied to just about anything in the world followed by some criticism of something totally irrelevant (udacity course) when the discussion is on KA videos.
I often see people talking about how great they personally found a MOOC course, but that self-assessment is largely useless. To put it bluntly: you don't know what aspects of a topic you don't understand, were not told about, or whether the information you received was at all accurate, because you lack the expertise to determine that.
For example, people thought they had learned a lot from the Udacity Statistics 101 course and it received glowing reviews from participants. But unfortunately they did not learn about some of the most important basic ideas in statistics, and some of the things they did learn were just wrong:
http://www.angrymath.com/2012/09/udacity-statistics-101.html
the course is amazingly, shockingly awful. It is poorly structured; it evidences an almost complete lack of planning for the lectures; it routinely fails to properly define or use standard terms or notation; it necessitates occasional massive gaps where “magic” happens; and it results in nonstandard computations that would not be accepted in normal statistical work.
Astoundingly, the Udacity Introduction to Statistics course manages to go almost its entire length without ever mentioning or making any distinction between the population and sample in a study. I say I'm “astounded” because in my classes (and any one I've surveyed or looked at), this is the key idea in introductory inferential statistics. It's the very first thing that is mentioned in my class (or the book), and it's the very last thing on the last day, too. It's the entire reason why inferential statistics is necessary in the first place. In fact, the very word “statistics” means measures for one (sample) and not the other (population) – but you'll never learn that from this class.